整合Sentinel-1 和 Sentinel-2 衛星影像進行水稻田土壤濕度監測—以桃園灌區為例

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徐庭偉(Ting-Wei Hsu) 查詢紙本館藏

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遙測科技碩士學位學程

論文名稱

整合Sentinel-1 和 Sentinel-2 衛星影像進行水稻田土壤濕度監測—以桃園灌區為例
(Integrating Sentinel-1 and Sentinel-2 satellite images for soil moisture monitoring in paddy field—A case study of the Taoyuan irrigation district)

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摘要(中)

水稻是台灣最重要的作物，在農業種植面積和產量上都佔有相當大的比例，同時也是水資源用量最多的作物。然而隨著氣候變遷加劇，乾旱、水災等天然災害發生的頻率越來越高，這對台灣的經濟和糧食安全造成巨大的威脅。遙測的發展提供了在大範圍的情況下監測水稻的生長狀況，如何及時且準確地監測稻田土壤水分狀況能夠更好地配置水資源確保食物供應的穩定性，使有效的農業水資源應用成為桃園灌區的重要議題。
估計植被覆蓋下的土壤濕度狀況是目前遙測在農業應用上的重要難題，本研究使用水雲模型 (Water-Cloud Model) 來降低植被覆蓋對於土壤溼度估計的影響，該模型透過結合合成孔徑雷達（Synthetic Aperture Radar, SAR）和光學資訊來模擬不同植被覆蓋下的雷達散射狀況。水雲模型的應用需要根據分析SAR回波、光學植被指數和現場量測的地表土壤濕度之間的關係進行校準，因此本研究蒐集了台灣桃園市桃園灌區2018年至2022年的Sentinel-1雷達影像和Sentinel-2光學影像和中央大學大氣水文觀測站所量測的土壤濕度，並考量了模型變數的影響分別測試了不同類型的雷達極化和針對不同環境變化進行校正的植生指數對於水雲模型校準的性能差異，最後利用精度最高的模型監測桃園灌區水稻田的土壤濕度狀況。
研究結果顯示，在不受到降雨所影響的條件下使用同極化VV搭配耐大氣植生指數 (Atmospherically Resistant Vegetation Index, ARVI) 時的效果最好 (R2=0.55, RMSE=4.16) ，然而，由於缺乏不同條件的土壤濕度量測資料和農業活動的不確定性，本研究所使用的方法需要進一步的分析和實驗以改進模型的性能。

摘要(英)

Paddy rice is the crop with the largest planting area and the most significant irrigation water demand in Taiwan, and timely and accurate monitoring of soil moisture in paddy rice fields can make a better allocation of water resources and secure the stability of the food supply. Previous studies have focused on rice mapping using remote sensing techniques. However, irrigation and soil moisture which also significantly influence the rice yield has not yet been fully considered in the rice production estimation.
In this study, to monitor soil moisture in paddy rice fields, this study uses the modified water-cloud model (WCM) which is able to estimate surface moisture in different vegetation covers by integrating Synthetic Aperture Radar (SAR) and optical information. The model application requires calibrating work that is based on analyzing the relationships between SAR backscattering, optical vegetation index, and measured surface moisture content. Specifically, this study collected Sentinel-1 and Sentinel-2 satellite images to perform the soil estimation, and ground measurements from 2018 to 2022 were also obtained for calibrating and validating estimation results. The soil moisture content is measured and recorded using Frequency-domain sensors (FDR) by National Central University (NCU) Atmosphere and Hydrology Observation station. To calibrate the model, this study used the SAR backscattering data from Sentinel-1 and tested various vegetation indexes calculated by using Sentinel-2 imagery. Among them, the highest R2 value of 0.55 can be obtained when the Atmospherically Resistant Vegetation Index (ARVI) is applied. However, the model can be further improved by calibrating the model with more soil moisture observations from various conditions of vegetation covers.

關鍵字(中)

★ 土壤濕度
★ 水稻
★ Sentinel 1&2
★ 水雲模型
★ 合成孔徑雷達

關鍵字(英)

★ Soil moisture
★ Paddy rice
★ Sentinel 1&2
★ Water Cloud Model
★ SAR

論文目次

摘要 I
ABSTRACT II
目錄 III
圖目錄 VI
表目錄 IX
第一章緒論 1
1.1 研究背景及動機 1
1.2 研究目的 4
1.3 論文架構 4
第二章文獻回顧 6
2.1 水稻生長概述 6
2.2 土壤濕度估計方法 10
2.2.1 光學估計方法 13
2.2.2 雷達估計方法 17
2.2.3 整合雷達及光學之估計方法 21
第三章研究方法 23
3.1 研究架構 23
3.2 水雲模型 (Water-Cloud Model) 24
3.3 植生指數 27
3.3.1 常態化差值植生指數 (NDVI) 28
3.3.2 土壤校正植生指數 (SAVI) 29
3.3.3 轉換土壤校正植生指數 (TSAVI) 29
3.3.4 優化土壤校正植生指數 (OSAVI) 30
3.3.5 耐大氣植生指數 (ARVI) 30
3.3.6 增強植生指數 (EVI) 31
3.3.7 常態化水體指數 (NDWI) 32
3.4 模式評估 32
第四章研究對象及材料 34
4.1 研究流程 34
4.2 研究範圍 36
4.3 研究資料 38
4.3.1 Sentinel-1 40
4.3.2 Sentinel-2 43
4.3.3中央大學大氣水文觀測站 45
4.4 影響資料選取與處理 47
第五章研究結果 59
5.1 水雲模型校準 59
5.1.1 不同極化下的土壤濕度估計性能 59
5.1.2 不同植生指數下的模型校準性能 60
5.2 土壤濕度觀測值及估計值的比較 63
5.3 桃園灌區各期土壤濕度 69
5.4 水稻田對於土壤濕度的物候變化 75
第六章研究討論 79
6.1 水雲模型對於降低植被覆蓋影響的效果 79
6.2 不同模型變數對於水雲模型校準的影響 79
6.3 土壤濕度估計效能和過去研究之比較 81
6.4 土壤濕度在稻田中時間及空間上的變化 83
6.5 研究限制及應用 85
第七章結論及建議 87
7.1 結論 87
7.2 建議 88
參考文獻 90
附錄一水雲模型衛星影像搭配表 104
附錄二水雲模型效準之詳細公式 107

參考文獻

洪浩倫、王志添、陳錕山 (2007) 。利用多尺度匹配自動套疊衛星雷達影像。 Journal of Photogrammetry and Remote Sensing，12 (4) ，381-401。
環境保護局空氣品質保護科 (2020) 。桃園市空氣汙染防制計畫。桃園市政府環境保護局
郭益全、蔡金川、蔣永正、王鐘和、張義璋、余志儒 (2000) 。稻作生長與環境關係之精準管理。水稻精準農業 (耕) 體系之研究，25-52。
陳烈夫、魏夢麗、鄭統隆、廖大經、陳正昌、曾東海、劉大江 (1996) 。台灣水稻產量的一些生理問題。農業試驗所專刊，59，79-88。
陳錦麟 (2018) 。利用Sentinel-1 & 2遙測影像推估土壤含水量空間分佈-以桃園石門灌區為例。國立臺灣大學生物環境系統工程學研究所碩士論文。
陳繼藩 (2011) 。遙測衛星影像應用於地表乾燥指標及地表土壤含水量之研究。國立中央大學太空及遙測研究中心。
黃郁晴 (2020) 。應用多時序 Sentinel-1 雷達影像進行崩塌地偵測。國立中央大學土木工程研究所碩士論文。
楊承憲 (2021) 。遙測影像應用機器學習推估台灣山區土壤含水量分佈。國立成功大學資源工程研究所碩士論文。
劉玫婷、李欣輯、徐永衡、陳永明 (2021) 。2021年乾旱事件農作物損失調查紀實。國家災害防救科技中心災害防救電子報，194。
鄭友誠 (2016) 。氣候變遷下農業灌溉水資源調適因應策略。行政院農委會農田水利處。
盧虎生 (2004) 。水稻之發育過程與健康管理。水稻健康管理研討會，17-32。
Alahacoon, N., & Edirisinghe, M. (2022) . A comprehensive assessment of remote sensing and traditional based drought monitoring indices at global and regional scale. Geomatics, Natural Hazards and Risk, 13 (1) , 762–799. https://doi.org/10.1080/19475705.2022.2044394
Artiola, J., Pepper, I., & Brusseau, M. (2004) . Environmental Monitoring and Characterization.
Attema, E. P. W., & Ulaby, F. T. (1978) . Vegetation modeled as a water cloud. Radio Science, 13 (2) , 357–364. https://doi.org/10.1029/RS013i002p00357
Babaeian, E., Sadeghi, M., Jones, S. B., Montzka, C., Vereecken, H., & Tuller, M. (2019) . Ground, Proximal, and Satellite Remote Sensing of Soil Moisture. Reviews of Geophysics, 57 (2) , 530–616. https://doi.org/10.1029/2018RG000618
Baghdadi, N. N., El Hajj, M., Zribi, M., & Fayad, I. (2016) . Coupling SAR C-Band and Optical Data for Soil Moisture and Leaf Area Index Retrieval Over Irrigated Grasslands. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 9 (3) , 1229–1243. https://doi.org/10.1109/JSTARS.2015.2464698
Baghdadi, N., Choker, M., Zribi, M., Hajj, M. E., Paloscia, S., Verhoest, N. E. C., Lievens, H., Baup, F., & Mattia, F. (2016) . A New Empirical Model for Radar Scattering from Bare Soil Surfaces. Remote Sensing, 8 (11) , Article 11. https://doi.org/10.3390/rs8110920
Baghdadi, N., El Hajj, M., Zribi, M., & Bousbih, S. (2017) . Calibration of the Water Cloud Model at C-Band for Winter Crop Fields and Grasslands. Remote Sensing, 9 (9) , Article 9. https://doi.org/10.3390/rs9090969
Baghdadi, N., Zribi, M., Paloscia, S., Verhoest, N. E. C., Lievens, H., Baup, F., & Mattia, F. (2015) . Semi-Empirical Calibration of the Integral Equation Model for Co-Polarized L-Band Backscattering. Remote Sensing, 7 (10) , Article 10. https://doi.org/10.3390/rs71013626
Bao, Y., Lin, L., Wu, S., Kwal Deng, K. A., & Petropoulos, G. P. (2018) . Surface soil moisture retrievals over partially vegetated areas from the synergy of Sentinel-1 and Landsat 8 data using a modified water-cloud model. International Journal of Applied Earth Observation and Geoinformation, 72, 76–85. https://doi.org/10.1016/j.jag.2018.05.026
Baret, F., & Guyot, G. (1991) . Potentials and limits of vegetation indices for LAI and APAR assessment. Remote Sensing of Environment, 35 (2) , 161–173. https://doi.org/10.1016/0034-4257 (91) 90009-U
Baret, F., Guyot, G., & Major, D. J. (1989) . TSAVI: A Vegetation Index Which Minimizes Soil Brightness Effects On LAI And APAR Estimation. 12th Canadian Symposium on Remote Sensing Geoscience and Remote Sensing Symposium, 3, 1355–1358. https://doi.org/10.1109/IGARSS.1989.576128
Baret, F., Jacquemoud, S., & Hanocq, J. F. (1993) . About the soil line concept in remote sensing. Advances in Space Research, 13 (5) , 281–284. https://doi.org/10.1016/0273-1177 (93) 90560-X
Bayat, B., van der Tol, C., & Verhoef, W. (2020) . Retrieval of land surface properties from an annual time series of Landsat TOA radiances during a drought episode using coupled radiative transfer models. Remote Sensing of Environment, 238, 110917. https://doi.org/10.1016/j.rse.2018.09.030
Bazzi, H., Baghdadi, N., El Hajj, M., Zribi, M., Minh, D. H. T., Ndikumana, E., Courault, D., & Belhouchette, H. (2019) . Mapping Paddy Rice Using Sentinel-1 SAR Time Series in Camargue, France. Remote Sensing, 11 (7) , Article 7. https://doi.org/10.3390/rs11070887
Bogena, H. R., Huisman, J. A., Oberdörster, C., & Vereecken, H. (2007) . Evaluation of a low-cost soil water content sensor for wireless network applications. Journal of Hydrology, 344 (1) , 32–42. https://doi.org/10.1016/j.jhydrol.2007.06.032
Bounoua, L., Collatz, G. J., Los, S. O., Sellers, P. J., Dazlich, D. A., Tucker, C. J., & Randall, D. A. (2000) . Sensitivity of Climate to Changes in NDVI. Journal of Climate, 13 (13) , 2277–2292. https://doi.org/10.1175/1520-0442 (2000) 013<2277:SOCTCI>2.0.CO;2
Champion, I., Prevot, L., & Guyot, G. (2000) . Generalized semi-empirical modelling of wheat radar response. International Journal of Remote Sensing, 21 (9) , 1945–1951. https://doi.org/10.1080/014311600209869
Dobson, M. C., & Ulaby, F. T. (1986) . Active Microwave Soil Moisture Research. IEEE Transactions on Geoscience and Remote Sensing, GE-24 (1) , 23–36. https://doi.org/10.1109/TGRS.1986.289585
Dubois, P. C., van Zyl, J., & Engman, T. (1995) . Measuring soil moisture with imaging radars. IEEE Transactions on Geoscience and Remote Sensing, 33 (4) , 915–926. https://doi.org/10.1109/36.406677
El Hajj, M., Baghdadi, N., Zribi, M., & Bazzi, H. (2017) . Synergic Use of Sentinel-1 and Sentinel-2 Images for Operational Soil Moisture Mapping at High Spatial Resolution over Agricultural Areas. Remote Sensing, 9 (12) , Article 12. https://doi.org/10.3390/rs9121292
El Hajj, M., Baghdadi, N., Zribi, M., Belaud, G., Cheviron, B., Courault, D., & Charron, F. (2016) . Soil moisture retrieval over irrigated grassland using X-band SAR data. Remote Sensing of Environment, 176, 202–218. https://doi.org/10.1016/j.rse.2016.01.027
Eshqi Molan, Y. (2020) . Soil Moisture Contributions to InSAR Phase and Decorrelation. Earth Sciences Theses and Dissertations. https://scholar.smu.edu/hum_sci_earthsciences_etds/15
Ezzahar, J., Ouaadi, N., Zribi, M., Elfarkh, J., Aouade, G., Khabba, S., Er-Raki, S., Chehbouni, A., & Jarlan, L. (2020) . Evaluation of Backscattering Models and Support Vector Machine for the Retrieval of Bare Soil Moisture from Sentinel-1 Data. Remote Sensing, 12 (1) , Article 1. https://doi.org/10.3390/rs12010072
Firdaus, R. B. R., Leong Tan, M., Rahmat, S. R., & Senevi Gunaratne, M. (2020) . Paddy, rice and food security in Malaysia: A review of climate change impacts. Cogent Social Sciences, 6 (1) , 1818373. https://doi.org/10.1080/23311886.2020.1818373
Foroughi, H., Naseri, A. A., Nasab, S. B., Hamzeh, S., Sadeghi, M., Tuller, M., & Jones, S. B. (2020) . A new mathematical formulation for remote sensing of soil moisture based on the Red-NIR space. International Journal of Remote Sensing, 41 (20) , 8034–8047. https://doi.org/10.1080/01431161.2020.1770365
Gao, Z., Gao, W., & Chang, N.-B. (2011) . Integrating temperature vegetation dryness index (TVDI) and regional water stress index (RWSI) for drought assessment with the aid of LANDSAT TM/ETM+ images. International Journal of Applied Earth Observation and Geoinformation, 13 (3) , 495–503. https://doi.org/10.1016/j.jag.2010.10.005
Gao, Z., Xu, X., Wang, J., Yang, H., Huang, W., & Feng, H. (2013) . A method of estimating soil moisture based on the linear decomposition of mixture pixels. Mathematical and Computer Modelling, 58 (3) , 606–613. https://doi.org/10.1016/j.mcm.2011.10.054
Gaultier, L., Collard, F., Hanna, Z. E. K., Guitton, G., Herlédan, S., & Séach, G. L. (2020) . Taking advantage of Multisensor Synergy: New discovery and analysis tools (No. EGU2020-22600) . EGU2020. Copernicus Meetings. https://doi.org/10.5194/egusphere-egu2020-22600
Gherboudj, I., Magagi, R., Berg, A. A., & Toth, B. (2011) . Soil moisture retrieval over agricultural fields from multi-polarized and multi-angular RADARSAT-2 SAR data. Remote Sensing of Environment, 115 (1) , 33–43. https://doi.org/10.1016/j.rse.2010.07.011
Ghulam, A., Qin, Q., & Zhan, Z. (2007) . Designing of the perpendicular drought index. Environmental Geology, 52 (6) , 1045–1052. https://doi.org/10.1007/s00254-006-0544-2
Ghulam, A., Qin, Q., Teyip, T., & Li, Z.-L. (2007) . Modified perpendicular drought index (MPDI) : A real-time drought monitoring method. ISPRS Journal of Photogrammetry and Remote Sensing, 62 (2) , 150–164. https://doi.org/10.1016/j.isprsjprs.2007.03.002
Glenn, E. P., Huete, A. R., Nagler, P. L., & Nelson, S. G. (2008) . Relationship Between Remotely-sensed Vegetation Indices, Canopy Attributes and Plant Physiological Processes: What Vegetation Indices Can and Cannot Tell Us About the Landscape. Sensors, 8 (4) , Article 4. https://doi.org/10.3390/s8042136
Grace, J., Nichol, C., Disney, M., Lewis, P., Quaife, T., & Bowyer, P. (2007) . Can we measure terrestrial photosynthesis from space directly, using spectral reflectance and fluorescence. Global Change Biology. http://dx.doi.org/10.1111/j.1365-2486.2007.01352.x
Greifeneder, F., Notarnicola, C., & Wagner, W. (2021) . A Machine Learning-Based Approach for Surface Soil Moisture Estimations with Google Earth Engine. Remote Sensing, 13 (11) , Article 11. https://doi.org/10.3390/rs13112099
Guan, X., Huang, C., Liu, G., Meng, X., & Liu, Q. (2016) . Mapping Rice Cropping Systems in Vietnam Using an NDVI-Based Time-Series Similarity Measurement Based on DTW Distance. Remote Sensing, 8 (1) , Article 1. https://doi.org/10.3390/rs8010019
Hajj, M. E., Baghdadi, N., Belaud, G., Zribi, M., Cheviron, B., Courault, D., Hagolle, O., & Charron, F. (2014) . Irrigated Grassland Monitoring Using a Time Series of TerraSAR-X and COSMO-SkyMed X-Band SAR Data. Remote Sensing, 6 (10) , Article 10. https://doi.org/10.3390/rs61010002
He, B., Xing, M., & Bai, X. (2014) . A Synergistic Methodology for Soil Moisture Estimation in an Alpine Prairie Using Radar and Optical Satellite Data. Remote Sensing, 6 (11) , Article 11. https://doi.org/10.3390/rs61110966
Hsing, Y. I. C. (2008) . Rice in Taiwan. In H. Selin (Ed.) , Encyclopaedia of the History of Science, Technology, and Medicine in Non-Western Cultures (pp. 1–3) . Springer Netherlands. https://doi.org/10.1007/978-94-007-3934-5_10245-1
Huang, S., Tang, L., Hupy, J. P., Wang, Y., & Shao, G. (2021) . A commentary review on the use of normalized difference vegetation index (NDVI) in the era of popular remote sensing. Journal of Forestry Research, 32 (1) , 1–6. https://doi.org/10.1007/s11676-020-01155-1
Huete, A. R. (1988) . A soil-adjusted vegetation index (SAVI) . Remote Sensing of Environment, 25 (3) , 295–309. https://doi.org/10.1016/0034-4257 (88) 90106-X
Kalantari, Z., Ferreira, C. S. S., Koutsouris, A. J., Ahlmer, A.-K., Cerdà, A., & Destouni, G. (2019) . Assessing flood probability for transportation infrastructure based on catchment characteristics, sediment connectivity and remotely sensed soil moisture. Science of The Total Environment, 661, 393–406. https://doi.org/10.1016/j.scitotenv.2019.01.009
Kashyap, B., & Kumar, R. (2021) . Sensing Methodologies in Agriculture for Soil Moisture and Nutrient Monitoring. IEEE Access, 9, 14095–14121. https://doi.org/10.1109/ACCESS.2021.3052478
Kaufman, Y. J., & Tanre, D. (1992) . Atmospherically resistant vegetation index (ARVI) for EOS-MODIS. IEEE Transactions on Geoscience and Remote Sensing, 30 (2) , 261–270. https://doi.org/10.1109/36.134076
Khabbazan, S., Vermunt, P., Steele-Dunne, S., Ratering Arntz, L., Marinetti, C., van der Valk, D., Iannini, L., Molijn, R., Westerdijk, K., & van der Sande, C. (2019) . Crop Monitoring Using Sentinel-1 Data: A Case Study from The Netherlands. Remote Sensing, 11 (16) , Article 16. https://doi.org/10.3390/rs11161887
Kornelsen, K. C., & Coulibaly, P. (2013) . Advances in soil moisture retrieval from synthetic aperture radar and hydrological applications. Journal of Hydrology, 476, 460–489. https://doi.org/10.1016/j.jhydrol.2012.10.044
Lang, R. H., & Saleh, H. A. (1985) . Microwave Inversion of Leaf Area and Inclination Angle Distributions from Backscattered Data. IEEE Transactions on Geoscience and Remote Sensing, GE-23 (5) , 685–694. https://doi.org/10.1109/TGRS.1985.289387
Li, J., & Wang, S. (2018) . Using SAR-Derived Vegetation Descriptors in a Water Cloud Model to Improve Soil Moisture Retrieval. Remote Sensing, 10 (9) , Article 9. https://doi.org/10.3390/rs10091370
Liang, J., Liang, G., Zhao, Y., & Zhang, Y. (2021) . A synergic method of Sentinel-1 and Sentinel-2 images for retrieving soil moisture content in agricultural regions. Computers and Electronics in Agriculture, 190, 106485. https://doi.org/10.1016/j.compag.2021.106485
Lievens, H., & Verhoest, N. E. C. (2011) . On the Retrieval of Soil Moisture in Wheat Fields From L-Band SAR Based on Water Cloud Modeling, the IEM, and Effective Roughness Parameters. IEEE Geoscience and Remote Sensing Letters, 8 (4) , 740–744. https://doi.org/10.1109/LGRS.2011.2106109
Lievens, H., Vernieuwe, H., Álvarez-Mozos, J., De Baets, B., & Verhoest, N. E. C. (2009) . Error in Radar-Derived Soil Moisture due to Roughness Parameterization: An Analysis Based on Synthetical Surface Profiles. Sensors, 9 (2) , Article 2. https://doi.org/10.3390/s90201067
Liu, G. R., Liang, C. K., Kuo, T. H., Lin, T. H., & Huang, S. J. (2004) . Comparison of the NDVI, ARVI and AFRI vegetation index, along with their relations with the AOD using SPOT 4 vegetation data. Terrestrial, Atmospheric and Oceanic Sciences, 15 (1) , 15–31. https://doi.org/10.3319/tao.2004.15.1.15 (a)
Liu, H. Q., & Huete, A. (1995) . A feedback based modification of the NDVI to minimize canopy background and atmospheric noise. IEEE Transactions on Geoscience and Remote Sensing, 33 (2) , 457–465. https://doi.org/10.1109/TGRS.1995.8746027
Liu, H., Zhao, Z., & Jezek, K. C. (2004) . Correction of Positional Errors and Geometric Distortions in Topographic Maps and DEMs Using a Rigorous SAR Simulation Technique. Photogrammetric Engineering & Remote Sensing, 70 (9) , 1031–1042. https://doi.org/10.14358/PERS.70.9.1031
Liu, Y., Qian, J., & Yue, H. (2021) . Combined Sentinel-1A With Sentinel-2A to Estimate Soil Moisture in Farmland. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14, 1292–1310. https://doi.org/10.1109/JSTARS.2020.3043628
Liu, Y., Qian, J., & Yue, H. (2021) . Comprehensive Evaluation of Sentinel-2 Red Edge and Shortwave-Infrared Bands to Estimate Soil Moisture. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14, 7448–7465. https://doi.org/10.1109/JSTARS.2021.3098513
Liu, Y., Yue, H., Wang, H., & Zhang, W. (2017) . Comparison of SMMI, PDI and its applications in Shendong mining area. IOP Conference Series: Earth and Environmental Science, 57, 012025. https://doi.org/10.1088/1755-1315/57/1/012025
Longo-Minnolo, G., Consoli, S., Vanella, D., Ramírez-Cuesta, J. M., Greimeister-Pfeil, I., Neuwirth, M., & Vuolo, F. (2022) . A stand-alone remote sensing approach based on the use of the optical trapezoid model for detecting the irrigated areas. Agricultural Water Management, 274, 107975. https://doi.org/10.1016/j.agwat.2022.107975
Lu, Z., & Meyer, D. J. (2002) . Study of high SAR backscattering caused by an increase of soil moisture over a sparsely vegetated area: Implications for characteristics of backscattering. International Journal of Remote Sensing, 23 (6) , 1063–1074. https://doi.org/10.1080/01431160110040035
Manjunath, K. R., More, R. S., Jain, N. K., Panigrahy, S., & Parihar, J. S. (2015) . Mapping of rice-cropping pattern and cultural type using remote-sensing and ancillary data: A case study for South and Southeast Asian countries. International Journal of Remote Sensing, 36 (24) , 6008–6030. https://doi.org/10.1080/01431161.2015.1110259
McVicar, T. R., & Jupp, D. L. B. (1998) . The current and potential operational uses of remote sensing to aid decisions on drought exceptional circumstances in Australia: A review. Agricultural Systems, 57 (3) , 399–468. https://doi.org/10.1016/S0308-521X (98) 00026-2
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 (3) , 246–263. https://doi.org/10.1016/0034-4257 (94) 90020-5
Naeimi, V., Scipal, K., Bartalis, Z., Hasenauer, S., & Wagner, W. (2009) . An Improved Soil Moisture Retrieval Algorithm for ERS and METOP Scatterometer Observations. IEEE Transactions on Geoscience and Remote Sensing, 47 (7) , 1999–2013. https://doi.org/10.1109/TGRS.2008.2011617
Nazir, A., Ullah, S., Saqib, Z. A., Abbas, A., Ali, A., Iqbal, M. S., Hussain, K., Shakir, M., Shah, M., & Butt, M. U. (2021) . Estimation and Forecasting of Rice Yield Using Phenology-Based Algorithm and Linear Regression Model on Sentinel-II Satellite Data. Agriculture, 11 (10) , Article 10. https://doi.org/10.3390/agriculture11101026
Oh, Y., Sarabandi, K., & Ulaby, F. T. (1992) . An empirical model and an inversion technique for radar scattering from bare soil surfaces. IEEE Transactions on Geoscience and Remote Sensing, 30 (2) , 370–381. https://doi.org/10.1109/36.134086
Paloscia, S., Pettinato, S., Santi, E., Notarnicola, C., Pasolli, L., & Reppucci, A. (2013) . Soil moisture mapping using Sentinel-1 images: Algorithm and preliminary validation. Remote Sensing of Environment, 134, 234–248. https://doi.org/10.1016/j.rse.2013.02.027
Paloscia, S., Pettinato, S., Santi, E., Notarnicola, C., Pasolli, L., & Reppucci, A. (2013) . Soil moisture mapping using Sentinel-1 images: Algorithm and preliminary validation. Remote Sensing of Environment, 134, 234–248. https://doi.org/10.1016/j.rse.2013.02.027
Panciera, R., Tanase, M. A., Lowell, K., & Walker, J. P. (2014) . Evaluation of IEM, Dubois, and Oh Radar Backscatter Models Using Airborne L-Band SAR. IEEE Transactions on Geoscience and Remote Sensing, 52 (8) , 4966–4979. https://doi.org/10.1109/TGRS.2013.2286203
Pendey, V., & Pandey, P. K. (2010) . Spatial and Temporal Variability of Soil Moisture. International Journal of Geosciences, 1 (2) , Article 2. https://doi.org/10.4236/ijg.2010.12012
Petropoulos, G. P., Ireland, G., & Barrett, B. (2015) . Surface soil moisture retrievals from remote sensing: Current status, products & future trends. Physics and Chemistry of the Earth, Parts A/B/C, 83–84, 36–56. https://doi.org/10.1016/j.pce.2015.02.009
Pipia, L., Amin, E., Belda, S., Salinero-Delgado, M., & Verrelst, J. (2021) . Green LAI Mapping and Cloud Gap-Filling Using Gaussian Process Regression in Google Earth Engine. Remote Sensing, 13 (3) , Article 3. https://doi.org/10.3390/rs13030403
Qin, Y., Xiao, X., Dong, J., Zhou, Y., Zhu, Z., Zhang, G., Du, G., Jin, C., Kou, W., Wang, J., & Li, X. (2015) . Mapping paddy rice planting area in cold temperate climate region through analysis of time series Landsat 8 (OLI) , Landsat 7 (ETM+) and MODIS imagery. ISPRS Journal of Photogrammetry and Remote Sensing, 105, 220–233. https://doi.org/10.1016/j.isprsjprs.2015.04.008
Quesney, A., Le Hégarat-Mascle, S., Taconet, O., Vidal-Madjar, D., Wigneron, J. P., Loumagne, C., & Normand, M. (2000) . Estimation of Watershed Soil Moisture Index from ERS/SAR Data. Remote Sensing of Environment, 72 (3) , 290–303. https://doi.org/10.1016/S0034-4257 (99) 00102-9
Ranjbar, S., Akhoondzadeh, M., Brisco, B., Amani, M., & Hosseini, M. (2021) . Soil Moisture Change Monitoring from C and L-band SAR Interferometric Phase Observations. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14, 7179–7197. https://doi.org/10.1109/JSTARS.2021.3096063
Richardson, A. J., & Weigand, C. L. (1977) . DISTINGUISHING VEGETATION FROM SOIL BACKGROUND INFORMATION. Photogrammetric Engineering and Remote Sensing, 43 (12) . https://trid.trb.org/view/60764
Sadeghi, M., Babaeian, E., Tuller, M., & Jones, S. B. (2017) . The optical trapezoid model: A novel approach to remote sensing of soil moisture applied to Sentinel-2 and Landsat-8 observations. Remote Sensing of Environment, 198, 52–68. https://doi.org/10.1016/j.rse.2017.05.041
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 (2) , 213–224. https://doi.org/10.1016/S0034-4257 (01) 00274-7
Schmidt, H., & Karnieli, A. (2001) . Sensitivity of vegetation indices to substrate brightness in hyper-arid environment: The Makhtesh Ramon Crater (Israel) case study. International Journal of Remote Sensing, 22 (17) , 3503–3520. https://doi.org/10.1080/01431160110063779
Sharma, P., Kumar, D., & Srivastava, H. (2018) . Assessment of Different Methods for Soil Moisture Estimation: A Review. Journal of Remote Sensing & GIS, 9, 57–73.
Sheng, R. T.-C., Huang, Y.-H., Chan, P.-C., Bhat, S. A., Wu, Y.-C., & Huang, N.-F. (2022) . Rice Growth Stage Classification via RF-Based Machine Learning and Image Processing. Agriculture, 12 (12) , Article 12. https://doi.org/10.3390/agriculture12122137
Said, S., Kothyari, U. C., & Arora, M. K. (2012) . Vegetation effects on soil moisture estimation from ERS-2 SAR images. Hydrological Sciences Journal, 57 (3) , 517–534. https://doi.org/10.1080/02626667.2012.665608
Son, N. T., Chen, C. F., Chen, C. R., Chang, L. Y., 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, 34 (20) , 7275–7292. https://doi.org/10.1080/01431161.2013.818258
Son, N.-T., Chen, C.-F., Chen, C.-R., & Guo, H.-Y. (2020) . Classification of multitemporal Sentinel-2 data for field-level monitoring of rice cropping practices in Taiwan. Advances in Space Research, 65 (8) , 1910–1921. https://doi.org/10.1016/j.asr.2020.01.028
Srinivasa Rao, S., Dinesh kumar, S., Das, S. N., Nagaraju, M. S. S., Venugopal, M. V., Rajankar, P., Laghate, P., Reddy, M. S., Joshi, A. K., & Sharma, J. R. (2013) . Modified Dubois Model for Estimating Soil Moisture with Dual Polarized SAR Data. Journal of the Indian Society of Remote Sensing, 41 (4) , 865–872. https://doi.org/10.1007/s12524-013-0274-3
Tanre, D., Holben, B. N., & Kaufman, Y. J. (1992) . Atmospheric correction algorithm for NOAA-AVHRR products: Theory and application. IEEE Transactions on Geoscience and Remote Sensing, 30 (2) , 231–248. https://doi.org/10.1109/36.134074
Ulaby, F. T., Allen, C. T., Eger, G., & Kanemasu, E. (1984) . Relating the microwave backscattering coefficient to leaf area index. Remote Sensing of Environment, 14 (1) , 113–133. https://doi.org/10.1016/0034-4257 (84) 90010-5
Ulaby, F. T., McDonald, K., Sarabandi, K., & Dobson, M. C. (1988) . Michigan Microwave Canopy Scattering Models (MIMICS) . International Geoscience and Remote Sensing Symposium, “Remote Sensing: Moving Toward the 21st Century”., 2, 1009–1009. https://doi.org/10.1109/IGARSS.1988.570506
Urban, M., Berger, C., Mudau, T. E., Heckel, K., Truckenbrodt, J., Onyango Odipo, V., Smit, I. P. J., & Schmullius, C. (2018) . Surface Moisture and Vegetation Cover Analysis for Drought Monitoring in the Southern Kruger National Park Using Sentinel-1, Sentinel-2, and Landsat-8. Remote Sensing, 10 (9) , Article 9. https://doi.org/10.3390/rs10091482
Vereecken, H., Huisman, J. A., Bogena, H., Vanderborght, J., Vrugt, J. A., & Hopmans, J. W. (2008) . On the value of soil moisture measurements in vadose zone hydrology: A review. Water Resources Research, 44 (4) . https://doi.org/10.1029/2008WR006829
Verhoest, N. E. C., Lievens, H., Wagner, W., Álvarez-Mozos, J., Moran, M. S., & Mattia, F. (2008) . On the Soil Roughness Parameterization Problem in Soil Moisture Retrieval of Bare Surfaces from Synthetic Aperture Radar. Sensors, 8 (7) , Article 7. https://doi.org/10.3390/s8074213
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 (3) , 299–314. https://doi.org/10.1016/j.rse.2005.12.016
Vreugdenhil, M., Wagner, W., Bauer-Marschallinger, B., Pfeil, I., Teubner, I., Rüdiger, C., & Strauss, P. (2018) . Sensitivity of Sentinel-1 Backscatter to Vegetation Dynamics: An Austrian Case Study. Remote Sensing, 10 (9) , Article 9. https://doi.org/10.3390/rs10091396
Wang, D., Morton, D., Masek, J., Wu, A., Nagol, J., Xiong, X., Levy, R., Vermote, E., & Wolfe, R. (2012) . Impact of sensor degradation on the MODIS NDVI time series. Remote Sensing of Environment, 119, 55–61. https://doi.org/10.1016/j.rse.2011.12.001
Wang, Hui, Liu, Mao-hua, Wang, Yue-xuan, Dao-kun, Ma, & Hai-xia, Li. (2008) . Development of farmland soil moisture and temperature monitoring system based on wireless sensor network [J]. Journal of Jilin University (Engineering and Technology Edition) , 3,604-608.
Yadav, V. P., Prasad, R., Bala, R., & Vishwakarma, A. K. (2020) . An improved inversion algorithm for spatio-temporal retrieval of soil moisture through modified water cloud model using C- band Sentinel-1A SAR data. Computers and Electronics in Agriculture, 173, 105447. https://doi.org/10.1016/j.compag.2020.105447
Yin, Q., Liu, M., Cheng, J., Ke, Y., & Chen, X. (2019) . Mapping Paddy Rice Planting Area in Northeastern China Using Spatiotemporal Data Fusion and Phenology-Based Method. Remote Sensing, 11 (14) , Article 14. https://doi.org/10.3390/rs11141699
Zhu, A.-X., Zhao, F.-H., Pan, H.-B., & Liu, J.-Z. (2021) . Mapping Rice Paddy Distribution Using Remote Sensing by Coupling Deep Learning with Phenological Characteristics. Remote Sensing, 13 (7) , Article 7. https://doi.org/10.3390/rs13071360
Zribi, M., Gorrab, A., & Baghdadi, N. (2014) . A new soil roughness parameter for the modelling of radar backscattering over bare soil. Remote Sensing of Environment, 152, 62–73. https://doi.org/10.1016/j.rse.2014.05.009

指導教授

姜壽浩(Shou-Hao Chiang)

審核日期

2023-7-27

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