Abstract
Remote sensing-based crop monitoring has evolved unprecedentedly to supply multispectral imagery with high spatial-temporal resolution for the assessment of crop evapotranspiration (ETc). Several methodologies have shown a high correlation between the Vegetation Indices (VIs) and the crop coefficient (Kc). This work analyzes the estimation of the crop coefficient (Kc) as a spectral function of the product of two variables: VIs and green vegetation cover fraction (fv). Multispectral images from experimental maize plots were classified to separate pixels into three classes (vegetation, shade and soil) using the OBIA (Object Based Image Analysis) approach. Only vegetation pixels were used to estimate the VIs and fv variables. The spectral Kcfv:VI models were compared with Kc based on Cumulative Growing Degree Days (CGDD) (Kc-cGDD). The maximum average values of Normalized Difference Vegetation Index (NDVI), WDRVI, amd EVI2 indices during the growing season were 0.77, 0.21, and 1.63, respectively. The results showed that the spectral Kcfv:VI model showed a strong linear correlation with Kc-cGDD (R2 > 0.80). The model precision increases with plant densities, and the Kcfv:NDVI with 80,000 plants/ha had the best fitting performance (R2 = 0.94 and RMSE = 0.055). The results indicate that the use of spectral models to estimate Kc based on high spatial and temporal resolution UAV-images, using only green pixels to compute VI and fv crop variables, offers a powerful and simple tool for ETc assessment to support irrigation scheduling in agricultural areas.
Highlights
Crop water requirement (CWR) is the water needed to compensate for water vapour released into the atmosphere by evapotranspiration (ETc) and is one of the most critical variables in irrigated agriculture to better match water demand with irrigation supply [1]
All Vegetation Indices (VIs) were less sensitive to detect Kc changes because of reduced canopy density in the final stages. These results show that the fv: Normalized Difference Vegetation Index (NDVI) and fv: EVI2 models disaggregate the vegetation canopy cover from the soil more effectively than fv WDRVI at vegetative stages (12 leaves)
Our results reveal that the model that best estimates Kc is fv:NDVI since it showed lower root mean square error (RMSE) (0.055–0.061), higher efficiency (E = 92–96%), and lower coefficient of variation (CV) (7.78–6.55); fv:WRDVI and fv:EVI2 models presented RMSE > 0.064, E < 95% and CV > 6.97
Summary
Crop water requirement (CWR) is the water needed to compensate for water vapour released into the atmosphere by evapotranspiration (ETc) and is one of the most critical variables in irrigated agriculture to better match water demand with irrigation supply [1]. There are several practical methodologies to estimate ETc. The most used method in the irrigation practice is well described in the FAO-56 manual [2], which is based on the knowledge of two variables: the reference evapotranspiration (ETo), representing the atmospheric evaporative demand, and the basal crop coefficient (Kcb), representing the water crop factors. According to the FAO-56 method, Kcb values are represented by four simplified lines, which vary according to the phenological stages: seedling, vegetative growth, intermediate, and maturity. These Kcb values must be calibrated to local conditions [5]
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