土壤表層濕度資訊對於水資源管理非常重要，同時對於農作物管理與產量推估
上也有賴於作物生長區域的資訊。本研究旨在應用中尺度影像光譜儀(MODIS)衛星影像
來探討越南湄公河三角洲土壤表層濕度變化與稻米作物耕種型態的相關性。土壤表層
濕度主要是根據2002 年至2007 年，每年一月到四月的MODIS 影像資料推導估計溫度
植生乾旱指標(TVDI)而來；此指標主要是利用MODIS 影像資料的地表溫度(LST)與植生
差異指標以經驗參數化推導而來。
從土壤表層濕度的估計結果可知，2006 年研究區的低土壤表層濕度區域範圍相
較於其他研究年分而言是最大的，因此本研究選擇以2006 為極端年與2002 為平常年，
分析這兩年的土壤表層濕度變化與稻米耕作型態分布的關聯性。稻作型態的空間分布
主要是以2002 年與2006 年MODIS NDVI 250 公尺解析度的時序影像資料進行分類後產
製：MODIS NDVI 時序資料先以經驗模態分解法(EMD)過濾雜訊；其後，再分別以硬分類：
線性混合模式(LMM) ，及軟分類：支持向量機(SVMs)分類演算法分別進行稻作型態之
分類及製圖。這兩種分類演算方法使用的目的是比較它們的分類優劣，同時本研究也
使用不同的空間與非空間資料來評估TVDI與稻作分類的精度。
推導結果顯示LST-NDVI 像點資料的空間分佈呈現非常清楚的三角形散佈特徵：
這表示研究區土壤表層濕度有大範圍的分布差異。其後再藉由逐日降雨資料來驗證研
究區的TDVI。結果顯示TDVI 與逐日降雨資料有相當的一致性與敏感性。從2002 年到
2005 年，較低的土壤表層濕度區域主要分布在海岸地區；但在2006 與20007 年就逐漸
擴展到三角洲中部區域。同時2006 年，低土壤表層濕度的區域範圍達到最大，呼應著
在2006 年當時湄公河三角洲區域，湄公河與巴塞河在旱季時因特殊原因所致逕流量大
減所造成的乾旱現象。
vii
在稻米生長週期偵測案例研究中，以過濾剖線偵測出的播種與高株分蘗期的日
期與田野調查資料的比較結果中顯示：使用EMD 濾波相較於小波(wavelet)濾波方法，
EMD 過濾的稻作生長曲線保持較佳的稻作生長NDVI 資訊。這些NDVI 濾波的剖線型態反
映了不同稻作耕種型態在不同稻米成長季節的變化。通過了解不同稻作型態的NDVI 時
序剖線特徵，接著以軟分類LMM 與硬分類SVMs 的分類方法分類由EMD 濾波的NDVI 時
序剖線，以產製研究區的稻作型態分類。然後2002 年與2006 年稻作分類圖再以地真
資料與政府統計資料進行比對。比對結果顯示，對研究區的稻作分類而言，LMM 與
SVMs的兩種分類法都是極佳的分類方法。
由分類結果與地真資料的比較可知，SVMs 的分類成果較LMM 的分類成果稍佳。
SVMs的總體分類精度與Kappa 係數在 2002 年分別為84.0% 與 0.79；2006 年為85.1%
與0.80；LMM 的分類成果值較SVMs 低；其總體分類精度與Kappa 係數在 2002 年分別
為81.8%與0.76,；2006 年分別為79.9%與0.73。同時這些由MODIS 資料推導出的稻作
分類結果皆與官方省級尺度的稻作統計資料有很高相關性(R2 > 0.85)。兩種分類方法
以z-test 檢測分類差異，在2002 年與2006 年的估計值分別為 0.299 與 0.275，皆
小於95%的信賴區間值1.96，通過檢測，顯示兩種分類方法並這兩年皆無統計上的分
類差異。
對於土壤表層濕度與稻作形態的關聯性分析，本研究以2002 年與2006 年土壤
表層濕度較 乾與非常乾類別的組圖與稻米分類區進行比較。結果顯示在該時期，土壤
表層濕度較乾與非常乾的地區，

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
ix
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
x
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.

Adams, J.B., Smith, M.O., Johnson, P.E., 1986. Spectral mixture modelling: A new analysis
of rock and soil types at the Viking Lander I Site. Journal of Geophysical Research 79,
8098-8112.
Boardmann, J.W., Kruse, F.A., Green, R.O., 1995. Mapping target signatures via partial
unmixing of aviris data. The fifth JPL Airborne Earth Science Workshop. JPL, pp. 23–
26.
Boegh, E., Soegaard, H., Hanan, N., Kabat, P., Lesch, L., 1998. A remote sensing study of the
NDVI-Ts relationship and the transpration from sparse vegetation in the Sahel based
on high resolution satellite data. Remote Sensing of Environment 69, 224–240.
Bolstad, P., Lillesand, T.M., 1991. Rapid Maximum Likelihood classification.
Photogrammetric Engineering and Remote Sensing 57, 67-74.
Bolstead, P., Lillesand, T.M., 1991. Automated GIS integration in land cover classification.
ACSM-ASPRS Annual Convention, pp. 23-32.
Bosdogianni, P., Petrou, M., Kittler, J., 1997. Mixed Pixel Classification for Robust Statistics.
IEEE Transaction on Geoscience and Remote Sensing 35, 551-559.
Boser, B.E., Guyon, I., Vapnik, V., 1992. A training algorithm for optimal margin classiers.
The Fifth Annual Workshop on Computational Learning Theory. ACM Press.
Bouvet, A., Le-Toan, T., Lam-Dao, N., 2009. Monitoring of the rice cropping system in the
Mekong Delta using Envisat/Asar dual polarization data. IEEE Transactions on
Geoscience and Remote Sensing 47, 517-526.
Bruce, L.M., Morgan, C., Larsen, S., 2001. Automated detection of subpixel hyperspectral
targets with continuous and discrete wavelet transforms. IEEE Transactions on
Geoscience and Remote Sensing 39, 2217-2226.
Bui, C.B., Nguyen, T.L., 2004. Improving rice productivity under water constraints in the
Mekong Delta, Vietnam. In: Seng, V., Craswell, E., Fukai, S. and Fisher, K. eds.
Water in agriculture. ACIAR, pp. 196-202.
Burrus, C.S., Gopinath, R.A., Guo, H., 1998. Introduction to wavelets and wavelet transforms:
A primer. Prentice-Hall, Englewood Cliffs, NJ.
Campbell, J.B. (Ed), 2002. Introduction to remote sensing. Guilford Press, New York.
Camps-Valls, G.a.B., L., 2005. Kernel-based methods for hyperspectral image classification.
IEEE Transactions on Geoscience and Remote Sensing 43, 1351–1362.
Canisius, F., Turral, H. and Molden, D., 2007. Fourier analysis of historical NOAA time
series data to estimate bimodal agriculture. International Journal of Remote Sensing 28,
5503-5522
Carlson, T., Gillies, R., Perry, E., 1994. A method to make use of thermal infrared
temperature and NDVI measurements to infer surface soil water content and fractional
vegetation cover. Remote Sensing Reviews 9, 161–173.
Carlson, T.N., 2005. Review: an overview of the ‘‘triangle method’’ for estimating surface
evapotranspiration and soil moisture from satellite imagery. Sensors 7, 1612–1629.
113
Carreiras, J.M.B., Shimabukuro, Y.E., Pereira, J.M.C., 2002. Fraction images derived from
SPOT-4 vegetation data to assess land-cover change over the State of Mato Grosso,
Brazil. International Journal of Remote Sensing 23, 4979-4983.
Chauhan, N.S., 2003. Spaceborn soil moisture estimation at high resolution: a microwaveoptical/
IR synergistic approach. International Journal of Remote Sensing 24, 4599–
4622.
Chen, Q., Li, Z., Wang, L., Wei, X.L., 2007. The study of estimating soil moisture using ERS
wind scatterometer. Journal of Remote Sensing in China 11, 803–810.
Chen, S.T., Yu, P.S., 2007. Real-time probabilistic forecasting of flood stages. Journal of
Hydrology 340, 63–77.
Chiew, F.H.S., Peel, M.C., Amirthanathan, G.E., Pegram, G.G.S., 2005. Identification of
oscillations in historical global streamflow data using empirical mode decomposition.
Seventh IAHS Scientific Assembly at Fozdo Iguaçu, Brazil. IAHS Publisher.
Claps, P., Laguardia, G., 2004. Assessing spatial variability of soil water content through
Thermal Inertia and NDVI. In: Owe, M., D''Urso, G., Moreno, J. F., Calera, A., eds.
Remote Sensing for Agriculture, Ecosystems, and Hydrology V. Proceedings of SPIE,
Bellingham, SPIE.
Congalton, R.G., Green, K., 2008. Assessing the accuracy of remotely sensed data: Principles
and practices. CRC.
Cortes, C., Vapnik, V., 1995. Support-vector network. Machine Learning 20, 273-297.
Czajkowski, K., Goward, S.N., Stadler, S.J., Waltz, A., 2000. Thermal remote sensing of near
surface environmental variables: application over the Oklahoma Mesonet. Professional
Geographer 52, 345–357.
de Troch, F.P., Troch, P.A., Su, Z., Lin, D.S., 1996. Application of remote sensing for
hydrological modelling. Kluwer Academic Publishers.
Defries, R.S., Hansen, M.C., Townshend, J.R.G., 2000. Global continuous fields of vegetation
characteristics: a linear mixture model applied to multi-year 8 km AVHRR data.
International Journal of Remote Sensing 21, 1389-1414.
Dewan, A.M., Yamaguchi, Y., 2009. Land use and land cover change in Greater Dhaka,
Bangladesh: Using remote sensing to promote sustainable urbanization. Applied
Geography 29, 390-401.
Duchemin, B., Hadria, R., Er-Raki, S., Boulet, G., Maisongrande, P., Chehbouni, A.,
Escadafal, R., Ezzahar, J., Hoedjes, J., Kharrou, M.H., Khabba, S., Mougenot, B.,
Olioso, A., Rodriguez, J.C., Simonneaux, V., 2006. Monitoring wheat phenology and
irrigation in Center of Morocco: on the use of relationship between evapotranspiration,
crops coefficients, leaf area index and remotely-sensed vegetation indices. Agricultural
Water Management 79, 1-27.
Dupigny-Giroux, L., Lewis, J.E., 1999. A moisture index for surface characterization over a
semiarid area. Photogrammetric Engineering and Remote Sensing 65, 937–946.
Elmore, A.J., Mustrard, J.F., Manning, S., Lobell, D.B., 2003. Quantifying vegetation change
in semiarid environments: Precision and accuracy of spectral mixture analysis and the
Normalized Difference Vegetation Index. Remote Sensing of Environment 73, 87-102.
Engman, E.T., 1990. Progress in microwave remote sensing of soil moisture. Canadian
Journal of Remote Sensing 16, 6–14.
114
Envitutorial, 2010. Envi version 4.5 Environment for Visualising Images.
Evans, J.P., Geerken, R., 2006. Classifying rangeland vegetation type and coverage using a
Fourier component based similarity measure. Remote Sensing of Environment 105, 1-
8.
FAO, 2005. Drought-resistant soils: optimization of soil moisture for sustainable plant
production. Rome, Italy.
Flandrin, P., 1999. Tame-frequency/time-scale analysis. Academic Press, San Diego, CA.
Flandrin, P., Goncalves, P., 2004. Empirical mode decomposition as data-driven wavelet-like
expansions. International Journal of Wavelets, Multiresolution and Information
Processing 2, 477-496.
Foody, G.M., 1991. Soil moisture content ground data for remote sensing investigation of
agriculture region. International Journal of Remote Sensing 12, 1461–1469.
Foody, G.M., Mathur, A., 2004a. A relative evaluation of multiclass image classification by
support vector machines. Geoscience and Remote Sensing, IEEE Transactions on 42,
1335-1343.
Foody, G.M., Mathur, A., 2004b. Toward intelligent training of supervised image
classifications: directing training data acquisition for SVM classification. Remote
Sensing of Environment 93, 107-117.
Friedl, M.A., Davis, F.W., 1994. Sources of variation in radiometric surface temperature over
a tall-grass prairie. Remote Sensing of Environment 48, 1–17.
Galford, G.L., Mustarda, J.F., Melillo, J., Gendrin, A., Cerri, C.C., 2008. Wavelet analysis of
MODIS time series to detect expansion and intensification of row-crop agriculture in
Brazil. Remote Sensing of Environment 112, 576-587.
Garcia-Haro, F.J., Gilabert, M.A., Melia, J., 1996. Linear spectral mixture modeling to
estimate vegetation amount from optical spectral data. International Journal of Remote
Sensing 17, 3373-3400.
Gillies, R.R., Kustas, W.P., Humes, K.S., 1997. A verification of the “triangle” method for
obtaining surface soil water content and energy fluxes from remote measurements of
the Normalized Difference Vegetation Index (NDVI) and surface radiant temperature.
International Journal of Remote Sensing 18, 3145–3166.
Gloersen, P., Huang, N.E., 2003. Comparison of interannual intrinsic modes in hemispheric
sea ice covers and other geophysical parameters. IEEE Transactions on Geoscience
and Remote Sensing 41, 1062-1074.
Goetz, S.J., 1997. Multisensor analysis of NDVI, surface temperature and biophysical
variables at a mixed grassland site. Intnernational Journal of Remote Sensing 18, 71-
94.
Goward, S.N., Xue, Y., Czarjkowski, K.P., 2002. Evaluating land surface moisture conditions
from the remotely sensed temperature/vegetation index measurement: An exploration
with the simplified simple biosphere model. Remote Sensing of Environment 79, 225-
242.
Grochenig, K., 2001. Foundations of time-frequency analysis. Birkhauser.
GSO, 2007. Statistical yearbook of Vietnam. Hanoi, Vietnam.
115
Gualtieri, J.A., Cromp, R.F., 1998. Support vector machines for hyperspectral remote sensing
classification. The 27th AIPR Workshop, Advances in Computer Assisted Recognition,
Proceeding of the SPIE.
Hall, F.G., Townshend, J.R., Engman, E.T., 1995. Status of remote sensing algorithms for
estimation of land surface state parameters. Remote Sensing of Environment 51, 138–
156.
Han, Y., Wang, Y., Zhao, Y., 2010. Estimating soil moisture conditions of the Greater
Changbai mountains by land Surface temperature and NDVI. IEEE Transactions On
Geoscience and Remote Sensing 48, 2509 - 2515.
Heinz, D.C., Chang, C.I., 2001. Fully constrained least squares linear spectral mixture
analysis method for material quantification in hyperspectral imagery. IEEE
Transactions on Geoscience and Remote Sensing 39, 529-545.
Horwitz, H.M., Nalepka, R.F., Hyde, P.D., Morganstern, J.P., 1971. Estimating the proportion
of objects within a single resolution element of a multispectral scanner. The 7th
International Symposium on Remote Sensing of Environment, Environmental
Research Institute of Michigan, USA.
Howie, C., 2005. High dykes in the Mekong Delta in Vietnam bring social gains and
environmental pains. Aquaculture News, pp. 15 – 17.
Hsu, C.W., Chang, C.C., C.J., L., 2010. A practical guide to support vector classication.
Department of Computer Science, National Taiwan University, Taiwan.
Huang, C., Davis, L.S., Townshend, J.R., 2002. An assessment of support vector machines for
land cover classification. International Journal of Remote Sensing 23, 725-749.
Huang, C., Davis, L.S. and J.R.G., 2002. Townshend An assessment of support vector
machines for land cover classification. International Journal of Remote Sensing 23,
725–749.
Huang, N.E., Chen, C.C., Huang, K., Salvino, L.W., Long, S.R., Fan, K.L., 2001. A new
spectral analysis of station TCU129, Chi-Chi, Taiwan, 21 September 1999. Bulletin of
the Seismological of America 91, 1310–1338.
Huang, N.E., Shen, S.S.P., 2005. Hilbert-Huang transform and its applications. World
Scientific Publishing Co. Pte. Ltd., Singapore.
Huang, N.E., Shen, Z., Long, S.R., 1999. A new view of nonlinear water waves: the Hilbert
spectrum. Annual Review of Fluid Mechanics 31, 417-457.
Huang, N.E., Shen, Z., Long, S.R., Wu, M.C., Shih, H.H., Zheng, Q., Yen, N.C., Tung, C.C.,
Liu, H.H., 1998. The empirical mode decomposition and the Hilbert spectrum for
nonlinear and non-stationary time series analysis. Proceedings of the Royal Society
London A 454, 903 995.
Huang, N.E., Wu, M.C., Long, S.R., Shen, S.S.P., Qu, P., Gloersen, P., Fan, K.L., 2003b. A
confidence limit for the empirical mode decomposition and Hilbert spectral analysis.
Proceedings of the Royal Society of London 495 2037-2345.
Jackson, T.J., Chen, D., Cosh, M., Li, F., Anderson, M., Walthall, C., Doriaswamy, P., Hunt,
E.R., 2004. Vegetation water content mapping using Landsat data derived normalized
difference water index for corn and soybeans. Remote Sensing of Environment 92,
475–482.
116
Jensen, J.R., 1996. Introductory digital image processing: A remote sensing perspective.
Prentice-Hall Inc., London.
Johnson, R.W., Tothill (Eds.), 1985. Definition and broad geographic outline of savanna lands,
In Ecology and Management of the World’s Savannas Edited by J.C. Tothill, J.J.Mott.
Australlian Academia of Science.
Justice, C.O., Vermote, E.F., Townshend, J.R.G., Defries, R.S., Roy, D.P., Hall, D.K.,
Salomonson, V.V., Privette, J.L., Riggs, G., Strahler, A.H., Lucht, W., Myneni, R.B.,
Knyazikhin, Y., Running, S.W., Nemani, R.R., Wan, Z., Huete, A.R., Van Leeuwen,
W.J.D., Wolfe, R.E., Giglio, L., Muller, J.P., Lewis, P., Barnsley, M.J., 1998. The
Moderate Resolution Imaging Spectroradiometer (MODIS): land remote sensing for
global change research. IEEE Transactions on Geoscience and Remote Sensing 36,
1228-1249.
Keerthi, S., Lin, C.J., 2003. Asymptotic behaviors of support vector machines with Gaussian
kerne. Neural Computation 15, 1667–1689.
Kerdiles, H.A., Grondona, M.O., 1995. NOAA-AVHRR NDVI decomposition and subpixel
classification using linear mixing in the Argentinean Pampa. International Journal of
Remote Sensing 16.
Kerdiles, H.a.G., M.O., 1995. NOAA-AVHRR NDVI decomposition and subpixel
classification using linear mixing in the Argentinean Pampa. International Journal of
Remote Sensing 16, 1303-1325.
Kramer, P.J., 1969. Plant and soil water relationship. McGraw Hill Inc.
Kruse, F.A., Boardman, J.W., Lefkoff, A.B., Heidebrecht, K.B., Shapiro, A.T., Barloon, P.J.,
Goetz, A.F.H., 1993. The spectral image processing system (SIPS): Interactive
visualization and analysis of imaging spectrometer data. Remote Sensing of
Environment 44, 145-163.
Kustas, W.P., Moran, M.S., Norman, J.M., 2003. Evaluating the spatial distribution of
evaporation. John Wiley & Sons.
Lam-Dao, N., Le-Toan, T., Apan, A., Bouvet, A., Young, F., Le-Van, T., 2009. Effects of
changing rice cultural practices on C-band synthetic aperture radar backscatter using
Envisat advanced synthetic aperture radar data in the Mekong River Delta. Journal of
Applied Remote Sensing 3, 1-17.
Lambin, E.F., Ehrlich, D., 1996. The surface temperature-vegetation index space for land
cover and land-cover change analysis. International Journal of Remote Sensing 17,
463–487.
Le-Toan, T., Feurer, D., Florsch, G., 2003. RISAR Validation report – Rice mapping in the
Mekong delta, ESA contract No 13201/98/NL/SB CCN 002. CESBIO.
Li, X., 2006. Temporal structure of neuronal population oscillations with empirical mode
decomposition. Physics Letters A 356, 237–241.
Liang, H., Bressler, S.L., Desimone, R., Fries, P., 2005. Empirical mode decomposition: A
method for analyzing neural data. Neurocomputing 65–66, 801–807.
Lillesand, T.M., Kiefer, R.W., 1999. Remote sensing and image interpretation. John Wiley
and Sons, New York.
Lobell, D.B., Asner, G.P., 2004. Cropland distributions from temporal unmixing of MODIS
data. Remote Sensing of Environment 93, 12-422.
117
Loutridis, S.J., 2005. Instantaneous energy density as a feature for gear fault detection.
Mechanical Systems and Signal Processing 20, 239–1253.
Mallat, S., 1989. A Theory for Multiresolution Signal Decomposition: the Wavelet
Representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 11,
674-693.
Mathur, A., Foody, G.M., 2008a. Multiclass and binary SVM classification: Implications for
training and classification users. IEEE Geoscience and Remote Sensing Letters 5,
241–245.
Matsumoto, M., Nishimura, T., 1998. Mersenne twister: A 623-dimensionally equidistributed
uniform pseudo-random number generator. ACM Transactions on Modeling and
Computer Simulation 8, 3–30.
Meer, F.v.d., 1999. Spatial Statistics for Remote Sensing. Kluwer Academic Publishers.
Melgani, F., Bruzzone, L., 2004. Classification of hyperspectral remote sensing images with
support vector machine. IEEE Trans. on Geoscience and Remote Sensing 42, 1778-
1790.
Meyer, Y., 1993. Wavelet Algorithms and Applications. Philadelphia: SIAM.
Mingwei, Z., Qingbo, Z., Zhongxin, C., Jia, L., Yong, Z., Chongfa, C., 2008. Crop
discrimination in Northern China with double cropping systems using Fourier analysis
of time-series MODIS data. International Journal of Applied Earth Observation and
Geoinformation, 10, 476-485.
Minh, H.N., Kawaguchi, T., 2002. Overview of rice production system in the Mekong Delta-
Vietnam. Journal of the Faculty of Agriculture, Kyushu University, Japan 47, 221–
231.
Mitra, D.S., Majumdar, T.J., 2004. Thermal inertia mapping over the Brahmaputra basin,
India using NOAA-AVHRR data and its possible geological applications.
International Journal of Remote Sensing of Environment 225, 3245-3260.
MONRE, 2009. Mekong Delta faces fresh water shortage.
Moran, M.S., Clarke, T.R., Inoue, Y., Vidal, A., 1994. Estimating crop water deficit using the
relation between surface-air temperature and spectral vegetation index. Remote
Sensing of Environment 49, 246–263.
Moran, M.S., Watts, J.M., Peters-Lidard, C.D., McElroy, S.A., 2004a. Estimating soil
moisture at the watershed scale with satellite-based radar and land surface models.
Canadian Journal of Remote Sensing 30.
Moran, M.S., Watts, J.M., Peters-Lidard, C.D., McElroy, S.A., 2004b. Estimating soil
moisture at the watershed scale with satellite-based radar and land surface models.
Canadian Journal of Remote Sensing 30, 805–826.
MRC, 2002. Annual Report 2001.
MRC, 2004. MRC joins global research effort to produce more food with less water. .
MRC, 2008. Preparing the Greater Mekong Subregion flood and drought risk management
and mitigation project.
NASA, 1999. Soil moisture. USA.
NASA, 2011a. Data products.
118
NASA, 2011b. MCST calibration Information.
Nash, D.B., Conel, J.E., 1974. Spectral refectrance systematics for mixtures of powdered
hypersthene, labradorite, and ilmenite. Journal of Geophysical Research 79, 615-1621.
Nemani, R., Pierce, L., Running, S.N., Goward, S.N., 1993. Developing satellite-derived
estimates of surface moisture status. Journal of Applied Meteorology 32, 548–557.
Nguyen, V.L., 2000. Crop diversification in Vietnam. In K. P. Minas, & J. D. Frank (Eds.),
Crop diversification in the Asia-Pacific Region. . Bangkok’ FAO Regional Office for
Asia and the Pacific.
Olhede, S., Walden, A.T., 2004. The Hilbert spectrum via wavelet projections. Proceedings of
the Royal Society London A 460, 955-975.
Oommen, T., Misra, D., Twarakavi, N.K.C., Prakash, A., Sahoo, B., Bandopadhyay, S., 2008.
An objective analysis of support vector machine based classification for remote
sensing. Mathematical Geosciences 40, 409–424.
Oryza, 2009. Mekong Delta won’t support future rice: Experts.
Otukei, J.R., Blaschke, T., 2010. Land cover change assessment using decision trees, support
vector machines and maximum likelihood classification algorithms. International
Journal of Applied Earth Observation and Geoinformation, 27–31.
Pal, M., Mather, P.M., 2004. Assessment of the effectiveness of support vector machines for
hyperspectral data. Future Generation Computer Systems 20, 1215–1225.
Pal, M., Mather, P.M., 2005. Support vector machines for classification in remote sensing.
International Journal of Remote Sensing 26, 1007–1011.
Patel, N.R., Anapashsha, R., Kumar, S., Saha, S.K., Dadhwal, V.K., 2009. Assessing potential
of MODIS derived temperature/vegetation condition index (TVDI) to infer soil
moisture status. International Journal of Remote Sensing 30, 23-29.
Peel, M.C., Amirthanathan, G.E., Pegram, G.G.S., McMahon, T.A., Chiew, F.H.S., 2005.
Empirical Mode Decomposition: Improvement and application. International Congress
on Modelling and Simulation.
Peng, Z.K., Tse, P.W., Chu, F.L., 2005. A comparison study of improved Hilbert-Huang
transform and wavelet transform: Application to fault diagnosis for rolling bearing.
Mechanical Systems and Signal Processing 19, 974-988.
Prince, S.D., Goward, S.N., 1995. Global Primary Production: a remote sensing approach.
Journal of Biogeography 22, 815-835.
Rao, A.R., Hsu, E.C., 2008. Hilbert-Huang Transform analysis of hydrological and
environmental time series. Springer, The Netherlands.
Ren, H., 2000. Unsupervised and generalized orthogonal subspace projection and constrained
energy minimization for target detection and classification in remotely sensed imagery.
Richards, J.A., Jia, X., 1999. Remote sensing digital image analysis: An introduction.
Springer-Verlag.
Rioul, O., 1993. A discrete-time multiresolution theory. IEEE Transactions on Signal
Processing, 2591−2606.
119
Robin, A., Hegarat-Mascle, S.L., Moisan, L., Poilve, H., 2005. Land cover classification from
coarse resolution time series. International Symposium on Remote Sensing of
Environment, Saint Petersbourg.
Sadeghi, A.M., Hancock, G.D., Waite, W.P., Scott, H.D., Rand, J.A., 1984. Microwave
measurements of moisture distributions in the upper soil profile. Water Resources
Research 20, 927–934.
Sakamoto, T., Nhan, N.V., Ohno, H., Ishitsuka, N., Yokozawa, M., 2006. Spatial-temporal
distribution of rice phenology and cropping systems in the Mekong delta with special
reference to the seasonal water flow of the Mekong and Bassac rivers. Remote Sensing
of Environment 100, 1-16.
Sakamoto, T., Phung, V.C., Kotera, A., Khang, D.N., Yokozawa, M., 2009. Analysis of rapid
expansion of inland aquaculture and triple rice-cropping areas in a coastal area of the
Vietnamese Mekong Delta using MODIS time-series imagery. Landscape and Urban
Planning 92, 34-46.
Sakamoto, T., Yokozawa, M., Toritani, H., Shibayama, M., Ishitsuka, N., Ohno, H., 2005. A
crop phenology detection method using time-series MODIS data. Remote Sensing of
Environment 96, 366-374.
Sandholt, I., Rasmussen, K., Andersen, J., 2002. A simple interpretation of the surface
temperature/vegetation index space for assessment of surface moisture status. Remote
Sensing of Environment 79, 213– 224.
Sellers, P.J., Berry, J.A., Collatz, G.J., Field, C.B., Hall, F.G., 1992. Canopy reflectance,
photosynthesis, and transpiration. III. A reanalysis using improved leaf models and a
new canopy integration scheme. Remote Sensing of Environment 42, 187-216.
Smith, M.O., Johnson, P.E., Adams, J.B., 1985. Quantitative determination of mineral types
and abundances from reflectance spectra using principal components analysis. Journal
of Geophysical Research 90, 797-804.
Smith, M.O., Ustin, S.L., Adams, J.B., Gillespie, A.R., 1990. Vegetation in deserts: I. A
regional measure of abundance from multispectral images. Remote Sensing of
Environment 31, 1-36.
Smith, R.C.G., Choudhury, B.J., 1991. Analysis of normalized difference and surface
temperature observations over southeastern Australia. International Journal of Remote
Sensing 12, 2021–2044.
Sobrino, J.A., El Kharraz, M.H., 1999. Combining afternoon and morning NOAA satellites
for thermal inertia estimation 2. Methodology and application. Journal of Geophysical
Research 104, 9455-9465.
Song, K., Zhou, X., Fan, Y., 2009. Empirically adopted IEM for retrieval of soil moisture
from radar backscattering coefficients. IEEE Transsaction on Geoscience Remote
Sensing 47, 1662–1672.
Sub-NIAPP, 2000. Land-use map of the Mekong Delta. Vietnam.
Sun, W., Wang, P.X., Zhang, S.Y., Zhu, D.H., Liu, J.M., Chen, J.H., Yang, H.S., 2008. Using
the vegetation temperature condition index for time series drought occurrence
monitoring in the Guanzhong Plain, PR China. International Journal of Remote
Sensing 29, 5133-5144.
120
Townshend, J.R.G., 1992. Land cover. International Journal of Remote Sensing 13, 1319–
1328.
Tuong, N.T., 2001. Sea level measurement and sea level rise in Vietnam. UK.
Vapnik, V., 1995. The Nature of Statistical Learning Theory. Springer-Verlag, New York.
Verbeiren, S., Eerens, H., Piccard, I., Bauwens, I., Orshoven, J.V., 2006. Sub-pixel
classification of SPOT-VEGETATION time series for the assessment of regional crop
areas in Belgium. International Journal of Applied Earth Observation and
Geoinformation 10, 486-497.
Vermote, E.F., Kotchenova, S.Y., Ray, J.P., 2008. MODIS surface reflectance user’s guide.
Vermote, E.F., Vermeulen, A., 1999. Algorithm technical background document:
Atmospheric correction algorithm: spectral correction reflectances (MOD 09) (Version
4.0).
Verstraeten, W.W., Veroustraete, F., van der Sande, C.J., Grootaers, I., Feyen, J., 2006. Soil
moisture retrieval using thermal inertia, determined with visible and thermal
spaceborne data, validated for European forests. Remote Sensing of Environment 101,
299-314.
VietnamNet, 2009. Mekong Delta: more flood and drought.
VMWR, 1994. Strategy and action plan for mitigating water disasters in Vietnam. Department
of Humanitarian Affairs (DHA), United Nations Development Program.
VNS, 2007. Mekong Delta residents battle drought to save crops.
Wagenseil, H., Samimi, C., 2006. Assessing spatio-temporal variations in plant phenology
using Fourier analysis on NDVI time series: results from a dry savannah environment
in Namibia. International Journal of Remote Sensing 27, 3455–3471.
Walker, J., 1999. Estimating soil moisture profile dynamics from near-surface soil moisture
measurements and standard meteorological data. The University of Newcastle. The
University of Newcastle, Australia.
Wan, Z., Wang, P., Li, X., 2004. Using MODIS land surface temperature and Normalized
Difference Vegetation Index products for monitoring drought in the southern Great
Plains, USA. International Journal of Remote Sensing 25, 61–72.
Wang, C., Qi, S., Niu, Z., Wang, J., 2004. Evaluating soil moisture status in China using the
temperature–vegetation dryness index (TVDI). Canadian Journal of Remote Sensing
30, 671-679.
Wang, H., Li, X., Long, H., Xu, X., Bao, Y., 2010. Monitoring the effects of land use and
cover type changes on soil moisture using remote-sensing data: A case study in
China''s Yongding River basin. Catena 82, 35–145.
Wang, L., Qu, J., 2009. Satellite remote sensing applications for surface soil moisture
monitoring: A review. Frontiers of Earth Science in China 3, 237–247.
Wang, P.X., Li, X.W., Gong, J.Y., Song, C.H., 2001. Vegetation temperature condition index
and its application for drought monitoring. Proceedings of International Geoscience
and Remote Sensing Symposium, Sydney, Australia, pp. 141–143.
Wardlow, B.D., Egbert, S.L., Kastens, J.H., 2007. Analysis of time-series MODIS 250 m
vegetation index data for crop classification in the U.S. Central Great Plains. Remote
Sensing of Environment 108, 290−310.
121
Westra, T., De Wulf, R.R., 2007. Monitoring Sahelian floodplains using Fourier analysis of
MODIS time-series data and artificial neural networks. International Journal of
Remote Sensing 28, 1595-1610.
White, I., Melville, M., Sammut, J., 1996. Possible impacts of saline water intrusion
floodgates in Vietnam''s Lower Mekong Delta.
Witold, P., 1989. Fuzzy control and fuzzy system. John Wiley & Sons, New York, USA.
Wu, Z., Schneider, E.K., Hu, Z.Z., Cao, L., 2001. The impact of global warming on ENSO
variability in climate records. COLA Technical Report, CTR 110.
Xin, F., Tian, G., Liu, Q., Chen, L., 2006. Combining vegetation index and remotely sensed
temperature for estimation of soil moisture in China. International Journal of Remote
Sensing 27, 2071-2075.
Xue, H., Ni, S., 2006. Progress in the study on monitoring of soil moisture with thermal
infrared remote sensing. Agricultural Research in the Arid Areas 24, 168–172.
Xue, Y., Cracknell, A.P., 1995. Advanced thermal modeling. International Journal of Remote
Sensing 16, 431 446.
Yoshida, S., 1981. Fundamentals of rice crop science. The International Rice Research
Institute, Los Banos, Laguna, Philippines.
Zhang, M., Zhou, Q., Chen, Z., Liu, J., Zhou, Y., Cai, C., 2008. Crop discrimination in
Northern China with double cropping systems using Fourier analysis of time-series
MODIS data. International Journal of Applied Earth Observation and Geoinformation,
10.
Zhang, X., Friedl, M.A., Schaaf, C.B., Strahler, A.H., Hodges, J.C.F., Gao, F., Reed, B.C.,
Huete, A., 2003. Monitoring vegetation phenology using MODIS. Remote Sensing of
Environment 84, 471–475.
Zhu, L., Tateishi, R., 2001. Applilcation of linear mixture model to time series AVHRR
NDVI data. AARS, Singapore.