Identification of Infiltration Features and Hydraulic Properties of Soils Based on Crop Water Stress Derived from Remotely Sensed Data

Abstract

Knowledge of the spatial variability of soil hydraulic properties is important for many reasons, e.g., for soil erosion protection, or the assessment of surface and subsurface runoff. Nowadays, precision agriculture is gaining importance for which knowledge of soil hydraulic properties is essential, especially when it comes to the optimization of nitrogen fertilization. The present work aimed to exploit the ability of vegetation cover to identify the spatial variability of soil hydraulic properties through the expression of water stress. The assessment of the spatial distribution of saturated soil hydraulic conductivity (Ks) and field water capacity (FWC) was based on a combination of ground-based measurements and thermal and hyperspectral airborne imaging data. The crop water stress index (CWSI) was used as an indicator of crop water stress to assess the hydraulic properties of the soil. Supplementary vegetation indices were used. The support vector regression (SVR) method was used to estimate soil hydraulic properties from aerial data. Data analysis showed that the approach estimated Ks with good results (R2 = 0.77) for stands with developed crop water stress. The regression coefficient values for estimation of FWC for topsoil (0–0.3 m) ranged from R2 = 0.38 to R2 = 0.99. The differences within the study sites of the FWC estimations were higher for the subsoil layer (0.3–0.6 m). R2 values ranged from 0.12 to 0.99. Several factors affect the quality of the soil hydraulic features estimation, such as crop water stress development, condition of the crops, period and time of imaging, etc. The above approach is useful for practical applications for its relative simplicity, especially in precision agriculture.

Normalised difference moisture index in water stress assessment of maize crops

“…Masina et al (2020) provided the proof that NDMI, calculated using the aerospace imagery from Sentinel-2 satellite, is a promising substitute for CWSI (crop water stress index) to evaluate and predict water stress levels in maize crops. In addition, NDMI is significantly correlated with surface moisture, and it is applied to facilitate more accurate estimation of field water capacity in the arable (0-30 cm) soil layer and provide the best decisions on irrigation (Brom et al, 2021). In the current study, cascade diagrams and cumulative NDMI calculation provided reliable and convenient estimation of the water supply status in maize crops, as the index was strongly dependent on moisture income and intake by the plants.…”