Evaluation of MODIS surrogates for meteorological humidity data in east Africa

Abstract

Satellite remote-sensing technology has shown promising results in characterizing the environment in which plants and animals thrive. Scientists, biologists, and epidemiologists are adopting remotely sensed imagery to compensate for the paucity of weather information measured by weather stations. With measured humidity from three stations as baselines, our study reveals that normalized difference vegetation index (NDVI) and atmospheric saturation deficit at the 780 hPa pressure level (D_MODIS), both of which were derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor, were significantly correlated with station saturation deficits (D_stn) (|r| = 0.42–0.63, p < 0.001). These metrics have the potential to estimate saturation deficits over east Africa. Four to nine days of lags were found in the NDVI responding to D_stn. For the daily estimation of D_stn, D_MODIS yielded better performance than the NDVI. However, both of them poorly explained variation in daily D_stn using simple regression models (adj. R² = 0.17–0.39). When the estimation temporal scale was changed to 16 days, performance was similar, and both were better than daily estimations. For D_stn estimation at coarser geographic scales, given that many factors such as soil, vegetation, slope, aspect, and wind speed might complicate NDVI response lags and model construction, D_MODIS is preferable as a proxy to saturation deficit over ground due to its simple relationship with D_stn.

Acknowledgements

This research was supported by the National Institutes of Health, Office of the Director, Roadmap Initiative, and NIGMS: award RGM084704A; and by a Ministry of Science and Technology Grant. We thank Dr Joseph Maitima of Ecodym Africa, Nairobi, Kenya, for his help in managing the weather stations. We also thank Shaun Langley and Mark H. DeVisser at Michigan State University for collecting the weather station data from Kenya.