Integrating random forest and crop modeling improves the crop yield prediction of winter wheat and oil seed rape - Dhillon, Maninder Singh; Dahms, Thorsten; Kuebert-Flock, Carina; Rummler, Thomas; Arnault, Joel; Steffan-Dewenter, Ingolf; Ullmann, Tobias (Frontiers in Remote Sensing, 2023)
The fast and accurate yield estimates with the increasing availability and variety of global satellite products and the rapid development of new algorithms remain a goal for precision agriculture and food security. However, the consistency and reliability of suitable methodologies that provide accurate crop yield outcomes still need to be explored. The study investigates the coupling of crop modeling and machine learning (ML) to improve the yield prediction of winter wheat (WW) and oil seed rape (OSR) and provides examples for the Free State of Bavaria (70,550 km²), Germany, in 2019. The main objectives are to find whether a coupling approach (Light Use Efficiency (LUE) + Random Forest (RF)) would result in better and more accurate yield predictions compared to results provided with other models not using the LUE. Four different RF models (RF1 (input: NDVI), RF2 (input: climate variables), RF3 (input: Normalized Difference Vegetation Index (NDVI) + climate variables), RF4 (input: LUE generated biomass + climate variables), and one semi-empiric LUE model were designed with different input requirements to find the best predictors of crop monitoring. The results indicate that the individual use of the NDVI (in RF1) and the climate variables (in RF2) could not be the most accurate, reliable, and precise solution for crop monitoring; however, their combined use (in RF3) resulted in higher accuracies. Notably, the study suggested the coupling of the LUE model variables to the RF4 model can reduce the relative root mean square error (RRMSE) from -8% (WW) and -1.6% (OSR) and increase the R2 by 14.3% (for both WW and OSR), compared to results just relying on LUE. Moreover, the research compares models yield outputs by inputting three different spatial inputs: Sentinel-2(S)-MOD13Q1 (10m), Landsat (L)-MOD13Q1 (30m), and MOD13Q1(MODIS) (250m). The S-MOD13Q1 data has relatively improved the performance of models with higher mean R2 (0.80 (WW)/0.69 (OSR)), and lower RRMSE (%) (9.18, 10.21) compared to L-MOD13Q1 (30 m) and MOD13Q1 (250 m). Satellite-based crop biomass, solar radiation, and temperature are found to be the most influential variables in the yield prediction of both crops.