Evaluating multispectral remote sensing and spectral unmixing analysis for crop residue mapping

Tillage practices can affect the long term sustainability of agricultural soils as well as a variety of soil processes that impact the environment. Crop residue retention is considered a soil conservation practice given that it reduces soil losses from water and wind erosion and promotes sequestration of carbon in the soil. Spectral unmixing estimates the fractional abundances of surface targets at a sub-pixel level and this technique could be helpful in mapping and monitoring residue cover. This study evaluated the accuracy with which spectral unmixing estimated percent crop residue cover using multispectral Landsat and SPOT data. Spectral unmixing produced crop residue estimates with root mean square errors of 17.29% and 20.74%, where errors varied based on residue type. The model performed best when estimating corn and small grain residue. Errors were higher on soybean fields, due to the lower spectral contrast between soil and soybean residue. Endmember extraction is a critical step to successful unmixing. Small gains in accuracy were achieved when using the purest crop residue- and soil-specific endmembers as inputs to the spectral unmixing model. To assist with operational implementation of crop residue monitoring, a simple endmember extraction technique is described.