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Open AccessArticle

Improvement and Validation of NASA/MODIS NRT Global Flood Mapping

Center for Spatial Information Science and Systems, George Mason University, Fairfax, VA 22032, USA
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(2), 205;
Received: 5 December 2018 / Revised: 11 January 2019 / Accepted: 11 January 2019 / Published: 21 January 2019
(This article belongs to the Special Issue Selected Papers from Agro-Geoinformatics 2018)
The remote-sensing based Flood Crop Loss Assessment Service System (RF-CLASS) is a web service based system developed and managed by the Center for Spatial Information Science and Systems (CSISS). The system uses Moderate Resolution Imaging Spectroradiometer (MODIS)-based flood data, which was implemented by the Dartmouth Flood Observatory (DFO), to provide an estimation of crop loss from floods. However, due to the spectral similarity between water and shadow, a noticeable amount of false classification of shadow can be found in the DFO flood products. Traditional methods can be utilized to remove cloud shadow and part of mountain shadow. This paper aims to develop an algorithm to filter out noise from permanent mountain shadow in the flood layer. The result indicates that mountain shadow was significantly removed by using the proposed approach. In addition, the gold standard test indicated a small number of actual water surfaces were misidentified by the proposed algorithm. Furthermore, experiments also suggest that increasing the spatial resolution of the slope helped reduce more noise in mountains. The proposed algorithm achieved acceptable overall accuracy (>80%) in all different filters and higher overall accuracies were observed when using lower slope filters. This research is one of the very first discussions on identifying false flood classification from terrain shadow by using the highly efficient method. View Full-Text
Keywords: MODIS; false water classification; terrain shadow; noise reduction MODIS; false water classification; terrain shadow; noise reduction
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MDPI and ACS Style

Lin, L.; Di, L.; Tang, J.; Yu, E.; Zhang, C.; Rahman, M.S.; Shrestha, R.; Kang, L. Improvement and Validation of NASA/MODIS NRT Global Flood Mapping. Remote Sens. 2019, 11, 205.

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