UAV multispectral remote sensing for the estimation of SPAD values at various growth stages of maize under different irrigation levels

Abstract

Chlorophyll is crucial for photosynthesis in plants and the readings by a SPAD meter (Soil and Plant Analyzer Development) can be used to represent leaf chlorophyll content for monitoring crop growth status and predicting grain yield. Remote sensing technology has shown potential in non-destructive monitoring of SPAD values over large areas, but current SPAD inversion models are limited in their ability to incorporate multiple principal components besides spectral parameters, adapt to other variables such as water stress, and predict SPAD only throughout the entire growth period. This two-year study used crop parameters (plant height and leaf area index) and vegetation indices (VI) derived from unmanned aerial vehicle (UAV) multispectral images to develop SPAD prediction models for maize under different irrigation levels in the 2018 and 2019 growing seasons in Inner Mongolia, China. Two nonlinear machine learning models, random forest (RF) and support vector regression (SVR), and a multiple statistical regression method (partial least squares regression (PLSR)) were used to modeling SPAD. The results showed that the VIs with a high correlation with SPAD varied at each growth stage and the accuracy of SPAD estimation model can be improved significantly by dividing different growth stages (R² increased by more than 104 %). PLSR performed better than RF and SVR for each growth period, especially at the reproductive R stage (R² = 0.79, RMSE = 2.25). LAI and PH did not always improve prediction accuracy, but adding crop parameters did increase the correlation coefficient between predicted values and biomass by 8.3 %. This study provides valuable insights into the estimation of SPAD at different growth stages of maize under varying water stress levels using UAV data and crop parameters, offering guidance for farmland management and yield prediction.