使用多時期MODIS影像與ENVISAT ASAR合成孔徑雷達影像進行越南湄公河三角洲區域之稻米產量估測

簡易檢索 / 詳目顯示

回結果列表

研究生：	雷蒂楓 Thi-phuong Le
論文名稱：	使用多時期MODIS影像與ENVISAT ASAR合成孔徑雷達影像進行越南湄公河三角洲區域之稻米產量估測 RICE CROP YIELD ESTIMATION USING MULTI-TEMPORAL MODIS AND ENVISAT ASAR DATA IN THE MEKONG DELTA, VIETNAM
指導教授：	陳繼藩 Chi-farn Chen
口試委員:
學位類別：	碩士 Master
系所名稱：	太空及遙測研究中心 - 遙測科技碩士學位學程 Master of Science Program in Remote Sensing Science and Technology
論文出版年：	2014
畢業學年度：	102
語文別：	英文
論文頁數：	116
中文關鍵詞：	English
相關次數：	點閱：18 下載：0
分享至:	分享至facebook 分享至twitter

查詢本校圖書館目錄查詢臺灣博碩士論文知識加值系統勘誤回報

越南的稻米生產不僅在亞，洲也是世界上的主要生產國之一。大部分的水稻產於被視為越南米倉的湄公河三角洲，該區域每年生產國家近一半的米糧，並且提供超過80%的稻米出口量。稻作產量估測的顯著性對區域與國家的農業經營與政策發展扮演十分重要的角色。本研究意欲使用2007與2008年的MODIS與ENVISAT合成孔徑雷達(ASAR)影像發展湄公河三角洲區域稻米生產模式的估算方法。資料處理流程有：(1)建立時序MODIS影像的 NDVI與EVI資料；(2)以小波轉換進行時序NDVI與EVI資料的去噪；(3)MODIS與ENVI ASAR影像融合；(4)建立稻作產量預測模型建立稻作產量預測模型；(5)以均方根誤差(RMSE)成果評估、平均絕對差(MAE)與平均偏誤差(MBE)進行成果評估。
在本研究中，MODIS NDVI/EVI 時序資料與 ASAR影像分別進行稻米產量的預測，最後融合彼此進行另一個估測的比較；同時，研究中亦測試了NDVI-LST(地表溫度), EVI-LST 與 EVI-ASAR，檢測是否可以增強預測的結果。稻作產量統計與不同參數亦以線性、多變數與二次方程進行回歸分析。
由回歸分析的結果可以發現統計模式的稻米產量預測模型有很好的成果，同時使用二次方程回歸模式較線性回歸模式為佳。整合兩個光學與雷達影像的模式較其他個別使用的影像模式有更高精準的預測成果，2007年一期稻與二期稻的相關係數分別為0.83 與 0.77；2008年一期稻與二期稻的相關係數分別為0.77 與 0.75。
建立模式的穩健性以2007年與2008年20個抽樣區的預測產量與現地統計產量進行比較分析。比較結果揭示了在這兩年由MODIS EVI與ASAR後向散射係數結合的二次項預測模式都有令人滿意的結果。實際產量與預測產量的百分率差異都在可以接受的限制內(約10%，p-value <0.05)。在2007年冬春季稻產量預測RMSE、MAE與MBE分別為10.85%、 9.39% 與 -3.39%；對於當年夏秋季稻作則各為12.01%、9.99% 與 9.31 %。在2008年冬春季稻產量預測RMSE、MAE與MBE各為8.39%、6.6% 與0.54%，當年夏秋季稻作則各為 8.96%、7.29%與0.45%。這些結果明確地說明了預測的產量與統計產量的高相關性，並且也顯示了建立的模式可以用以預測研究區的稻作產量。
事實上，許多的因素如蟲害、稻作病害與雨季期氣候變化的狀況都會降低稻作的產量估測的精度。本研究探索在湄公河三角洲在收穫季節前，使用MODIS NDVI /EVI 時序資料與ASAR影像對稻米產量估測的潛力與有效性。本研究方法亦可移植至其他區域的研究上。

Vietnam is one of the most important countries in producing rice in Asia as well as in the world. The majority of rice is produced in the Mekong Delta (MD) which was known as the rice bowl of Vietnam. Annually, it produces approximately a half of the country's rice and account for more than 80% amount of rice export. The significance of rice crop yield estimation plays a critical role in agricultural management and policy development at regional and national scale. This study aims to develop an approach for rice crop yield prediction in the Mekong Delta, Vietnam using MODIS and ENVISAT ASAR data for rice crop seasons in 2007 and 2008. The data were processed through five main steps: (1) constructing time-series MODIS NDVI/EVI data, (2) noise filtering of the time-series NDVI/EVI data using the wavelet transform, (3) Fusion of MODIS and ENVISAT ASAR images, (4) developing a rice-crop yield prediction models, and (5) result verification using the root mean square error (RMSE), the mean absolute error (MAE), and the mean bias error (MBE).
In this study, an attempt has been made to study the potential of MODIS NDVI/EVI time-series data and ASAR images individually for the purpose of rice yield forecasting. Then, fusion data was also used as another case to estimate rice crop yield. At the same time, the combinations between NDVI-LST, EVI-LST and EVI-ASAR were also implemented to test if there is an improvement in the correlation and prediction results. The regression analysis between rice crop yield statistics and different parameters was implemented using linear and quadratic models.
From the regression analysis results, it was found that the statistical model-based can be successfully used for the purpose of rice yield estimation in the study area. The rice crop yield in MD could be better modeled using quadratic models compared to linear models. The quadratic model using combination of 2two variables (MODIS EVI and Backscattering coefficients) is the best one and gave more accurate prediction results than others, with correlation coefficients of 0.83 and 0.77 for the first and second crop in 2007 and R2 were 0.77 and 0.75 for crops in 2008, respectively.
The robustness of the established models was evaluated by comparisons between the predicted yields and crop yield statistic for 20 sampling districts in 2007 and 2008. The comparisons revealed satisfactory results obtained from the quadratic model using combination of MODIS EVI and Backscattering coefficients (derived from ASAR data) in both years. The percentage difference of the predicted from the actual yield is within acceptable limit (around 10%) and p-value < 0.05. The root mean square error (RMSE), mean absolute error (MAE) and mean bias error (MBE) were used to evaluate the prediction results. In 2007, The RMSE, MAE and MAE were 10.85%, 9.39% and -3.39% for winter-spring crop respectively. And for the summer-autumn crop, those parameters were 12.01%, 9.99% and 9.31 %. In 2008, for the first crop, the RMSE, MAE and MBE were 8.39%, 6.6%, and -0.54%. For the second crop, the RMSE, MAE and MBE were 8.96%, 7.29% and 0.45%. Those results were clear that there was a good correlation between the predicted yield and the rice yield statistics and the established model can be used to estimate rice crop yield in the study area.
In fact, there are many factors like pest, rice diseases, and the variations of climate conditions in the rainy season could lower the accuracy in rice crop yield prediction results. This study explored the potential of MODIS NDVI/EVI time-series and ASAR data for rice crop yield estimation in Mekong Delta before the harvesting period. The methods used in this study could be transferable to the other regions.

TABLE OF CONTENTS
摘要    i
ABSTRACT    iii
ACKNOWEDGMENT    v
LIST OF TABLES    ix
LIST OF FIGURES    xii
CHAPTER 1: INTRODUCTION    1
1. Background    1
2. Statement of the Problem    5
3. Research Objectives    6
4. Research Questions    6
5. Thesis Outlines    7
CHAPTER 2: LITERLITURE REVIEW    8
1. Moderate Resolution Image Spectroradiometer (MODIS)    8
1.1. MODIS Surface Reflectance Product (MOD09A1)    8
1.2. MODIS Land Surface Temperature and Emissivity (MOD11A2)    9
1.3. Overview of MODIS Data Preprocessing    10
3. ENVISAT ASAR Wide Swath Moderate Resolution Mode (ENVISAT ASAR WSM)    11
3. Overview of Noise Filtering Method for MODIS Time-Series and Speckle Noise Reduction on ENVISAT ASAR Data    12
3.1. Noise Filtering From MODIS Time-Series Data    12
3.2. Speckle Reduction for ENVISAT ASAR WSM Data.    14
4. Fusion Techniques of MODIS and ENVISAT ASAR WSM Data.    15
5. Applications of Remote Sensing for Rice Crop Yield Estimation    16
CHAPTER 3: STUDY AREA AND DATA COLLECTION    19
1. General of the Study Area    19
2. Climate Conditions    20
3. Hydrology    21
4. Rice Cropping Systems    21
5. Description of Rice Growth Stages    23
6. Data Collection    24
6.1. Remote Sensing Data    24
6.2. Ancillary Data    27
CHAPTER 4: METHODOLOGY    29
1. General Framework    29
2. MODIS Time-series NDVI/EVI/LST Construction    31
2.1. NDVI/EVI Time-series Construction    31
2.2. Noise filtering of Time-series NDVI/EVI Data using Wavelet Transforms    33
3. ENVISAT ASAR Data Processing    35
4. MODIS-ENVISATASAR Data Fusion    36
4.1. Fusion method    38
4.2. Evaluation of Fused images    39
5. Non-rice areas masking    39
6. Establishment of rice yield models    41
6.1. Input variables for prediction establishment    41
6.2. Sampling selection    43
6.3. Models    44
7. Model validation    45
CHAPTER 5: RESULTS AND DISCUSSION    46
1. Wavelet Filtering of Time-series NDVI/EVI Data    46
2. Temporal Characteristics of Rice Crop NDVI and EVI Profiles    48
3. Relationship between Rice Crop Yield Statistics and NDVI/EVI/Backscattering Parameters    50
3.1. Correlation between Rice Crop Yield Statistics and NDVI/EVI    50
3.2. Correlation between Rice Crop Yield Statistics and Backscattering Coefficients    54
4. Fusion results between MODIS and ENVISAT ASAR Data    58
5. Rice yield prediction models    61
6. Validation results    80
CHAPTER 6: CONCLUSIONS AND RECOMMENDATIONS    84
1. Conclusions    84
2. Recommendations    86
APPENDIX    87
REFERENCES    108


                                

A.Boutve, T. Le Toan, 2011. Use of ENVISAT/ASAR wide-swath data for timely rice fields mapping in the Mekong River Delta. Remote Sensing of Environment 115, 1090-1101.
Aiazzi, B., L. Alparone, S. Baroni, and A. Garzelli, 2002. Contextdriven fusion of spatial and spectral resolution images based on oversampled multiresolution analysis, IEEE Transactions on Geoscience and Remote Sensing, 40(10):2300–2312.
Alvarez, R., 2009. Predicting average regional yield and production of wheat in the Argentine Pampas by an artificial neural network approach. European. Journal of Agronomy 30, 2009, 70–77.
Aschbacher, J.; Lichtenegger, J., 1990. Complementary nature of SAR and optical data: a case study in the Tropics. Earth Observation Quarterly, 31, 4-8.
Atkinson, P.M.; Jeganathan, C.; Dash, J.; Atzberger, C., 2012 Inter-comparison of four models for smoothing satellite sensor time-series data to estimate vegetation phenology. Remote Sens. Environ. 123, 400−417.
Bala, S.K., and A.S. Islam, 2009. Correlation between potato yield and MODIS-derived vegetation indices. International Journal of Remote Sensing 30 (10),: 2491-507.
Benedetti, R., Rossini, P., 1993. On the use of NDVI profiles as a tool for agricultural statistics: the case study of wheat yield estimate and forecast in Emilia Rogna. Remote Sens. Environ. 45, 311–326.
Boegh, E., Soegaard, H., Broge, N., Hasager, C. B., Jensen, N. O., Schelde, K., et al., 2002. Airborne multispectral data for quantifying leaf area index, nitrogen concentration, and photosynthetic efficiency in agriculture. Remote Sensing of Environment, 81, 179−193.
Bouvet, A., Le-Toan, T., 2011. Use of ENVISAT/ASAR wide swath data for time-ly rice field mapping in the Mekong Delta. Remote Sensing of Environment 115, 1090-1101.
C.F.Chen, N.T. Son, L.Y Chang, 2012. Monotoring of rice cropping intensity in the upper MeKong Delta, Vietnam using time-series MODIS data. Advance Space Research 49, 292-301. Published by Elserved Ltd. All rights reserved. Doi:10.1016/j.sar.2011.09.011.
Canisius, F., Turral, H., & Molden, D., 2007. Fourier analysis of historical NOAA time series data to estimate bimodal agriculture. International Journal of Remote Sensing,28, pp. 5503–5522.
Chavez, P. S., Sides, S. C., and Anderson, J. A., 1991, Comparison of three different methods to merge multiresolution and multispectral data Landsat TM and SPOT Panchromatic. Photogrammetric Engineering and Remote Sensing, 57, 295–303.
Chen, C.F., Son, N.T., Chen, C.R., Chang, L.Y., 2012. Wavelet filtering of time-series moderate resolution imaging spectroradiometer data for rice crop mapping using support vector machines and maximum likelihood classifier. Journal of Applied Remote Sensing, vol.5.
Chen, X., Vierling, L., Deering, D., 2005. A simple and effective radiometric correction method to improve landscape change detection across sensors and across time. Remote Sensing of Enviroment 84 (4), 63-79.
D.G.Leckei, 1990. Synergism of Synthetic Aperture Radar and visible/infrared data for forest type discrimination, Photogramm. Eng. Remote Sens., Vol.56, pp.1237-1246.
Ehlers, M., 1991. Multisensor image fusion techniques in remote sensing. ISPRS Journal of Photogrammetry and Remote Sensing, 46 (1), 19–30.
Frost, V.S., Stiles, J.A., Shanmugan, K.S., Holtzman, J.C., 1982. A model for Radar images and its application to adaptive digital filtering of multiplicative noise. IEEE Trans. Pattern Anal. March. Intell., vol. 4, no. 2, p. 157-166.
G.Hong, Y. Zhang, and B. Mercer, 2009. A Wavelet and IHS Integration Method to Fuse High Resolution SAR with Moderate Resolution Multispectral Images. Photogrammetric Engineering & Remote Sensing
Groten, S.M.E., 1993. NDVI-Crop monitoring and early yieldassessment of Burkina Faso. Int. J. Remote Sens. 14, 1495–1515.
Gusso, A., Ducati, J.R., Veronez, M.R., Arvor, D., L.G. da Silveria Jr., 2013. Spectral model for soybean yield estimate using MODIS/EVI data. International Journal of Geosciences, 1, 1233-1241.
H.McNairn, C. Champagne,J. Chang, D. Holmstrom, G. Reichert, 2009. Intergration of optical and Synthetic Aperture Radar (SAR( imagagy for delivering operational annual crop inventories. ISPRS Journal of Photogrammetry and Remote Sensing 64, p. 434-449.
Hayes, J.M., Decker, W.L., 1996. Using NOAA AVHRR data to estimate maize production in the United States Corn Belt. Int. J.Remote Sens. 17, 3189–3200.
Helmy, A. K.; Nasr, A. H.; El-Taweel, G.H.S., 2010. Assessment and Evaluation of Different Data Fusion Techniques. International Journal of Computers4(4), 107-115.
Holecz, F., Dwyer, E., Monaco, S., Schmid, B., Frei, U. & Fischer, R., 2000. An operational rice field mapping tool using spaceborne SAR data. The ERS-Envisat symposium. Gothenburg, Germany.
Holzman, M.E., Rivas, R., Piccolo, M.C., 2014. Estimating soil moisture and the relationship with crop yield using surface temperature and vegetation index. International Journal of Applied Earth Observation and Geoinformation 28 (2014) 181–192.
Huang, S., Liu, D., 2007. Some uncertain factor analysis and improvement in spaceborne synthetic aperture radar imaging. Signal processing, vol. 78, p. 3202-3217.
Huete, A. R., Didan, K., Miura, T., Rodriguez, E. P., Gao, X., & Ferreira, L. G., 2002. Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing of Environment, 83,195−213.
Huete, A., Justice, C., & Liu, H., 1994. Development of vegetation and soil indices for MODIS–EOS.Remote Sensing of Environment, 49, 224−234.
Huete, A.R., Justice C., 1999. MODIS vegetation index (MOD13) algorithm theoretical basis documents. Ver. 3.
Huete, H. Q. Liu, K. Batchily, and W. van Leeuwen, 1997. A comparison of vegetation indices over a global set of TM images for EOS–MODIS, Remote Sensing of Environment 59, pp. 440–451
Huete, K. Didan, T. Miura, E. P. Rodriguez, X. Gao, and L. G., 2002. Ferreira, Overview of the radiometric and biophysical performance of the MODIS vegetation indices, Remote Sensing of Environment 83, pp. 195–213.
J.Amorós-López, L. Gosmez-Chova, L. Alonso, L. Guanter, R. Zurita-Milla, J. Moreno, G. Camps-Valls, 2013. Multitemporal fusion of Landsat/TM and ENVISAT/MERIS for crop monitoring. International Journal of Applied Earth Observation and Geoinformation 23, 132-141.
Jonsson, P., and L. Eklundh, 2002. Seasonality extraction by function fitting to time-series of satellite sensor data, IEEE Transactions on Geoscience and Remote Sensing, 40(8):1824–1832.
Julien, Y., A.Sobrino, J., Mattar, C., B.Ruescas, A., Juan C. Jime´Nez-Mu˜ Noz, Guillem S`Oria, Victoria Hidalgo, Mariam Atitar, Belen Franch And Juan Cuenca, 2009. Temporal analysis of normalized difference vegetation index (NDVI) and land surface temperature (LST) parameters to detect changes in the Iberian land cover between 1981 and 2001. International Journal of Remote Sensing, Vol. 32, No. 7, 10 April 2011, 2057–2068.
Kajalainen, M., Kuittinen, R., Vesa, J., Tuomo, K., Hieu, N. M. & Ha, T. T. T., 2000. Rice yield estimation using SAR images, meteorological data and GIS. The ERS-Envisat symposium. Gothenburg, Germany
Kropff, M. J., Laar, H. H. V. & Matthews, R. B., 1994. Oryza 1: An ecophysiological model for irrigated rice production. IN M.J., K., H.H., V. L. & R.B., M. (Eds.) SARP Research.
Kuan, D.T., Sawchuk, A.A., Strand, T.C., Chavell, P., 1985. Adaptive noise smoothing filter for images with signal-dependent noise. IEEE Trans. Pattern Anal. Mach. Intell. Vol 7, no. 2, p. 165-177.
Kuan, D.T., Sawchuk, A.A., Strand, T.C., Chavell, P., 1987. Adaptive restoration of image with speckle. IEEE Trans. Acoust. Speech Signal Sprocess., vol. 35, no. 3, p. 373-383.
Kumar, A. S., Kartikeyan, B. and Majumdar, K. L., 2000. Band sharpening of IRS-multispectral imagery by cubic spline wavelets.,International Journalof Remote Sensingvol. 21, no. 3, pp. 581–594.
Labus, M.P., Neilsen, G.A., Lawrence, R.L., Engel, R., Long, D.S., 2002. Wheat yield estimates using multi-temporal NDVI satellite imagery. International Journal Of Remote Sensing, vol. 23, no. 20, 4169-4180.
Lam-Dao, N., Le-Toan, T. & Floury, N., 2005. The Use of SAR Data for Rice Crop Monitoring - A Case Study ofMekong River Delta – Vietnam. The 26th Asian Conference on Remote Sensing. Ha Noi, Vietnam.
Lee, J.S., 1986. Speckle suppression and analysis for synthetic aperture radar. Optical Engineering, vol. 25, p. 639-643.
Lee, J.S.,1980. Digital image enhancement and noise filtering by use of local statistic. IEEE Trans. Pattern Anal. Mach. Intel. Vol. 2, no. 2, p. 165-186.
Le-Toan, T., Bouvet, A., Tan, B., Zengyuan, L., Wei, H., Bingbai, L., Pingping, Z. & Bondeau, A., 2005. Rice monitoring in China - Midterm report. The 2005 Dragon Symposium "Mid Term results". Santorini, Greece.
Lewis, J.E., Rowland, J., Nadeau, A., 1998. Estimating maize production in Kenya using NDVI: some statistical considerations. Int. J.Remote Sensing 19 (13), 2609–2617.
Li, H., B.S. Manjunath, and S.K. Mitra, 1995. Miltisensor image fusion using the wavelet transform. Graphic Models and Image Processing, 57 (3): 235-245.
Li, Y., Liao, Q., Li, X., Liao, S., Chi, G. & Peng, S., 2003. Towards an operational system for regional-scale rice yield estimation using a time-series of Radarsat ScanSAR images. International Journal of Remote Sensing, 24, 4207-4220.
Liu, W.T., and F. Kogan, 2002. Monitoring Brazilian soybean production using NOAA/AVHRR based vegetation condition indices. International Journal of Remote Sensing 23 (6): 1161-79.
Lopes, A., Nezry, E., Touzi, R., Laur, H., 1993. Structure detection and statistical adaptive speckle filtering in SAR images. International Journal of Remote Sensing 14 (9), 1735-1758.
Lopes, A., Touzi, R., Nezry, E., 1990. Adaptive speckle filters and scene heterogeneity. IEEE Trans. Geosc. Remote Sensing, vol. 28, no. 28, p. 992-1000.
M. R de Leeuw and L. M. T. de Carvalho, 2009. Performance evaluation of several adaptive speckel filters for SAR imaging. Anais XIV Simpósio Brasileiro de Sensoriamento Remoto, Natal, Brasil, 25-30 abril 2009, INPE, p. 7299-7305.
M. S., Mahey, R.K., Sidhu, S.S, 1999. Cotton yield prediction through spectral parameters. Journal of the Indian Society of Remote Sensing, vol. 27, no. 4.
Ma, B.L., Dwyer, L.M., Costa, C., Cober, E.R., Morrison, M.J., 2001. Early prediction of soybean yield from canopy reflectance measurements. Agron. J. 93, 1227–1234.
Mercer, B., D. Edwards, G. Hong, Y. Zhang, and J. Maduck, 2005. Fusion of InSAR high resolution imagery and low resolution multi-spectra; optical imagery. Proceedings of the ISPRS Hannover Workshop, 17-20 May, Hannover Germany.
Mkhabela, M.S., Bullock, P., Raj, S., Wang, S., Yang, Y., 2011. Crop yield forecasting on the Canadian Prairies using MODIS NDVI data. Agricultural and Forest Meteorology 151, 385–393.
Mkhabela, M.S., Mkhabela, M.S., Mashinini, N.N., 2005. Early maize yield forecasting in the four agro-ecological regions of Swaziland using NDVI data derived from NOAA’s-AVHRR. Agric. Forest Meteorol. 129 (1–2), 1–9.
Moriondo M, Maselli F, Bindi M, 2007. A simple model of regional wheat yield based on NDVI data. European Journal of Agronomy 26: 266–274. doi: 10.1016/j.eja.2006.10.007.
Panda, S.S., P.Ames, D., Pannigrahi, S., 2010. Application of vegetation indicies for Agriculture crop yield prediction using neural networl techniques. Remote Sensing, 2, 673-696.
Paul C. Doraiswamy, Bakhyt Akhmedov, Larry Beard, Alan Stern, and Richard Mueller, 2007. Operational Prediction Of Crop Yields Using Modis Data And Products.Proc. 2007 International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences.
Prasad, A.K., Chai, L., Singh, R.P., Kafatos, M., 2006. Crop yield estimation model for Iowa using remote sensing and surface parameters. Int. J. Appl. Earth Observ. Geoinformat. 8 (1), 26–33
Quarmby, N.A., MIlnes, Hindle, T.N., Silleos, N., 1993. The use of mutil-temporal NDVI measurements from AVHRR data for crop yield estimation and prediction. Int. J. Remote Sensing 14, 100-210.
Ranchin, T., and L. Wald, 2000. Fusion of high spatial and spectral resolution images: The ARSIS concept and its implementation, Photogrammetric Engineering & Remote Sensing, 66(1):49–61. Remote Sensing of Environment, 89,519−534.
Ren, J., Chen, Z., Zhou, Q., Tang, H., 2008. Regional yield estimation for winter wheat with MODIS-NDVI data in Shandong, China. International Journal of Applied Earth Observation and Geoinformatic 10, 403-413.
Ribbes, F. & Le-Toan, T., 1999b. Rice field mapping and monitoring with Radarsat data. International Journal of Remote Sensing, 20, 745-765.
Saarikko RA, 2000. Applying a site based crop model to estimate regional yields under current and changed climates. Ecol Model 131:191–206.
Sakamoto, T., Van Nguyen, N., Ohno, H., Ishitsuka, N., Yokozawa, M., 2006. Spatial–temporal distribution of rice phenology and cropping systems in theMekong Delta with special reference to the seasonal water flow of the Mekong and Bassac rivers. Remote Sens. Environ. 100, 1-16.
Sakamoto, T., Yokozawa, M., Toritani, H., Shibayama, M., Ishitsuka, N., Ohno, H., 2005. A crop phenology detection method using timeseries MODIS data. Remote Sensing Environ. 96 (3–4), 366–374.
Shao, Y., Fan, X., Liu, H., Xiao, J., Ross, S., Brisco, B., Brown, R., Staples, G., 2001. Rice monitoring and production estimation using multitemporal RADARSAT. Remote Sensing of Environment 76, 310-325.
Sharma, P.K., Chaurasia, R., Mahey, R.K., 2000. Wheat productionbforecasts using remote sensing and other techniques—experiencevof Punjab State. Indian J. Agric. Econ. 55 (2), 68–80.
Son, N.T., Chen, C.R., Chen, C.F., Chang, L., Duc, H.N, Nguyen, L.D., 2013. Prediction of rice crop yield using MODIS EVI-LAI data in the Mekong Delta, Vietnam. International Journal Of Remote Sensing, vol. 30, p. 7275-7292.
Sun, D., Kafatos, M., 2007. Note on the NDVI-LST relationship and the use of temperature-related drought indices over North America. Geophysical Research Letters, Vol. 34, L24406, doi:10.1029/2007GL031485.
Sunar, F., and Musaoglu, N., 1998. Merging multiresolution SPOT P and Landsat TM data: the effects and advantages. International Journal of Remote Sensing, vol.19, pp.219–224. Vol. 75, No. 10, October 2009, pp. 1213–1223.
Wald, L., Ranchin, T., Mangolini, M., 1997. Fusion of Satellite Images of Different Spatial Resolutions: Assessing the Quality of Resulting Images. Photogrametric Engineering & Remote Sensing, Vol. 63, n. 6, pp. 691-699.
Wardlow, B.D., Egbert, S.L., Kastens, J.H., 2007. Analysis of timeseries MODIS 250 m vegetation index data for crop classification in the U.S. Central Great Plains. Remote Sensing Environ. 108 (3),290–310.
Weigand, C.L., 1984. The value of direct observation of crop canopies for indicating growing conditions and yield. The 18th International Symposium on Remote Sensing of Environment, Paris, France, October 1-5, pp. 127-150.
Weigand, C.L., and Ricgardson, A.J., 1990. Use of spectral vegetation indices to infer leaf area, evapotranspiration and yield: I. Rationale. Journal of Argonomy, 82, 623-629.
Wu, C., Munger, J.W., Niu, Z., Kuang, D., 2010. Comparison of multiple models for estimating gross primary production using MODIS and eddy covariance data in Harvard Forest. Remote Sensing of Environment 114 (2010) 2925–2939
X. Gao, A. R. Huete, W. Ni, and T. Miura, 2000. Optical–biophysical relationships of vegetation spectra without background contamination, Remote Sensing of Environment 74, pp. 609–620.
X. Wang, L. Ge, Xiaojing, Li, 2012. Evaluation of Filters for ENVISAT ASAR Speckle Suppression in Pasture Area. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume I-7, 2012 XXII ISPRS Congress, 25 August – 01 September 2012, Melbourne, Australia.
Xiao, X., Boles, S., Frolking, S., Li, C., Babu, J.Y., Salas, W., and Moore, B. III., 2006. Mapping paddy rice agriculture in South and Southeast Asia using multiple temporal MODIS images, Remote Sensing of Environment, 100:95-113.
Xiao, X., Hollinger, D., Aber, J., Goltz, M., Davidson, E. A., Zhang, Q., et al., 2004. Satellitebased modeling of gross primary production in an evergreen needleleaf forest.
Xiao, X.M., B. Stephen, J.Y. Liu, and D.F. Zhang, 2002. Characterization of forest types in Northeastern China, using multi-temporal SPOT-4 Vegetation sensor data, Remote Sensing of Environment, 82:335–348.
Xiaoliang Lu, Ronggao Liu, Jiyuan Liu, and Shunlin Liang, 2007. Removal of Noise by Wavelet Method to Generate High Quality Temporal Data of Terrestrial MODIS Products. Photogrammetric Engineering & Remote Sensing, Vol. 73, No. 10, October 2007, pp. 1129–1139.
Yocky, D.A., 1995. Image merging and data fusion using the discrete two-dimension wavelet transform . Journal of the Optical Society of America, 12 (9): 1834-1841.
Yocky, D.A., 1996. Multiresolution wavelet decomposition image merger of Landsat thematic mapper and SPOT panchromatic data.Photogrametric Engeneering & Remote Sensing, Vol 62, n. 9, pp. 1067-1074.
Yun, J.I., 2003. Predicting regional rice production in South Korea using data and crop-growth modelling. Agricultural Systems, 77, 23-38.
Z. Jiang, A.R. Huete, K. Didan, T. Miura, 2008. Development of two-band enhanced vegetation index without a blue band. Remote Sensing of Environment 112, 3833-3845.
Zeng, Y.; Zhang, J.; Genderen, J. L. Van; Zhang, Y., 2010. Image fusion for land cover change detection. International Journal of Image and Data Fusion, 1 (2), 193-215.
Zhou, J., Civco, D. L., Silander, J. A., 1998. A wavelet transform method to merge Landsat TM and SPOT Panchromatic data. International Journal of Remote Sensing, Vol. 19, n.4, pp. 743-757.

簡易檢索 / 詳目顯示

相關論文