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

The spatiotemporal 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) with three steps for FSR images. Then, the large-scale fractional biocrust coverage (FBC) in deserts was mapped from Sentinel-2 images with a spatial resolution of 10 m. First, we develop a fraction biocrust cover index (FBCI) based on radiative transfer theory. Next, a multiangle calculation equation involving 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 FBCI is used to calculate 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 FBC estimated by EMBC is highly consistent with field measurements (root-mean-square error (RMSE) = 0.0774 and systematic deviation = -4.05%). Furthermore, the values of FBC estimated by EMBC and the two other models have a high level of consistency in terms of spatial distribution (RMSE < 0.0998). We conclude that our EMBC can accurately estimate FBC in a desert, which is an important technique for monitoring drought in an arid environment.