@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{liu_li_wang_gao_2022, title={Assessing the effects of urban green landscape on urban thermal environment dynamic in a semiarid city by integrated use of airborne data, satellite imagery and land surface model}, volume={107}, ISSN={["1872-826X"]}, DOI={10.1016/j.jag.2021.102674}, abstractNote={The response of the urban thermal environment to green space landscapes has been studied previously, while its detailed pattern is insufficiently explored owing to the constraints of spatial and temporal resolution of available datasets. This study uses integrated Thermal Airborne Spectrographic Imager (TASI) data, Landsat TM/ETM and MODIS satellite imagery, and Noah land surface model output to investigate the effects of landscape pattern on the urban thermal environment across Shijiazhuang, China. The present study not merely proposes a generalized framework for the spatiotemporal analysis of urban thermal environment, but also affords several insights into the cooling effects related to urban green space landscapes in semiarid cities. Firstly, trees and lawns show a noticeable disparity in land surface temperature (LST) response to urban green landscape metrics, primarily due to the difference in cooling efficiency via evapotranspiration. This disparity can be explained further by the radiation-shading effect of trees. Secondly, analysis confirms that the composition of urban green space has a substantial impact on LST throughout summer. This pattern is largely stable for trees owing to the constant Bowen ratio, but they are altered for lawns. Conversely, the configurations of urban green space exhibit less impact on LST. These effects vary temporally in magnitude and can be enhanced notably in humid conditions. The lower correlation between the configuration metrics of urban green and LST has to do with the surface resistance alterations and additional cooling effects. Finally, consistent patterns of the impact of urban green space landscape metrics on LST are illustrated at different spatial scales and region sizes but greater effects are revealed for smaller analytical units, further confirming the impacts of urban green landscape on urban thermal environment.}, journal={INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION}, author={Liu, Kai and Li, Xueke and Wang, Shudong and Gao, Xiaojie}, year={2022}, month={Mar} }
 @article{zhang_dai_li_gao_zhang_gong_lu_wang_ji_wang_et al._2022, title={Crop classification for UAV visible imagery using deep semantic segmentation methods}, ISSN={["1752-0762"]}, DOI={10.1080/10106049.2022.2032387}, abstractNote={Abstract Unmanned aerial vehicle (UAV) has become a mainstream data collection platform in precision agriculture. For more accessible UAV–visible imagery, the high spatial resolution brings the rich geometric texture features triggered large differences in same crop image's features. We proposed an encoder–decoder's fully convolutional neural network combined with a visible band difference vegetation index (VDVI) to perform deep semantic segmentation of crop image features. This model ensures the accuracy and the generalization ability, while reducing parameters and the operation cost. A case study of crop classification was conducted in Chengdu, China, where classified four crops, namely, maize, rice, balsam pear, and Loropetalum chinese, it was shown more effective results. In addition, this study explores a fine crop classification method based on visible light features, which is feasible with low equipment cost, and has a prospect of application in crop survey based on UAV low altitude remote sensing.}, journal={GEOCARTO INTERNATIONAL}, author={Zhang, Shiqi and Dai, Xiaoai and Li, Jingzhong and Gao, Xiaojie and Zhang, Fuxi and Gong, Fanxi and Lu, Heng and Wang, Meilian and Ji, Fujiang and Wang, Zekun and et al.}, year={2022}, month={Feb} }
 @article{yang_dai_wang_gao_qu_li_li_lu_wang_2022, title={The dynamics of Paiku Co lake area in response to climate change}, ISSN={["2408-9354"]}, DOI={10.2166/wcc.2022.083}, abstractNote={
 With the drastic change in global climate, the wide distribution of natural lakes over the Qinghai-Tibet Plateau (TP) has attracted extensive attention due to their high climate sensitivity. In this paper, we investigated the dynamics of Paiku Co, the largest inland lake in the Qomolangma Natural Reserve, with the associated response to climate change in the past three decades. The methods used contain the water index method, the spatial and temporal fusion model, the statistical mono-window algorithm, and multi-variable linear regression. Lake area fluctuated greatly in 1990–2000, followed by a continuous shrinkage in 2000–2010, and stabled after that 2010–2020. We forecasted that Paiku Co would enter a slow expansion period. Conjoint analysis with climate factors showed that the area variation of Paiku Co was not significantly related to precipitation change, but negatively related to the change of air temperature and lake temperature. We found that the lake change was not dominated by a single factor but showed different climate sensitivity in each period. Especially, there was a common inflection point around 2013 that might herald the occurrence of a new trend of climate change. This article provides new ideas and solutions for the research of lakes in the Qinghai-Tibet Plateau and offers a reference for water resource management.}, journal={JOURNAL OF WATER AND CLIMATE CHANGE}, author={Yang, Zhichong and Dai, Xiaoai and Wang, Zekun and Gao, Xiaojie and Qu, Ge and Li, Weile and Li, Jingzhong and Lu, Heng and Wang, Youlin}, year={2022}, month={Jun} }
 @article{bo_li_liu_wang_zhang_gao_zhang_2022, title={Three Decades of Gross Primary Production (GPP) in China: Variations, Trends, Attributions, and Prediction Inferred from Multiple Datasets and Time Series Modeling}, volume={14}, ISSN={["2072-4292"]}, DOI={10.3390/rs14112564}, abstractNote={The accurate estimation of gross primary production (GPP) is crucial to understanding plant carbon sequestration and grasping the quality of the ecological environment. Nevertheless, due to the inconsistencies of current GPP products, the variations, trends and short-term predictions of GPP have not been sufficiently well studied. In this study, we explore the spatiotemporal variability and trends of GPP and its associated climatic and anthropogenic factors in China from 1982 to 2015, mainly based on the optimum light use efficiency (LUEopt) product. We also employ an autoregressive integrated moving average (ARIMA) model to forecast the monthly GPP for a one-year lead time. The results show that GPP experienced an upward trend of 2.268 g C/m2 per year during the studied period, that is, an increasing rate of 3.9% per decade since 1982. However, these trend changes revealed distinct heterogeneity across space and time. The positive trends were mainly distributed in the Yellow River and Huaihe River out of the nine major river basins in China. We found that the dynamics of GPP were concurrently affected by climate factors and human activities. While air temperature and leaf area index (LAI) played dominant roles at a national level, the effects of precipitation, downward shortwave radiation (SRAD), carbon dioxide (CO2) and aerosol optical depth (AOD) exhibited discrepancies in terms of degree and scope. The ARIMA model achieved satisfactory prediction performance in most areas, though the accuracy was influenced by both data values and data quality. The model can potentially be generalized for other biophysical parameters with distinct seasonality. Our findings are further verified and corroborated by four widely used GPP products, demonstrating a good consistency of GPP trends and prediction. Our analysis provides a robust framework for characterizing long-term GPP dynamics that shed light on the improved assessment of the environmental quality of terrestrial ecosystems.}, number={11}, journal={REMOTE SENSING}, author={Bo, Yong and Li, Xueke and Liu, Kai and Wang, Shudong and Zhang, Hongyan and Gao, Xiaojie and Zhang, Xiaoyuan}, year={2022}, month={Jun} }
 @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{yoshizumi_coffer_collins_gaines_gao_jones_mcgregor_mcquillan_perin_tomkins_et al._2020, title={A Review of Geospatial Content in IEEE Visualization Publications}, DOI={10.1109/VIS47514.2020.00017}, abstractNote={Geospatial analysis is crucial for addressing many of the world’s most pressing challenges. Given this, there is immense value in improving and expanding the visualization techniques used to communicate geospatial data. In this work, we explore this important intersection – between geospatial analytics and visualization – by examining a set of recent IEEE VIS Conference papers (a selection from 2017-2019) to assess the inclusion of geospatial data and geospatial analyses within these papers. After removing the papers with no geospatial data, we organize the remaining literature into geospatial data domain categories and provide insight into how these categories relate to VIS Conference paper types. We also contextualize our results by investigating the use of geospatial terms in IEEE Visualization publications over the last 30 years. Our work provides an understanding of the quantity and role of geospatial subject matter in recent IEEE VIS publications and supplies a foundation for future meta-analytical work around geospatial analytics and geovisualization that may shed light on opportunities for innovation.}, journal={2020 IEEE VISUALIZATION CONFERENCE - SHORT PAPERS (VIS 2020)}, author={Yoshizumi, Alexander and Coffer, Megan M. and Collins, Elyssa L. and Gaines, Mollie D. and Gao, Xiaojie and Jones, Kate and McGregor, Ian R. and McQuillan, Katie A. and Perin, Vinicius and Tomkins, Laura M. and et al.}, year={2020}, pages={51–55} }