@misc{roman_justice_paynter_boucher_devadiga_endsley_erb_friedl_gao_giglio_et al._2024, title={Continuity between NASA MODIS Collection 6.1 and VIIRS Collection 2 land products}, volume={302}, ISSN={["1879-0704"]}, DOI={10.1016/j.rse.2023.113963}, abstractNote={This paper provides a review and summary status of the research underway by the NASA Terra Aqua Suomi-NPP Land Discipline Team to provide continuity of global land data products from the NASA Moderate resolution Imaging Spectroradiometer (MODIS) to the Visible Infrared Imaging Radiometer Suite (VIIRS). The two MODIS instruments on the NASA Earth Observing System Terra (morning overpass) and Aqua (afternoon overpass) platforms have provided more than twenty years of data. The peer-reviewed land products generated from MODIS are now being transitioned to production using VIIRS inputs, with the intention of providing dynamic continuity for the Aqua observations. As part of that process, the products from the two instruments are undergoing intercomparison and evaluation. These results are provided where available and show promising levels of agreement and accuracy in all cases. The paper also offers options for establishing continuity of Terra MODIS data products.}, journal={REMOTE SENSING OF ENVIRONMENT}, author={Roman, Miguel O. and Justice, Chris and Paynter, Ian and Boucher, Peter B. and Devadiga, Sadashiva and Endsley, Arthur and Erb, Angela and Friedl, Mark and Gao, Huilin and Giglio, Louis and et al.}, year={2024}, month={Mar} }
 @article{gray_choi_friedl_griffiths_2024, title={Drought Changes Growing Season Length and Vegetation Productivity}, url={https://doi.org/10.5194/egusphere-egu24-14774}, DOI={10.5194/egusphere-egu24-14774}, abstractNote={Meteorological droughts are increasing in intensity, frequency, and duration due to climate change. These events may have substantial impacts on vegetation productivity that influence the global carbon balance. Effects vary considerably, however, with the intensity of the drought as well as local abiotic and biotic conditions such as vegetation type, soil type, and the timing of the drought. Productivity is primarily reduced because droughts decrease the efficiency with which plants can convert atmospheric CO2 into carbohydrates, largely because of stomatal closure when energy is not limiting. However, another aspect by which droughts can reduce productivity is by shortening the growing season length (GSL). GSL reduction may be particularly pronounced in vegetation communities already sensitive to precipitation variability, in particular, short-rooted grassland and croplands ecosystems. Here, we use evidence from satellite observations of ecosystem activity, meteorological measurements, and data from eddy-covariance flux towers to reveal the impact of several large-scale meteorological droughts on vegetation productivity on natural and managed ecosystems. In particular, we show that the timing of the drought is important, with late droughts being particularly diminishing to productivity. We also demonstrate that while plant physiological responses to drought dominate the reduction in productivity, the diminishment of GSL plays an underappreciated role. These results have wide implications for the future carbon balance under a changing climate, and suggests that ecosystem models could better explain productivity by incorporating the effects of droughts on GSL.}, author={Gray, Josh and Choi, Eunhye and Friedl, Mark and Griffiths, Patrick}, year={2024}, month={Mar} }
 @article{hinks_gray_2024, title={From Satellites to Soil: Integrating Satellite and Household Survey Data to Assess the Impacts of Adaptations on Smallholder Farmers&#8217; Climate Resilience}, url={https://doi.org/10.5194/egusphere-egu24-14059}, DOI={10.5194/egusphere-egu24-14059}, abstractNote={Despite feeding the majority of the global population, small (<2 ha) farmers are among the poorest and disproportionately vulnerable to climate changes. Their ability to improve yields amid increasingly severe and frequent climate shocks will largely determine the success of the UN&#8217;s Sustainable Development Goals (SDGs) to eliminate poverty and hunger. Because smallholder farmers play a central role in efforts to achieve global food security, many governmental and private institutions have influenced smallholders&#8217; on-farm management practices through interventions. However, interventions led by different institutions have pushed communities of smallholders to adopt divergent adaptation strategies: Some communities have taken proactive measures by diversifying their crop rotations or implementing tree-based systems as natural climate solutions, while others have primarily used reactive measures, implementing adaptations that were directly informed by their recent experiences with extreme weather events (e.g., altering sow and harvest dates to avoid a period of extreme heat). Despite the deadly consequences of food shortages in smallholder communities, very little research has quantified the impact of specific adaptations on their sensitivity to inter-annual climate variability. Fortunately, the recent influx of satellite sensors has enabled us to remotely monitor changes in smallholder field-level cultivation practices and tree-based systems, and with high performance computing, we can scale these analyses across landscapes. Here, we integrated administrative yield data, multi-source satellite and weather data, and household and field survey data across India, Nepal, and Bangladesh in mixed-effect models to answer: Where, and how have smallholder communities adapted their cultivation practices? And, how have these adaptations impacted their resilience to weather shocks? The results of these findings were contextualized using household survey data of 2,000 smallholder farmers to understand the drivers of farmers&#8217; decisions and their perspectives on climate-induced adaptations. Our findings can inform future interventions in the region, and the algorithms will be directly transferable to other regions of smallholder agriculture where farmers adopt distinct adaptations and experience other climate threats.}, author={Hinks, Isabella and Gray, Josh}, year={2024}, month={Mar} }
 @article{smith_gao_gray_2024, title={Overcoming Big Data Challenges in Satellite Observation: A Variable Resolution Scheme for Modeling Land Surface Phenology}, url={https://doi.org/10.5194/egusphere-egu24-12119}, DOI={10.5194/egusphere-egu24-12119}, abstractNote={As the volume of satellite observation data experiences exponential growth, our ability to process this data and extract meaningful insights is struggling to keep pace. This challenge is particularly pronounced when dealing with dynamic and variable phenomena across diverse spatiotemporal scales. Achieving accurate representation of these nuances necessitates data generation at high spatial and temporal resolutions, resulting in significant redundancy in computation and storage.This issue is notably evident in the case of products that monitor plant phenology over time, which are crucial for assessing the impacts of climate change and monitoring agriculture. Computational complexities often limit these products to coarse resolutions (500m-1km) or short time frames, distorting our understanding of phenology across scales. In contrast, various approaches in hydrology and land surface modeling have utilized tiled grids and meshes to capture spatial heterogeneity and reduce dimensionality for complex modeling. This is accomplished through decomposing or aggregating modeling surfaces into response units representative of system drivers and have been shown to enable improved computational capabilities while still maintaining accurate approximations. We believe that similar modeling techniques can be leveraged to enable phenological modeling at higher resolutions.&#160;Building on these advancements, we develop a variable resolution scheme to represent land surface heterogeneity for modeling Land Surface Phenology (LSP) and decompose Landsat and Sentinel-2 Enhanced Vegetation Index (EVI) into adaptive areal units. Through this method we operationalize the Bayesian Land Surface Phenology (BLSP) model, a hierarchical Bayesian algorithm capable of constructing LSP data for the complete Landsat archive. While BLSP produces highly valuable results, it faces computational challenges for large-scale applications as its current time series approach necessitates each pixel to be computed individually. Our innovative approach reduces the dimensionality of modeling LSP by an order of magnitude to improve computational efficiency and enable the production of a 30 m BLSP product. These improvements are key to provide a region wide long-term phenometrics product at 30m resolution necessary to support studies into the long-term changes at a fine scale.}, author={Smith, Owen and Gao, Xiaojie and Gray, Josh}, year={2024}, month={Mar} }
 @article{choi_gray_2024, title={Understanding the role of vegetation responses to drought in regulating autumn senescence}, url={https://doi.org/10.5194/egusphere-egu24-13879}, DOI={10.5194/egusphere-egu24-13879}, abstractNote={Vegetation phenology is the recurring of plant growth, including the cessation and resumption of growth, and plays a significant role in shaping terrestrial water, nutrient, and carbon cycles. Changes in temperature and precipitation have already induced phenological changes around the globe, and these trends are likely to continue or even accelerate. While warming has advanced spring arrival in many places, the effects on autumn phenology are less clear-cut, with evidence for earlier, delayed, or even unchanged end of the growing season (EOS). Meteorological droughts are intensifying in duration and frequency because of climate change. Droughts intricately impact changes in vegetation, contingent upon whether the ecosystem is limited by water or energy. These droughts have the potential to influence EOS changes. Despite this, the influence of drought on EOS remains largely unexplored. This study examined moisture&#8217;s role in controlling EOS by understanding the relationship between precipitation anomalies, vegetation&#8217;s sensitivity to precipitation (SPPT), and EOS. We also assess regional variations in responses to the impact of SPPT on EOS.The study utilized multiple vegetation and water satellite products to examine the patterns of SPPT in drought and its impact on EOS across aridity gradients and vegetation types. By collectively evaluating diverse SPPTs from various satellite datasets, this work offers a comprehensive understanding and critical basis for assessing the impact of drought on EOS. We focused on the Northern Hemisphere from 2000 to 2020, employing robust statistical methods. This work found that, in many places, there was a stronger relationship between EOS and drought in areas with higher SPPT. Additionally, a non-linear negative relationship was identified between EOS and SPPT in drier regions, contracting with a non-linear positive relationship observed in wetter regions. These findings were consistent across a range of satellite-derived vegetation products. Our findings provide valuable insights into the effects of SPPT on EOS during drought, enhancing our understanding of vegetation responses to drought and its consequences on EOS and aiding in identifying drought-vulnerable areas.}, author={Choi, Eunhye and Gray, Josh}, year={2024}, month={Mar} }
 @article{rasmussen_abrahamson_tang_smith_gray_woodcock_bosch_2023, title={Assessment of Performance of Tree-Based Algorithms to Reduce Errors of Omisssion and Commission in Change Detection}, ISSN={["2153-6996"]}, DOI={10.1109/IGARSS52108.2023.10283320}, abstractNote={The ability to detect land use and land cover change quickly and accurately is crucial for earth system modeling, policy making, and sustainable land management. Remote sensing has been widely used to map and monitor land use and land cover change over very large areas. Many change detection algorithms (CDAs) have been developed with promising accuracy. However, accuracy of detecting specific types of change using these algorithms is often not satisfactory owing to errors of commission. We present a novel pixel-based broad area search (BAS) approach that detects and classifies heavy construction, which is an important indicator of human development and of interest to the intelligence community. The BAS system combines an online CDA, roboBayes, with a supervised tree-based classifier that removes the CDA’s errors of commission. To assess the performance of the classifier, we examined three tree-based algorithms – decision tree, random forest, and LightGBM – trained on roboBayes model parameters, tuning the models using a leave-one-region-out cross-validation strategy. We compared the performance of the tree-based classifiers against a baseline of filters created by the authors. Performance was evaluated at the pixel-level using precision, recall, and F1-score, which are analogues of commission error, omission error, and accuracy, respectively. The BAS system with optimized tree-based filters performed nearly 80% better than the BAS system without any filters and more than 50% better than the authors’ filters.}, journal={IGARSS 2023 - 2023 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM}, author={Rasmussen, Peter and Abrahamson, Jenna and Tang, Xiaojing and Smith, Owen and Gray, Josh and Woodcock, Curtis and Bosch, Marc}, year={2023}, pages={6676–6679} }
 @article{fidan_gray_doll_nelson_2023, title={Machine learning approach for modeling daily pluvial flood dynamics in agricultural landscapes}, volume={167}, ISSN={["1873-6726"]}, url={https://doi.org/10.1016/j.envsoft.2023.105758}, DOI={10.1016/j.envsoft.2023.105758}, abstractNote={Despite rural, agricultural landscapes being exposed to pluvial flooding, prior predictive flood modeling research has largely focused on urban areas. To improve and extend pluvial flood modeling approaches for use in agricultural regions, we built a machine learning model framework that uses remotely sensed imagery from Planet Labs, gridded rainfall data, and open-access geospatial landscape characteristics to produce a pluvial flood timeline. A Random Forest model was trained and daily flood timeline was generated for Hurricane Matthew (2016) at a 10-m resolution. The results show the model predicts pluvial flooding well, with overall accuracy of 0.97 and F1 score of 0.69. Further evaluation of model outputs highlighted that corn and soybean crops were most impacted by the pluvial flooding. The model may be used to identify agricultural areas susceptible to pluvial flooding, crops that may be potentially impacted, and characteristics of areas that experience pluvial flooding.}, journal={ENVIRONMENTAL MODELLING & SOFTWARE}, author={Fidan, Emine and Gray, Josh and Doll, Barbara and Nelson, Natalie G.}, year={2023}, month={Sep} }
 @article{gao_mcgregor_gray_friedl_moon_2023, title={Observations of Satellite Land Surface Phenology Indicate That Maximum Leaf Greenness Is More Associated With Global Vegetation Productivity Than Growing Season Length}, volume={37}, ISSN={["1944-9224"]}, url={https://doi.org/10.1029/2022GB007462}, DOI={10.1029/2022GB007462}, abstractNote={Vegetation green leaf phenology directly impacts gross primary productivity (GPP) of terrestrial ecosystems. Satellite observations of land surface phenology (LSP) provide an important means to monitor the key timing of vegetation green leaf development. However, differences between satellite‐derived LSP proxies and in situ measurements of GPP make it difficult to quantify the impact of climate‐induced changes in green leaf phenology on annual GPP. Here, we used 1,110 site‐years of GPP measurements from eddy‐covariance towers in association with time series of satellite LSP observations from 2000 to 2014 to show that while satellite LSP explains a large proportion of variation in annual GPP, changes in green‐leaf‐based growing season length (GSL, leaf development period from spring to autumn) had less impact on annual GPP by ∼30% than GSL changes in GPP‐based photosynthetic duration. Further, maximum leaf greenness explained substantially more variance in annual GPP than green leaf GSL, highlighting the role of future vegetation greening trends on large‐scale carbon budgets. Site‐level variability contributes a substantial proportion of annual GPP variance in the model based on LSP metrics, suggesting the importance of local environmental factors altering regional GPP. We conclude that satellite LSP‐based inferences regarding large‐scale dynamics in GPP need to consider changes in both green leaf GSL and maximum greenness.}, number={3}, journal={GLOBAL BIOGEOCHEMICAL CYCLES}, author={Gao, Xiaojie and McGregor, Ian R. R. and Gray, Josh M. M. and Friedl, Mark A. A. and Moon, Minkyu}, year={2023}, month={Mar} }
 @article{wendelberger_gray_wilson_houborg_reich_2022, title={Multiresolution Broad Area Search: Monitoring Spatial Characteristics of Gapless Remote Sensing Data}, url={https://doi.org/10.6339/22-JDS1072}, DOI={10.6339/22-JDS1072}, abstractNote={Global earth monitoring aims to identify and characterize land cover change like construction as it occurs. Remote sensing makes it possible to collect large amounts of data in near real-time over vast geographic areas and is becoming available in increasingly fine temporal and spatial resolution. Many methods have been developed for data from a single pixel, but monitoring pixel-wise spectral measurements over time neglects spatial relationships, which become more important as change manifests in a greater number of pixels in higher resolution imagery compared to moderate resolution. Building on our previous robust online Bayesian monitoring (roboBayes) algorithm, we propose monitoring multiresolution signals based on a wavelet decomposition to capture spatial change coherence on several scales to detect change sites. Monitoring only a subset of relevant signals reduces the computational burden. The decomposition relies on gapless data; we use 3 m Planet Fusion Monitoring data. Simulations demonstrate the superiority of the spatial signals in multiresolution roboBayes (MR roboBayes) for detecting subtle changes compared to pixel-wise roboBayes. We use MR roboBayes to detect construction changes in two regions with distinct land cover and seasonal characteristics: Jacksonville, FL (USA) and Dubai (UAE). It achieves site detection with less than two thirds of the monitoring processes required for pixel-wise roboBayes at the same resolution.}, journal={Journal of Data Science}, author={Wendelberger, Laura J. and Gray, Josh M. and Wilson, Alyson G. and Houborg, Rasmus and Reich, Brian J.}, year={2022} }
 @article{sodiya_parajuli_abt_gray_2022, title={Spatial Analysis of Forest Product Manufacturers in North Carolina}, volume={12}, ISSN={["1938-3738"]}, url={https://doi.org/10.1093/forsci/fxac045}, DOI={10.1093/forsci/fxac045}, abstractNote={
 Spatial analysis of industrial locations is an important tool for cluster-based economic development that helps identify hot spots for attracting new businesses in a particular region. The forest product industry in North Carolina (NC) is the top employer among all manufacturing sectors, with a substantial contribution to the state economy. Using geographic information system tools, we examined the current spatial distribution of the primary and secondary forest product manufacturers (FPM) and available forest resources to identify major hot spots in NC. Additionally, by estimating count data models, this study evaluated factors influencing the location of FPMs across counties in NC. Our results suggested that primary FPMs exhibit a higher spatial dependency relative to secondary FPMs. Similarly, regression results suggested that the counties near cities with high population, hot spots of raw materials, and better county economy are more likely to host both primary and secondary FPMs in the counties of NC. The findings of this study shed light on how the clustering of forest product manufacturing firms may influence competition between FPMs, sustainable supply of raw materials, and supply-chain networks in forest-dependent rural regions.
 Study Implications: Results suggested that counties with a presence of primary forest product manufacturers are more likely to host secondary forest product manufacturers, which reinforces the coagglomeration of primary and secondary forest product manufacturers (FPMs) in North Carolina. The interaction between primary and secondary FPMs is therefore important for the sustainable supply-chain network in the forest product industry. Moreover, the identified hot spots, especially in counties that do not currently host a forest products industry, could be potential locations for new mills. As our findings suggest that roads are an important determinant in locating both primary and secondary FPMs, more investment in the transportation system could boost the forest product industry in certain counties in NC.}, journal={FOREST SCIENCE}, author={Sodiya, Olakunle E. and Parajuli, Rajan and Abt, Robert C. and Gray, Joshua}, year={2022}, month={Dec} }
 @article{mei_wang_fouhey_zhou_hinks_gray_van berkel_jain_2022, title={Using Deep Learning and Very-High-Resolution Imagery to Map Smallholder Field Boundaries}, volume={14}, ISSN={["2072-4292"]}, DOI={10.3390/rs14133046}, abstractNote={The mapping of field boundaries can provide important information for increasing food production and security in agricultural systems across the globe. Remote sensing can provide a viable way to map field boundaries across large geographic extents, yet few studies have used satellite imagery to map boundaries in systems where field sizes are small, heterogeneous, and irregularly shaped. Here we used very-high-resolution WorldView-3 satellite imagery (0.5 m) and a mask region-based convolutional neural network (Mask R-CNN) to delineate smallholder field boundaries in Northeast India. We found that our models had overall moderate accuracy, with average precision values greater than 0.67 and F1 Scores greater than 0.72. We also found that our model performed equally well when applied to another site in India for which no data were used in the calibration step, suggesting that Mask R-CNN may be a generalizable way to map field boundaries at scale. Our results highlight the ability of Mask R-CNN and very-high-resolution imagery to accurately map field boundaries in smallholder systems.}, number={13}, journal={REMOTE SENSING}, author={Mei, Weiye and Wang, Haoyu and Fouhey, David and Zhou, Weiqi and Hinks, Isabella and Gray, Josh M. and Van Berkel, Derek and Jain, Meha}, year={2022}, month={Jul} }
 @article{gao_gray_reich_2021, title={Long-term, medium spatial resolution annual land surface phenology with a Bayesian hierarchical model}, volume={261}, ISSN={["1879-0704"]}, DOI={10.1016/j.rse.2021.112484}, abstractNote={Land surface phenology (LSP) is a consistent and sensitive indicator of climate change effects on Earth's vegetation. Existing methods of estimating LSP require time series densities that, until recently, have only been available from coarse spatial resolution imagery such as MODIS (500 m) and AVHRR (1 km). LSP products from these datasets have improved our understanding of phenological change at the global scale, especially over the MODIS era (2001-present). Nevertheless, these products may obscure important finer scale spatial patterns and longer-term changes. Therefore, we have developed a Bayesian hierarchical model to retrieve complete annual sequences of LSP from Landsat imagery (1984-present), which has medium spatial resolution (30 m) but relatively sparse temporal frequency. Our approach uses Markov Chain Monte Carlo (MCMC) sampling to quantify individual phenometric uncertainty, which is especially important when considering long time series with variable observation quality and density, but has rarely been demonstrated. The estimated spring LSP had strong agreement with ground phenology records at Harvard Forest (R2 = 0.87) and Hubbard Brook Experimental Forest (R2 = 0.67). The estimated LSP were consistent with the recently released 30 m LSP product, MSLSP30NA, in its time period of 2016 to 2018 (R2 of 0.86 and 0.73 for spring and autumn phenology, respectively). Our Bayesian hierarchical model is an important step forward in extending medium resolution LSP records back in time as it accomplishes both critical goals of retrieving annual LSP from sparse time series and accurately estimating uncertainty.}, journal={REMOTE SENSING OF ENVIRONMENT}, author={Gao, Xiaojie and Gray, Josh M. and Reich, Brian J.}, year={2021}, month={Aug} }
 @article{gao_gray_cohrs_cook_albaugh_2021, title={Longer greenup periods associated with greater wood volume growth in managed pine stands}, volume={297}, ISSN={["1873-2240"]}, url={https://doi.org/10.1016/j.agrformet.2020.108237}, DOI={10.1016/j.agrformet.2020.108237}, abstractNote={Increasing forest productivity is important to meet future demand for forest products, and to improve resilience in the face of climate change. Forest productivity depends on many things, but the timing of leaf development (hereafter: “plant phenology”) is especially important. However, our understanding of how plant phenology affects the productivity of managed forests, and how silviculture may in turn affect phenology, has been limited because of the spatial scale mismatch between phenological data and field experimental observations. In this study, we take advantage of a new 30 m satellite land surface phenology dataset and stand growth measurements from long-term experimental pine plantation sites in the southeastern United States to investigate the question: is stand growth related to remotely sensed phenology metrics? Multiple linear regression and random forest models were fitted to quantify the effect of phenology and silvicultural treatments on stand growth. We found that 1) Greater wood volume growth was associated with longer green up periods; 2) Fertilization elevated EVI2 measurement values during the whole growing season, especially in the winter; 3) Competing vegetation could affect remotely sensed observations and complicates interpretation of remotely sensed phenology metrics.}, journal={AGRICULTURAL AND FOREST METEOROLOGY}, publisher={Elsevier BV}, author={Gao, Xiaojie and Gray, Josh and Cohrs, Chris W. and Cook, Rachel and Albaugh, Timothy J.}, year={2021}, month={Feb} }
 @article{johnson_reich_gray_2021, title={Multisensor fusion of remotely sensed vegetation indices using space-time dynamic linear models}, volume={5}, ISSN={["1467-9876"]}, DOI={10.1111/rssc.12495}, abstractNote={High spatiotemporal resolution maps of surface vegetation from remote sensing data are desirable for vegetation and disturbance monitoring. However, due to the current limitations of imaging spectrometers, remote sensing datasets of vegetation with high temporal frequency of measurements have lower spatial resolution, and vice versa. In this research, we propose a space‐time dynamic linear model to fuse high temporal frequency data (MODIS) with high spatial resolution data (Landsat) to create high spatiotemporal resolution data products of a vegetation greenness index. The model incorporates the spatial misalignment of the data and models dependence within and across land cover types with a latent multivariate Matérn process. To handle the large size of the data, we introduce a fast estimation procedure and a moving window Kalman smoother to produce a daily, 30‐m resolution data product with associated uncertainty.}, journal={JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES C-APPLIED STATISTICS}, author={Johnson, Margaret C. and Reich, Brian J. and Gray, Josh M.}, year={2021}, month={May} }
 @article{bolton_gray_melaas_moon_eklundh_friedl_2020, title={Continental-scale land surface phenology from harmonized Landsat 8 and Sentinel-2 imagery}, volume={240}, ISSN={["1879-0704"]}, DOI={10.1016/j.rse.2020.111685}, abstractNote={Dense time series of Landsat 8 and Sentinel-2 imagery are creating exciting new opportunities to monitor, map, and characterize temporal dynamics in land surface properties with unprecedented spatial detail and quality. By combining imagery from the Landsat 8 Operational Land Imager and the MultiSpectral Instrument on-board Sentinel-2A and -2B, the remote sensing community now has access to moderate (10–30 m) spatial resolution imagery with repeat periods of ~3 days in the mid-latitudes. At the same time, the large combined data volume from Landsat 8 and Sentinel-2 introduce substantial new challenges for users. Land surface phenology (LSP) algorithms, which estimate the timing of phenophase transitions and quantify the nature and magnitude of seasonality in remotely sensed land surface conditions, provide an intuitive way to reduce data volumes and redundancy, while also furnishing data sets that are useful for a wide range of applications including monitoring ecosystem response to climate variability and extreme events, ecosystem modelling, crop-type discrimination, and land cover, land use, and land cover change mapping, among others. To support the need for operational LSP data sets, here we describe a continental-scale land surface phenology algorithm and data product based on harmonized Landsat 8 and Sentinel-2 (HLS) imagery. The algorithm creates high quality times series of vegetation indices from HLS imagery, which are then used to estimate the timing of vegetation phenophase transitions at 30 m spatial resolution. We present results from assessment efforts evaluating LSP retrievals, and provide examples illustrating the character and quality of information related to land cover and terrestrial ecosystem properties provided by the continental LSP dataset that we have developed. The algorithm is highly successful in ecosystems with strong seasonal variation in leaf area (e.g., deciduous forests). Conversely, results in evergreen systems are less interpretable and conclusive.}, journal={REMOTE SENSING OF ENVIRONMENT}, author={Bolton, Douglas K. and Gray, Josh M. and Melaas, Eli K. and Moon, Minkyu and Eklundh, Lars and Friedl, Mark A.}, year={2020}, month={Apr} }
 @article{singh_gray_2020, title={Mapping Understory Invasive Plants in Urban Forests with Spectral and Temporal Unmixing of Landsat Imagery}, volume={86}, ISSN={["2374-8079"]}, DOI={10.14358/PERS.86.8.509}, abstractNote={Successful eradication and management of invasive plants require frequent and accurate maps. Detection of invasive plants is difficult at moderate resolution because target species are often located in the forest understory among other vegetation types, and so produce mixed spectral
 signatures. Spectral unmixing approaches can help to decompose these spectral mixtures; however, they are typically applied to only one or a few images, and thus neglect phenological variability that may improve invasive species discrimination. We compared two approaches to multiple endmember
 spectral mixture analysis for detecting Ligustrum sinense in the southeastern United States: the use of temporal signatures of endmembers from full and select-date normalized difference vegetation index time series, and conventional spectral unmixing using a single image date. Our results
 suggest that using temporal signatures from all available imagery may be a good choice, with minimal impact to achievable accuracy, if a priori information on phenological differences between endmembers is unavailable, or imagery for periods of high phenological difference are unavailable.}, number={8}, journal={PHOTOGRAMMETRIC ENGINEERING AND REMOTE SENSING}, author={Singh, Kunwar K. and Gray, Josh}, year={2020}, month={Aug}, pages={509–518} }
 @article{hannon_moorman_schultz_gray_deperno_2020, title={Predictors of fire-tolerant oak and fire-sensitive hardwood distribution in a fire-maintained longleaf pine ecosystem}, volume={477}, ISSN={0378-1127}, url={http://dx.doi.org/10.1016/j.foreco.2020.118468}, DOI={10.1016/j.foreco.2020.118468}, abstractNote={The longleaf pine (Pinus palustris) ecosystem has been reduced to a fraction of its original extent, and where this ecosystem does occur, it is often degraded by hardwood encroachment. The reduction of hardwood tree cover is often a desirable longleaf pine community restoration outcome, though hardwood midstory and overstory trees have been recognized as an important natural component of the communities. Moreover, the appropriate amount of hardwood tree cover in a restored longleaf pine community is debated, as more hardwood tree cover can benefit mixed forest and mast-dependent wildlife (e.g., fox squirrels [Sciurus niger], white-tailed deer [Odocoileus virginianus]), and less hardwood tree cover is critical to the federally endangered red-cockaded woodpecker (Leuconotopicus borealis). To inform the debate, we assessed the environmental (e.g., topography, edaphic conditions, and pine basal area) and management (e.g., distance to firebreaks, prescribed fire history) factors that influenced abundance of upland hardwood trees in xeric longleaf pine communities on a site where frequent growing-season fire has been ongoing since 1991. We counted upland hardwoods ≥5 cm diameter at breast height (DBH) at 307 random field plots (0.04 ha) and categorized all hardwood trees as belonging to either a guild of fire-tolerant oaks or a guild of fire-sensitive hardwood species. We used generalized linear models (GLM) to determine the most important predictors of abundance for both guilds. The predictors of abundance differed between the two guilds, with fire-tolerant oak abundance increasing with greater slope and proximity to ignition sources and decreasing with greater pine basal area. Fire-sensitive hardwood abundance increased with mesic site conditions and decreased with the number of growing-season fires and greater pine basal area. Although seasonality in fire history was an important predictor of fire-sensitive hardwood abundance, variables related to long-term fire-history were not important predictors of fire-tolerant oak abundance in longleaf pine communities. However, with limited variation in fire return interval across the study area, our ability to draw inferences regarding the role of fire return interval was limited. Where hardwood encroachment is not a problem, and hardwood levels are below desired, balanced target levels, hardwood abundance in longleaf pine communities can be increased by reducing pine basal area and reducing prescribed fire intensity.}, journal={Forest Ecology and Management}, publisher={Elsevier BV}, author={Hannon, Daniel R. and Moorman, Christopher E. and Schultz, Alan D. and Gray, Josh M. and DePerno, Christopher S.}, year={2020}, month={Dec}, pages={118468} }
 @article{cohrs_cook_gray_albaugh_2020, title={Sentinel-2 Leaf Area Index Estimation for Pine Plantations in the Southeastern United States}, volume={12}, ISSN={2072-4292}, url={http://dx.doi.org/10.3390/rs12091406}, DOI={10.3390/rs12091406}, abstractNote={Leaf area index (LAI) is an important biophysical indicator of forest health that is linearly related to productivity, serving as a key criterion for potential nutrient management. A single equation was produced to model surface reflectance values captured from the Sentinel-2 Multispectral Instrument (MSI) with a robust dataset of field observations of loblolly pine (Pinus taeda L.) LAI collected with a LAI-2200C plant canopy analyzer. Support vector machine (SVM)-supervised classification was used to improve the model fit by removing plots saturated with aberrant radiometric signatures that would not be captured in the association between Sentinel-2 and LAI-2200C. The resulting equation, LAI = 0.310SR − 0.098 (where SR = the simple ratio between near-infrared (NIR) and red bands), displayed good performance ( R 2 = 0.81, RMSE = 0.36) at estimating the LAI for loblolly pine within the analyzed region at a 10 m spatial resolution. Our model incorporated a high number of validation plots (n = 292) spanning from southern Virginia to northern Florida across a range of soil textures (sandy to clayey), drainage classes (well drained to very poorly drained), and site characteristics common to pine forest plantations in the southeastern United States. The training dataset included plot-level treatment metrics—silviculture intensity, genetics, and density—on which sensitivity analysis was performed to inform model fit behavior. Plot density, particularly when there were ≤618 trees per hectare, was shown to impact model performance, causing LAI estimates to be overpredicted (to a maximum of X i + 0.16). Silviculture intensity (competition control and fertilization rates) and genetics did not markedly impact the relationship between SR and LAI. Results indicate that Sentinel-2’s improved spatial resolution and temporal revisit interval provide new opportunities for managers to detect within-stand variance and improve accuracy for LAI estimation over current industry standard models.}, number={9}, journal={Remote Sensing}, publisher={MDPI AG}, author={Cohrs, Chris W. and Cook, Rachel L. and Gray, Josh M. and Albaugh, Timothy J.}, year={2020}, month={Apr}, pages={1406} }
 @inproceedings{bolton_melaas_gray_moon_eklundh_friedl_2019, title={A Land Surface Phenology Product for North America from Harmonized Landsat 8 and Sentinel-2 imagery}, number={B32E–05}, booktitle={American Geophysical Union, Fall Meeting}, author={Bolton, Douglas Kane and Melaas, Eli K. and Gray, Josh and Moon, Minkyu and Eklundh, Lars and Friedl, Mark A.}, year={2019}, pages={B32E–05} }
 @article{stanimirova_cai_melaas_gray_eklundh_jonsson_friedl_2019, title={An Empirical Assessment of the MODIS Land Cover Dynamics and TIMESAT Land Surface Phenology Algorithms}, volume={11}, ISSN={["2072-4292"]}, DOI={10.3390/rs11192201}, abstractNote={Observations of vegetation phenology at regional-to-global scales provide important information regarding seasonal variation in the fluxes of energy, carbon, and water between the biosphere and the atmosphere. Numerous algorithms have been developed to estimate phenological transition dates using time series of remotely sensed spectral vegetation indices. A key challenge, however, is that different algorithms provide inconsistent results. This study provides a comprehensive comparison of start of season (SOS) and end of season (EOS) phenological transition dates estimated from 500 m MODIS data based on two widely used sources of such data: the TIMESAT program and the MODIS Global Land Cover Dynamics (MLCD) product. Specifically, we evaluate the impact of land cover class, criteria used to identify SOS and EOS, and fitting algorithm (local versus global) on the transition dates estimated from time series of MODIS enhanced vegetation index (EVI). Satellite-derived transition dates from each source are compared against each other and against SOS and EOS dates estimated from PhenoCams distributed across the Northeastern United States and Canada. Our results show that TIMESAT and MLCD SOS transition dates are generally highly correlated (r = 0.51-0.97), except in Central Canada where correlation coefficients are as low as 0.25. Relative to SOS, EOS comparison shows lower agreement and higher magnitude of deviations. SOS and EOS dates are impacted by noise arising from snow and cloud contamination, and there is low agreement among results from TIMESAT, the MLCD product, and PhenoCams in vegetation types with low seasonal EVI amplitude or with irregular EVI time series. In deciduous forests, SOS dates from the MLCD product and TIMESAT agree closely with SOS dates from PhenoCams, with correlations as high as 0.76. Overall, our results suggest that TIMESAT is well-suited for local-to-regional scale studies because of its ability to tune algorithm parameters, which makes it more flexible than the MLCD product. At large spatial scales, where local tuning is not feasible, the MLCD product provides a readily available data set based on a globally consistent approach that provides SOS and EOS dates that are comparable to results from TIMESAT.}, number={19}, journal={REMOTE SENSING}, author={Stanimirova, Radost and Cai, Zhanzhang and Melaas, Eli K. and Gray, Josh M. and Eklundh, Lars and Jonsson, Per and Friedl, Mark A.}, year={2019}, month={Oct} }
 @inproceedings{moon_seyednasrollah_richardson_gray_friedl_2019, title={Climate controls on springtime phenology in Eastern Temperate Forests of North America}, booktitle={American Geophysical Union, Fall Meeting}, author={Moon, Minkyu and Seyednasrollah, Bijan and Richardson, Andrew D. and Gray, Josh and Friedl, Mark A.}, year={2019}, pages={B33K–2629} }
 @article{sulla-menashe_gray_abercrombie_friedl_2019, title={Hierarchical mapping of annual global land cover 2001 to present: The MODIS Collection 6 Land Cover product}, volume={222}, ISSN={["1879-0704"]}, DOI={10.1016/j.rse.2018.12.013}, abstractNote={Land cover and land use maps provide an important basis for characterizing the ecological state and biophysical properties of Earth's land areas. The Collection 5 MODIS Global Land Cover Type product, initially released in 2010, was produced at annual time steps and has been widely used in the land science community. In this paper we describe refinements and improvements, in both the algorithm and the resulting map data sets, that have been implemented in the MODIS Collection 6 Global Land Cover Type product. Unlike the Collection 5 product, which was based on the 17-class International Geosphere-Biosphere Programme (IGBP) legend, the Collection 6 algorithm uses a hierarchical classification model where the classes included in each level of the hierarchy reflect structured distinctions between land cover properties. The resulting suite of nested classifications is combined to create eight distinct classification schemes including the five legacy schemes included in Collection 5, and three new legends based on the FAO-Land Cover Classification System (LCCS) that distinguish between land cover, land use, and surface hydrologic state. The Collection 6 algorithm also incorporates a state-space multitemporal modeling framework based on hidden Markov models that reduce spurious land cover changes introduced by classification uncertainty in individual years. Among other changes, relative to Collection 5, the Collection 6 product includes less area mapped as forests, open shrublands, and cropland/natural vegetation mosaics, and more area mapped as woodlands and grasslands. Accuracy assessment indicates that the Collection 6 product has an overall accuracy of 73.6% for the primary LCCS layer and that the amount of spurious land cover change has been substantially reduced in Collection 6 relative to Collection 5 (1.6% in C6 and 11.4% in C5).}, journal={REMOTE SENSING OF ENVIRONMENT}, author={Sulla-Menashe, Damien and Gray, Josh M. and Abercrombie, S. Parker and Friedl, Mark A.}, year={2019}, month={Mar}, pages={183–194} }
 @article{moon_zhang_henebry_liu_gray_melaas_friedl_2019, title={Long-term continuity in land surface phenology measurements: A comparative assessment of the MODIS land cover dynamics and VIIRS land surface phenology products}, volume={226}, ISSN={["1879-0704"]}, DOI={10.1016/j.rse.2019.03.034}, abstractNote={Vegetation phenology contributes to, and is diagnostic of, seasonal variation in ecosystem processes and exerts important controls on land-atmosphere exchanges of carbon, water, and energy. Satellite remote sensing provides a valuable source of data for monitoring the phenology of terrestrial ecosystems and has been widely used to map geographic and interannual variation in land surface phenology (LSP) over large areas. The Visible Infrared Imaging Radiometer Suite (VIIRS) land surface phenology product provides global data sets characterizing the annual LSP of terrestrial ecosystems, and is designed to support long-term continuity of LSP measurements from the Moderate Resolution Imaging Spectroradiometer (MODIS). We used data from VIIRS and MODIS to evaluate the agreement and characterize the similarities and differences between LSP data from each instrument. Specifically, we compare data from the Collection 6 MODIS Land Cover Dynamics (LCD) product with data from the newly developed VIIRS LSP product over the most common land cover types in North America. To do this, we assessed the overall agreement between time series of vegetation indices from VIIRS and MODIS, evaluated the correspondence between retrieved phenometrics from each instrument, and analyzed sources of differences between phenometrics from the each product. As part of this analysis, we also compared phenometrics from MODIS and VIIRS with phenometrics derived from Landsat Analysis Ready Data and PhenoCam time series. Results show that two-band enhanced vegetation index (EVI2) values from VIIRS and MODIS are similar (R2 > 0.81; root mean square deviation < 0.062), but that VIIRS EVI2 time series show more high frequency variation than time series from MODIS. Further, even though the VIIRS and MODIS products are generated using different instruments and algorithms, phenometrics from each product are similar and show only minor differences within and across land cover types. Systematic differences between phenometrics from the two products were generally less than one week (absolute bias 4.8 ± 3.0 days), and RMSDs were less than two weeks for most phenometrics across different land cover classes (10.7 ± 4.3 days). Comparison of VIIRS and MODIS LSP data with corresponding metrics estimated from Landsat and PhenoCam data consistently showed high agreement among the data sets. Overall, results from this analysis indicate that the VIIRS LSP product provides excellent continuity with the MODIS record. However, studies attempting to create high-fidelity long-term LSP time series by merging these products should exploit the overlap period of MODIS and VIIRS to estimate land cover-specific corrections for modest systematic bias in the MODIS LCD product relative to the VIIRS LSP product.}, journal={REMOTE SENSING OF ENVIRONMENT}, author={Moon, Minkyu and Zhang, Xiaoyang and Henebry, Geoffrey M. and Liu, Lingling and Gray, Josh M. and Melaas, Eli K. and Friedl, Mark A.}, year={2019}, month={Jun}, pages={74–92} }
 @book{seyednasrollah_young_hufkens_milliman_friedl_frolking_richardson_abraha_allen_apple_et al._2019, title={PhenoCam Dataset v2. 0: Vegetation Phenology from Digital Camera Imagery, 2000-2018}, DOI={10.3334/ORNLDAAC/1674}, journal={ORNL DAAC}, author={Seyednasrollah, Bijan and Young, A.M. and Hufkens, K. and Milliman, T. and Friedl, M.A. and Frolking, S. and Richardson, A.D. and Abraha, M. and Allen, D.W. and Apple, M. and et al.}, year={2019}, month={Sep} }
 @inproceedings{kruskamp_singh_jones_gray_meentemeyer_2019, title={Web-based Decision Analytics For Mapping Host Species Distributions and Forecasting the Spread of Forest Pests and Pathogens}, number={IN52A–03}, booktitle={American Geophysical Union, Fall Meeting}, author={Kruskamp, Nicholas and Singh, Kunwar K. and Jones, C.K. and Gray, Josh and Meentemeyer, Ross Kendall}, year={2019}, pages={IN52A–03} }
 @inproceedings{zhang_martin_gray_stevenson_yao_2018, title={Evaluating machine learning approaches for mapping flood risk}, booktitle={American Geophysical Union, Fall Meeting}, author={Zhang, Zhenzhen and Martin, Katherine and Gray, Joshua and Stevenson, Kathryn and Yao, Yuan}, year={2018}, pages={H41M–2286} }
 @article{zhang_jayavelu_liu_friedl_henebry_liu_schaaf_richardson_gray_2018, title={Evaluation of land surface phenology from VIIRS data using time series of PhenoCam imagery}, volume={256}, ISSN={["1873-2240"]}, DOI={10.1016/j.agrformet.2018.03.003}, abstractNote={Land surface phenology (LSP) has been widely retrieved from time series of various satellite instruments in order to monitor climate change and ecosystem dynamics. However, any evaluation of the quality of LSP data sets is quite challenging because the in situ observations on a limited number of individual trees, shrubs, or other plants are rarely representative of the landscape sampled in a single satellite pixel. Moreover, vegetation indices detecting biophysical features of vegetation seasonality are different from (but related to) the specific plant life history stages observed by humans at ground level. This study is the first comprehensive evaluation of the LSP product derived from Visible Infrared Imaging Radiometer Suite (VIIRS) data using both MODIS LSP products and observations from the PhenoCam network across the Contiguous United States during 2013 and 2014. PhenoCam observes vegetation canopy over a landscape at very high frequency, providing nearly continuous canopy status and enabling the estimate of discrete phenophase using vegetation indices that are conceptually similar to satellite data. Six phenological dates (greenup onset, mid-greenup phase, maturity onset, senescence onset, mid-senescence phase, and dormancy onset) were retrieved separately from daily VIIRS NDVI (normalized difference vegetative index) and EVI2 (two-band enhanced vegetation index) time series. Similarly, the six phenological dates were also extracted from the 30-min time series of PhenoCam data using GCC (green chromatic coordinate) and VCI (vegetation contrast index) separately. Phenological dates derived from VIIRS NDVI and EVI2 and PhenoCam GCC and VCI were generally comparable for the vegetation greenup phase, but differed considerably for the senescence phase. Although all indices captured green leaf development effectively, performance discrepancies arose due to their ability to track the mixture of senescing leaf colors. PhenoCam GCC and VCI phenological observations were in better agreement with the phenological dates from VIIRS EVI2 than from VIIRS NDVI. Further, the VIIRS EVI2 phenological metrics were more similar to those from PhenoCam VCI than from PhenoCam GCC time series. Overall, the average absolute difference between the VIIRS EVI2 and PhenoCam VCI phenological dates was 7–11 days in the greenup phase and 10–13 days in the senescence phase. The difference was smaller in forests, followed by grasslands and croplands, and then savannas. Finally, the phenological dates derived from VIIRS EVI2 were compared with MODIS detections, which showed a good agreement with an average absolute difference less than a week except for the senescence onset. These results for the first time demonstrate the upper boundary of uncertainty in VIIRS LSP detections and the continuity to MODIS LSP product.}, journal={AGRICULTURAL AND FOREST METEOROLOGY}, author={Zhang, Xiaoyang and Jayavelu, Senthilnath and Liu, Lingling and Friedl, Mark A. and Henebry, Geoffrey M. and Liu, Yan and Schaaf, Crystal B. and Richardson, Andrew D. and Gray, Joshua}, year={2018}, month={Jun}, pages={137–149} }
 @article{singh_chen_smart_gray_meentemeyer_2018, title={Intra-annual phenology for detecting understory plant invasion in urban forests}, volume={142}, ISSN={0924-2716}, url={http://dx.doi.org/10.1016/J.ISPRSJPRS.2018.05.023}, DOI={10.1016/J.ISPRSJPRS.2018.05.023}, abstractNote={Accurate and repeatable mapping of biological plant invasions is essential to develop successful management strategies for conserving native biodiversity. While overstory invasive plants have been successfully detected and mapped using multiple methods, understory invasive detection remains a challenge, particularly in dense forested environments. Very few studies have utilized an approach that identifies and aligns the acquisition timing of remote sensing imagery with peak phenological differences between understory and overstory vegetation types. We investigated this opportunity by analyzing a monthly time-series of 2011 Landsat TM data to identify acquisition periods with the highest phenological differences between understory and overstory vegetation for detecting the spatial distribution of the exotic understory plant Ligustrum sinense Lour., a rapidly spreading invader in urbanizing regions of the southeastern United States. We used vegetation indices (VI) to establish intra-annual phenological trends for L. sinense, evergreen forest, and deciduous forest located in Mecklenburg County, North Carolina, USA. We developed Random Forest (RF) models to detect L. sinense from those time steps exhibiting the highest phenological differences. We assessed the relative contribution of VI and topographic indices (TI) to the detection of L. sinense. We compared the top performing models and used the best overall model to produce a map of L. sinense for the study area. RF models that included VI, TI, and Landsat TM bands for March 13 and 29, 2011 (the periods with highest detected phenological differences), produced the highest overall accuracy and Kappa estimates, outperforming the combination of VI and TI by 8.5% in accuracy and 20.5% in Kappa. The top performing model from the RF produced a Kappa of 0.75. Our findings suggest that selecting remote sensing data for a period when phenological differences between L. sinense and forest types are at their peak can improve the detection and mapping of L. sinense.}, journal={ISPRS Journal of Photogrammetry and Remote Sensing}, publisher={Elsevier BV}, author={Singh, Kunwar K. and Chen, Yin-Hsuen and Smart, Lindsey and Gray, Josh and Meentemeyer, Ross K.}, year={2018}, month={Aug}, pages={151–161} }
 @inproceedings{friedl_sulla-menashe_gray_2018, title={Mapping Annual Land Cover and Phenology from MODIS: Global Data Sets Supporting Modeling and Global Change Science}, number={GC14B–08}, booktitle={American Geophysical Union, Fall Meeting}, author={Friedl, Mark A. and Sulla-menashe, Damien J. and Gray, Joshua}, year={2018}, pages={GC14B–08} }
 @inproceedings{kruskamp_gray_meentemeyer_2018, title={Quantifying emerging infectious disease impacts on above ground biomass}, booktitle={American Geophysical Union, Fall Meeting}, author={Kruskamp, Nicholas and Gray, Josh M. and Meentemeyer, Ross Kendall}, year={2018}, pages={B14B–06} }
 @article{singh_madden_gray_meentemeyer_2018, title={The managed clearing: An overlooked land-cover type in urbanizing regions?}, volume={13}, ISSN={["1932-6203"]}, DOI={10.1371/journal.pone.0192822}, abstractNote={Urban ecosystem assessments increasingly rely on widely available map products, such as the U.S. Geological Service (USGS) National Land Cover Database (NLCD), and datasets that use generic classification schemes to detect and model large-scale impacts of land-cover change. However, utilizing existing map products or schemes without identifying relevant urban class types such as semi-natural, yet managed land areas that account for differences in ecological functions due to their pervious surfaces may severely constrain assessments. To address this gap, we introduce the managed clearings land-cover type–semi-natural, vegetated land surfaces with varying degrees of management practices–for urbanizing landscapes. We explore the extent to which managed clearings are common and spatially distributed in three rapidly urbanizing areas of the Charlanta megaregion, USA. We visually interpreted and mapped fine-scale land cover with special attention to managed clearings using 2012 U.S. Department of Agriculture (USDA) National Agriculture Imagery Program (NAIP) images within 150 randomly selected 1-km2 blocks in the cities of Atlanta, Charlotte, and Raleigh, and compared our maps with National Land Cover Database (NLCD) data. We estimated the abundance of managed clearings relative to other land use and land cover types, and the proportion of land-cover types in the NLCD that are similar to managed clearings. Our study reveals that managed clearings are the most common land cover type in these cities, covering 28% of the total sampled land area– 6.2% higher than the total area of impervious surfaces. Managed clearings, when combined with forest cover, constitutes 69% of pervious surfaces in the sampled region. We observed variability in area estimates of managed clearings between the NAIP-derived and NLCD data. This suggests using high-resolution remote sensing imagery (e.g., NAIP) instead of modifying NLCD data for improved representation of spatial heterogeneity and mapping of managed clearings in urbanizing landscapes. Our findings also demonstrate the need to more carefully consider managed clearings and their critical ecological functions in landscape- to regional-scale studies of urbanizing ecosystems.}, number={2}, journal={PLOS ONE}, author={Singh, Kunwar K. and Madden, Marguerite and Gray, Josh and Meentemeyer, Ross K.}, year={2018}, month={Feb} }
 @article{richardson_hufkens_milliman_aubrecht_chen_gray_johnston_keenan_klosterman_kosmala_et al._2018, title={Tracking vegetation phenology across diverse North American biomes using PhenoCam imagery}, volume={5}, ISSN={["2052-4463"]}, DOI={10.1038/sdata.2018.28}, abstractNote={Vegetation phenology controls the seasonality of many ecosystem processes, as well as numerous biosphere-atmosphere feedbacks. Phenology is also highly sensitive to climate change and variability. Here we present a series of datasets, together consisting of almost 750 years of observations, characterizing vegetation phenology in diverse ecosystems across North America. Our data are derived from conventional, visible-wavelength, automated digital camera imagery collected through the PhenoCam network. For each archived image, we extracted RGB (red, green, blue) colour channel information, with means and other statistics calculated across a region-of-interest (ROI) delineating a specific vegetation type. From the high-frequency (typically, 30 min) imagery, we derived time series characterizing vegetation colour, including "canopy greenness", processed to 1- and 3-day intervals. For ecosystems with one or more annual cycles of vegetation activity, we provide estimates, with uncertainties, for the start of the "greenness rising" and end of the "greenness falling" stages. The database can be used for phenological model validation and development, evaluation of satellite remote sensing data products, benchmarking earth system models, and studies of climate change impacts on terrestrial ecosystems.}, journal={SCIENTIFIC DATA}, author={Richardson, Andrew D. and Hufkens, Koen and Milliman, Tom and Aubrecht, Donald M. and Chen, Min and Gray, Josh M. and Johnston, Miriam R. and Keenan, Trevor F. and Klosterman, Stephen T. and Kosmala, Margaret and et al.}, year={2018}, month={Mar} }
 @inproceedings{gray_khan_friedl_2018, title={USA-NPN Observations Reveal the Ecological Relevance of Remotely Sensed Phenology}, number={B53C–05}, booktitle={American Geophysical Union, Fall Meeting}, author={Gray, J.M. and Khan, Anam and Friedl, Mark A.}, year={2018}, pages={B53C–05} }
 @article{pickard_gray_meentemeyer_2017, title={Comparing Quantity, Allocation and Configuration Accuracy of Multiple Land Change Models}, volume={6}, ISSN={["2073-445X"]}, DOI={10.3390/land6030052}, abstractNote={The growing numbers of land change models makes it difficult to select a model at the beginning of an analysis, and is often arbitrary and at the researcher’s discretion. How to select a model at the beginning of an analysis, when multiple are suitable, represents a critical research gap currently understudied, where trade-offs of choosing one model over another are often unknown. Repeatable methods are needed to conduct cross-model comparisons to understand the trade-offs among models when the same calibration and validation data are used. Several methods to assess accuracy have been proposed that emphasize quantity and allocation, while overlooking the accuracy with which a model simulates the spatial configuration (e.g., size and shape) of map categories across landscapes. We compared the quantity, allocation, and configuration accuracy of four inductive pattern-based spatial allocation land change models (SLEUTH, GEOMOD, Land Change Modeler (LCM), and FUTURES). We simulated urban development with each model using identical input data from ten counties surrounding the growing region of Charlotte, North Carolina. Maintaining the same input data, such as land cover, drivers of change, and projected quantity of change, reduces differences in model inputs and allows for focus on trade-offs in different types of model accuracy. Results suggest that these four land change models produce representations of urban development with substantial variance, where some models may better simulate quantity and allocation at the trade-off of configuration accuracy, and vice versa. Trade-offs in accuracy exist with respect to the amount, spatial allocation, and landscape configuration of each model. This comparison exercise illustrates the range of accuracies for these models, and demonstrates the need to consider all three types of accuracy when assessing land change model’s projections.}, number={3}, journal={LAND}, author={Pickard, Brian and Gray, Joshua and Meentemeyer, Ross}, year={2017}, month={Sep} }
 @article{zhang_wang_gao_liu_schaaf_friedl_yu_jayavelu_gray_liu_et al._2017, title={Exploration of scaling effects on coarse resolution land surface phenology}, volume={190}, ISSN={0034-4257}, url={http://dx.doi.org/10.1016/J.RSE.2017.01.001}, DOI={10.1016/J.RSE.2017.01.001}, abstractNote={Numerous land surface phenology (LSP) datasets have been produced from various coarse resolution satellite data and different detection algorithms from regional to global scales. In contrast to field-observed phenological events that are defined by clearly evident organismal changes with biophysical meaning, current approaches to detecting transitions in LSP only determine the timing of variations in remotely sensed observations of surface greenness. Since activities to bridge LSP and field observations are challenging and limited, our understanding of the biophysical characteristics of LSP transitions is poor. Therefore, we set out to explore the scaling effects on LSP transitions at the nominal start of growing season (SOS) by comparing detections from coarse resolution data with those from finer resolution imagery. Specifically, using a hybrid piecewise-logistic-model-based LSP detection algorithm, we detected SOS in the agricultural core of the United States—central Iowa—at two scales: first, at a finer scale (30 m) using reflectance generated by fusing MODIS data with Landsat 8 OLI data (OLI SOS) and, second, at a coarser resolution of 500 m using Visible Infrared Imaging Radiometer Suite (VIIRS) observations. The VIIRS SOS data were compared with OLI SOS that had been aggregated using a percentile approach at various degrees of heterogeneity. The results revealed the complexities of SOS detections and the scaling effects that are latent at the coarser resolution. Specifically, OLI SOS variation defined using standard deviation (SD) was as large as 40 days within a highly spatially heterogeneous VIIRS pixel; whereas, SD could be < 10 days for a more homogeneous set of pixels. Furthermore, the VIIRS SOS detections equaled the OLI SOS (with an absolute difference less than one day) in > 60% of OLI pixels within a homogeneous VIIRS pixel, but in < 20% of OLI pixels within a spatially heterogeneous VIIRS pixel. Moreover, the SOS detections in a coarser resolution pixel reflected the timing at which vegetation greenup onset occurred in 30% of area, despite variation in SOS heterogeneities. This result suggests that (1) the SOS detections at coarser resolution are controlled more by the earlier SOS pixels at the finer resolution rather than by the later SOS pixels, and (2) it should be possible to well simulate the coarser SOS value by selecting the timing at 30th percentile SOS at the finer resolution. Finally, it was demonstrated that in homogeneous areas the VIIRS SOS was comparable with OLI SOS with an overall difference of < 5 days.}, journal={Remote Sensing of Environment}, publisher={Elsevier BV}, author={Zhang, Xiaoyang and Wang, Jianmin and Gao, Feng and Liu, Yan and Schaaf, Crystal and Friedl, Mark and Yu, Yunyue and Jayavelu, Senthilnath and Gray, Joshua and Liu, Lingling and et al.}, year={2017}, month={Mar}, pages={318–330} }
 @inproceedings{riveros-iregui_moser_christenson_gray_hedgespeth_jass_lowry_martin_nichols_stewart_et al._2017, place={Washington, D.C.}, title={Impacts of Extreme Flooding on Hydrologic Connectivity and Water Quality in the Atlantic Coastal Plain and Implications for Vulnerable Populations}, booktitle={American Geophysical Union, Fall Meeting}, publisher={American Geophysical Union}, author={Riveros-Iregui, Diego A. and Moser, Haley A. and Christenson, Elizabeth C. and Gray, Joshua and Hedgespeth, Melanie L. and Jass, Theodore Lawrence and Lowry, David Shane and Martin, Katherine and Nichols, Elizabeth G. and Stewart, Jill R. and et al.}, year={2017} }
 @book{richardson_hufkens_milliman_aubrecht_chen_gray_johnston_keenan_klosterman_kosmala_et al._2017, place={Oak Ridge, TN, USA}, title={PhenoCam Dataset v1. 0: Vegetation phenology from digital camera imagery, 2000–2015}, DOI={10.3334/ORNLDAAC/1511}, journal={ORNL DAAC}, author={Richardson, A.D. and Hufkens, K. and Milliman, T. and Aubrecht, D.M. and Chen, M. and Gray, J.M. and Johnston, M.R. and Keenan, T.F. and Klosterman, S.T. and Kosmala, M. and et al.}, year={2017}, month={Dec} }
 @inproceedings{gray_sills_amanatides_2017, title={Using Remote Sensing and Synthetic Controls to Understand Deforestation Drivers and their Moderation by Forest Use in Kalimantan, Indonesia}, number={GC52C–07}, booktitle={American Geophysical Union, Fall Meeting}, author={Gray, Josh M. and Sills, Erin O. and Amanatides, M.M.}, year={2017}, pages={GC52C–07} }
 @inproceedings{singh_gray_2017, title={Water savings from reduced alfalfa cropping in California’s Upper San Joaquin Valley}, number={IN51F–0069}, booktitle={American Geophysical Union, Fall Meeting}, author={Singh, Kunwar K. and Gray, Joshua}, year={2017}, pages={IN51F–0069} }
 @article{chen_melaas_gray_friedl_richardson_2016, title={A new seasonal-deciduous spring phenology submodel in the Community Land Model 4.5: impacts on carbon and water cycling under future climate scenarios}, volume={22}, ISSN={1354-1013}, url={http://dx.doi.org/10.1111/gcb.13326}, DOI={10.1111/gcb.13326}, abstractNote={A spring phenology model that combines photoperiod with accumulated heating and chilling to predict spring leaf‐out dates is optimized using PhenoCam observations and coupled into the Community Land Model (CLM) 4.5. In head‐to‐head comparison (using satellite data from 2003 to 2013 for validation) for model grid cells over the Northern Hemisphere deciduous broadleaf forests (5.5 million km2), we found that the revised model substantially outperformed the standard CLM seasonal‐deciduous spring phenology submodel at both coarse (0.9 × 1.25°) and fine (1 km) scales. The revised model also does a better job of representing recent (decadal) phenological trends observed globally by MODIS, as well as long‐term trends (1950–2014) in the PEP725 European phenology dataset. Moreover, forward model runs suggested a stronger advancement (up to 11 days) of spring leaf‐out by the end of the 21st century for the revised model. Trends toward earlier advancement are predicted for deciduous forests across the whole Northern Hemisphere boreal and temperate deciduous forest region for the revised model, whereas the standard model predicts earlier leaf‐out in colder regions, but later leaf‐out in warmer regions, and no trend globally. The earlier spring leaf‐out predicted by the revised model resulted in enhanced gross primary production (up to 0.6 Pg C yr−1) and evapotranspiration (up to 24 mm yr−1) when results were integrated across the study region. These results suggest that the standard seasonal‐deciduous submodel in CLM should be reconsidered, otherwise substantial errors in predictions of key land–atmosphere interactions and feedbacks may result.}, number={11}, journal={Global Change Biology}, publisher={Wiley}, author={Chen, Min and Melaas, Eli K. and Gray, Josh M. and Friedl, Mark A. and Richardson, Andrew D.}, year={2016}, month={May}, pages={3675–3688} }
 @article{melaas_sulla-menashe_gray_black_morin_richardson_friedl_2016, title={Multisite analysis of land surface phenology in North American temperate and boreal deciduous forests from Landsat}, volume={186}, ISSN={0034-4257}, url={http://dx.doi.org/10.1016/J.RSE.2016.09.014}, DOI={10.1016/J.RSE.2016.09.014}, abstractNote={Forests play important roles in the Earth's climate system and global carbon cycle. Therefore, a critical need exists to improve our understanding of how the growing seasons of forests are changing, and by extension, how the composition and function of forests will respond to future climate change. Coarse spatial resolution satellite remote sensing has been widely used to monitor and map the phenology of terrestrial ecosystems at regional to global scales, and despite widespread agreement that the growing season of Northern Hemisphere forests is changing, the spatial resolution of these data sources imposes significant limitations on the character and quality of inferences that can be drawn from them. In particular, the spatial resolution afforded by instruments such as MODIS does not resolve ecologically important landscape-scale patterns in phenology. With this issue in mind, here we evaluate the ability of a newly developed Landsat phenology algorithm (LPA) to reconstruct a 32-year time series for the start and end of the growing season in North American temperate and boreal forests. We focus on 13 “sidelap” regions located between overlapping Landsat scenes that span a large geographic range of temperate and boreal forests, and evaluate the quality and character of LPA-derived start and end of growing season (SOS and EOS) dates using several independent data sources. On average, SOS and EOS dates were detected for about two-thirds of the 32 years included in our analysis, with the remaining one-third missing due to cloud cover. Moreover, there was generally better agreement between ground observations and LPA-derived estimates of SOS dates than for EOS across the 13 sites included in our study. Our results demonstrate that, despite the presence of time series gaps, LPA provides a robust basis for retrospective analysis of long-term changes in spring and autumn deciduous forest phenology over the last three decades. Finally, our results support the potential for monitoring land surface phenology at 30 m spatial resolution in near real-time by combining time series from multiple sensors such as the Landsat Operational Land Imager and the Sentinel 2 MultiSpectral Instrument.}, journal={Remote Sensing of Environment}, publisher={Elsevier BV}, author={Melaas, Eli K. and Sulla-Menashe, Damien and Gray, Josh M. and Black, T. Andrew and Morin, Timothy H. and Richardson, Andrew D. and Friedl, Mark A.}, year={2016}, month={Dec}, pages={452–464} }
 @inproceedings{gray_friedl_singh_2016, place={Washington, D.C.}, title={Multisource Image Kalman Filtering for Rapid Phenological Monitoring and Forecasting}, booktitle={American Geophysical Union, Fall Meeting Abstracts}, publisher={American Geophysical Union}, author={Gray, J.M. and Friedl, Mark A. and Singh, Kunwar K.}, year={2016}, pages={B43B–0599} }
 @inproceedings{zhang_jayavelu_wang_henebry_gray_friedl_liu_schaaf_shuai_2016, place={Washington, D.C.}, title={Validation of VIIRS Land Surface Phenology using Field Observations, PhenoCam Imagery, and Landsat data.}, booktitle={American Geophysical Union, Fall Meeting Abstracts}, publisher={American Geophysical Union}, author={Zhang, Xiaoyang and Jayavelu, Senthilnath and Wang, Jianmin and Henebry, Geoffrey M. and Gray, Josh M. and Friedl, Mark A. and Liu, Yan and Schaaf, Crystal and Shuai, An}, year={2016}, pages={B33J–06} }
 @inproceedings{gray_friedl_2015, place={Washington, D.C.}, title={Incorporating phenology into yield models}, booktitle={American Geophysical Union, Fall Meeting Abstracts}, publisher={American Geophysical Union}, author={Gray, J.M. and Friedl, M.A.}, year={2015}, pages={B43A–0540} }
 @inproceedings{melaas_sulla-menashe_gray_friedl_2015, place={Washington, D.C.}, title={Using three decades of Landsat data to characterize changes and vulnerability of temperate and boreal forest phenology to climate change}, booktitle={American Geophysical Union, Fall Meeting Abstracts}, publisher={American Geophysical Union}, author={Melaas, Eli K. and Sulla-menashe, Damien J. and Gray, J.M. and Friedl, Mark A.}, year={2015}, pages={B21G–0548} }
 @article{friedl_gray_melaas_richardson_hufkens_keenan_bailey_o’keefe_2014, title={A tale of two springs: using recent climate anomalies to characterize the sensitivity of temperate forest phenology to climate change}, volume={9}, ISSN={1748-9326}, url={http://dx.doi.org/10.1088/1748-9326/9/5/054006}, DOI={10.1088/1748-9326/9/5/054006}, abstractNote={By the end of this century, mean annual temperatures in the Northeastern United States are expected to warm by 3–5 °C, which will have significant impacts on the structure and function of temperate forests in this region. To improve understanding of these impacts, we exploited two recent climate anomalies to explore how the springtime phenology of Northeastern temperate deciduous forests will respond to future climate warming. Specifically, springtime temperatures in 2010 and 2012 were the warmest on record in the Northeastern United States, with temperatures that were roughly equivalent to the lower end of warming scenarios that are projected for this region decades from now. Climate conditions in these two years therefore provide a unique empirical basis, that complements model-based studies, for improving understanding of how northeastern temperate forest phenology will change in the future. To perform our investigation, we analyzed near surface air temperatures from the United States Historical Climatology Network, time series of satellite-derived vegetation indices from NASA’s Moderate Resolution Imaging Spectroradiometer, and in situ phenological observations. Our study region encompassed the northern third of the eastern temperate forest ecoregion, extending from Pennsylvania to Canada. Springtime temperatures in 2010 and 2012 were nearly 3 °C warmer than long-term average temperatures from 1971–2000 over the region, leading to median anomalies of more than 100 growing degree days. In response, satellite and ground observations show that leaf emergence occurred up to two weeks earlier than normal, but with significant sensitivity to the specific timing of thermal forcing. These results are important for two reasons. First, they provide an empirical demonstration of the sensitivity of springtime phenology in northeastern temperate forests to future climate change that supports and complements model-based predictions. Second, our results show that subtle differences in the character of thermal forcing can substantially alter the timing of leaf emergence and canopy development. By explicitly comparing and contrasting the timing of thermal forcing and leaf phenology in 2010 and 2012, we show that even though temperatures were warmer in 2012 than in 2010, the nature and timing of thermal forcing in 2010 lead to leaf emergence that was almost a week earlier than 2012.}, number={5}, journal={Environmental Research Letters}, publisher={IOP Publishing}, author={Friedl, Mark A and Gray, Josh M and Melaas, Eli K and Richardson, Andrew D and Hufkens, Koen and Keenan, Trevor F and Bailey, Amey and O’Keefe, John}, year={2014}, month={May}, pages={054006} }
 @article{gray_frolking_kort_ray_kucharik_ramankutty_friedl_2014, title={Direct human influence on atmospheric CO2 seasonality from increased cropland productivity}, volume={515}, ISSN={0028-0836 1476-4687}, url={http://dx.doi.org/10.1038/NATURE13957}, DOI={10.1038/NATURE13957}, abstractNote={Ground- and aircraft-based measurements show that the seasonal amplitude of Northern Hemisphere atmospheric carbon dioxide (CO2) concentrations has increased by as much as 50 per cent over the past 50 years. This increase has been linked to changes in temperate, boreal and arctic ecosystem properties and processes such as enhanced photosynthesis, increased heterotrophic respiration, and expansion of woody vegetation. However, the precise causal mechanisms behind the observed changes in atmospheric CO2 seasonality remain unclear. Here we use production statistics and a carbon accounting model to show that increases in agricultural productivity, which have been largely overlooked in previous investigations, explain as much as a quarter of the observed changes in atmospheric CO2 seasonality. Specifically, Northern Hemisphere extratropical maize, wheat, rice, and soybean production grew by 240 per cent between 1961 and 2008, thereby increasing the amount of net carbon uptake by croplands during the Northern Hemisphere growing season by 0.33 petagrams. Maize alone accounts for two-thirds of this change, owing mostly to agricultural intensification within concentrated production zones in the midwestern United States and northern China. Maize, wheat, rice, and soybeans account for about 68 per cent of extratropical dry biomass production, so it is likely that the total impact of increased agricultural production exceeds the amount quantified here.}, number={7527}, journal={Nature}, publisher={Springer Science and Business Media LLC}, author={Gray, Josh M. and Frolking, Steve and Kort, Eric A. and Ray, Deepak K. and Kucharik, Christopher J. and Ramankutty, Navin and Friedl, Mark A.}, year={2014}, month={Nov}, pages={398–401} }
 @article{klosterman_hufkens_gray_melaas_sonnentag_lavine_mitchell_norman_friedl_richardson_2014, title={Evaluating remote sensing of deciduous forest phenology at multiple spatial scales using PhenoCam imagery}, volume={11}, ISSN={1726-4189}, url={http://dx.doi.org/10.5194/bg-11-4305-2014}, DOI={10.5194/bg-11-4305-2014}, abstractNote={Plant phenology regulates ecosystem services at local and global scales and is a sensitive indicator of global change. Estimates of phenophase transition dates, such as the start of spring or end of fall, can be derived from sensor- based time series, but must be interpreted in terms of bio- logically relevant events. We use the PhenoCam archive of digital repeat photography to implement a consistent proto- col for visual assessment of canopy phenology at 13 temper- ate deciduous forest sites throughout eastern North America, and to perform digital image analysis for time-series-based estimation of phenophase transition dates. We then compare these results to remote sensing metrics of phenophase tran- sition dates derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Advanced Very High Reso- lution Radiometer (AVHRR) sensors. We present a new type of curve fit that uses a generalized sigmoid function to es- timate phenology dates, and we quantify the statistical un- certainty of phenophase transition dates estimated using this method. Results show that the generalized sigmoid provides estimates of dates with less statistical uncertainty than other curve-fitting methods. Additionally, we find that dates de- rived from analysis of high-frequency PhenoCam imagery have smaller uncertainties than satellite remote sensing met- rics of phenology, and that dates derived from the remotely sensed enhanced vegetation index (EVI) have smaller uncer- tainty than those derived from the normalized difference veg- etation index (NDVI). Near-surface time-series estimates for the start of spring are found to closely match estimates de- rived from visual assessment of leaf-out, as well as satel- lite remote-sensing-derived estimates of the start of spring. However late spring and fall phenology metrics exhibit larger differences between near-surface and remote scales. Differ- ences in late spring phenology between near-surface and re- mote scales are found to correlate with a landscape metric of deciduous forest cover. These results quantify the effect of landscape heterogeneity when aggregating to the coarser spatial scales of remote sensing, and demonstrate the impor- tance of accurate curve fitting and vegetation index selection when analyzing and interpreting phenology time series.}, number={16}, journal={Biogeosciences}, publisher={Copernicus GmbH}, author={Klosterman, S. T. and Hufkens, K. and Gray, J. M. and Melaas, E. and Sonnentag, O. and Lavine, I. and Mitchell, L. and Norman, R. and Friedl, M. A. and Richardson, A. D.}, year={2014}, month={Aug}, pages={4305–4320} }
 @inproceedings{keenan_bohrer_friedl_gray_hollinger_munger_schmid_toomey_richardson_wing_et al._2014, place={Munchen}, series={Geophysical Research Abstracts}, title={Increased carbon uptake in the eastern US due to warming induced changes in phenology}, volume={16}, booktitle={European Geosciences Union General Assembly Abstracts}, publisher={European Geosciences Union}, author={Keenan, Trevor and Bohrer, Gil and Friedl, Mark and Gray, Josh and Hollinger, David and Munger, J.William and Schmid, Hans Peter and Toomey, Michael and Richardson, Andrew and Wing, Ian Sue and et al.}, year={2014}, collection={Geophysical Research Abstracts} }
 @article{gray_friedl_frolking_ramankutty_nelson_gumma_2014, title={Mapping Asian Cropping Intensity With MODIS}, volume={7}, ISSN={1939-1404 2151-1535}, url={http://dx.doi.org/10.1109/jstars.2014.2344630}, DOI={10.1109/jstars.2014.2344630}, abstractNote={Agricultural systems are geographically extensive, have profound significance to society, and affect regional energy, climate, and water cycles. Since most suitable lands worldwide have been cultivated, there is a growing pressure to increase yields on existing agricultural lands. In tropical and subtropical regions, multicropping is widely used to increase food production, but regional-to-global information related to multicropping practices is poor. The high temporal resolution and moderate spatial resolution of the MODIS sensors provide an ideal source of information for characterizing cropping practices over large areas. Relative to studies that document agricultural extensification, however, systematic assessment of agricultural intensification via multicropping has received relatively little attention. The goal of this work was to help close this information gap by developing methods that use multitemporal remote sensing to map multicropping systems in Asia. Image time-series analysis is especially challenging in this part of the world because atmospheric conditions including clouds and aerosols lead to high frequencies of missing or low-quality observations, especially during the Asian Monsoon. The methodology that we developed builds upon the algorithm used to produce the MODIS Land Cover Dynamics product (MCD12Q2), but uses an improved methodology optimized for crops. We assessed our results at the aggregate scale using state, district, and provincial level inventory statistics reporting total cropped and harvested areas, and at the field scale using survey results for 191 field sites in Bangladesh. While the algorithm highlighted the dominant continental-scale patterns in agricultural practices throughout Asia, and produced reasonable estimates of state and provincial level total harvested areas, field-scale assessment revealed significant challenges in mapping high cropping intensity due to abundant missing data.}, number={8}, journal={IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing}, publisher={Institute of Electrical and Electronics Engineers (IEEE)}, author={Gray, Josh and Friedl, Mark and Frolking, Steve and Ramankutty, Navin and Nelson, Andrew and Gumma, Murali Krishna}, year={2014}, month={Aug}, pages={3373–3379} }
 @article{li_friedl_xin_gray_pan_frolking_2014, title={Mapping Crop Cycles in China Using MODIS-EVI Time Series}, volume={6}, ISSN={2072-4292}, url={http://dx.doi.org/10.3390/rs6032473}, DOI={10.3390/rs6032473}, abstractNote={Abstract: As the Earth’s population continues to grow and demand for food increases, the need for improved and timely information related to the properties and dynamics of global agricultural systems is becoming increasingly important. Global land cover maps derived from satellite data provide indispensable information regarding the geographic distribution and areal extent of global croplands. However, land use information, such as cropping intensity (defined here as the number of cropping cycles per year), is not routinely available over large areas because mapping this information from remote sensing is challenging. In this study, we present a simple but efficient algorithm for automated mapping of cropping intensity based on data from NASA’s (NASA: The National Aeronautics and Space Administration) MODerate Resolution Imaging Spectroradiometer (MODIS). The proposed algorithm first applies an adaptive Savitzky-Golay filter to smooth Enhanced Vegetation Index (EVI) time series derived from MODIS surface reflectance data. It then uses an iterative moving-window methodology to identify cropping cycles from the smoothed EVI time series. Comparison of results from our algorithm with}, number={3}, journal={Remote Sensing}, publisher={MDPI AG}, author={Li, Le and Friedl, Mark and Xin, Qinchuan and Gray, Josh and Pan, Yaozhong and Frolking, Steve}, year={2014}, month={Mar}, pages={2473–2493} }
 @article{keenan_gray_friedl_toomey_bohrer_hollinger_munger_o’keefe_schmid_wing_et al._2014, title={Net carbon uptake has increased through warming-induced changes in temperate forest phenology}, volume={4}, ISSN={1758-678X 1758-6798}, url={http://dx.doi.org/10.1038/NCLIMATE2253}, DOI={10.1038/NCLIMATE2253}, number={7}, journal={Nature Climate Change}, publisher={Springer Science and Business Media LLC}, author={Keenan, Trevor F. and Gray, Josh and Friedl, Mark A. and Toomey, Michael and Bohrer, Gil and Hollinger, David Y. and Munger, J. William and O’Keefe, John and Schmid, Hans Peter and Wing, Ian Sue and et al.}, year={2014}, month={Jun}, pages={598–604} }
 @inproceedings{milliman_richardson_klosterman_gray_hufkens_aubrecht_chen_friedl_2014, place={Washington, D.C.}, title={Standardizing PhenoCam Image Processing and Data Products}, booktitle={American Geophysical Union, Fall Meeting Abstracts}, publisher={American Geophysical Union}, author={Milliman, Thomas E. and Richardson, Andrew D. and Klosterman, Stephen and Gray, J.M. and Hufkens, Koen and Aubrecht, Donald and Chen, Min and Friedl, Mark A.}, year={2014}, pages={B41K–019} }
 @inproceedings{friedl_melaas_sulla-menashe_gray_2014, place={Washington, D.C.}, title={Using Time Series of Landsat Data to Improve Understanding of Short-and Long-Term Changes to Vegetation Phenology in Response to Climate Change}, booktitle={American Geophysical Union, Fall Meeting Abstracts}, publisher={American Geophysical Union}, author={Friedl, M.A. and Melaas, E.K. and Sulla-menashe, D.J. and Gray, J.M.}, year={2014} }
 @article{gray_song_2013, title={Consistent classification of image time series with automatic adaptive signature generalization}, volume={134}, ISSN={0034-4257}, url={http://dx.doi.org/10.1016/J.RSE.2013.03.022}, DOI={10.1016/J.RSE.2013.03.022}, abstractNote={Long-term data archives such as Landsat offer the potential for understanding land cover dynamics over large areas, but limited progress has been made towards realizing this potential due to data availability and computational limitations. Those limitations are less relevant now, and there is renewed interest in developing reliable methods of automatically and consistently classifying time series of remotely sensed images. Our objective was to develop a method of automatically classifying temporally irregular time series (i.e., non-anniversary date images in consecutive years) of images with a minimum of parameterization and a priori information. In contrast to traditional signature extension methods, the automatic adaptive signature generalization procedure (AASG) adapts class spectral signatures to individual images and therefore requires no image correction procedure. Class signatures are derived from pixels with stable land cover through time. We tested the performance of AASG relative to traditional signature extension with various image corrections, and explored the sensitivity of AASG to a thresholding parameter (c) controlling stable site identification. AASG performed as well as signature extension with atmospheric correction (κ = 0.68), and better than signature extension with relative (κ = 0.65) and TOA reflectance (κ = 0.56) image corrections for a summer–summer image pair. Additionally, we demonstrated the unique ability of AASG to adapt class signatures to phenological differences by classifying a summer–winter image pair with a modest reduction in overall accuracy (κ = 0.66). Observed sensitivity to c supported the hypothesis of an optimum value yielding enough training sites to describe class spectral variability, but conservative enough to minimize contamination of signatures due to classification errors. AASG offers significant advantages over traditional signature extension, particularly for temporally irregular time series. Although we demonstrated a simple implementation, the AASG approach is flexible and we outline several refinements which stand to improve performance. This development represents significant progress towards realizing the potential of long-term data archives to gain long-term understandings of global land cover dynamics.}, journal={Remote Sensing of Environment}, publisher={Elsevier BV}, author={Gray, Josh and Song, Conghe}, year={2013}, month={Jul}, pages={333–341} }
 @inproceedings{gray_friedl_frolking_ramankutty_nelson_2013, place={Washington, D.C.}, title={Large scale maps of cropping intensity in Asia from MODIS}, booktitle={American Geophysical Union, Fall Meeting Abstracts}, publisher={American Geophysical Union}, author={Gray, Josh M. and Friedl, Mark A. and Frolking, Steve and Ramankutty, Navin and Nelson, Andrew}, year={2013}, pages={B41A–0385} }
 @inproceedings{frick_friedl_melaas_gray_2012, place={Washington, D.C.}, title={A comparison of phenophase transition dates calculated from MODIS EVI and NBAR-EVI}, booktitle={American Geophysical Union, Fall Meeting Abstracts}, publisher={American Geophysical Union}, author={Frick, E.A. and Friedl, M.A. and Melaas, E.K. and Gray, J.M.}, year={2012}, pages={B11C–0438} }
 @article{gray_song_2012, title={Mapping leaf area index using spatial, spectral, and temporal information from multiple sensors}, volume={119}, ISSN={0034-4257}, url={http://dx.doi.org/10.1016/j.rse.2011.12.016}, DOI={10.1016/j.rse.2011.12.016}, abstractNote={Leaf area index (LAI) is one of the most important biophysical parameters for modeling ecosystem processes such as carbon and water fluxes. Remote sensing provides the only feasible option for mapping LAI continuously over landscapes, but existing methodologies have significant limitations. There is a tradeoff between spatial and temporal resolutions inherent in remotely sensed images, i.e. high spatial resolution images may only be collected infrequently, whereas imagery with fine temporal resolution has necessarily coarser spatial resolution. LAI products created using a single sensor inherit the spatial and temporal characteristics of that sensor. Moreover, the majority of developed algorithms in the literature use spectral information alone, which suffers from the serious limitation of signal saturation at moderately high LAI. We developed a novel approach for mapping effective LAI (Le) using spectral information from Landsat, spatial information from IKONOS, and temporal information from MODIS, which overcomes these limitations. The approach is based on an empirical model developed between Le measured on the ground and spectral and spatial information from remotely sensed images to map annual maximum and minimum Le. A phenological model was fit to a time series of MODIS vegetation indices which was used to model the trajectory between annual minimum and maximum Le. This approach was able to generate maps of Le at Landsat spatial resolution with daily temporal resolution. We tested the approach in the North Carolina Piedmont and generated daily maps of Le for a 100 km2 area. Modeled Le compared well with time series of LAI estimates from two AmeriFlux sites within the study area. A comparison of the MODIS LAI product with spatially averaged Le estimates from our model showed general agreement in forested areas, but large differences in developed areas. This model takes advantage of multidimensional information available from multiple remote sensors and offers significant improvements for mapping leaf area index, particularly for forested areas where spectral indices tend to saturate.}, journal={Remote Sensing of Environment}, publisher={Elsevier BV}, author={Gray, Josh and Song, Conghe}, year={2012}, month={Apr}, pages={173–183} }
 @inproceedings{friedl_richardson_pless_frolking_milliman_klosterman_toomey_gray_2012, place={Washington, D.C.}, title={PhenoCam: A Continental Observatory in Support of Monitoring, Modeling, and Forecasting Phenological Responses to Climate Change}, booktitle={American Geophysical Union, Fall Meeting Abstracts}, publisher={American Geophysical Union}, author={Friedl, Mark A. and Richardson, A.D. and Pless, R. and Frolking, Steve and Milliman, T.E. and Klosterman, Stephen and Toomey, M.P. and Gray, Josh M.}, year={2012}, pages={GC54A–06} }
 @phdthesis{gray_2012, place={Chapel Hill, North Carolina}, title={Understanding regional water resource dynamics due to land-cover/land-use and climate changes in the North Carolina Piedmont}, DOI={10.17615/gfa6-ec52}, school={University of North Carolina-Chapel Hill}, author={Gray, Joshua Michael}, year={2012} }
 @inbook{song_gray_gao_2011, title={Remote Sensing of Vegetation with Landsat Imagery}, ISBN={9781420091755 9781420091816}, ISSN={2155-1839}, url={http://dx.doi.org/10.1201/b10599-3}, DOI={10.1201/b10599-3}, booktitle={Advances in Environmental Remote Sensing}, publisher={CRC Press}, author={Song, Conghe and Gray, Joshua and Gao, Feng}, year={2011}, month={Feb}, pages={3–29} }
 @inproceedings{song_gray_zhang_2008, place={Washington, D.C.}, title={Retrieving LAI from Remotely Sensed Images: Spectral Indices vs Spatial Texture}, booktitle={American Geophysical Union, Fall Meeting Abstracts}, publisher={American Geophysical Union}, author={Song, C. and Gray, J.M. and Zhang, S.}, year={2008}, pages={B33D–03} }