Spatial modeling of soil organic carbon using remotely sensed indices and environmental field inventory variables

Modeling canopy water content in the assessment for rainfall induced surface and groundwater nitrate contamination: The Bilate cropland sub watershed

“…Plant canopy water content can be predicted from the reflectance of the plant-enhanced vegetation index of MODIS 250 m EVI for each image pixel value of the cropland [ 21 ]. The principle of geographically weighted regression modeling (GWR) for predicting [ 95 , 89 , 99 , 100 ] crop water balance based on crop growth curve for crop heterogeneity as model parameter calibration [ 101 ] proved to be conceptually important. In this concept, crop data is considered as model input and its extension of OLS (Ordinary Least Squares) regression allows to consider locally varying parameters as spatial instability in a sample and the stochastic working principle of the (GWR) model [ 102 ] as follows in the (equation (5) ).…”