To be acceptable for precision agriculture applications, satellite imagery must be converted to surface reflectance. To be economical, the analytics must be delivered completely by automation and free of error to preserve farmer trust. CMAC (closed-form method for atmospheric correction) software was tested for this application along with established applications, Sen2Cor and FORCE—all three software packages seek to retrieve Sentinel-2 surface reflectance. Forty-three Sentinel-2 images were selected of farmland near Burley, Idaho, corrected by this software and evaluated as reflectance time series extracted from three irrigated corn fields. NDVI of irrigated corn presented an ideal test of precision and accuracy for surface reflectance retrieval. If accurate and precise, a plotted time series will smoothly display logistic growth during crop establishment followed by a plateau, then gradual senesce before harvest: divergences from this pattern indicate errors. CMAC followed the expected smooth pattern for this dataset while, in both FORCE and Sen2Cor, divergence occurred both above and below the CMAC time series for NDVI and from individual spectral band reflectance. These divergences were systematic and directly related to the degree of atmospheric effect—overcorrecting when clear, under-correcting when hazy. Only CMAC provided surface reflectance with the accuracy required for precision agriculture: applicable for Sentinel-2 as Tier 1 data and when haze or cloud- affected and unreliable, as Tier 2 infill from daily smallsat data. Additional analyses of the CMAC-corrected dataset were performed that were also applicable to Tier 2 daily-cadence smallsat data. Further analysis of this dataset indicated that, applied as NDVI, the application of broadband NIR, though sensitive to atmospheric water vapor, exhibited minimal errors compared to NDVI from narrowband NIR. These CMAC-corrected data provided an application to index crop start dates and were capable of distinguishing the uncorrectable data of cloud, cloud shadow, or extreme haze for removal under complete automation.
Groeneveld et al. (Mon,) studied this question.