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Preprints
https://doi.org/10.5194/hessd-10-7963-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/hessd-10-7963-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.

  21 Jun 2013

21 Jun 2013

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This preprint was under review for the journal HESS but the revision was not accepted.

Estimating Sahelian and East African soil moisture using the Normalized Difference Vegetation Index

A. McNally1, C. Funk2, G. J. Husak1, J. Michaelsen1, B. Cappelaere3, J. Demarty3, T. Pellarin4, T. P. Young5, K. K. Caylor6, C. Riginos7, and K. E. Veblen8 A. McNally et al.
  • 1Department of Geography, Climate Hazards Group, UC Santa Barbara, Santa Barbara, CA, USA
  • 2US Geological Survey and Climate Hazards Group, University of California, Santa Barbara, CA, USA
  • 3Institut de recherche pour le développement (IRD), UMR HydroSciences, Montpellier, France
  • 4Universite de Grenoble, Grenoble, France
  • 5Department of Plant Sciences, UC Davis, Davis, CA, USA
  • 6Department of Civil and Environmental Engineering Princeton University, Princeton, NJ, USA
  • 7Conservation Research Center, Teton Science Schools, Jackson, WY, USA
  • 8Department of Wildland Resources, Utah State University, Logan, UT, USA

Abstract. Rainfall gauge networks in Sub-Saharan Africa are inadequate for assessing Sahelian agricultural drought, hence satellite-based estimates of precipitation and vegetation indices such as the Normalized Difference Vegetation Index (NDVI) provide the main source of information for early warning systems. While it is common practice to translate precipitation into estimates of soil moisture, it is difficult to quantitatively compare precipitation and soil moisture estimates with variations in NDVI. In the context of agricultural drought early warning, this study quantitatively compares rainfall, soil moisture and NDVI using a simple statistical model to translate NDVI values into estimates of soil moisture. The model was calibrated using in-situ soil moisture observations from southwest Niger, and then used to estimate root zone soil moisture across the African Sahel from 2001–2012. We then used these NDVI-soil moisture estimates (NSM) to quantify agricultural drought, and compared our results with a precipitation-based estimate of soil moisture (the Antecedent Precipitation Index, API), calibrated to the same in-situ soil moisture observations. We also used in-situ soil moisture observations in Mali and Kenya to assess performance in other water-limited locations in sub Saharan Africa.

The separate estimates of soil moisture were highly correlated across the semi-arid, West and Central African Sahel, where annual rainfall exhibits a uni-modal regime. We also found that seasonal API and NDVI-soil moisture showed high rank correlation with a crop water balance model, capturing known agricultural drought years in Niger, indicating that this new estimate of soil moisture can contribute to operational drought monitoring. In-situ soil moisture observations from Kenya highlighted how the rainfall-driven API needs to be recalibrated in locations with multiple rainy seasons (e.g., Ethiopia, Kenya, and Somalia). Our soil moisture estimates from NDVI, on the other hand, performed well in Niger, Mali and Kenya. This suggests that the NDVI-soil moisture relationship may be more robust across rainfall regimes than the API because the relationship between NDVI and plant available water is less reliant on local characteristics (e.g., infiltration, runoff, evaporation) than the relationship between rainfall and soil moisture.

A. McNally et al.

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