Mapping Water Infiltration Rate Using Ground and UAV Hyperspectral Data: A Case Study of Alento, Italy

Abstract

Water infiltration rate (WIR) into the soil profile was investigated through a comprehensive study harnessing spectral information of the soil surface. As soil spectroscopy provides invaluable information on soil attributes, and as WIR is a soil surface-dependent property, field spectroscopy may model WIR better than traditional laboratory spectral measurements. This is because sampling for the latter disrupts the soil-surface status. A field soil spectral library (FSSL), consisting of 114 samples with different textures from six different sites over the Mediterranean basin, combined with traditional laboratory spectral measurements, was created. Next, partial least squares regression analysis was conducted on the spectral and WIR data in different soil texture groups, showing better performance of the field spectral observations compared to traditional laboratory spectroscopy. Moreover, several quantitative spectral properties were lost due to the sampling procedure, and separating the samples according to texture gave higher accuracies. Although the visible near-infrared–shortwave infrared (VNIR–SWIR) spectral region provided better accuracy, we resampled the spectral data to the resolution of a Cubert hyperspectral sensor (VNIR). This hyperspectral sensor was then assembled on an unmanned aerial vehicle (UAV) to apply one selected spectral-based model to the UAV data and map the WIR in a semi-vegetated area within the Alento catchment, Italy. Comprehensive spectral and WIR ground-truth measurements were carried out simultaneously with the UAV–Cubert sensor flight. The results were satisfactorily validated on the ground using field samples, followed by a spatial uncertainty analysis, concluding that the UAV with hyperspectral remote sensing can be used to map soil surface-related soil properties.

Soil Salinity Inversion in Coastal Corn Planting Areas by the Satellite-UAV-Ground Integration Approach

“…(2) Francos et al found that the traditional field non-imaging spectral data ar discrete and easily affected by the soil background [50], while the ground hyperspectr…”

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

“…In comparison with published works, the presented results provide a comparable estimate of the observed characteristics of soil hydraulic properties, e.g., Ambrosone et al [39] report R 2 = 0.73 for the linear method to determine soil water content, using multispectral data from the Sentinel 2 satellite and the OPTRAM model [117], and R 2 = 0.8 for the nonlinear method. Francos et al [33] performed a detailed analysis of the spectral characteristics of soils in relation to the infiltration rate. The results showed a very good agreement between laboratory and spectral data; however, the result varied according to the different types of soils and their physical properties.…”

“…The present method eliminates some of the limitations of the above approaches. In monitoring soil without vegetation cover using optical spectral data, the structural properties of soils and the content of color elements, such as hematite or goethite, can play an important role, which can significantly affect the spectral information [33]. Evaluation of soil hydraulic properties using spectral vegetation indices (e.g., the RGB index, [27]) may have only a limited possibility to identify short-term changes in the stand water regime.…”

“…The use of high spatial resolution multispectral satellite data (e.g., Sentinel 2 and Landsat 8 OLI/TIRS) and very high resolution aerial and UAV (unmanned aerial vehicle) data seems to be more appropriate. In particular, the use of hyperspectral [33,34] and thermal data [35][36][37] has great potential.…”

“…The use of optical spectral data is based on the assumption of a change in spectral reflectance as a function of the change in soil or vegetation canopy water content due to stand water stress. A detailed analysis of the spectral response of different soil types with various water infiltration rate was carried out by Francos et al [33], showing good agreement between the spectral properties of the soil and the rate of water infiltration into the soil. The combination of Sentinel 2 satellite data and the OPTRAM (OPtical TRApezoid Model proposed by Sadeghi et al [38]) model showed good agreement between spectral data and soil moisture [39].…”