Actual Evapotranspiration and Biomass of Maize from a Red–Green-Near-Infrared (RGNIR) Sensor on Board an Unmanned Aerial Vehicle (UAV)

• Main Authors: Robson Argolo dos Santos, Everardo Chartuni Mantovani, Roberto Filgueiras, Elpídio Inácio Fernandes-Filho, Adelaide Cristielle Barbosa da Silva, Luan Peroni Venancio
• Format: Article
• Language: English
• Published: MDPI AG 2020-08-01
• Series: Water
• Subjects: aerial remote sensing; vegetation index; K_cb; water balance
• Online Access: https://www.mdpi.com/2073-4441/12/9/2359

Surface reflectance data acquisition by unmanned aerial vehicles (UAVs) are an important tool for assisting precision agriculture, mainly in medium and small agricultural properties. Vegetation indices, calculated from these data, allow one to estimate the water consumption of crops and predict dry biomass and crop yield, thereby enabling a priori decision-making. Thus, the present study aimed to estimate, using the vegetation indices, the evapotranspiration (ET) and aboveground dry biomass (AGB) of the maize crop using a red–green-near-infrared (RGNIR) sensor onboard a UAV. For this process, 15 sets of images were captured over 61 days of maize crop monitoring. The images of each set were mosaiced and subsequently subjected to geometric correction and conversion from a digital number to reflectance to compute the vegetation indices and basal crop coefficients (K_cb). To evaluate the models statistically, 54 plants were collected in the field and evaluated for their AGB values, which were compared through statistical metrics to the data estimated by the models. The K_cb values derived from the Soil-Adjusted Vegetation Index (SAVI) were higher than the K_cb values derived from the Normalized Difference Vegetation Index (NDVI), possibly due to the linearity of this model. A good agreement (R² = 0.74) was observed between the actual transpiration of the crop estimated by the K_cb derived from SAVI and the observed AGB, while the transpiration derived from the NDVI had an R² of 0.69. The AGB estimated using the evaporative fraction with the SAVI model showed, in relation to the observed AGB, an RMSE of 0.092 kg m⁻² and an R² of 0.76, whereas when using the evaporative fraction obtained through the NDVI, the RMSE was 0.104 kg m⁻², and the R² was 0.74. An RGNIR sensor onboard a UAV proved to be satisfactory to estimate the water demand and AGB of the maize crop by using empirical models of the K_cb derived from the vegetation indices, which are an important source of spatialized and low-cost information for decision-making related to water management in agriculture.