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Evaluation of the Performance of SM2RAIN-Derived Rainfall Products over Brazil

1
Department of Civil Engineering, University of the Western Plains Ezequiel Zamora, San Carlos Campus 2201 CO, Venezuela
2
Unidade Acadêmica de Ciências Atmosféricas, Universidade Federal de Campina Grande, Av. Aprígio Veloso, 58109-970 PB Campina Grande, Brazil
3
Laboratório de Análise e processamento de Imagens de Satélites (LAPIS), Instituto de Ciências Atmosféricas, Universidade Federal de Alagoas, A. C. Simões Campus, 57072-900 MA Alagoas, Brazil
4
Daugherty Water for Food Global Institute, University of Nebraska-Lincoln, Lincoln, NE 68501, USA
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(9), 1113; https://doi.org/10.3390/rs11091113
Received: 30 March 2019 / Revised: 27 April 2019 / Accepted: 7 May 2019 / Published: 9 May 2019
(This article belongs to the Special Issue Precipitation and Water Cycle Measurements using Remote Sensing)
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Abstract

Microwave-based satellite soil moisture products enable an innovative way of estimating rainfall using soil moisture observations with a bottom-up approach based on the inversion of the soil water balance Equation (SM2RAIN). In this work, the SM2RAIN-CCI (SM2RAIN-ASCAT) 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) (from the Advanced SCATterometer (ASCAT) soil moisture data) were evaluated against in situ rainfall observations under different bioclimatic conditions in Brazil. The research V7 version of the Tropical Rainfall Measurement Mission Multi-satellite Precipitation Analysis (TRMM TMPA) was also used as a state-of-the-art rainfall product with an up-bottom approach. Comparisons were made at daily and 0.25° scales, during the time-span of 2007–2015. The SM2RAIN-CCI, SM2RAIN-ASCAT, and TRMM TMPA products showed relatively good Pearson correlation values (R) with the gauge-based observations, mainly in the Caatinga (CAAT) and Cerrado (CER) biomes (R median > 0.55). SM2RAIN-ASCAT largely underestimated rainfall across the country, particularly over the CAAT and CER biomes (bias median < −16.05%), while SM2RAIN-CCI is characterized by providing rainfall estimates with only a slight bias (bias median: −0.20%), and TRMM TMPA tended to overestimate the amount of rainfall (bias median: 7.82%). All products exhibited the highest values of unbiased root mean square error (ubRMSE) in winter (DJF) when heavy rainfall events tend to occur more frequently, whereas the lowest values are observed in summer (JJA) with light rainfall events. The SM2RAIN-based products showed larger contribution of systematic error components than random error components, while the opposite was observed for TRMM TMPA. In general, both SM2RAIN-based rainfall products can be effectively used for some operational purposes on a daily scale, such as water resources management and agriculture, whether the bias is previously adjusted. View Full-Text
Keywords: satellite rainfall; soil moisture; SM2RAIN; ASCAT; microwave sensors; Brazil satellite rainfall; soil moisture; SM2RAIN; ASCAT; microwave sensors; Brazil
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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).
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Paredes-Trejo, F.; Barbosa, H.; dos Santos, C.A.C. Evaluation of the Performance of SM2RAIN-Derived Rainfall Products over Brazil. Remote Sens. 2019, 11, 1113.

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