遥感反演土壤水分指数的适用性研究
Applicability Research of Parameters in Soil Moisture Inversion
DOI: 10.12677/GSER.2022.113039, PDF,    国家自然科学基金支持
作者: 刘子琪, 郑 杰, 朱忠礼*, 曲裕泉, 徐自为：北京师范大学地理科学学部，地表过程与资源生态国家重点实验室，北京
关键词: 土壤水分；遥感反演指数；适用性研究；青藏高原；Soil Moisture； Parameters of Soil Moisture Inversion； Applicability Research； Qinghai-Tibet Plateau
摘要: 土壤水分是地表过程的核心变量之一，实时准确掌握土壤水分的空间分布信息有利于水资源管理、洪水预测以及全球气候变化研究，且对于农业发展及灌溉等都具有重要的意义。遥感反演土壤水分指数发展十分迅速，但对于不同指数的适用性还缺乏定量的认识。本文利用2012至2014年那曲地区MODIS数据计算多种遥感反演土壤水分指数，并与地面观测数据进行相关性分析，结果表明：在非冻融时期，ATI、SEE以及TVDI与土壤水分相关性较好，从相关系数来看，在不考虑植被覆盖条件和低植被覆盖条件下与土壤水分相关性较好的指数依次是ATI、SEE和TVDI；而在高植被覆盖条件下与土壤水分相关性较好的指数依次是SEE、TVDI、SWCTI和ATI；同时本文认为综合使用多种指数是实现土壤水分监测一种有效的途径，在不考虑植被覆盖和低植被覆盖条件下综合使用ATI、SEE组合，在高植被覆盖条件下综合使用SEE、ATI、NDVI组合可以更有效地监测土壤水分的时空分布。
Abstract: Soil moisture is one of the core variables of the surface process, and accurately grasping the spatial distribution information of soil moisture in real time is not only beneficial to water resources management, flood forecasting and global climate change, but also can be important for agricultural development and irrigation management. At present research, remote sensing parameters for retrieving soil moisture have been well developed, however, the applicability of different parameters has not yet been quantitatively recognized. In this paper, the MODIS data from Naqu area are used to calculate a variety of remote sensing soil water parameters, and carry out the correlation analysis with ground observation data, the result shows that ATI, SEE and TVDI are well correlated with soil moisture during non-freezing period. From the correlation coefficient, the parameters related to soil moisture under arbitrary vegetation and low vegetation cover conditions are ATI, SEE and TVDI. The parameters related to soil moisture under high vegetation cover conditions are SEE, TVDI, SWCTI and ATI. At the same time, this paper believes that the comprehensive use of multiple parameters is an effective way to achieve soil moisture. Under the condition of not considering vegetation and low vegetation cover, the combination of ATI and SEE can be used, and under the condition of high vegetation coverage, the combination of SEE, ATI and NDVI can be used.
文章引用：刘子琪, 郑杰, 朱忠礼, 曲裕泉, 徐自为. 遥感反演土壤水分指数的适用性研究[J]. 地理科学研究, 2022, 11(3): 395-406. https://doi.org/10.12677/GSER.2022.113039

参考文献

[1] David, R.L. (2011) Soil Moisture: A Central and Unifying Theme in Physical Geography. Progress in Physical Geography, 35, 65-86. [Google Scholar] [CrossRef] 
[2] 覃湘栋, 庞治国, 江威, 等. 土壤水分微波反演方法进展和发展趋势[J]. 地球信息科学学报, 2021, 23(10): 1728-1742.
[3] 肖德安, 王世杰. 土壤水研究进展与方向评述[J]. 生态环境学报, 2009, 18(3): 1182-1188.
[4] Zhu, W.B., et al. (2017) A Time Domain Solution of the Modified Temperature Vegetation Dryness Index (MTVDI) for Continuous Soil Moisture Monitoring. Remote Sensing of Environment, 200, 1-17. [Google Scholar] [CrossRef] 
[5] Maria, J.E. and Pere, Q. (2016) Comparison of Remote Sensing and Simulated Soil Moisture Datasets in Mediterranean Landscapes. Remote Sensing of Environment, 180, 99-114. [Google Scholar] [CrossRef] 
[6] 周壮, 赵少杰, 蒋玲梅. 被动微波遥感土壤水分产品降尺度方法研究综述[J]. 北京师范大学学报(自然科学版), 2016, 52(4): 479-485.
[7] 陈书林, 刘元波, 温作民. 卫星遥感反演土壤水分研究综述[J]. 地球科学进展, 2012, 27(11): 1192-1203.
[8] 施建成, 杜阳, 杜今阳, 等. 微波遥感地表参数反演进展[J]. 中国科学: 地球科学, 2012, 42(6): 814-842.
[9] 侯英雨, 何延波, 柳钦火, 等. 干旱监测指数研究[J]. 生态学杂志, 2007(6): 892-897.
[10] Qiu, B., Huang, Y., Chen, C., et al. (2018) Mapping Spatiotemporal Dynamics of Maize in China from 2005 to 2017 through Designing Leaf Moisture Based Indicator from Normalized Multi-Band Drought Index. Computers and Electronics in Agriculture, 153, 82-93. [Google Scholar] [CrossRef] 
[11] 余涛, 田国良. 热惯量法在监测土壤表层水分变化中的研究[J]. 遥感学报, 1997(1): 24-31.
[12] 杨树聪, 沈彦俊, 郭英, 等. 基于表观热惯量的土壤水分监测[J]. 中国生态农业学报, 2011, 19(5): 1157-1161.
[13] Watson, K., Rowan, L.C. and Offield, T.W. (1971) Application of Thermal Modeling in the Geologic Interpretation of IR Images (Thermal Modeling for IR Images Geologic Interpretation, Discussing Physical Parameters Role in Materials Natural Environmental Diurnal Temperature Behavior): International Symposium on Remote Sensing of Environment, 7th. University of Michigan, Ann Arbor.
[14] Inge, S., Kjeld, R. and Jens, A. (2002) A Simple Interpretation of the Surface Temperature/Vegetation Index Space for Assessment of Surface Moisture Status. Remote Sensing of Environment, 79, 213-224. [Google Scholar] [CrossRef] 
[15] Rasmus, F. and Inge, S. (2003) Derivation of a Shortwave Infrared Water Stress Index from MODIS Near- and Short-Wave Infrared Data in a Semiarid Environment. Remote Sensing of Environment, 87, 111-121. [Google Scholar] [CrossRef] 
[16] 姚云军, 秦其明, 赵少华, 等. 基于MODIS短波红外光谱特征的土壤含水量反演[J]. 红外与毫米波学报, 2011, 30(1): 9-14.
[17] 赵泽斌, 晋锐, 田伟, 等. 基于SiB2模型的土壤水分降尺度指标的适用性研究[J]. 遥感技术与应用, 2017, 32(2): 195-205.
[18] 杨永民, 邱建秀, 苏红波, 等. 基于热红外的四种土壤含水量估算方法对比[J]. 红外与毫米波学报, 2018, 37(4): 459-467.
[19] Abduwasit, G., Qiming, Q., Tashpolat, T., et al. (2007) Modified Perpendicular Drought Index (MPDI): A Real-Time Drought Monitoring Method. ISPRS Journal of Photogrammetry and Remote Sensing, 62, 150-164. [Google Scholar] [CrossRef] 
[20] Zhao, Z., Wang, H., Qin, D., et al. (2021) Large-Scale Monitoring of Soil Moisture Using Temperature Vegetation Quantitative Index (TVQI) and Exponential Filtering: A Case Study in Beijing. Agricultural Water Management, 252, Article ID: 106896. [Google Scholar] [CrossRef] 
[21] Yang, K., Qin, J., Zhao, L., et al. (2013) A Multiscale Soil Moisture and Freeze-Thaw Monitoring Network on the Third Pole. Bulletin of the American Meteorological Society, 94, 1907-1916. [Google Scholar] [CrossRef] 
[22] 马春芽, 王景雷, 黄修桥. 遥感监测土壤水分研究进展[J]. 节水灌溉, 2018(5): 70-74.
[23] 赵英时, 等. 遥感应用分析原理与方法[M]. 北京: 科学出版社, 2013: 462-472.
[24] Ghulam, A., Qin, Q. and Zhan, Z. (2006) Designing of the Perpendicular Drought Index. Environmental Geology, 52, 1045-1052. [Google Scholar] [CrossRef] 
[25] 胡荣辰, 朱宝, 孙佳丽. 干旱遥感监测中不同指数方法的比较研究[J]. 安徽农业科学, 2009, 37(17): 8289-8291.
[26] Nemani, R., Pierce, L., Running, S., et al. (1993) Developing Satellite-Derived Estimates of Surface Moisture Status. Journal of Applied Meteorology, 32, 548-557. [Google Scholar] [CrossRef] 
[27] 王鹏新, 龚健雅, 李小文. 条件植被温度指数及其在干旱监测中的应用[J]. 武汉大学学报(信息科学版), 2001(5): 412-418.
[28] Molero, B., Merlin, O., Malbéteau, Y., et al. (2016) SMOS Disaggregated Soil Moisture Product at 1 km Resolution: Processor Overview and First Validation Results. Remote Sensing of Environment, 180, 361-376. [Google Scholar] [CrossRef] 
[29] Hong, Z., Zhang, W., Yu, C., et al. (2018) SWCTI: Surface Water Content Temperature Index for Assessment of Surface Soil Moisture Status. Sensors, 18, 2875. [Google Scholar] [CrossRef] [PubMed]
[30] Wang, L. and Qu, J.J. (2007) NMDI: A Normalized Multi-Band Drought Index for Monitoring Soil and Vegetation Moisture with Satellite Remote Sensing. Geophysical Research Letters, 34, L20405. [Google Scholar] [CrossRef] 
[31] 张红卫, 陈怀亮, 周官辉, 等. 归一化多波段干旱指数在农田干旱监测中的应用[J]. 科技导报, 2009, 27(11): 23-26.
[32] 张洁, 武建军, 周磊, 等. 基于MODIS数据的农业干旱监测方法对比分析[J]. 遥感信息, 2012, 27(5): 48-54.
[33] 魏华, 杜晓, 王世新, 等. 一种新的基于MODIS的地表含水量模型构造与验证[J]. 武汉大学学报信息科学版, 2007, 32(3): 205.
[34] Zhang, H., et al. (2013) VSDI: A Visible and Shortwave Infrared Drought Index for Monitoring Soil and Vegetation Moisture Based on Optical Remote Sensing. International Journal of Remote Sensing, 34, 4585-4609. [Google Scholar] [CrossRef] 
[35] Qin, J., Yang, K., Lu, N., et al. (2013) Spatial Upscaling of In-Situ Soil Moisture Measurements Based on MODIS- Derived Apparent Thermal Inertia. Remote Sensing of Environment, 138, 1-9. [Google Scholar] [CrossRef] 
[36] Gao, S., Zhu, Z., Weng, H., et al. (2017) Upscaling of Sparse in Situ Soil Moisture Observations by Integrating Auxiliary Information from Remote Sensing. International Journal of Remote Sensing, 38, 4782-4803. [Google Scholar] [CrossRef] 
[37] 吴小丹, 闻建光, 肖青, 等. 关键陆表参数遥感产品真实性检验方法研究进展[J]. 遥感学报, 2015, 19(1): 75-92.
[38] 吴黎, 张有智, 解文欢, 等. 改进的表观热惯量法反演土壤含水量[J]. 国土资源遥感, 2013, 25(1): 44-49.
[39] Cho, J., Lee, Y. and Lee, H. (2014) Assessment of the Relationship between Thermal-Infrared-Based Temperature- Vegetation Dryness Index and Microwave Satellite-Derived Soil Moisture. Remote Sensing Letters, 5, 627-636. [Google Scholar] [CrossRef] 
[40] Schneider, J.M., et al. (2003) Spatiotemporal Variations in Soil Water: First Results from the ARM SGP CART Network. Journal of Hydrometeorology, 4, 106-120. [Google Scholar] [CrossRef] 
[41] Goetz, S.J. (1997) Multi-Sensor Analysis of NDVI, Surface Temperature and Biophysical Variables at a Mixed Grassland Site. International Journal of Remote Sensing, 18, 71-94. [Google Scholar] [CrossRef] 
[42] 晏红波, 周国清. 地表土壤湿度光学遥感反演方法研究进展[J]. 亚热带资源与环境学报, 2017, 12(2): 82-89.
[43] 游士兵, 严研. 逐步回归分析法及其应用[J]. 统计与决策, 2017(14): 31-35.
[44] 王根绪, 沈永平, 钱鞠, 等. 高寒草地植被覆盖变化对土壤水分循环影响研究[J]. 冰川冻土, 2003, 25(6): 653-659.

为你推荐