Abstract
The NASA ISRO Synthetic Aperture Radar (NISAR) Mission plans to generate >40TB of raw data daily to support open access and operational L-band science. This includes Ecosystems applications for agriculture. To further prepare, the Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) platform was used to observe cropland sites across the southern United States to support the development of L-band prototype science products. Major crops include corn, cotton, pasture, peanut, rice, and soybean. A suite of cropland classification experiments applied a set of strategic algorithms to synergistically assess performance, scattering mechanisms, and limitations. SAR terms with sensitivity to volume scattering performed well and consistently across mapping experiments achieving accuracy greater than 80% for cropland vs not cropland. Volume scattering and cross-pol terms were most useful across the different ML techniques with overall accuracy and Kappa consistently over 90% and. 85, respectively, for crop type by late growth stages for both L-band observations.
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