Work

Comparison of Soil Water Content from SCATSAR-SWI and Cosmic Ray Neutron Sensing at Four Agricultural Sites in Northern Italy: Insights from Spatial Variability and Representativeness

Year: 2024

Type: article

Abstract: Monitoring soil water content (SWC) is vital for various applications, particularly in agriculture. This study compares the SWC estimated by means of SCATSAR-SWI remote sensing (RS) at different depths (T-values) with Cosmic Ray Neutron Sensing (CRNS) across four agricultural sites in northern Italy. Additionally, it examines spatial mismatch and representativeness of SWC products’ footprints based on the Normalized Difference Vegetation Index (NDVI), soil properties (sand, silt, clay, Soil Organic Carbon (SOC)), and irrigation information within the areas. Results reveal that RS-derived SWC, particularly at T=2 depth, exhibits moderate positive linear correlation (mean Pearson correlation coefficient, R=0.6) and mean unbiased Root-Mean-Square Difference (ubRMSD) of 14.90%SR (Degree of Saturation (%)). However, lower agreement is observed during summer and autumn, attributed to factors such as high biomass growth. Sites with less variation in vegetation and soil properties within RS pixels rank better in comparing SWC products. Although a weak correlation (mean R=0.35) exists between median NDVI differences of footprints and disparities in SWC product performance metrics, the influence of vegetation greenness on the results is clearly identified. Additionally, RS pixels with a lower percentage of sand and SOC, and silt loam soil type correlate with decreased agreement between SWC products. Finally, localized irrigation practices also partially explain some differences in the SWC products. Overall, the results highlight how RS-pixel variability can explain differences between SWC products and how this information should be considered when selecting optimal ground-based measurement locations for remote sensing comparison.

Source: Remote Sensing

DOI: 10.3390/rs16183384

Authors: Sadra Emamalizadeh, Alessandro Pirola, Cinzia Alessandrini, Anna Balenzano, Gabriele Baroni

Institutions: University of Bologna, Istituto per il Rilevamento Elettromagnetico dell'Ambiente, National Research Council

Language: English

Topic: Soil Moisture and Remote Sensing

Subfield: Environmental Engineering

Field: Environmental Science

Domain: Physical Sciences

SDG: Zero hunger

Open Access status: gold

Estimated APC paid: $3,069