Measuring full-range soil hydraulic properties for the prediction of crop water availability using gamma-ray attenuation and inverse modeling

Accurate knowledge of soil hydraulic properties (K-θ-h) for the entire range of crop available water is essential for the prediction of soil water movement and related processes by mechanistic models, including the partitioning of surface energy fluxes into transpiration and evaporation and the dynamics of root water uptake, mandatory processes for adjustments of crop water use efficiency. We implemented an experimental and numerical protocol to obtain K-θ-h of eleven soils with a broad spectrum of texture and land use. Measurements of the soil water content during evaporation experiments using gamma-ray beam attenuation, a non-invasive technique, were adopted as an alternative approach to conventional measurements of the soil water pressure head. Inverse parameter optimization was performed using Hydrus-1D. The optimized K-θ-h functions were interpreted with respect to crop available water, where results calculated by a proposed “dynamic” method were compared with those determined using the conventional “static” criteria with standardized pressure heads. The evaporation experiment protocol allowed the determination of the K-θ-h relationships by inverse modeling from near-saturation to the dry range (∼ −150 m) with satisfactory accuracy. Soil water retention curves of the fine-textured soils determined by the conventional method (pressure plates) deviated from those estimated by the inverse optimization near saturation and in the dry range, with the conventional method predicting larger water content values. In terms of crop available water, the “dynamic” method allowed incorporating system characteristics (atmospheric demand and crop properties) and K-θ-h in a process-based way, contrarily to the “static” method. Considering a specific scenario, for the fine-textured soils the “static” and “dynamic” approaches performed similarly, however, for the coarse-textured soils, they diverged significantly. No tendency could be revealed for crop water availability under different land uses, and, in general, crop available water for soils under forest use was very similar to their counterparts under agricultural use

Spectral range (FRX-VIS-NIR-SWIR-MIR) interaction on the organic matter prediction.

Organic matter (OM) is an important indicator of soil quality and, therefore, must be quantified quickly and efficiently. Besides traditional methods, and countless studies with sensors have been advanced, but few integratively. This work aimed to develop models for OM content prediction using the FRX-VIS-NIR-SWIR-MIR regions separately and together. A total of 22 soil samples were collected in the state of São Paulo, Brazil (80-100 cm). Each sample was oven dried at 45 ° C for 48 hours, milled and sieved to a diameter of 0.150 mm and had a series of 8 treatments, considering the addition of humified organic material (MOH) in different amounts (0, 5, 10, 15, 20, 30, 40, 50 g). Afterwards, the treated samples were read in the sensors FIELDSPEC-PRO (VISNIRSWIR), ALPHA FTIR (MIR), and portable FRX for the extraction of the spectral responses. For the creation of the best model by Partial least squares regression (PLSR), 70% of the samples were used for calibration and 30% for validation and different preprocessing techniques were used: transformation of the reflectance data for absorbance, smoothing, first derivative Savitzky-Golay (SGD), standard normal variation (SNV) and multiplicative signal correction (MSC). The spectral data were tested separately and together using the absorbance transformation as a fixed preprocessing to which other was added. The calibration results for all models presented R² greater than 0.8 for the best preprocessing, which varies according to the spectral region analyzed. The best validation occurred for the preprocessed model with only absorbance and smoothing using the VIS-NIR-SIWIR-MIR spectral regions and presented values of R², RMSE and RPIQ equal to 0.8, 6.8 and 4.55, respectively. When using all the regions (RX-VIS-NIR-SWIR-MIR) simultaneously, validation values similar to those cited above (R² = 0.8, RMSE = 6.96 and RPIQ = 4.45)were obtained, indicating that the X-ray region does not influence the improvement of the prediction of OM. Models created only with a the spectral x-ray region presented the worst validation values. OM considerably influences on the spectral response from visible to medium infrared regions and, in this manner, they can be used for more accurate prediction of its values