植被参数与蒸发的遥感反演方法及区域干旱评估应用研究
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摘要
水资源短缺已成为制约我国社会经济尤其是农业发展的主要因素,合理利用和优化配置有限的水资源,需要深入研究作物的耗水规律和及时了解作物旱情。传统的田间观测方法由于下垫面的复杂性难以进行区域扩展,而遥感具有空间连续性和时间动态变化特点,在区域耗水和旱情评估方面具有广阔的应用前景。本文利用MODIS遥感数据,对植被参数反演和区域蒸散发估计的方法和模型进行了研究,在此基础上探讨了遥感在农田缺水状况和干旱评估中的应用。
论文首先分析了MODIS不同反射率数据集的不确定性,并且通过植被指数和辐射传输模型两种方法分析了这种不确定性对植被参数反演结果的影响。研究发现:相对Collection 4(C4)的日反射率数据,改进后的Collection 5(C5)数据在可见光和近红外波段的反射率的不确定性明显降低,时相合成后的C4数据的不确定性也明显降低,根据这两种数据反演的植被参数结果也更可靠。
在此基础上,提出了利用时相合成的MODIS数据进行植被参数反演的方法,建立了遥感单层模型和双层模型,并结合位山灌区地表水热通量观测对这两种模型进行了比较研究。针对双层模型求解,提出了一种新的组分通量初始化方法,采用基于MODIS叶面积指数定义的表面阻抗的彭曼公式计算植被组分腾发量,通过与地面观测值比较发现,该方法的模拟结果比通常采用Priestley-Taylor公式初始化的双层模型要好。研究还发现双层模型对输入参数十分敏感,在地气温差较小并且植被覆盖度较大的地区,双层模型模拟结果并不优于单层模型。
本文还研究了陆面过程模型与遥感的同化方法。初步结果表明:初始土壤水分误差比较大时,采用土壤水分观测值的同化能有效提高模型模拟的土壤水分精度,而采用MODIS地表温度的同化能有效改善地表热通量的模拟结果,说明陆面过程同化模型在区域水热循环研究和干旱评估应用中具有较大的潜力。
最后,采用基于遥感反演的实际蒸散发定义的作物缺水指数CWSI和基于植被指数和地表温度的温度植被旱情指数TVDI,分别对2005-2007年位山灌区冬小麦的缺水状况和2006年川东和重庆地区的旱情进行了评估。地面验证结果表明CWSI不受当地气候条件和作物生长变化影响,能更合理的评估旱情。
The water shortage and uneven distribution in time and space is a major limiting factor for the social economic development especially for agricultural production in China. For better managing the limited water resources, it demands quantitatively assessing water consumption and monitoring drought situation. Due to great heterogeneity in land surface, it is difficult to make regional estimation from the traditional field observations. Remote sensing have obvious advantages on quick monitoring of large scale land surface and has been widely used for regional water cycle study. This thesis focused on physically-based model of vegetation parameter retrieval and evapotranspiration estimation based on MODIS data, and its application to assessing regional drought.
Firstly, the uncertainties in the MODIS surface reflectance products were discussed. And the effects of uncertainty on the vegetation parameter retrieval were investigated using both the vegetation index method and radiative transfer model inversion method. It was found that the MODIS Collection 4 (C4) product had greater uncertainty than its following product Collection 5 (C5). After temporal composite, the uncertainty in the daily C4 product was reduced. It was also found that the vegetation parameter retrievals using the two methods from the C5 data and 8-day composite C4 data were greatly improved comparing to the daily C4 data.
Based on the above understanding, a vegetation parameterization retrieval scheme using the temporal composite of MODIS data was proposed, and the single-layer and double-layer models were developed for estimating the actual evapotranspiration during the winter wheat growing season. The flux observation in the Weishan Irrigation Zone was used to validate the model results. It was proposed the Peman-Monteith algorithm with surface resistance derived from MODIS LAI data was used to make initial estimation of the canopy transpiration in the double-layer model. It was shown that the double-layer model can simulate the actual evapotranspiration better than the model using the Priestley-Taylor method for initialization. It was also found the double-layer model was sensitive to land surface parameters and had no obvious advantages than the single-layer model in this semi-humid zone with dense vegetation cover and small difference between land surface and air temperature.
The land surface data assimilation based on the SiB2 model and MODIS data was also tested in this research. The initial soil moisture had a large impact on the modeling results. With the error in the initial soil moisture, the soil moisture results were much improved with the assimilation scheme using soil moisture observation, and the surface heat fluxes results were improved through the assimilation scheme using MODIS surface temperature. It was confirmed that the land data assimilation system combining land surface model and remote sensing had great potential application for studying the water and energy cycles and assessing regional drought.
Finally, the crop water stress index (CWSI) based on the modeled actual evaporation was used for assessing the water stress of winter wheat during the main growing period from 2005 to 2007 in Weishan Irrigation Zone. And the temperature vegetation dryness index (TVDI) was used for assessing the drought occurred in the eastern Sichuan Province and Chongqing in 2006. Comparing with the field observations, it was found that the CWSI was less affected by the regional climate conditions and crop growth, and therefore it was a more reliable index for assessing the drought situation.
论文首先分析了MODIS不同反射率数据集的不确定性,并且通过植被指数和辐射传输模型两种方法分析了这种不确定性对植被参数反演结果的影响。研究发现:相对Collection 4(C4)的日反射率数据,改进后的Collection 5(C5)数据在可见光和近红外波段的反射率的不确定性明显降低,时相合成后的C4数据的不确定性也明显降低,根据这两种数据反演的植被参数结果也更可靠。
在此基础上,提出了利用时相合成的MODIS数据进行植被参数反演的方法,建立了遥感单层模型和双层模型,并结合位山灌区地表水热通量观测对这两种模型进行了比较研究。针对双层模型求解,提出了一种新的组分通量初始化方法,采用基于MODIS叶面积指数定义的表面阻抗的彭曼公式计算植被组分腾发量,通过与地面观测值比较发现,该方法的模拟结果比通常采用Priestley-Taylor公式初始化的双层模型要好。研究还发现双层模型对输入参数十分敏感,在地气温差较小并且植被覆盖度较大的地区,双层模型模拟结果并不优于单层模型。
本文还研究了陆面过程模型与遥感的同化方法。初步结果表明:初始土壤水分误差比较大时,采用土壤水分观测值的同化能有效提高模型模拟的土壤水分精度,而采用MODIS地表温度的同化能有效改善地表热通量的模拟结果,说明陆面过程同化模型在区域水热循环研究和干旱评估应用中具有较大的潜力。
最后,采用基于遥感反演的实际蒸散发定义的作物缺水指数CWSI和基于植被指数和地表温度的温度植被旱情指数TVDI,分别对2005-2007年位山灌区冬小麦的缺水状况和2006年川东和重庆地区的旱情进行了评估。地面验证结果表明CWSI不受当地气候条件和作物生长变化影响,能更合理的评估旱情。
The water shortage and uneven distribution in time and space is a major limiting factor for the social economic development especially for agricultural production in China. For better managing the limited water resources, it demands quantitatively assessing water consumption and monitoring drought situation. Due to great heterogeneity in land surface, it is difficult to make regional estimation from the traditional field observations. Remote sensing have obvious advantages on quick monitoring of large scale land surface and has been widely used for regional water cycle study. This thesis focused on physically-based model of vegetation parameter retrieval and evapotranspiration estimation based on MODIS data, and its application to assessing regional drought.
Firstly, the uncertainties in the MODIS surface reflectance products were discussed. And the effects of uncertainty on the vegetation parameter retrieval were investigated using both the vegetation index method and radiative transfer model inversion method. It was found that the MODIS Collection 4 (C4) product had greater uncertainty than its following product Collection 5 (C5). After temporal composite, the uncertainty in the daily C4 product was reduced. It was also found that the vegetation parameter retrievals using the two methods from the C5 data and 8-day composite C4 data were greatly improved comparing to the daily C4 data.
Based on the above understanding, a vegetation parameterization retrieval scheme using the temporal composite of MODIS data was proposed, and the single-layer and double-layer models were developed for estimating the actual evapotranspiration during the winter wheat growing season. The flux observation in the Weishan Irrigation Zone was used to validate the model results. It was proposed the Peman-Monteith algorithm with surface resistance derived from MODIS LAI data was used to make initial estimation of the canopy transpiration in the double-layer model. It was shown that the double-layer model can simulate the actual evapotranspiration better than the model using the Priestley-Taylor method for initialization. It was also found the double-layer model was sensitive to land surface parameters and had no obvious advantages than the single-layer model in this semi-humid zone with dense vegetation cover and small difference between land surface and air temperature.
The land surface data assimilation based on the SiB2 model and MODIS data was also tested in this research. The initial soil moisture had a large impact on the modeling results. With the error in the initial soil moisture, the soil moisture results were much improved with the assimilation scheme using soil moisture observation, and the surface heat fluxes results were improved through the assimilation scheme using MODIS surface temperature. It was confirmed that the land data assimilation system combining land surface model and remote sensing had great potential application for studying the water and energy cycles and assessing regional drought.
Finally, the crop water stress index (CWSI) based on the modeled actual evaporation was used for assessing the water stress of winter wheat during the main growing period from 2005 to 2007 in Weishan Irrigation Zone. And the temperature vegetation dryness index (TVDI) was used for assessing the drought occurred in the eastern Sichuan Province and Chongqing in 2006. Comparing with the field observations, it was found that the CWSI was less affected by the regional climate conditions and crop growth, and therefore it was a more reliable index for assessing the drought situation.
引文
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