Improvement of the Soil Moisture Retrieval Procedure Based on the Integration of UAV Photogrammetry and Satellite Remote Sensing Information

In countries characterized by arid and semi-arid climates, a precise determination of soil moisture conditions on the field scale is critically important, especially in the first crop growth stages, to schedule irrigation and to avoid wasting water. The objective of this study was to apply the operative methodology that allowed surface soil moisture (SSM) content in a semi-arid environment to be estimated. SSM retrieval was carried out by combining two scattering models (IEM and WCM), supplied by backscattering coefficients at the VV polarization obtained from the C-band Synthetic Aperture Radar (SAR), a vegetation descriptor NDVI obtained from the optical sensor, among other essential parameters. The inversion of these models was performed by Neural Networks (NN). The combined models were calibrated by the Sentinel 1 and Sentinel 2 data collected on bare soil, and in cereal, pea and onion crop fields. To retrieve SSM, these scattering models need accurate measurements of the roughness surface parameters, standard deviation of the surface height (hrms) and correlation length (L). This work used a photogrammetric acquisition system carried on Unmanned Aerial Vehicles (UAV) to reconstruct digital surface models (DSM), which allowed these soil roughness parameters to be acquired in a large portion of the studied fields. The obtained results showed that the applied improved methodology effectively estimated SSM on bare and cultivated soils in the principal early growth stages. The bare soil experimentation yielded an R-2 = 0.74 between the estimated and observed SSMs. For the cereal field, the relation between the estimated and measured SSMs yielded R-2 = 0.71. In the experimental pea fields, the relation between the estimated and measured SSMs revealed R-2 = 0.72 and 0.78, respectively, for peas 1 and peas 2. For the onion experimentation, the highest R-2 equaled 0.5 in the principal growth stage (leaf development), but the crop R-2 drastically decreased to 0.08 in the completed growth phase. The acquired results showed that the applied improved methodology proves to be an effective tool for estimating the SSM on bare and cultivated soils in the principal early growth stages.

Automated summary

The study utilized scattering models and Neural Networks to estimate surface soil moisture in a semi-arid environment, and used UAVs to acquire digital surface models and soil parameters, demonstrating the effectiveness of the method in early crop growth stages.