Spatial and Temporal Biomass and Growth for Grain Crops Using NDVI Time Series

Abstract

Remote sensing from optical radiometers in space offers a nondestructive approach to estimating above ground biomass (AGB) with high spatial and temporal resolution, but the application is challenged by cloud cover and differences in soil background and crop phenology. We present a framework based on Sentinel-2 imagery for relating the adjusted summed NDVI measurements to the AGB. The resulting R2 values for the measured and estimated AGB ranged from 0.79 to 0.98 for individual paddocks, and the R2 from a pooled dataset (multiple crops, years, and locations) was 0.86. Application of the pooled dataset model to a separate validation dataset resulted in an R2 of 0.88; however, there was a bias that resulted in the underestimation of the measured biomass. Analysis of the impacts of the gaps in the time series showed a decrease of 0.43% per gap day for the summed NDVI values. To address the impacts of clouds, we demonstrate the use of active optical and additional satellite imagery to fill the gaps due to clouds in the Sentinel-2 imagery. The framework presented results of the spatial daily estimates of the AGB and crop growth rates.