Using broadband crop residue angle index to estimate the fractional cover of vegetation, crop residue, and bare soil in cropland systems

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Accurate estimation of fractional vegetation cover (FVC), crop residue cover (CRC), and bare soil (BS) in agricultural and vegetation-soil ecosystems is critically important. The traditional triangular space method uses a CRC spectral index (SI) and vegetation SI to create a two-dimensional scatter map in which the three vertices represent pure vegetation (FVC = 1), crop residue (CRC = 1), and bare soil (BS = 1). With this method, the CRC, FVC, and BS of each pixel are calculated based on their spatial locations in the triangular space. In practice, soil moisture and crop residue moisture (SM-CRM) affects the values of CRC spectral indices (SIs) for pure crop residue and soil, thereby reducing the accuracy of broadband remote sensing estimates of CRC, FVC, and BS. In the current work, we propose a new method for estimating fractional cover that uses a broadband spectral angle index (BAI) to estimate CRC. The proposed BAI is the included angle between (i) the line from the reflectance at band a to the reflectance at band b and (ii) the line from the reflectance at band b to the reflectance at band c, where bands a and b represent the VIS or NIR bands, respectively, and band c represents the SWIR1 or SWIR2 of the broadband remote sensing band. The proposed BAI method can mitigate the effects of soil and crop residue moisture content on spectral reflectance. This study evaluates proposed BAIs to estimate CRC and BAI-NDVI triangular space method to estimate CRC, FVC, and BS in cropland where water content varies greatly. Several different BAIs were validated using both laboratory-based measurements and field-based experiments using Sentinel-2 multispectral instrument imaging. We used two laboratory-based treatments (mixed spectral reflectance of dry, saturated soil and crop residue and the mixed spectral reflectance of winter wheat leaf and dry, saturated soil and crop residue) to analyze the response of BAIs to CRC, SM-CRM, FVC, and vegetation water content. Next, we evaluated the performance of different BAIs in determining CRC based on the mixed spectral reflectance of crop residue and soil, as well as the performance of the BAI-NDVI triangular space method and linear spectral unmixing analysis to estimate CRC, FVC, and BS from mixed spectral reflectance measurements. Our results indicate that the proposed methods reduce the influence of moisture on broadband CRC SIs, provide accurate estimates of cropland CRC and fractional estimates of CRC, FVC, and BS, and may be applied in croplands where soil and crop residue moisture content varies greatly.