Articles | Volume 25, issue 4
Hydrol. Earth Syst. Sci., 25, 1827–1847, 2021
Hydrol. Earth Syst. Sci., 25, 1827–1847, 2021

Research article 09 Apr 2021

Research article | 09 Apr 2021

Field-scale soil moisture bridges the spatial-scale gap between drought monitoring and agricultural yields

Noemi Vergopolan et al.

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Cited articles

Adegoke, J. O. and Carleton, A. M.: Relations between Soil Moisture and Satellite Vegetation Indices in the U.S. Corn Belt, J. Hydrometeorol., 3, 395–405,<0395:rbsmas>;2, 2002. a
Aghighi, H., Azadbakht, M., Ashourloo, D., Shahrabi, H. S., and Radiom, S.: Machine Learning Regression Techniques for the Silage Maize Yield Prediction Using Time-Series Images of Landsat 8 OLI, IEEE J. Sel. Top. Appl., 11, 4563–4577,, 2018. a
Alfani, F., Arslan, A., McCarthy, N., Cavatassi, R., and Sitko, N.: Climate-change vulnerability in rural Zambia: the impact of an El Niño-induced shock on income and productivity, available at: (last access: 18 May 2020), 2019. a, b, c, d
Archer, K. J. and Kimes, R. V.: Empirical characterization of random forest variable importance measures, Comput. Stat. Data An., 52, 2249–2260,, 2008. a, b
Azzari, G., Jain, M., and Lobell, D. B.: Towards fine resolution global maps of crop yields: Testing multiple methods and satellites in three countries, Remote Sens. Environ., 202, 129–141,, 2017. a, b
Short summary
Drought monitoring and yield prediction often rely on coarse-scale hydroclimate data or (infrequent) vegetation indexes that do not always indicate the conditions farmers face in the field. Consequently, decision-making based on these indices can often be disconnected from the farmer reality. Our study focuses on smallholder farming systems in data-sparse developing countries, and it shows how field-scale soil moisture can leverage and improve crop yield prediction and drought impact assessment.