dc.description.abstract | Information on surface soil moisture is important for water management, while
information on rice growing areas is vital for crop management and production prediction.
This study aims to investigate surface soil moisture variability in relation to rice cropping
systems in the Mekong Delta (MD), Vietnam using the Moderate Resolution Imaging
Spectroradiometer (MODIS) data. The surface soil moisture was estimated from the MODIS
data acquired during January to April from 2002 to 2007 using the Temperature Vegetation
Dryness Index (TVDI) method. This index was empirically calculated by parameterizing the
relationship between the MODIS Land Surface Temperature (LST) and the Normalized
Difference Vegetation Index (NDVI) data.
From the results of soil moisture estimation, it was found that the low soil moisture
occurred in 2006 and occupied the largest region of the study area compared to other years.
Therefore, this extreme year 2006 and a normal year, in this case 2002, were selected for
analysis of soil moisture variability in relation to the distribution of rice cropping systems.
The spatial distribution of rice cropping systems was obtained from classification of the timeseries
MODIS NDVI 250-m data acquired in 2002 and 2006. Data were processed using the
empirical mode decomposition (EMD) method for noise filtering of the time-series NDVI
data. Soft and hard classification algorithms, namely linear mixture model (LMM) and
support vector machines (SVMs), were used for classifying rice cropping systems. These two
classification algorithms were used for the sake of comparing their classification performance.
Various spatial and non-spatial data were also gathered for accuracy assessment of the TVDI
and classification results.
The results showed that the LST-NDVI space was well-defined. The pixels in each
scatter plot could form a triangle. This indicated a wide range of surface soil moisture in the
study area. The TVDI validation results were achieved by comparing TVDI values with daily
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rainfall throughout the study area. The comparison results revealed good agreement and
sensitivity between TVDI and daily rainfall data. The areas with low soil moisture were
mainly distributed in coastal areas from 2002 to 2005, but expanded into the middle region in
2006 and 2007. The largest area of low soil moisture was observed in 2006, reflecting the fact
that the MD was faced with drought in 2006 because the amount of water in the Mekong and
Bassac Rivers in the dry season was reduced drastically.
In a case study of rice crop phenology detection, the comparison results between the
estimated sowing/heading dates and the field survey data indicated that the use of smooth
time profiles extracted from the EMD-based filtered time-series MODIS NDVI 250-m data
for detecting phenological dates gave better results than the wavelet transform-based data.
The EMD acted a good filter for noise reduction of the time-series NDVI data. The smooth
NDVI profiles extracted from the EMD-based filtered NDVI data could well preserve the
amplitude of NDVI values better than those extracted from the wavelet transform. These
NDVI patterns reflected the seasonal changes in crop phenology of rice cropping systems,
which was important for understanding the temporal NDVI responses of different rice fields
of cropping patterns in the study area. The LMM and SVMs were applied to the EMD-based
filtered data for classification of rice cropping systems in the region. The classification maps
for 2002 and 2006 were compared with the ground truth data and government rice area
statistics. The comparison results indicated that both classification methods (LMM and SVMs)
were promising for rice crop mapping in the region.
The comparison results between the classification results and the ground truth data
indicated that the SVMs gave slightly better classification results than the LMM. The overall
accuracy and Kappa coefficient achieved by the SVMs for the year 2002 data were 84.0% and
0.79, while the values for the LMM were 81.8% and 0.76, respectively. Similarly, the overall
accuracy and Kappa coefficient achieved by the SVMs for the year 2006 data were 85.1% and
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0.80, and those for the LMM were 81.8% and 0.76, respectively. These comparison results
reaffirmed good agreement between the MODIS-derived rice areas with the government rice
area statistics at the provincial level (R2 > 0.85 in all cases). However, a significance test of
difference between two classification methods using Z-test method revealed that the
classification accuracy between these two classification methods (i.e., LMM and SVMs) were
not statistically significant different. The Z-test values between the classification methods
reported for the year 2002 and 2006 data were 0.299 and 0.275, respectively. These values
were smaller than the critical value of 1.96.
To relate surface soil moisture variations with rice cropping systems, the composite soil
moisture maps (considering dry and very dry classes) were aggregated with the rice crop
maps for the years 2002 and 2006. The results indicated a remarkable increase in the area of
double and triple irrigated rice cropping systems in areas of low soil moisture (i.e., dry and
very dry conditions) during this period. Approximately, 6.3% and 9.9% of the area of double
and triple irrigated rice cropping systems identified as low soil moisture in 2002 increased to
14.9% and 16.3% in 2006, respectively. This study has demonstrated merits of using MODIS
data for studying soil moisture variability in relation to rice cropping systems, which is
important for crop and water management.
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