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1. Feasibility of tundra vegetation height retrieval from Sentinel-1 and Sentinel-2 data

   https://doi.org/10.1016/j.rse.2019.111515  

   Bartsch, Annett ; Widhalm, Barbara ; Leibman, Marina ; Ermokhina, Ksenia ; Kumpula, Timo ; Skarin, Anna ; Wilcox, Evan J. ; Jones, Benjamin M. ; Frost, Gerald V. ; Höfler, Angelika ; et al ( February 2020 , Remote Sensing of Environment)
2. Crop Type and Land Cover Mapping in Northern Malawi Using the Integration of Sentinel-1, Sentinel-2, and PlanetScope Satellite Data

   https://doi.org/10.3390/rs13040700  

   Kpienbaareh, Daniel ; Sun, Xiaoxuan ; Wang, Jinfei ; Luginaah, Isaac ; Bezner Kerr, Rachel ; Lupafya, Esther ; Dakishoni, Laifolo ( February 2021 , Remote Sensing)

   Mapping crop types and land cover in smallholder farming systems in sub-Saharan Africa remains a challenge due to data costs, high cloud cover, and poor temporal resolution of satellite data. With improvement in satellite technology and image processing techniques, there is a potential for integrating data from sensors with different spectral characteristics and temporal resolutions to effectively map crop types and land cover. In our Malawi study area, it is common that there are no cloud-free images available for the entire crop growth season. The goal of this experiment is to produce detailed crop type and land cover maps in agricultural landscapes using the Sentinel-1 (S-1) radar data, Sentinel-2 (S-2) optical data, S-2 and PlanetScope data fusion, and S-1 C2 matrix and S-1 H/α polarimetric decomposition. We evaluated the ability to combine these data to map crop types and land cover in two smallholder farming locations. The random forest algorithm, trained with crop and land cover type data collected in the field, complemented with samples digitized from Google Earth Pro and DigitalGlobe, was used for the classification experiments. The results show that the S-2 and PlanetScope fused image + S-1 covariance (C2) matrix + H/α polarimetric decomposition (an entropy-based decomposition method) fusion outperformed all other image combinations, producing higher overall accuracies (OAs) (>85%) and Kappa coefficients (>0.80). These OAs represent a 13.53% and 11.7% improvement on the Sentinel-2-only (OAs < 80%) experiment for Thimalala and Edundu, respectively. The experiment also provided accurate insights into the distribution of crop and land cover types in the area. The findings suggest that in cloud-dense and resource-poor locations, fusing high temporal resolution radar data with available optical data presents an opportunity for operational mapping of crop types and land cover to support food security and environmental management decision-making. 

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3. Tracking changes in coastal land cover in the Yellow Sea, East Asia, using Sentinel-1 and Sentinel-2 time-series images and Google Earth Engine

   https://doi.org/10.1016/j.isprsjprs.2022.12.029  

   Liu, Yongchao ; Xiao, Xiangming ; Li, Jialin ; Wang, Xinxin ; Chen, Bangqian ; Sun, Chao ; Wang, Jie ; Tian, Peng ; Zhang, Haitao ( February 2023 , ISPRS Journal of Photogrammetry and Remote Sensing)
4. Constraining Mountain Streamflow Constituents by Integrating Citizen Scientist Acquired Geochemical Samples and Sentinel‐1 SAR Wet Snow Time‐Series for the Shimshal Catchment in the Karakoram Mountains of Pakistan

   https://doi.org/10.1029/2022WR032171  

   Lund, Jewell ; Forster, Richard R. ; Jameel, Yusuf ; Rupper, Summer B. ; Deeb, Elias J. ; Dars, Ghulam Hussain ; Zaheer, Azhar ; Ali, Masood ; Ghafoor, Abdul ; Khan, Garee ; et al ( March 2023 , Water Resources Research)

   Upper Indus Basin (UIB) streamflow originates largely from glacier and snow melt in the upstream Himalaya, Karakoram, and Hindu Kush mountain ranges and is extremely vulnerable because of its projected climate changes, dense populations, and hydropolitical tensions. Accurate knowledge of streamflow constituents is required for resilient water resources management; this is precluded by a paucity of measurement as well as climatological and topographic complexity. Here we integrate citizen scientist acquired geochemical samples, collected from October 2018 through September 2019 in the Shimshal watershed of the Karakoram Mountains of Pakistan, with Sentinel‐1 (S1) synthetic aperture radar (SAR)‐derived wet snow maps, to better understand streamflow constituents for the high altitude and heavily glaciated catchment. We use Bayesian end‐member mixture analysis to separate river flows into baseflow and meltwater constituents, using fixed and time‐variant melt end‐member values. We compare river hydrograph separation results with S1 wet snow time series maps for the same timeframe. We then utilize S1 imagery to inform end‐member mixture analysis to separate meltwaters into snow and glacier melt. For the Shimshal catchment, we find that about 85% of annual river flows are derived from snow and glacier melt; 45% of annual flows are derived from snow melt and 40% glacier melt. Engaged and committed citizen scientists enabled geochemical sample collection and analysis on a significant temporal and spatial scale. In the future, co‐produced knowledge that both implements local expertise and that is also planned and utilized by diverse stakeholders may increase climatological awareness and resilience in the UIB.

    

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5. Spatiotemporal variations of wetland backscatter: The role of water depth and vegetation characteristics in Sentinel-1 dual-polarization SAR observations

   https://doi.org/10.1016/j.rse.2021.112864  

   Zhang, Boya ; Wdowinski, Shimon ; Gann, Daniel ; Hong, Sang-Hoon ; Sah, Jay ( March 2022 , Remote Sensing of Environment)