A comprehensive evaluation of satellite-based and reanalysis soil moisture products over the upper Blue Nile Basin, Ethiopia
Bibliographic record
Abstract
Soil moisture data is crucial for enhancing drought monitoring, optimizing water management, refining irrigation schedules, forecasting floods, and understanding climate change impacts. Despite the existence of long-term global satellite and reanalysis products, the performance of global satellite products in Ethiopia is underexplored, highlighting a need for comprehensive assessments to effectively utilize these resources and address critical environmental challenges. This research evaluates various operational satellites and reanalysis soil moisture datasets over the Gilgel Abay watershed. The datasets include the European Space Agency's Climate Change Initiative Soil Moisture (ESA-CCI SM), Soil Moisture and Ocean Salinity (SMOS), NASA's Soil Moisture Active Passive mission (SMAP Enhanced), the European Centre for Medium-Range Weather Forecasts Fifth Generation Reanalysis (ECMWF ERA5), Climate Forecast System reanalysis (CFSRv2), NASA's Short-term Prediction Research and Transition Center - Land Information System (SPoRT-LIS), and NASA's Global Land Data Assimilation System (GLDAS). After applying bias correction, the Kolmogorov-Smirnov two-sample t-tests, Bonferroni correction, and statistical error metrics, the evaluation reveals that all products, except NASA-GLDAS, effectively capture soil moisture dynamics. SMAP shows superior temporal dynamics, followed by SMOS, ESA-CCI, CFSRv2, LIS and ERA5. Using Spearman's rank correlation coefficient (r s ), SMAP (r s = 0.68) and SMOS (r s = 0.67) identified as the most accurate soil moisture products, with SMOS excelling in spatial representation and closely aligning with the Topographic Wetness Index (TWI). However, the lack of sufficient in situ monitoring networks limits the ability to perform a thorough evaluation. Establishing these networks is essential for improving satellite retrievals and modelling in the upper Blue Nile Basin, Ethiopia. • The research critically examines the global soil moisture datasets in Ethiopia. • SMAP excels in temporal dynamics, with SMOS closely aligning to the Topographic Wetness Index. • In situ monitoring is key to improving soil moisture data retrieval and modelling in the Blue Nile.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".