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Remote Sens. 2017, 9(11), 1134; doi:10.3390/rs9111134

Improving Rainfall Erosivity Estimates Using Merged TRMM and Gauge Data

1
Institute of Agricultural Remote Sensing and Information Technology Application, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China
2
School of Earth and Ocean Science, Cardiff University, Cardiff CF10 3XQ, UK
3
College of Resource and Environment, Tibet University, Nyingchi 860114, China
*
Author to whom correspondence should be addressed.
Received: 8 August 2017 / Revised: 20 October 2017 / Accepted: 2 November 2017 / Published: 6 November 2017
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Abstract

Soil erosion is a global issue that threatens food security and causes environmental degradation. Management of water erosion requires accurate estimates of the spatial and temporal variations in the erosive power of rainfall (erosivity). Rainfall erosivity can be estimated from rain gauge stations and satellites. However, the time series rainfall data that has a high temporal resolution are often unavailable in many areas of the world. Satellite remote sensing allows provision of the continuous gridded estimates of rainfall, yet it is generally characterized by significant bias. Here we present a methodology that merges daily rain gauge measurements and the Tropical Rainfall Measuring Mission (TRMM) 3B42 data using collocated cokriging (ColCOK) to quantify the spatial distribution of rainfall and thereby to estimate rainfall erosivity across China. This study also used block kriging (BK) and TRMM to estimate rainfall and rainfall erosivity. The methodologies are evaluated based on the individual rain gauge stations. The results from the present study generally indicate that the ColCOK technique, in combination with TRMM and gauge data, provides merged rainfall fields with good agreement with rain gauges and with the best accuracy with rainfall erosivity estimates, when compared with BK gauges and TRMM alone. View Full-Text
Keywords: TRMM; rain gauge; merged rainfall; collocated cokriging; rainfall erosivity TRMM; rain gauge; merged rainfall; collocated cokriging; rainfall erosivity
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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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Teng, H.; Ma, Z.; Chappell, A.; Shi, Z.; Liang, Z.; Yu, W. Improving Rainfall Erosivity Estimates Using Merged TRMM and Gauge Data. Remote Sens. 2017, 9, 1134.

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