A novel framework for multi-layer soil moisture estimation with high spatio-temporal resolution based on data fusion and automated machine learning

Shenglin Li, Yang Han, Caixia Li and Jinglei Wang

Agricultural Water Management, 2024, vol. 306, issue C

Abstract: High spatiotemporal resolution monitoring of multi-layer soil moisture (SM) is crucial for optimizing agricultural water management and precision irrigation strategy. However, achieving high temporal resolution at a 30 m spatial scale remains challenging given the confine of current satellite sensors. To overcome this, we developed an innovative framework synergizing multi-source remote sensing data, reanalysis data, auxiliary information (topography and soil texture), and ground-based SM observation. Initially, we generated seamless 30 m resolution metrics, including the normalized difference vegetation index (NDVI), land surface temperature (LST), and surface albedo, by employing the modified neighborhood similar pixel interpolator (MNSPI) in conjunction with the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM). These variables, combined with reanalysis data, auxiliary data, and ground-based SM observations, were input into an Automated Machine Learning (AutoML) workflow to estimate SM at 0–20, 20–40, and 40–60 cm soil layers. Validation conducted in the People's Victory Canal irrigation area revealed depth-dependent prediction accuracy, with Pearson correlation coefficient (R) values of 0.806, 0.772, and 0.680, root mean square errors (RMSEs) of 0.038, 0.047, and 0.054 cm³/cm³, and relative root mean square errors (RRMSEs) of 16.170 %, 20.346 %, and 22.689 % for the 0–20, 20–40, and 40–60 cm soil layers, respectively. This framework shows significant potential for enhancing water resources management at the field scale by providing accurate, high-resolution SM estimates across multiple depths.

Keywords: Soil moisture; High resolution; Data Integration; Automated machine learning; Multimodal data; Remote sensing (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0378377424005092
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:306:y:2024:i:c:s0378377424005092

DOI: 10.1016/j.agwat.2024.109173

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 ().