Root zone soil moisture mapping at very high spatial resolution using radar-derived surface soil moisture product

Nadia Ouaadi, Abdelghani Chehbouni, Emna Ayari, Bouchra Ait Hssaine, Jamal ElFarkh, Michel Le Page, Salah Er-Raki and Aaron Boone

Agricultural Water Management, 2025, vol. 314, issue C

Abstract: Root zone soil moisture (RZSM) is a key variable controlling the soil-vegetation-atmosphere exchanges. Its estimation is vital for monitoring hydrological, meteorological and agricultural processes. A number of large-scale products exist but with a coarse resolution (>1 km), which is not suitable for plot-scale studies. The aim of this work is to map RZSM, for the first time, at very high spatial resolution using a very high spatial resolution surface soil moisture (SSM) product and a recursive exponential filter. SSM is estimated from Sentinel-1 data using the water cloud model at a resolution of approximately 50 m. The approach was evaluated on a database consisting of 12 fields, including 7 winter wheat and 5 summer maize fields, irrigated using different techniques. The results show that the approach performs reasonably well using Sentinel-1 SSM product with correlation coefficient (R) between 0.3 and 0.82, root-mean-square error (RMSE) between 0.05 and 0.12 m3/m3 and a bias in the range −0.1–0.07 m3/m3, at 15–20 cm depth. This is equivalent to R = 0.6, RMSE = 0.12 m3/m3 and bias = 0.07 m3/m3 using the entire database, which is quite low compared to the use of in situ SSM measurements (R = 0.81, RMSE = 0.07 m3/m3 and bias = 0.03 m3/m3). This is related to inaccuracies in the SSM product, where fields with good SSM estimation also resulted in good RZSM estimation and conversely. In addition to SSM, the approach is also sensitive to its time constant T. Analysis of RZSM sensitivity to T shows that the optimum T value depends on soil texture, climate and measurement depth. In particular, low optimum T values (1 day) are obtained for loamy and sandy loam soils, while higher values (5–10 days) are optimal for soils with a high clay fraction, at 15–20 cm depth. These values increase with soil depth and are influenced by seasonal atmospheric demand. Combined to reasonable statistical metrics, the spatial variability depicted by the RZSM maps opens up prospects for high-resolution RZSM mapping from Sentinel-1 SSM data using a simple approach over annual crops. This is of prime relevance for agricultural applications requiring very high-resolution estimation at plot scale, such as crop yield, irrigation and fertilizer management, as well as for the assessment of inter-plot variability.

Keywords: Root zone soil moisture; Surface soil moisture; Radar remote sensing; Exponentiel filter; Crops (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0378377425002215
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:agiwat:v:314:y:2025:i:c:s0378377425002215

DOI: 10.1016/j.agwat.2025.109507

Access Statistics for this article

Agricultural Water Management is currently edited by B.E. Clothier, W. Dierickx, J. Oster and D. Wichelns

More articles in Agricultural Water Management from Elsevier
Bibliographic data for series maintained by Catherine Liu ().