Remote sensing leaf water stress in coffee (Coffea arabica) using secondary effects of water absorption and random forests

Water management is an important component in agriculture, particularly for perennial tree crops such as coffee. Proper detection and monitoring of water stress therefore plays an important role not only in mitigating the associated adverse impacts on crop growth and productivity but also in reducing expensive and environmentally unsustainable irrigation practices. Current methods for water stress detection in coffee production mainly involve monitoring plant physiological characteristics and soil conditions. In this study, we tested the ability of selected wavebands in the VIS/NIR range to predict plant water content (PWC) in coffee using the random forest algorithm. An experiment was set up such that coffee plants were exposed to different levels of water stress and reflectance and plant water content measured. In selecting appropriate parameters, cross-correlation identified 11 wavebands, reflectance difference identified 16 and reflectance sensitivity identified 22 variables related to PWC. Only three wavebands (485 nm, 670 nm and 885 nm) were identified by at least two methods as significant. The selected wavebands were trained (n = 36) and tested on independent data (n = 24) after being integrated into the random forest algorithm to predict coffee PWC. The results showed that the reflectance sensitivity selected bands performed the best in water stress detection (r = 0.87, RMSE = 4.91% and pBias = 0.9%), when compared to reflectance difference (r = 0.79, RMSE = 6.19 and pBias = 2.5%) and cross-correlation selected wavebands (r = 0.75, RMSE = 6.52 and pBias = 1.6). These results indicate that it is possible to reliably predict PWC using wavebands in the VIS/NIR range that correspond with many of the available multispectral scanners using random forests and further research at field and landscape scale is required to operationalize these findings.