the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 29 May 2026
Connecting earth observation anomalies to farmer surveys for monitoring impacts of agricultural drought on rainfed rice yields in Nigeria
Abstract. Agricultural drought threatens rainfed rice production in Nigeria, where smallholder farmers depend on rainfall and have limited capacity to buffer climate shocks. While meteorological drought indices such as the SPI and the SPEI are widely used in national early warning systems, their ability to capture the impacts of droughts on rainfed rice yields at the smallholder field-level remains uncertain. This study evaluates the added value of earth observation (EO)-derived vegetation and soil moisture anomalies for monitoring and predicting drought impacts on rainfed rice yields in Nigeria. Satellite-based Normalized Difference Vegetation Index anomalies (NDVIA) and Soil Water Index anomalies (SWIA) were derived using a zonal clustering and thresholding approach and combined with farmer survey and yield data collected from 146 rainfed rice farmers across four major rice-growing states between 2019 and 2024. Multivariate regression models were used to assess the relationships between EO indicator anomalies and annual yield changes, and the effects of different zonal clustering and anomaly thresholds on anomaly calculation were evaluated. Results show that SPI and SPEI explain a substantial share of yield variability in some years, particularly when droughts coincide with sensitive phenological stages. However, EO-based anomaly indicators, especially SWIA (maximum improved R² = 0.25), provide complementary information and significantly improve yield predictions in years when meteorological indices alone perform poorly. The timing of anomalies relative to rice phenology was critical, with droughts during panicle initiation having the largest yield impacts. Integrating EO-based vegetation and soil moisture anomaly indicators with existing meteorological indices can contribute to the monitoring of agricultural droughts and improve the operational relevance of early warning systems for rainfed rice farmers in Nigeria.
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Status: open (until 05 Aug 2026)
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RC1: 'Comment on egusphere-2026-2636', Matteo Zampieri, 06 Jul 2026
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AC1: 'Reply on RC1', Nick Gutkin, 15 Jul 2026
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Dear Reviewer,
Thank you very much for your detailed review and suggestions. We agree with the spatial mismatch issue, and will clearly state it as a potential limitation in the revised version of the manuscript after the review process. Furthermore, we will also add to the discussion the suggestions for future work incorporating the additional drought indicators as mentioned by the Reviewer. We will also consider these indicators for our own future work building on the results from this study.
Thank you,
Nick GutkinCitation: https://doi.org/10.5194/egusphere-2026-2636-AC1
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AC1: 'Reply on RC1', Nick Gutkin, 15 Jul 2026
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RC2: 'Comment on egusphere-2026-2636', Anonymous Referee #2, 15 Jul 2026
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The objective of this article is to evaluate the effectiveness of Earth Observation indicators (vegetation (NDVIA) and soil moisture (SWIA) anomalies) for monitoring the impact of droughts on rain-fed rice yields in Nigeria, in comparison with traditional meteorological indices (SPI, SPEI). The goal is to predict crop losses due to droughts and help farmers adapt.
To do this, the authors create various combinations of indices, covering different stages of rice growth, and calculate averages across areas of varying sizes.
Results show that models that include NDVIA and SWIA tends to better predict rice yields than those using only SPI/SPEI, especially for years when the meteorological indices fail (e.g., 2023) even if R² values are still relatively low.
The paper is clearly written and well-organized and the subject is appropriate to EGUSphere. I would regret the lack of rice yield timeseries in t/ha. There is no typical value of rice yield in the paper, only some RSME in t/ha in Figure A4. Nevertheless, I would recommend the paper for publication with only one minor comments.
Can authors add a figure showing rice-yield temporal evolution, and example of estimated rice yields using one or tow combination of indices.
Citation: https://doi.org/10.5194/egusphere-2026-2636-RC2
Data sets
MOFODRONI dataset Gutkin, N., Ehiemere, C.I., De Vos, K., Ehiemere, N., Degerickx, J., Gebruers, S. https://zenodo.org/records/19593937
Model code and software
MOFODRONI Gutkin, N., De Vos, K., Degerickx, J., Gebruers, S. https://github.com/gutkinn/MOFODRONI
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The study is timely and important in the context of the increasing pressures that climate change places on agriculture. It focuses on rainfed rice production in Nigeria and relates meteorological indicators (SPI and SPEI), as well as satellite-derived indicators (NDVI and soil moisture), to yield variability and drought occurrence reported at the farm level.
The analysis is very detailed and there is no major issue with it. One minor concern is the mismatch in spatial scale between the meteorological indicators (25 km resolution) and the farm-level observations. However, this is not critical, as meteorological drought conditions are often considered spatially coherent. The explained variance is not particularly high, but this is expected when comparing meteorological indicators with farm-level data at such different spatial scales. However, the spatial resolution of the SPI and SPEI datasets is not reported and should be clearly stated in the manuscript as a potential limitation of the study. In any case, the improvement achieved by incorporating satellite-derived indicators is convincing, particularly during the drier year. This is the main result of the paper that deserves to be published.
As a suggestion for future work, the authors could also consider drought indicators derived from thermal remote sensing, such as ECOSTRESS and the Cooling Efficiency Factor Index (CEFI). These indicators have the potential to bridge the gap between driver-based drought indicators, such as SPI and SPEI, and impact-based indicators by directly detecting stomatal closure and the associated reduction in transpiration under drought stress. A brief mention of these approaches in the discussion could further strengthen the manuscript.