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A New Multiangle Method for Estimating Fractional Biocrust Coverage From Sentinel-2 Data in Arid Areas

Hui SunXinjiang Key Laboratory of Oasis Ecology, College of Geography and Remote Sensing Sciences, Xinjiang University, Urumqi, ChinaXu MaXinjiang Key Laboratory of Oasis Ecology, Post-Doctoral Mobile Stationm, College of Geography and Remote Sensing Sciences, Xinjiang University, Urumqi, ChinaYing LiuCollege of Life Science and Technology, Xinjiang University, Urumqi, ChinaGuiyun ZhouSchool of Resources and Environment, University of Electronic Science and Technology, Chengdu, ChinaJianli DingXinjiang Key Laboratory of Oasis Ecology, College of Geography and Remote Sensing Sciences, Xinjiang University, Urumqi, ChinaLei LuCollege of Earth and Environmental Sciences, Lanzhou University, Lanzhou, ChinaTiejun WangFaculty of Geo-Information Science and Earth Observation (ITC), University of Twente, Enschede, The NetherlandsQiuli YangXinjiang Key Laboratory of Oasis Ecology, College of Geography and Remote Sensing Sciences, Xinjiang University, Urumqi, ChinaQingtai ShuSchool of Forestry, Southwest Forestry University, Kunming, ChinaZhang FeiCollege of Geography and Environmental Sciences, Zhejiang Normal University, Jinhua, China

2024en

ABI

Annotatsiya

The spatio-temporal distribution of biocrusts can be used to monitor regional water resources in desert ecosystems. However, a lack of biocrust products from remotely sensed images with fine spatial resolution (FSR) limits scientific research in this area. To address this issue, we establish an estimation model for biocrusts (EMBC) in three steps for FSR images and map the large-scale fractional biocrust coverage ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FBC ) in deserts using Sentinel-2 images with a spatial resolution of 10 m. Firstly, we develop a fraction biocrust cover index ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FBCI ) based on radiative transfer theory. Next, a multi-angle calculation equation involving <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FBCI is established and the parameters for a pixel dichotomy model are solved by an inverse method using a linear kernel-driven model. Finally, this pixel dichotomy model with <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FBCI is used to calculate <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FBC . We validate the model using field measurements and compare the validation results with those estimated by a random forest model and a backpropagation neural network model. This comparison demonstrates that the value of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FBC estimated by EMBC is highly consistent with field measurements (root mean square error (RMSE) = 0.0774, systematic deviation = -4.05%). Furthermore, the values of FBC estimated with EMBC and the two other models show a high level of consistency in terms of spatial distribution (RMSE < 0.0998). We conclude that our EMBC can accurately estimate <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FBC in a desert and is an important technique for monitoring drought in an arid environment.

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DOI: 10.1109/tgrs.2024.3361249

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