@article{tiwari_tulbure_caineta_gaines_perin_kamal_krupnik_aziz_islam_2024, title={Automated in-season rice crop mapping using Sentinel time-series data and Google Earth Engine: A case study in climate-risk prone Bangladesh}, volume={351}, ISSN={["1095-8630"]}, DOI={10.1016/j.jenvman.2023.119615}, abstractNote={High-resolution mapping of rice fields is crucial for understanding and managing rice cultivation in countries like Bangladesh, particularly in the face of climate change. Rice is a vital crop, cultivated in small scale farms that contributes significantly to the economy and food security in Bangladesh. Accurate mapping can facilitate improved rice production, the development of sustainable agricultural management policies, and formulation of strategies for adapting to climatic risks. To address the need for timely and accurate rice mapping, we developed a framework specifically designed for the diverse environmental conditions in Bangladesh. We utilized Sentinel-1 and Sentinel-2 time-series data to identify transplantation and peak seasons and employed the multi-Otsu automatic thresholding approach to map rice during the peak season (April-May). We also compared the performance of a random forest (RF) classifier with the multi-Otsu approach using two different data combinations: D1, which utilizes data from the transplantation and peak seasons (D1 RF) and D2, which utilizes data from the transplantation to the harvest seasons (D2 RF). Our results demonstrated that the multi-Otsu approach achieved an overall classification accuracy (OCA) ranging from 61.18% to 94.43% across all crop zones. The D2 RF showed the highest mean OCA (92.15%) among the fourteen crop zones, followed by D1 RF (89.47%) and multi-Otsu (85.27%). Although the multi-Otsu approach had relatively lower OCA, it proved effective in accurately mapping rice areas prior to harvest, eliminating the need for training samples that can be challenging to obtain during the growing season. In-season rice area maps generated through this framework are crucial for timely decision-making regarding adaptive management in response to climatic stresses and forecasting area-wide productivity. The scalability of our framework across space and time makes it particularly suitable for addressing field data scarcity challenges in countries like Bangladesh and offers the potential for future operationalization.}, journal={JOURNAL OF ENVIRONMENTAL MANAGEMENT}, author={Tiwari, Varun and Tulbure, Mirela G. and Caineta, Julio and Gaines, Mollie D. and Perin, Vinicius and Kamal, Mustafa and Krupnik, Timothy J. and Aziz, Md Abdullah and Islam, A. F. M. Tariqul}, year={2024}, month={Feb} }
 @article{perin_tulbure_gaines_reba_yaeger_2022, title={A multi-sensor satellite imagery approach to monitor on-farm reservoirs}, volume={270}, ISSN={["1879-0704"]}, DOI={10.1016/j.rse.2021.112796}, abstractNote={Fresh water stored by on-farm reservoirs (OFRs) is an important component of surface hydrology and is critical for meeting global irrigation needs. Farmers use OFRs to store water during the wet season and for crop irrigation during the dry season, yet their seasonal and inter-annual variability and downstream impacts are not quantified. Therefore, OFRs' sub-weekly surface area changes are critical to understanding their dynamics and mitigating their downstream impacts. However, prior to the recent increase in satellite imagery availability and improvement in sensors' spatial resolution, monitoring the OFRs' sub-weekly surface area changes across space and time was challenging because OFRs occur in high numbers (i.e. hundreds) and are small water bodies (< 50 ha). We propose a novel multi-sensor approach to monitor OFRs surface areas, developed based on 736 OFRs in eastern Arkansas, USA, which leverages the use of PlanetScope (PS), RapidEye (RE), Sentinel 2 (S2), and Sentinel 1 (S1). First, we estimate the uncertainties in surface area for each sensor by comparing the surface area estimates to a validation dataset, and by comparing RE, S2 and S1 to PS—the sensor with the highest spatial resolution (i.e. 3.125 m). Second, we use the uncertainties of each sensor with a data assimilation algorithm based on the Kalman filter to obtain sub-weekly surface area time series for all OFRs. Our results show the lowest uncertainties for PS, followed by RE, S2 and S1. These uncertainties varied according to the OFRs' size and shape complexities. The surface area estimates derived from the Kalman filter including only the optical sensors resulted in high agreement (r2 > 0.95) and small uncertainties (4–8%) when compared to the validation dataset. We found higher uncertainties (5–14%) when adding S1 to the Kalman filter—this is related to the higher uncertainties found for S1 (~20%). The algorithm can assimilate optical and radar satellite data to increase the OFRs' surface area time series cadence allowing us to investigate sub-weekly surface area changes. The algorithm is not sensor-specific, and it accounts for the uncertainties in both the sensors observations and the resulting surface areas, which are key advantages when compared to other algorithms used to combine satellite data. By improving the surface area observations cadence and providing the surface area uncertainties, the approach presented in this study has the potential to enhance water conservation plans by allowing better assessment and management of the OFRs.}, journal={REMOTE SENSING OF ENVIRONMENT}, author={Perin, Vinicius and Tulbure, Mirela G. and Gaines, Mollie D. and Reba, Michele L. and Yaeger, Mary A.}, year={2022}, month={Mar} }
 @article{tulbure_broich_perin_gaines_ju_stheman_pavelsky_masek_yin_mai_et al._2022, title={Can we detect more ephemeral floods with higher density harmonized Landsat Sentinel 2 data compared to Landsat 8 alone?}, volume={185}, ISSN={["1872-8235"]}, DOI={10.1016/j.isprsjprs.2022.01.021}, abstractNote={Spatiotemporal quantification of surface water and flooding is essential given that floods are among the largest natural hazards. Effective disaster response management requires near real-time information on flood extent. Satellite remote sensing is the only way of monitoring these dynamics across vast areas and over time. Previous water and flood mapping efforts have relied on optical time series, despite cloud contamination. This reliance on optical data is due to the availability of systematically acquired and easily accessible optical data globally for over 40 years. Prior research used either MODIS or Landsat data, trading either high temporal density but lower spatial resolution or lower cadence but higher spatial resolution. Both MODIS and Landsat pose limitations as Landsat can miss ephemeral floods, whereas MODIS misses small floods and inaccurately delineates flood edges. Leveraging high temporal frequency of 3–4 days of the existing Landsat-8 (L8) and two Sentinel-2 (S2) satellites combined, in this research, we assessed whether the increased temporal frequency of the three sensors improves our ability to detect surface water and flooding extent compared to a single sensor (L8 alone). Our study area was Australia’s Murray-Darling Basin, one of the world’s largest dryland basins that experiences ephemeral floods. We applied machine learning to NASA’s Harmonized Landsat Sentinel-2 (HLS) Surface Reflectance Product, which combines L8 and S2 observations, to map surface water and flooding dynamics. Our overall accuracy, estimated from a stratified random sample, was 99%. Our user’s and producer’s accuracy for the water class was 80% (±3.6%, standard error) and 76% (±5.8%). We focused on 2019, one of the most recent years when all three HLS sensors operated at full capacity. Our results show that water area (permanent and flooding) identified with the HLS was greater than that identified by L8, and some short-lived flooding events were detected only by the HLS. Comparison with high resolution (3 m) PlanetScope data identified extensive mixed pixels at the 30 m HLS resolution, highlighting the need for improved spatial resolution in future work. The HLS has been able to detect floods in cases when one sensor (L8) alone was not, despite 2019 being one of the driest years in the area, with few flooding events. The dense optical time-series offered by the HLS data is thus critical for capturing temporally dynamic phenomena (i.e., ephemeral floods in drylands), highlighting the importance of harmonized data such as the HLS.}, journal={ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING}, author={Tulbure, Mirela G. and Broich, Mark and Perin, Vinicius and Gaines, Mollie and Ju, Junchang and Stheman, Stephen V. and Pavelsky, Tamlin G. and Masek, Jeffrey and Yin, Simon and Mai, Joachim and et al.}, year={2022}, month={Mar}, pages={232–246} }
 @article{gaines_tulbure_perin_2022, title={Effects of Climate and Anthropogenic Drivers on Surface Water Area in the Southeastern United States}, volume={58}, ISSN={["1944-7973"]}, DOI={10.1029/2021WR031484}, abstractNote={Abstract}, number={3}, journal={WATER RESOURCES RESEARCH}, author={Gaines, Mollie D. and Tulbure, Mirela G. and Perin, Vinicius}, year={2022}, month={Mar} }
 @article{perin_roy_kington_harris_tulbure_stone_barsballe_reba_yaeger_2021, title={Monitoring Small Water Bodies Using High Spatial and Temporal Resolution Analysis Ready Datasets}, volume={13}, ISSN={["2072-4292"]}, DOI={10.3390/rs13245176}, abstractNote={Basemap and Planet Fusion—derived from PlanetScope imagery—represent the next generation of analysis ready datasets that minimize the effects of the presence of clouds. These datasets have high spatial (3 m) and temporal (daily) resolution, which provides an unprecedented opportunity to improve the monitoring of on-farm reservoirs (OFRs)—small water bodies that store freshwater and play important role in surface hydrology and global irrigation activities. In this study, we assessed the usefulness of both datasets to monitor sub-weekly surface area changes of 340 OFRs in eastern Arkansas, USA, and we evaluated the datasets main differences when used to monitor OFRs. When comparing the OFRs surface area derived from Basemap and Planet Fusion to an independent validation dataset, both datasets had high agreement (r2 ≥ 0.87), and small uncertainties, with a mean absolute percent error (MAPE) between 7.05% and 10.08%. Pairwise surface area comparisons between the two datasets and the PlanetScope imagery showed that 61% of the OFRs had r2 ≥ 0.55, and 70% of the OFRs had MAPE <5%. In general, both datasets can be employed to monitor OFRs sub-weekly surface area changes, and Basemap had higher surface area variability and was more susceptible to the presence of cloud shadows and haze when compared to Planet Fusion, which had a smoother time series with less variability and fewer abrupt changes throughout the year. The uncertainties in surface area classification decreased as the OFRs increased in size. In addition, the surface area time series can have high variability, depending on the OFR environmental conditions (e.g., presence of vegetation inside the OFR). Our findings suggest that both datasets can be used to monitor OFRs sub-weekly, seasonal, and inter-annual surface area changes; therefore, these datasets can help improve freshwater management by allowing better assessment and management of the OFRs.}, number={24}, journal={REMOTE SENSING}, author={Perin, Vinicius and Roy, Samapriya and Kington, Joe and Harris, Thomas and Tulbure, Mirela G. and Stone, Noah and Barsballe, Torben and Reba, Michele and Yaeger, Mary A.}, year={2021}, month={Dec} }
 @article{perin_tulbure_gaines_reba_yaeger_2021, title={On-farm reservoir monitoring using Landsat inundation datasets}, volume={246}, ISSN={["1873-2283"]}, DOI={10.1016/j.agwat.2020.106694}, abstractNote={On-farm reservoirs (OFRs)—artificial water impoundments that retain water from rainfall and run-off—enable farmers to store water during the wet season to be used for crop irrigation during the dry season. However, monitoring the inter- and intra-annual change of these water bodies remains a challenging task because they are typically small (< 10 ha) and occur in high numbers. Therefore, we used two existing Landsat inundation datasets—the U.S. Geological Survey Dynamic Surface Water Extent (DSWE) and the European Commission’s Joint Research Centre (JRC) Global Monthly Water History—to assess surface water area change of OFRs located in eastern Arkansas, the third most irrigated state in the U.S. that has seen a rapid increase of OFRs occurrence. We used an existent OFRs dataset as ground-truth. We aimed (i) to compare the performance of the DSWE and the JRC when characterizing OFRs of varied sizes and (ii) to assess the impact of climate variables (i.e., precipitation and temperature) on surface water area of OFRs. We found the highest mean percent errors (MPE) in size (~20%) for OFRs between 0 and 5 ha, the smallest size class in our study. The DSWE had a smaller MPE and higher agreement with our ground-truth dataset when compared to the JRC for OFRs smaller than 5 ha (p-value < 0.05). Both inundation datasets enabled us to estimate the seasonality in surface area change of OFRs, with the highest surface water extent between March–May, the months when the region receives most of the annual precipitation. Our results showed that both DSWE and JRC can be used to enhance hydrological assessments in poorly monitored basins that have a concentration of OFRs, and the methods can be applied to other study regions if the inundation datasets are available.}, journal={AGRICULTURAL WATER MANAGEMENT}, author={Perin, Vinicius and Tulbure, Mirela G. and Gaines, Mollie D. and Reba, Michele L. and Yaeger, Mary A.}, year={2021}, month={Mar} }
 @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} }
 @article{perin_santos_lollato_ruiz-diaz_kluitenberg_2020, title={Impacts of ammonia volatilization from broadcast urea on winter wheat production}, volume={112}, ISSN={["1435-0645"]}, DOI={10.1002/agj2.20371}, abstractNote={Abstract}, number={5}, journal={AGRONOMY JOURNAL}, author={Perin, Vinicius and Santos, Eduardo A. and Lollato, Romulo and Ruiz-Diaz, Dorivar and Kluitenberg, Gerard J.}, year={2020}, pages={3758–3772} }