Paper
7 October 2011 Investigating agro-drought in the Lower Mekong Basin using MODIS NDVI and land surface temperature data
N. T. Son, C. F. Chen, C. R. Chen
Author Affiliations +
Abstract
Agro-drought usually refers to the shortage of water for crop irrigation in a short period, creating serve impacts on crop production due to insufficient soil moisture. This phenomenon has been considered as a challenge for rice farmers in the Lower Mekong Basin (LMB), especially in the dry season (from November to April). Thus, information on agro-drought is important for water scheduling to mitigate adverse impacts on rice production. The main objective of this study is to investigate the applicability of the monthly MODIS normalized difference vegetation index (NDVI) and land surface temperature (LST) data for drought monitoring from 2008 to 2010. The data was processed for the dry season because this period is usually suffered from droughts. A simple temperature vegetation difference index (TVDI) was used to estimate the surface soil moisture content. We investigated the sensitivity between the preliminary TVDI results and TRMM (Tropical Rainfall Measuring Mission) precipitation. The results revealed good agreement between the two datasets. TVDI was declined during or after rain events indicating greater soil moisture content, but increased again later indicating less soil moisture content. The results by analysis of TVDI showed that the moderate and serve droughts were spatially scattered over the region from November to March and returned to normal condition by the end of the dry season (April) with the onset of rainy season. The drought was found more serve and extensive in plains of Thailand and Cambodia. The larger area of serve drought was especially observed for the 2008-2009 dry seasons compared to 2010. The results achieved from this study could be useful for crop irrigation scheduling.
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N. T. Son, C. F. Chen, and C. R. Chen "Investigating agro-drought in the Lower Mekong Basin using MODIS NDVI and land surface temperature data", Proc. SPIE 8174, Remote Sensing for Agriculture, Ecosystems, and Hydrology XIII, 817411 (7 October 2011); https://doi.org/10.1117/12.897972
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KEYWORDS
MODIS

Soil science

Vegetation

Agriculture

Remote sensing

Data acquisition

Data processing

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