Next Article in Journal
Development and Performance Assessment of a Low-Cost UAV Laser Scanner System (LasUAV)
Next Article in Special Issue
Use of SMOS L3 Soil Moisture Data: Validation and Drought Assessment for Pernambuco State, Northeast Brazil
Previous Article in Journal
A Novel Object-Based Supervised Classification Method with Active Learning and Random Forest for PolSAR Imagery
Previous Article in Special Issue
Evaluation of SMOS, SMAP, ASCAT and Sentinel-1 Soil Moisture Products at Sites in Southwestern France
Article Menu
Issue 7 (July) cover image

Export Article

Open AccessArticle

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

1
Department of Civil Engineering, University of the Western Plains ‘Ezequiel Zamora’, San Carlos Campus 2201, Venezuela
2
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
3
IEEC/UPC and SMOS Barcelona Expert Centre, Universitat Politècnica de Catalunya, Jordi Girona 1-3UPC Campus Nord, Building D3, 08034 Barcelona, Spain
*
Author to whom correspondence should be addressed.
Remote Sens. 2018, 10(7), 1093; https://doi.org/10.3390/rs10071093
Received: 4 May 2018 / Revised: 1 July 2018 / Accepted: 6 July 2018 / Published: 9 July 2018
(This article belongs to the Special Issue Soil Moisture Remote Sensing Across Scales)
  |  
PDF [6253 KB, uploaded 9 July 2018]
  |  

Abstract

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. View Full-Text
Keywords: satellite rainfall; soil moisture; SM2RAIN; CHIRPS; MSWEP; microwave sensors; Northeast Brazil; CMORPH satellite rainfall; soil moisture; SM2RAIN; CHIRPS; MSWEP; microwave sensors; Northeast Brazil; CMORPH
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Paredes-Trejo, F.; Barbosa, H.A.; Rossato Spatafora, L. Assessment of SM2RAIN-Derived and State-of-the-Art Satellite Rainfall Products over Northeastern Brazil. Remote Sens. 2018, 10, 1093.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top