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Open AccessArticle

Evaluation of Remotely Sensed and Interpolated Environmental Datasets for Vector-Borne Disease Monitoring Using In Situ Observations over the Amhara Region, Ethiopia

1
Department of Earth and Environment, Florida International University, Miami, FL 33199, USA
2
Department of Geography and Environmental Sustainability, University of Oklahoma, Norman, OK 73019, USA
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(5), 1316; https://doi.org/10.3390/s20051316
Received: 2 February 2020 / Revised: 25 February 2020 / Accepted: 26 February 2020 / Published: 28 February 2020
Despite the sparse distribution of meteorological stations and issues with missing data, vector-borne disease studies in Ethiopia have been commonly conducted based on the relationships between these diseases and ground-based in situ measurements of climate variation. High temporal and spatial resolution satellite-based remote-sensing data is a potential alternative to address this problem. In this study, we evaluated the accuracy of daily gridded temperature and rainfall datasets obtained from satellite remote sensing or spatial interpolation of ground-based observations in relation to data from 22 meteorological stations in Amhara Region, Ethiopia, for 2003–2016. Famine Early Warning Systems Network (FEWS-Net) Land Data Assimilation System (FLDAS) interpolated temperature showed the lowest bias (mean error (ME) ≈ 1–3 °C), and error (mean absolute error (MAE) ≈ 1–3 °C), and the highest correlation with day-to-day variability of station temperature (COR ≈ 0.7–0.8). In contrast, temperature retrievals from the blended Advanced Microwave Scanning Radiometer on Earth Observing Satellite (AMSR-E) and Advanced Microwave Scanning Radiometer 2 (AMSR2) passive microwave and Moderate-resolution Imaging Spectroradiometer (MODIS) land-surface temperature data had higher bias and error. Climate Hazards group InfraRed Precipitation with Stations (CHIRPS) rainfall showed the least bias and error (ME ≈ −0.2–0.2 mm, MAE ≈ 0.5–2 mm), and the best agreement (COR ≈ 0.8), with station rainfall data. In contrast FLDAS had the higher bias and error and the lowest agreement and Global Precipitation Mission/Tropical Rainfall Measurement Mission (GPM/TRMM) data were intermediate. This information can inform the selection of geospatial data products for use in climate and disease research and applications. View Full-Text
Keywords: accuracy assessment; environmental data; EPIDEMIA; AMSR-E; AMSR2; FLDAS; MODIS; TRMM/GPM; CHIRPS; epidemiological data accuracy assessment; environmental data; EPIDEMIA; AMSR-E; AMSR2; FLDAS; MODIS; TRMM/GPM; CHIRPS; epidemiological data
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MDPI and ACS Style

Alemu, W.G.; Wimberly, M.C. Evaluation of Remotely Sensed and Interpolated Environmental Datasets for Vector-Borne Disease Monitoring Using In Situ Observations over the Amhara Region, Ethiopia. Sensors 2020, 20, 1316.

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