Assessment of SM2RAIN-Derived and State-of-the-Art Satellite Rainfall Products over Northeastern Brazil

Remote Sensing

Franklin Paredes-Trejo¹  Luciana Rossato Spatafora²  HumbertoAlves Barbosa³ 

[1] Department of Civil Engineering, University of the Western Plains ‘Ezequiel Zamora’, San Carlos Campus 2201, Venezuela;IEEC/UPC and SMOS Barcelona Expert Centre, Universitat Politècnica de Catalunya, Jordi Girona 1-3UPC Campus Nord, Building D3, 08034 Barcelona, Spain;Laboratório de Análise e processamento de Imagens de Satélites (LAPIS), Instituto de Ciências Atmosféricas, Universidade Federal de Alagoas, A. C. Simões Campus, 57072-900 Maceió, Alagoas, Brazil;

关键词: satellite rainfall;    soil moisture;    SM2RAIN;    CHIRPS;    MSWEP;    microwave sensors;    Northeast Brazil;    CMORPH;   

DOI  :  10.3390/rs10071093

来源: DOAJ

【 摘 要 】

Microwave-based satellite rainfall products offer an opportunity to assess rainfall-related events for regions where rain-gauge stations are sparse, such as in Northeast Brazil (NEB). Accurate measurement of rainfall is vital for water resource managers in this semiarid region. In this work, the SM2RAIN-CCI rainfall data obtained from the inversion of the microwave-based satellite soil moisture (SM) observations derived from the European Space Agency (ESA) Climate Change Initiative (CCI), and ones from three state-of-the-art rainfall products (Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS), Climate Prediction Center Morphing Technique (CMORPH), and Multi-SourceWeighted-Ensemble Precipitation (MSWEP)) were evaluated against in situ rainfall observations under different bioclimatic conditions at the NEB (e.g., AMZ, Amazônia; CER, Cerrado; MAT, Mata Atlântica; and CAAT, Caatinga). Comparisons were made at daily, 5-day, and 0.25° scales, during the time-span of 1998 to 2015. It was found that 5-day SM2RAIN-CCI has a reasonably good performance in terms of the correlation coefficient over the CER biome (R median: 0.75). In terms of the root mean square error (RMSE), it exhibits better performance in the CAAT biome (RMSE median: 12.57 mm). In terms of bias (B), the MSWEP, SM2RAIN-CCI, and CHIRPS datasets show the best performance in MAT (B median: −8.50%), AMZ (B median: −0.65%), and CER (B median: 0.30%), respectively. Conversely, CMORPH poorly represents the rainfall variability in all biomes, particularly in the MAT biome (R median: 0.43; B median: −67.50%). In terms of detection of rainfall events, all products show good performance (Probability of detection (POD) median > 0.90). The performance of SM2RAIN-CCI suggests that the SM2RAIN algorithm fails to estimate the amount of rainfall under very dry or very wet conditions. Overall, results highlight the feasibility of SM2RAIN-CCI in those poorly gauged regions in the semiarid region of NEB.

【 授权许可】

Unknown