Improving the Retrieval of Crop Canopy Chlorophyll Content Using Vegetation Index Combinations

Estimates of crop canopy chlorophyll content (CCC) can be used to monitor vegetation productivity, manage resources, and control disease pests. However, making these estimates using conventional ground-based methods is time-consuming resource-intensive when deployed over large areas. Although indices (VIs), derived from satellite sensor data, have been estimate CCC, they suffer problems related spectral saturation, soil background, structure. A new method was, therefore, proposed for combining the Medium Resolution Imaging Spectrometer (MERIS) terrestrial index (MTCI) LAI-related (LAI-VIs) increase accuracy CCC wheat soybeans. The PROSAIL-D reflectance model was simulate spectra that were resampled match response functions MERIS carried on ENVISAT satellite. Combinations MTCI LAI-VIs then via univariate linear regression, binary regression random forest regression. field data determined based measurements. All LAI-VI combinations selected techniques resulted in more accurate than use alone (field soybeans wheat: R2 = 0.62 RMSE 77.10 ?g cm?2; soybeans: 0.24 136.54 cm?2). better other two models. combination resulting best MTVI2 with 0.65 37.76 cm?2 data) 0.78 47.96 (MERIS data). Combining a represents further step towards improving estimation multispectral data.