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

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 R-2 values for the measured and estimated AGB ranged from 0.79 to 0.98 for individual paddocks, and the R-2 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 R-2 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.

Automated summary

Remote sensing from optical radiometers in space provides a nondestructive approach for estimating above ground biomass (AGB). However, challenges such as cloud cover, soil background differences, and crop phenology need to be addressed. In this study, a framework based on Sentinel-2 imagery is presented for estimating AGB using adjusted summed NDVI measurements. The results show high accuracy and reliability, with R-2 values ranging from 0.79 to 0.98. Additionally, the study demonstrates the use of active optical and additional satellite imagery to fill gaps caused by clouds in the Sentinel-2 imagery.