Machine learning based wheat yield early estimation using satellite-derived spectral indices, weather data and input fertilizers

Remote sensing data is crucial in modern agriculture, particularly to estimate wheat yields over large areas quickly and accurately. This is especially important in Morocco, where drought has led to low wheat production in the 2022/23 marketing year. This study aimed to evaluate variations in wheat yield within Moroccan fields. Four spectral indices were extracted from Sentinel-2 imagery (NDVI, GCVI, EVI and SATVI) for the agricultural season of 2020 and 2021, as well as data on total precipitation, maximum and minimum air temperatures, and total NPK fertilizer inputs. Three machine learning models were used for the analysis: Multiple Linear Regression (MLR), Random Forest (RF), and Extreme Gradient Boosting (Xgboost). The results showed that the non-linear models (RF and Xgboost) performed better than the linear model (MLR). The best performing algorithm was found to be Xgboost, with an R² value of 0.75 and a root mean square error of 689 kg.ha^-1 when only using spectral indices and climate data, and a root mean square error of 566 kg.ha^-1 when adding total NPK fertilizer data. The use of remote sensing indices, climate data, and NPK inputs with a machine learning technique was found to be effective in estimating wheat yields and can help to fill gaps in missing data.