ZHANG Feng, WU Bing-fang. Estimation of Monthly Rice-Planted Area in Thailand Using Remote Sensing Data[J]. Journal of Remote Sensing, 2004,(6):664-671.
ZHANG Feng, WU Bing-fang. Estimation of Monthly Rice-Planted Area in Thailand Using Remote Sensing Data[J]. Journal of Remote Sensing, 2004,(6):664-671. DOI: 10.11834/jrs.20040617.
Thailand is the largest exporter of rice in the world grain market with a reputation of high grain quality.One-half of the rice land was located in the Northeast region.Planted area changes monthly in tropical agriculture
unlike that of agriculture in temperate zones.This paper estimated the area planted with rice using remote sensing data in center-northeast of Thailand.We propose a method which can be used to estimated the rice planted area in tropical regions by RADAR data.The arable land area was measured using Landsat Thematic Mapper (TM) data acquired in the dry season and identified monthly rice planted fields using Synthertic Aperture Radar(SAR) data acquired in the rainy season(planting season).Landsat TM data (path-row:128-49) acquired on 7 April 2002 was used to identify the agricultural area.To detect the planted fields
RADARSAT SCN(ScanSAR Narrow)data were employed. The parameters of SHR are as follows:C-band
HH(horizontal transmit and horizontal receive) polarization
beam mode: W2
s5
s6
orbit: 35606 descending
scene center: 15°54′N and 102°41′E.Four images were acquired on 02-05-2002
9-06-2002
01-08-2002 and 31-08-2002.Arable land area was labeled using unsupervised classification of the TM data.According to statistic result about time series of backscatter coefficients of rice
four type of models were built to estimate the monthly change in planted area.Since rice-planting is not carried out simultaneously and the planted area change monthly
some assumptions were necessary for estimating the planted area.Assuming that the intensity of planting exhibits a normal distribution
there are five peaks due to the monthly planting.The monthly rice-planted area was estimated based on supervised classification using the defined model during the rice-planting season.The overall classification accuracy was 91%
and the rice information including arable land is 90%.