魏宝成, 玉山, 贾旭, 包玉海, 那日苏, 银山. 基于AMSR-2蒙古高原土壤水分反演及对气象因子响应分析[J]. 中国生态农业学报(中英文), 2016, 24(6): 837-844.
引用本文: 魏宝成, 玉山, 贾旭, 包玉海, 那日苏, 银山. 基于AMSR-2蒙古高原土壤水分反演及对气象因子响应分析[J]. 中国生态农业学报(中英文), 2016, 24(6): 837-844.
WEI Baocheng, YU Shan, JIA Xu, BAO Yuhai, NA Risu, YIN Shan. Analysis of soil moisture retrieval and response to meteorological factors using AMSR-2[J]. Chinese Journal of Eco-Agriculture, 2016, 24(6): 837-844.
Citation: WEI Baocheng, YU Shan, JIA Xu, BAO Yuhai, NA Risu, YIN Shan. Analysis of soil moisture retrieval and response to meteorological factors using AMSR-2[J]. Chinese Journal of Eco-Agriculture, 2016, 24(6): 837-844.

基于AMSR-2蒙古高原土壤水分反演及对气象因子响应分析

Analysis of soil moisture retrieval and response to meteorological factors using AMSR-2

  • 摘要: 土壤水分是地表水文过程研究的一个重要参数, 是众多环境因子综合作用的结果, 科学判定土壤水分对环境因子的响应特性, 对在蒙古高原地区开展干旱监测预警, 调整农业生产结构, 改善区域生态环境具有重要意义。本研究基于AMSR-2观测亮温、SPOT-NDVI数据, 利用微波辐射传输模型及粗糙地表发射率Qp模型, 构建适合蒙古高原的土壤水分反演方程, 同时将模型应用于2013年蒙古高原植被生长期土壤水分反演。在此基础上, 结合TRMM 3B43降雨量及气象站点气温数据, 探讨了蒙古高原土壤水分对气象因子及植被的响应特性。结果表明: 1)构建的蒙古高原表层土壤水分反演模型精度较高, 土壤水分反演值与实测值的判定系数为0.680 6, 均方根误差(RMSE)达0.031 6 cm3.cm-3, 反演结果明显优于JAXA提供的AMSR-2土壤水分产品数据(RMSE=0.044 1 cm3.cm-3)。2)TRMM 3B43降雨数据与实测降雨量线性拟合, 其判定系数为0.859 8, 直线拟合斜率K=0.941 5, 在数值上较站点实测值略微偏低, 表明TRMM 3B43数据精度较高, 在蒙古高原具有很好的适用性。3)蒙古高原植被生长期土壤水分、植被指数及降水量在空间格局上均表现出由北向南、由东北向西南逐渐减少的趋势。干旱区, 土壤水分对气温变化最敏感, 二者表现出显著正相关关系, 其次为降水和植被; 半干旱区, 植被是影响土壤水分的关键因子, 而气温与降水对土壤水分影响呈现出季节性变化; 半湿润区3个因子对土壤水分的影响程度表现为植被>降水>气温。总之, 利用土壤水分对气象因子和植被的响应特性, 可以采取适当措施降低蒙古高原灾害发生风险, 为区域生态环境建设提供科学依据。

     

    Abstract: Soil moisture is an important component of the hydrologic cycle in terrestrial ecosystems and it is critical for predicting and understanding various hydrological processes, including changes in weather conditions, precipitation patterns, runoff generation and irrigation scheduling. Soil moisture is a function of the total effect of environmental factors. The Mongolia Plateau is an ideal area for studying the interaction between soil moisture and environmental factors, because of its arid and semi-arid location and its high ecological fragility and sensitivity to global climate change. Therefore, it was necessary to study the response of soil moisture to environmental factors, which was favorable to monitor and predict droughts, adjust agricultural production structures and improve regional eco-environment in the Mongolia Plateau. A soil moisture retrieval model for the Mongolia Plateau was built using microwave radiance transfer function and Qp model based on AMSR-2 brightness temperature and SPOT normalized difference vegetation index (NDVI) data. Soil moisture was retrieved, and the retrieval precision was verified during vegetation growth period from April to October 2013 in the Mongolia Plateau. Combination with TRMM 3B43 precipitation and air temperature data acquired by meteorological stations, the study explored response characteristics between soil moisture, meteorological factors and vegetation. The results showed that 1) the coefficient of determination (r) between retrieved and ground-based soil moisture was 0.680 6, with a root-mean square error (RMSE) of 0.031 6 cm3.cm-3. The retrieval result was much better than soil moisture product data of JAXA (RMSE = 0.044 1 cm3.cm-3). 2) The developed model had a high accuracy and was applicable in surface soil moisture estimation. The regression coefficient of the linear fit of the TRMM 3B43 precipitation measure (rainfall) was 0.859 8 and with a slope line of 0.941 5, which suggested that TRMM 3B43 data were applicable in the Mongolia Plateau. 3) Total precipitation, mean NDVI and soil moisture during the growing season decreased gradually from north to south and from northeast to southwest. In the arid region of the study area, soil moisture was significantly and positively correlated with temperature, followed by precipitation and vegetation. In the semi-arid region of the study area, vegetation was the key factor driving soil moisture, and the effects of temperature and precipitation on soil moisture showed seasonal variations. The response of soil moisture to the three factors was in the order of vegetation > precipitation > temperature in the semi-humid region of the study area. In conclusion, the response of soil moisture to both environmental factors and vegetation could provide scientific basis for constructing healthy regional eco-environments with reducing disasters risk.

     

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