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

Abstract

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 R2 = 0.74 between the estimated and observed SSMs. For the cereal field, the relation between the estimated and measured SSMs yielded R2 = 0.71. In the experimental pea fields, the relation between the estimated and measured SSMs revealed R2 = 0.72 and 0.78, respectively, for peas 1 and peas 2. For the onion experimentation, the highest R2 equaled 0.5 in the principal growth stage (leaf development), but the crop R2 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.

Insights into Efficient Irrigation of Urban Landscapes: Analysis Using Remote Sensing, Parcel Data, Water Use, and Tiered Rates

“…We used the Excel app from Microsoft 365 along with SAS JMP Pro 16.1.0 for graphing and statistical analysis. Because of the large spatial scales involved, analysis of extensive landscapes often relies on remote sensing and GIS [45][46][47]. Using 4-band imagery, plant health can be assessed using the Normalized Difference Vegetation Index (NDVI) [48].…”

“…Because healthy vegetation with more chlorophyll reflects higher levels of light in the NIR and absorbs light in the red band, the NDVI distinguishes between areas of thick, healthy plant life vs. unhealthy and/or sparse plant life and has been used to both monitor plant health and drought conditions [49][50][51]. Researchers have shown that NDVI is directly related to the photosyn- Because of the large spatial scales involved, analysis of extensive landscapes often relies on remote sensing and GIS [45][46][47]. Using 4-band imagery, plant health can be assessed using the Normalized Difference Vegetation Index (NDVI) [48].…”

Drone-Based Multispectral Remote Sensing Inversion for Typical Crop Soil Moisture under Dry Farming Conditions

“…In recent years, the application of multispectral remote sensing technology in monitoring crop growth and analyzing soil characteristics has gradually increased [7][8][9][10][11]. In southern Africa, Ndlovu H S et al [12] utilized drone-based multispectral imagery to precisely estimate maize leaf water indices for crop monitoring and early warning system development, aiming to optimize agricultural production in smallholder farms.…”