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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 (DMODIS), both of which were derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor, were significantly correlated with station saturation deficits (Dstn) (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 toDstn. For the daily estimation ofDstn,DMODISyielded better performance than the NDVI. However, both of them poorly explained variation in dailyDstnusing 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. ForDstnestimation 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,DMODISis preferable as a proxy to saturation deficit over ground due to its simple relationship withDstn. [ABSTRACT FROM AUTHOR]

Abstract: Real time and spatially distributed T a (air temperature) data are desired for many applications. T s (land surface temperature) derived from remote sensors has been used to estimate T a in previous studies. Exploring MODIS Aqua T s and station measured daily maximum and minimum T a over east Africa, we found that T s did not agree well with T a during the day (MAE (Mean Absolute Error)=6.9±5.0°C) but had better agreement during the night (MAE=1.9±1.7°C). A stepwise linear regression method was applied to construct possible models to predict T a based on MODIS data. Our results showed that, only considering elevation, high spatial resolution T a could be obtained by simple linear models, with MAE=1.9°C, agreement index=0.79 for daily maximum T a, and MAE=1.9°C, agreement index=0.92 for daily minimum T a. MODIS T s data could provide temporal variation information and slightly improve the accuracy of model predictions (by 0.2°C of MAE). However, considering (i) major absences (about 2/3 of days) of T s data due to cloud cover and (ii) small T a variations in time (σ =2.1°C) over east Africa, models without T s might be more practical in particular applications such as tsetse fly distribution models. Other variables including solar zenith angle, low level precipitable water content, and vegetation index (NDVI and EVI) were insignificant in the daily maximum and minimum T a estimation models after elevation and T s had already been considered as predictors. [Copyright &y& Elsevier]