Estimating the leaf water content of Coffea arabica L. based on hyperspectral reflectance and dataset construction

Currently, irrigation decisions in coffee cultivation primarily rely on empirical knowledge, resulting in inefficient practices. Combining real-time leaf water content (LWC) data can help improve the accuracy of the irrigation planning. Spectral remote sensing is a fast, reliable, and non-invasive method to detect vegetation moisture content. In this study, a model to estimate the LWC of Coffea arabica L. was built using hyperspectral reflectance of the canopy under various irrigation levels. For this purpose, common spectral indices, two-band spectral indices ratio spectral index (RSI); difference spectral index (DSI); and normalized difference spectral index (NDSI), and three-band spectral indices were constructed. Feature bands were extracted using the successive projections algorithm (SPA). Optimal spectral indices were extracted using the correlation coefficient method, and the feature wavebands and spectral indices were combined into five datasets. These datasets were split into modeling and validation datasets by sample set partitioning based on the joint x-y distance (SPXY) algorithm. A linear model partial least squares regression (PLSR) and three non-linear models support vector machine (SVM); extreme learning machine (ELM); back propagation artificial neural network (BPANN) were built to estimate LWC of Coffea arabica L. The results indicated that the non-linear models surpassed the linear model. The accuracy was the highest when the modeling was performed using the dataset combination 5. Among various modeling methods, the predictive performance of ELM was the best (modeling dataset: R²=0.745, RMSE=2.241%, RRMSE=3.482%; validation dataset: R²=0.721, RMSE=2.142%, RRMSE=3.364%). ELM outperformed PLSR, SVM, and BPANN in LWC retrieval. The obtained results indicated that the dataset built by the combined use of different methods was superior to those from a single data source in accuracy. This study provides a scientific basis for the quantitative diagnosis of coffee tree water status, with significant implications for optimizing field irrigation management.