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

• Published in: Sensors (Basel, Switzerland) Switzerland), 2020-02, Vol.20 (5), p.1316
• Main Authors: Alemu, Woubet G, Wimberly, Michael C
• Format: Article
• Language: English
• Subjects: accuracy assessment; amsr-e; amsr2; Biological Monitoring - methods; chirps; Climate; environmental data; epidemia; epidemiological data; Ethiopia; fldas; Meteorology - methods; modis; Rain; Satellite Imagery - methods; Temperature; trmm/gpm; Vector Borne Diseases - diagnosis
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s20051316

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.