基于RS/GIS的土壤含水量估算模型与方法研究
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摘要
我国是一个农业大国,为防范洪涝灾害而进行的土壤水分监测一直是人们关心的问题。利用遥感手段能够快速方便地获取大区域的地表信息,因而在监测大面积土壤水分的研究和应用中具有不可替代的优势。国内外监测土壤水分的方法主要有:热惯量法、作物缺水指数法、距平植被指数法、土壤水分光谱法、热红外法、微波遥感法等。本研究首次基于像元反射光谱信息分解建立了适用于裸地的土壤含水量遥感信息模型。
本文以北京昌平地区作为研究试验区,研究步骤如下:(1)收集该区域的气象数据、土壤类型数据、土壤和水体的光谱特征曲线、多时相遥感影像等。(2)对原始资料进行预处理,将图像和属性数据存入GIS数据库中,便于进行分析和统计。(3)遥感影像处理,包括校正和恢复、剪切、增强等,为土壤含水量的定量反演奠定基础。(4)遥感影像分析和解译,结合调查资料进行土地利用类型和植被覆盖度划分。(5)基于土壤的光谱响应机制建立像元反射光谱信息分解模型。(6)将研究区域的遥感影像按照土壤类型划分成不同的数据层,根据传感器响应函数分别将每个波段的DN值矩阵转化为亮度值矩阵作为模型输入数据,计算出土壤容积含水率作为输出结果存入GIS数据库中。
对北京昌平地区多时相遥感数据进行计算,运用抽样调查理论对实测结果进行统计分析,结果表明:裸露耕地的监测精度最高(理论精度89.78%);山区受到当地的地形以及植被覆盖的影响,土壤含水量计算结果偏高且精度有所降低(理论精度83.19%);水体以及城市(密集建筑用地)的光谱特性不符合本模型的使用条件。影响模型监测精度的因素很多,主要包括:遥感数据的空间及光谱分辨率、地物波谱特性数据的精确程度及其代表性、植被覆盖程度、地形以及土地利用类型等。本文提出的依据土壤光谱相应机制建立像元反射光谱信息分解模型进行土壤含水量计算的方法具有一定的局限性,适用于无植被或植被稀疏的地区。
最后,全面系统地总结了本文的工作和研究成果,并提出有待改进的地方和需进一步开展的工作。
Being an agricultural country, monitoring of soil moisture is very important to flood-control in China. Remote Sensing (RS) is a good technological method for information acquisition of soil surface because of its convenience and speediness. Thus, Remote Sensing introduces important advantages in large-scale monitoring of soil moisture, sometimes inalienable in China. A lot of methods and algorithms have been developed for soil moisture monitoring. In this paper, we have tackled the soil moisture monitoring problem by introducing Remote Sensing strategies and offered a spectrum decomposed model for use, namely, Spectrum Decomposed Model Based Soil Moisture Computation (SDMBSMC). The principium of soil spectrum response was analyzed and the reflected spectral information was decomposed into three parts: dry soil reflectance, water reflectance and mirror reflectance of water membrane. A novel method was brought forward to estimate the soil water contents. The workflow of the method includes: (i) dry soil spectral information collection from existing spectrum database ;( ii) reflectivity extraction from remote sensing images ;( iii) soil moisture computation based on spectrum decomposed model.
A case study on Changpin District, Beijing was provided. The implementation steps of soil moisture monitoring based on spectrum decomposed model can be described as following:
(1) acquisitions of data, such as climatic data, soil type, spectrum characteristics of soil and water objects, multi-phrases remote sensing images, and statistical charts and data from the local government;
(2) Data pre-processing and storage of pre-processed data in GIS database;
(3) Processing of original remote sensing images, such as emendation, furbishment, cut, and amplifying. This work is the base for quantitative analysis of soil moisture;
(4) Remote Sensing images analysis and interpretation;
(5) Partition of soil usage type and vegetation overlay;
We use ArcGIS software as a problem solving environment. In ArcGIS software, implementation steps is as following:
(1) DN matrix transformation to a luminance matrix based on various sensor response functions for every wave band respectively;
(2) Remote Sensing image layer partition based on soil type;
(3) Soil moisture computation using SDMBSMC;
(4) Storage of computation results into database;
The computation results of this case study show that :
(1) The approximate precision of soil moisture monitoring on infrequent vegetation land is 89.78% and best;
(2) The approximate precision of soil moisture monitoring on a mountainous area is 83.19% and relatively lower due to the impact of the surrounding terrain and vegetation coverage;
(3) SDMBSMC is not applicable to water objects and urban areas (areas containing high density architectures) because of the various spectrum characteristics of them;
There are many factors that affect the precision of soil moisture monitoring, such as spectral and spatial resolutions of remote sensing images, resolutions of spectral characteristic data, vegetation coverage, the surrounding terrain, land usage types ,etc. Consequently, SDMBSMC has some constraints due to these factors when computing the soil moisture in an area.
At last, a detailed conclusion on the whole work and the research results on this paper are summarized, and the improved aspects and the proposals to the future are discussed.
本文以北京昌平地区作为研究试验区,研究步骤如下:(1)收集该区域的气象数据、土壤类型数据、土壤和水体的光谱特征曲线、多时相遥感影像等。(2)对原始资料进行预处理,将图像和属性数据存入GIS数据库中,便于进行分析和统计。(3)遥感影像处理,包括校正和恢复、剪切、增强等,为土壤含水量的定量反演奠定基础。(4)遥感影像分析和解译,结合调查资料进行土地利用类型和植被覆盖度划分。(5)基于土壤的光谱响应机制建立像元反射光谱信息分解模型。(6)将研究区域的遥感影像按照土壤类型划分成不同的数据层,根据传感器响应函数分别将每个波段的DN值矩阵转化为亮度值矩阵作为模型输入数据,计算出土壤容积含水率作为输出结果存入GIS数据库中。
对北京昌平地区多时相遥感数据进行计算,运用抽样调查理论对实测结果进行统计分析,结果表明:裸露耕地的监测精度最高(理论精度89.78%);山区受到当地的地形以及植被覆盖的影响,土壤含水量计算结果偏高且精度有所降低(理论精度83.19%);水体以及城市(密集建筑用地)的光谱特性不符合本模型的使用条件。影响模型监测精度的因素很多,主要包括:遥感数据的空间及光谱分辨率、地物波谱特性数据的精确程度及其代表性、植被覆盖程度、地形以及土地利用类型等。本文提出的依据土壤光谱相应机制建立像元反射光谱信息分解模型进行土壤含水量计算的方法具有一定的局限性,适用于无植被或植被稀疏的地区。
最后,全面系统地总结了本文的工作和研究成果,并提出有待改进的地方和需进一步开展的工作。
Being an agricultural country, monitoring of soil moisture is very important to flood-control in China. Remote Sensing (RS) is a good technological method for information acquisition of soil surface because of its convenience and speediness. Thus, Remote Sensing introduces important advantages in large-scale monitoring of soil moisture, sometimes inalienable in China. A lot of methods and algorithms have been developed for soil moisture monitoring. In this paper, we have tackled the soil moisture monitoring problem by introducing Remote Sensing strategies and offered a spectrum decomposed model for use, namely, Spectrum Decomposed Model Based Soil Moisture Computation (SDMBSMC). The principium of soil spectrum response was analyzed and the reflected spectral information was decomposed into three parts: dry soil reflectance, water reflectance and mirror reflectance of water membrane. A novel method was brought forward to estimate the soil water contents. The workflow of the method includes: (i) dry soil spectral information collection from existing spectrum database ;( ii) reflectivity extraction from remote sensing images ;( iii) soil moisture computation based on spectrum decomposed model.
A case study on Changpin District, Beijing was provided. The implementation steps of soil moisture monitoring based on spectrum decomposed model can be described as following:
(1) acquisitions of data, such as climatic data, soil type, spectrum characteristics of soil and water objects, multi-phrases remote sensing images, and statistical charts and data from the local government;
(2) Data pre-processing and storage of pre-processed data in GIS database;
(3) Processing of original remote sensing images, such as emendation, furbishment, cut, and amplifying. This work is the base for quantitative analysis of soil moisture;
(4) Remote Sensing images analysis and interpretation;
(5) Partition of soil usage type and vegetation overlay;
We use ArcGIS software as a problem solving environment. In ArcGIS software, implementation steps is as following:
(1) DN matrix transformation to a luminance matrix based on various sensor response functions for every wave band respectively;
(2) Remote Sensing image layer partition based on soil type;
(3) Soil moisture computation using SDMBSMC;
(4) Storage of computation results into database;
The computation results of this case study show that :
(1) The approximate precision of soil moisture monitoring on infrequent vegetation land is 89.78% and best;
(2) The approximate precision of soil moisture monitoring on a mountainous area is 83.19% and relatively lower due to the impact of the surrounding terrain and vegetation coverage;
(3) SDMBSMC is not applicable to water objects and urban areas (areas containing high density architectures) because of the various spectrum characteristics of them;
There are many factors that affect the precision of soil moisture monitoring, such as spectral and spatial resolutions of remote sensing images, resolutions of spectral characteristic data, vegetation coverage, the surrounding terrain, land usage types ,etc. Consequently, SDMBSMC has some constraints due to these factors when computing the soil moisture in an area.
At last, a detailed conclusion on the whole work and the research results on this paper are summarized, and the improved aspects and the proposals to the future are discussed.
引文
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[7] Watson K, Rowen L C, Offield T W. Application of Thermal Modeling in the Geologic Interpretation of IR Images.In: Proceedings of the 7th international symposium on remote sensing of environment. Ann Arbor, MI, USA: Univ. Michigan, 1971: 2017~2041
[8] Watson K, Pohn H A. Thermal Inertia Mapping from Satellites Discrimination of Geologic Units in Oman. J Res Geol Suvr, 1974, 2(2): 147~158
[9] Bijleveld R A.‘Tell Us’A Combined Surface Temperature, Soil Moisture and Evaporation Mapping Approach. Paper Presented at 12th Int.Symp.On Rem Sens Of Env, Manila, Philippines, 1978: 20~26
[10] Pratt A, Ellyett C D. The Thermal Inertia Approach to Mapping of Soil Moisture and Geology. Remote Sens. Environ., 1979, 8(2): 151~168
[11] Owe M. Estimating Surface Soil Moisture from Satellite Microwave Measurement and a Satellite Derived Vegetation Index. Remote Sens. Environ., 1988, 24(2): 331~345
[12] Seguin B. The Assessment of Regional Crop Water Conditions from Meteorological Satellite Thermal Infrared Data. Remote Sens Environ, 1991, 35(2~3): 141~148
[13] Price J C. On the Analysis of Thermal Infrared Imagery. The Limited Utility of Apparent Thermal Inertia. Remote Sens Environ, 1985, 18(1): 59~73
[14] Carlsorn T N. Regional-Scale Estimates of Surface Moisture Availability and Thermal Inertia Using Remote Thermal Measurements. Remote Sensing Reviews, 1986(1): 197~247
[15] Hall F G. Satellite Remote Sensing of Surface Energy Balance: Success, Failure, and Unsolved Issues in FIFE. J Geophy Res, 1992(97): 19061~19089
[16] Vining R G. Estimation of Sensible Heat Flux from Remotely Sensed Canopy Temperature. J Geophy Res, 1992, 97(D17): 18951~18954.
[17] Norman J M. Algorithm for Extracting Information from Remote Sensing Thermal-IR Observations of the Earth’sSurface. Remote Sens Environ, 1995(51): 157~168
[18] Matthews K B. Climate Soil Moisture Deficit Climate and Soil Data Integration in GIS. J. Climate, 1994, 28(3): 273~283
[19]隋洪智,田国良.热惯量方法监测土壤水分.黄河流域典型地区遥感动态研究.北京:科学出版社, 1990.75~136
[20]田国良,杨希华,郑柯.冬小麦旱情监测模型研究.环境遥感, 1992, 7(2): 83~89
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