博碩士論文 108022602 詳細資訊




以作者查詢圖書館館藏 以作者查詢臺灣博碩士 以作者查詢全國書目 勘誤回報 、線上人數:62 、訪客IP:3.215.186.30
姓名 沙吉塔(Disyacitta Awanda)  查詢紙本館藏   畢業系所 遙測科技碩士學位學程
論文名稱 衛星觀測土壤濕度與溫度植生乾燥指數之關係
(The Relationship between Soil Moisture and Temperature Vegetation Dryness Index based on Satellite Observation)
相關論文
★ 應用經驗模態分解法在福衛五號遙測照像儀之相對輻射校正★ 福爾摩沙衛星五號遙測儀之在軌絕對輻射校正
★ 應用衛星資料及地理資訊系統在印尼BALURAN國家公園野生牛棲息地之測繪★ 利用MISR衛星資料反演陸地區域氣膠光學厚度和地表反射率
★ 衛星資料在臺灣地區西南氣流降雨估算之應用★ 結合MODIS與MISR觀測資料在氣膠單次散射反照率反演之應用
★ 結合衛星資料與建物資訊解析台北市空間發展與都市熱島效應之鏈結★ Landsat-7衛 星 資 料 反 演 都 市 大 氣 氣膠光學厚度之研究與應用
★ 對數常態分布在氣膠消光係數廓線擬合之應用★ 氣膠光學厚度與懸浮微粒濃度關係之探討及其在衛星觀測之應用
★ 地球同步衛星(Himawari-8)在逐時大氣氣膠光學厚度之反演與分析★ 同時輻射率定法在向日葵八號氣膠光學厚度反演之應用
★ 應用Landsat衛星影像探討越南河內都市化所致土地利用改變在都市熱島效應強度之影響★ 結合衛星與地面觀測資料在台中地區能見度與氣膠參數變化之分析
★ 福爾摩沙衛星五號遙測儀升空前後等化係數之率定★ 應用氣膠種類與垂直分布建立衛星氣膠光學厚度和PM濃度之關係
檔案 [Endnote RIS 格式]    [Bibtex 格式]    [相關文章]   [文章引用]   [完整記錄]   [館藏目錄]   [檢視]  [下載]
  1. 本電子論文使用權限為同意立即開放。
  2. 已達開放權限電子全文僅授權使用者為學術研究之目的,進行個人非營利性質之檢索、閱讀、列印。
  3. 請遵守中華民國著作權法之相關規定,切勿任意重製、散佈、改作、轉貼、播送,以免觸法。

摘要(中) 土壤含水量是水文循環中的重要參數,而地表濕度的空間變化對於地表交互作用之解析至關重要。然而,地面觀測的空間分布通常是受限的,遙測技術配合衛星觀測具有提供全球和區域尺度的表面土壤濕度之潛能,但目前大多數的土壤濕度產品僅具有全球尺度或低空間解析的被動或主動-被動微波感測器所反演提供。另一方面,光學感測器可針對區域尺度以更高空間解析提供與土壤濕度相關的溫度植升乾燥指數(TVDI)來估算地表乾燥程度。根據之前的研究,大多數 TVDI是針對特定或單一地物種類/土地利用的現場測量來評估的,而大範圍區域土壤濕度的空間變化則需各地物種類/土地利用之訊息,例如裸地、草地、灌木地和林地覆蓋。因此,本研究提出基於主動-被動微波反演之土壤濕度產品(SMAP),評估每各地物種類/土地利用所對應光學感測器 MODIS 的 TVDI。整體結果顯示,MODIS TVDI 與 SMAP 土壤濕度的空間分布非常吻合,且明顯受地表溫度變化之影響。在本研究提出的精細修正後,大多數土地覆蓋類型的土壤濕度與 TVDI 的相關性有顯著的提升,R = >0.50,尤其是裸土區域,R = 0.70。根據 MODIS TVDI 與 SMAP 土壤濕度之相關性,應用 TVDI 在土壤濕度的估算獲得不錯之成果,尤其是在植被稀疏的土地覆蓋類型。
摘要(英) Soil moisture plays an important parameter to understand the various environmental phenomenon, especially in hydrological cycles. Spatial variability of the surface soil moisture condition is essential to a more comprehensive understanding of the surface interactions. However, the spatial information from ground-based observation is generally limited. Remote sensing technology could potentially provide a global and regional scale of surface soil moisture. Most soil moisture products in metrics unit (volumetric unit) are usually provided by passive or active-passive microwave sensor which has global scale or low spatial resolution. Optical sensor remote sensing can perform surface dryness index in terms of Temperature Vegetation Dryness Index (TVDI) related to the soil moisture condition in regional scale or finer spatial resolution. According to the previous study, most of the TVDI results were evaluated by in-situ measurements for specific land cover types. The spatial variation of soil moisture within the entire region would necessitate in each land cover/land use, such as bare land, grassland, shrubland, and forest land covers. Therefore, this research proposed to evaluate the TVDI from MODIS satellite imagery for each land cover type based on active-passive microwave remote sensing of soil moisture products, i.e. Soil Moisture Active-Passive (SMAP). The overall results show the spatial distribution of TVDI quite similar with soil moisture information from SMAP and heavily influenced by the land surface temperature variation of the landcover type. After the refined correlation proposed in this study, soil moisture in most of the landcover types significantly correlated with TVDI with R = >0.50, especially in bare soil with R = 0.70. The estimated soil moisture based on TVDI represents the soil moisture quite well, especially in the early dry season. The uncertainty on the high dense vegetation canopy was included to estimate the soil moisture in the shallow soil depth and the bare soil with sparse vegetation landcover type performed a better estimation due to less uncertainty. However, this research could provide the soil moisture content in 1 km spatial resolution based on optical sensor and the possibility to achieve finer spatial resolution.
關鍵字(中) ★ 土壤濕度, 主動-被動微波, 空間解析, 光學感測器, SMAP, MODIS, TVDI, 地物覆蓋種類 關鍵字(英) ★ soil moisture, active-passive mirowave, spatial resolution, optical sensor, SMAP, MODIS, TVDI, land cover type
論文目次 摘要 ii
Abstract iii
Acknowledgement iv
List of Figures vii
List of Table ix
CHAPTER I 1
INTRODUCTION 1
1.1. Background 1
1.2. Challenge and Objectives 5
CHAPTER II 6
LITERATURE REVIEW 6
2.1. Soil Moisture 6
2.2. Active-Passive Microwave Sensor for Soil Moisture 8
2.3. Temperature Vegetation Dryness Index 11
2.4. Related works 13
CHAPTER III 15
METHODOLOGY 15
3.1. Study area 15
3.2. Datasets and Pre-processing 17
3.2.1. Geolocate Soil Moisture Active-Passive (SMAP) 18
3.2.2. Land Surface Temperature 20
3.2.3. Normalized Vegetation Dryness Index 21
3.2.4. Global Land Use and Land Cover 23
3.3. Temperature Vegetation Dryness Index Calculation 24
3.4. Relationship of TVDI and Soil Moisture 27
3.5. Evaluation of Estimated Soil Moisture 28
3.6. Research workflow 30
CHAPTER IV 31
RESULTS AND DISCUSSION 31
4.1. Results 31
4.1.1. TVDI condition in land cover 31
4.1.2. Relationship of TVDI and soil moisture 36
4.1.3. Estimated soil moisture characteristic in land cover 37
4.1.4. Error assessment of estimated soil moisture 45
4.2. Discussion 47
CHAPTER V 50
CONCLUSIONS 50
References 51
參考文獻 References
Anand, A. et al. (2021) ‘Chapter 19 - GIS-based analysis for soil moisture estimation via kriging with external drift’, in Srivastava, P. K. et al. (eds) Agricultural Water Management. Academic Press, pp. 391–408. doi: https://doi.org/10.1016/B978-0-12-812362-1.00019-9.
Brocca, L. et al. (2017) ‘Soil moisture for hydrological applications: Open questions and new opportunities’, Water (Switzerland), 9(2). doi: 10.3390/w9020140.
Brodzik, M. J. et al. (2012) ‘EASE-Grid 2.0: Incremental but significant improvements for earth-gridded data sets’, ISPRS International Journal of Geo-Information, 1(1), pp. 32–45. doi: 10.3390/ijgi1010032.
Buchhorn, M. et al. (2020) ‘Copernicus Global Land Service: Land Cover 100m: collection 3: epoch 2019: Globe’. doi: 10.5281/ZENODO.3939050.
Chen, J. et al. (2011) ‘Estimating soil moisture using temperature-vegetation dryness index (TVDI) in the Huang-huai-hai (HHH) plain’, International Journal of Remote Sensing, 32(4), pp. 1165–1177. doi: 10.1080/01431160903527421.
Chen, S. et al. (2015) ‘Temperature vegetation dryness index estimation of soil moisture under different tree species’, Sustainability (Switzerland), 7(9), pp. 11401–11417. doi: 10.3390/su70911401.
Colliander, A. et al. (2018) ‘An assessment of the differences between spatial resolution and grid size for the SMAP enhanced soil moisture product over homogeneous sites’, Remote Sensing of Environment, 207(February), pp. 65–70. doi: 10.1016/j.rse.2018.02.006.
Devadiga, S. (2021) Vegetation Indices (MOD13): General Accuracy Statement. Available at: https://modis-land.gsfc.nasa.gov/ValStatus.php?ProductID=MOD13&_ga=2.220671320.1160654436.1623400544-316423107.1614267328 (Accessed: 11 June 2021).
Dorigo, W. A. et al. (2011) ‘The International Soil Moisture Network: a data hosting facility for global in situ soil moisture measurements’, Hydrology and Earth System Sciences, 15(5), pp. 1675–1698. doi: 10.5194/hess-15-1675-2011.
Entekhabi, B. D. et al. (2015) ‘The Soil Moisture Active Passive ( SMAP ) Mission’, 98(5).
Fang, B. and Lakshmi, V. (2014) ‘Soil moisture at watershed scale: Remote sensing techniques’, Journal of Hydrology, 516, pp. 258–272. doi: 10.1016/j.jhydrol.2013.12.008.
Gao, L., Sadeghi, M. and Ebtehaj, A. (2020) ‘Microwave retrievals of soil moisture and vegetation optical depth with improved resolution using a combined constrained inversion algorithm: Application for SMAP satellite’, Remote Sensing of Environment, 239(January), p. 111662. doi: 10.1016/j.rse.2020.111662.
Gao, Z., Gao, W. and Chang, N. Bin (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), pp. 495–503. doi: 10.1016/j.jag.2010.10.005.
Gevaert, A. I. et al. (2016) ‘Spatio-temporal evaluation of resolution enhancement for passive microwave soil moisture and vegetation optical depth’, International Journal of Applied Earth Observation and Geoinformation, 45, pp. 235–244. doi: 10.1016/j.jag.2015.08.006.
El Hajj, M. et al. (2018) ‘Evaluation of SMOS, SMAP, ASCAT and Sentinel-1 soil moisture products at sites in Southwestern France’, Remote Sensing, 10(4), pp. 1–17. doi: 10.3390/rs10040569.
Hoskera, A. K. et al. (2020) ‘Accuracies of soil moisture estimations using a semi-empirical model over bare soil agricultural croplands from Sentinel-1 SAR data’, Remote Sensing, 12(10), pp. 1–20. doi: 10.3390/rs12101664.
Januar, T. W. et al. (2020) ‘Modifying an image fusion approach for high spatiotemporal LST retrieval in surface dryness and evapotranspiration estimations’, Remote Sensing, 12(3), pp. 1–23. doi: 10.3390/rs12030498.
Kim, H. et al. (2020) ‘Global scale error assessments of soil moisture estimates from microwave-based active and passive satellites and land surface models over forest and mixed irrigated/dryland agriculture regions’, Remote Sensing of Environment, 251(February), p. 112052. doi: 10.1016/j.rse.2020.112052.
Li, C. et al. (2018) ‘The evaluation of SMAP enhanced soil moisture products using high-resolution model simulations and in-situ observations on the Tibetan Plateau’, Remote Sensing, 10(4), pp. 1–16. doi: 10.3390/rs10040535.
Liang, L. et al. (2014) ‘Drought change trend using MODIS TVDI and its relationship with climate factors in China from 2001 to 2010’, Journal of Integrative Agriculture, 13(7), pp. 1501–1508. doi: 10.1016/S2095-3119(14)60813-3.
Morgan, N. and McNamee, L. G. (2021) California, Encyclopedia Britannica. Available at: https://www.britannica.com/place/California-state (Accessed: 8 June 2021).
Pandey, D. K., Putrevu, D. and Misra, A. (2021) ‘Chapter 10 - Large-scale soil moisture mapping using Earth observation data and its validation at selected agricultural sites over Indian region’, in Srivastava, P. K. et al. (eds) Agricultural Water Management. Academic Press, pp. 185–207. doi: https://doi.org/10.1016/B978-0-12-812362-1.00010-2.
Parwati and Suwarsono (2008) ‘Model Indeks Tvdi (Temperature Vegetation Dryness Index) Untuk Mendeteksi Kekeringan Lahan Berdasarkan Data Modis-Terra’, Jurnal Penginderaan Jauh, 5, pp. 35–44. Available at: http://jurnal.lapan.go.id/index.php/jurnal_inderaja/article/viewFile/1167/1045.
Petropoulos, G. P., Ireland, G. and Barrett, B. (2015) ‘Surface soil moisture retrievals from remote sensing: Current status, products & future trends’, Physics and Chemistry of the Earth, 83–84, pp. 36–56. doi: 10.1016/j.pce.2015.02.009.
Przeździecki, K. et al. (2017) ‘Estimation of soil moisture across broad landscapes of Georgia and South Carolina using the triangle method applied to MODIS satellite imagery’, 51(4), pp. 1–19.
Przeździecki, K., Zawadzki, J. and Miatkowski, Z. (2018) ‘Use of the temperature–vegetation dryness index for remote sensing grassland moisture conditions in the vicinity of a lignite open-cast mine’, Environmental Earth Sciences, 77(17), pp. 1–13. doi: 10.1007/s12665-018-7815-6.
Rahimzadeh-Bajgiran, P., Omasa, K. and Shimizu, Y. (2012) ‘Comparative evaluation of the Vegetation Dryness Index (VDI), the Temperature Vegetation Dryness Index (TVDI) and the improved TVDI (iTVDI) for water stress detection in semi-arid regions of Iran’, ISPRS Journal of Photogrammetry and Remote Sensing, 68(1), pp. 1–12. doi: 10.1016/j.isprsjprs.2011.10.009.
Reichle, R. et al. (2020) SMAP L4 Global 3-hourly 9 km EASE-Grid Surface and Root Zone Soil Moisture Analysis Update, Version 5, NASA National Snow and Ice Data Center Distributed Active Archive Center. doi: https://doi.org/10.5067/0D8JT6S27BS9.
Robock, A. (2003) ‘HYDROLOGY | Soil Moisture’, in Holton, J. R. (ed.) Encyclopedia of Atmospheric Sciences. Oxford: Academic Press, pp. 987–993. doi: https://doi.org/10.1016/B0-12-227090-8/00169-X.
Romano, N. (2014) ‘Soil moisture at local scale: Measurements and simulations’, Journal of Hydrology, 516, pp. 6–20. doi: 10.1016/j.jhydrol.2014.01.026.
Sabaghy, S. et al. (2018) ‘Spatially enhanced passive microwave derived soil moisture: Capabilities and opportunities’, Remote Sensing of Environment, 209(March), pp. 551–580. doi: 10.1016/j.rse.2018.02.065.
Sandholt, I., Rasmussen, K. and Andersen, J. (2002) ‘A simple interpretation of the surface temperature/vegetation index space for assessment of surface moisture status’, Remote Sensing of Environment, 79(2–3), pp. 213–224. doi: 10.1016/S0034-4257(01)00274-7.
Schirmbeck, L. W. et al. (2017) ‘Understanding TVDI as an index that expresses soil moisture’, Journal of Hyperspectral Remote Sensing, 7(2), pp. 82–90. doi: 10.29150/jhrs.v7.2.p82-90.
Sehler, R. et al. (2019) ‘Investigating Relationship Between Soil Moisture and Precipitation Globally Using Remote Sensing Observations’, Journal of Contemporary Water Research & Education, 168(1), pp. 106–118. doi: 10.1111/j.1936-704x.2019.03324.x.
Seneviratne, S. I. et al. (2010) ‘Investigating soil moisture-climate interactions in a changing climate: A review’, Earth-Science Reviews, 99(3–4), pp. 125–161. doi: 10.1016/j.earscirev.2010.02.004.
Shen, R. et al. (2012) ‘Retrieving soil moisture by TVDI based on different vegetation index: A case study of Shanxi Province’, Proceedings - 2012 International Conference on Computer Science and Electronics Engineering, ICCSEE 2012, 3(Figure 2), pp. 418–422. doi: 10.1109/ICCSEE.2012.476.
Shrestha, R. and Boyer., A. G. (2019) Soil moisture data sets become fertile ground for applications, Eos. doi: https://doi.org/10.1029/2019EO114329.
Son, N. T. et al. (2019) ‘Multitemporal Landsat-MODIS fusion for cropland drought monitoring in El Salvador’, Geocarto International, 34(12), pp. 1363–1383. doi: 10.1080/10106049.2018.1489421.
Tagesson, T. et al. (2018) ‘Disaggregation of SMOS soil moisture over West Africa using the Temperature and Vegetation Dryness Index based on SEVIRI land surface parameters’, Remote Sensing of Environment, 206(December 2017), pp. 424–441. doi: 10.1016/j.rse.2017.12.036.
Velpuri, N. M., Senay, G. B. and Morisette, J. T. (2015) ‘Evaluating New SMAP Soil Moisture for Drought Monitoring in the Rangelands of the US High Plains’, Rangelands, 38(4), pp. 183–190. doi: 10.1016/j.rala.2016.06.002.
Wang, C. et al. (2004) ‘Evaluating soil moisture status in China using the temperature–vegetation dryness index (TVDI)’, Canadian Journal of Remote Sensing, 30(5), pp. 671–679. doi: 10.5589/m04-029.
Wang, J. et al. (2016) ‘Improving spatial representation of soil moisture by integration of microwave observations and the temperature-vegetation-drought index derived from MODIS products’, ISPRS Journal of Photogrammetry and Remote Sensing, 113, pp. 144–154. doi: 10.1016/j.isprsjprs.2016.01.009.
Weather Atlas (2021) Monthly weather forecast and climate California, USA. Available at: https://www.weather-us.com/en/california-usa-climate (Accessed: 6 June 2021).
West, H., Quinn, N. and Horswell, M. (2019) ‘Remote sensing for drought monitoring & impact assessment: Progress, past challenges and future opportunities’, Remote Sensing of Environment, 232(June), p. 111291. doi: 10.1016/j.rse.2019.111291.
Xia, C. et al. (2009) ‘Study on operational applications in crop growth and drought monitoring using multiple satellite data: Case study in Xinjiang, China’, International Geoscience and Remote Sensing Symposium (IGARSS), 3, pp. III-451-III–454. doi: 10.1109/IGARSS.2009.5418289.
Yuan, L. et al. (2020) Soil moisture estimation for the Chinese loess plateau using MODIS-derived ATI and TVDI, Remote Sensing. doi: 10.3390/RS12183040.
Zhang, F. et al. (2014) ‘Soil moisture monitoring based on land surface temperature-vegetation index space derived from MODIS data’, Pedosphere, pp. 450–460. doi: 10.1016/S1002-0160(14)60031-X.
Zhao, Y. et al. (2012) Hydropedology in the Ridge and Valley: Soil Moisture Patterns and Preferential Flow Dynamics in Two Contrasting Landscapes, Hydropedology. Elsevier B.V. doi: 10.1016/B978-0-12-386941-8.00012-5.
Zormand, S., Jafari, R. and Koupaei, S. S. (2017) ‘Assessment of PDI, MPDI and TVDI drought indices derived from MODIS Aqua/Terra Level 1B data in natural lands’, Natural Hazards, 86(2), pp. 757–777. doi: 10.1007/s11069-016-2715-0.
指導教授 林唐煌 審核日期 2021-7-21
推文 facebook   plurk   twitter   funp   google   live   udn   HD   myshare   reddit   netvibes   friend   youpush   delicious   baidu   
網路書籤 Google bookmarks   del.icio.us   hemidemi   myshare   

若有論文相關問題,請聯絡國立中央大學圖書館推廣服務組 TEL:(03)422-7151轉57407,或E-mail聯絡  - 隱私權政策聲明