基于Landsat8和Sentinel-1A数据的焦作广利灌区夏玉米土壤墒情监测方法研究
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摘要
土壤水分是影响农业生产活动的重要因素,在旱情监测、农作物估产等方面有重要意义。研究采用水云模型来消除研究区域植被对后向散射的影响。建立植被含水量和归一化水指数的关系提取模型中所需的植被含水量参数。利用AIEM模型结合粗糙度参数Zs建立研究区土壤墒情反演模型,将模型应用于河南省焦作广利灌区,反演结果和实测值相关性达0.7。将水云模型与AIEM模型联合反演土壤墒情,取得了较为满意的结果,该方法具有较高的适用性。
The soil moisture content is an important factor affecting the growth and development of crops. The water cloud model is used to eliminate the influence of vegetation on the backscattering in the study area. And the relationship between the normalized water index and the vegetation water content is establish to extract the vegetation water content parameters needed in the model. The AIEM model combined with the roughness parameter Zs is used to establish an empirical model for soil moisture inversion in the study area. The correlation between the inversion results of the model and the measured values is 0.7. The water cloud model and AIEM model are combined to retrieve soil moisture,and satisfactory results are obtained. The method has high applicability.
The soil moisture content is an important factor affecting the growth and development of crops. The water cloud model is used to eliminate the influence of vegetation on the backscattering in the study area. And the relationship between the normalized water index and the vegetation water content is establish to extract the vegetation water content parameters needed in the model. The AIEM model combined with the roughness parameter Zs is used to establish an empirical model for soil moisture inversion in the study area. The correlation between the inversion results of the model and the measured values is 0.7. The water cloud model and AIEM model are combined to retrieve soil moisture,and satisfactory results are obtained. The method has high applicability.
引文
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[12]JACKSON T J,CHEN D,COSH M,et al.Vegetation water content mapping using Landsat data derived normalized difference water index for corn and soybeans[J].Remote Sensing of Environment,2004,92(4):475-482.
[13]ZRIBI M,DECHAMBRE M.A new empirical model to retrieve soil moisture and roughness from radar data[J].Remote Sensing of Environment,2002,84(1):42-52.
[14]李震,廖静娟.合成孔径雷达地表参数反演模型与方法[M].北京:科学出版社,2011.
[2]杨涛,宫辉力,李小娟,等.土壤水分遥感监测研究进展[J].生态学报,2010,30(22):6 264-6 277.
[3]JACKSON T,MANSFIELD K,SAAFI M,et al.Measuring soil temperature and moisture using wireless MEMS sensors[J].Measurement,2008,41(4):381-390.
[4]JACKSON Tyronese,MANSFIELD Katrina,Mohamed Saafi,et al.Measuring soil temperature and moisture using wireless mems sensors[J].Measurement,2008,41(4):381-390.
[5]王珊,胡振华,张宝忠,等.基于有效最大含水量的土壤水分监测优化布设方法[J].中国农村水利水电,2018(5):1-5.
[6]刘虹利,王红瑞,吴泉源,等.基于MODIS数据的济南市农田区土壤含水量模型[J].中国农村水利水电,2012(8):12-15.
[7]JACKSON T,MANSFIELD K,Saafi M,et al.Measuring soil temperature and moisture using wireless MEMS sensors[J].Measurement,2008,41(4):381-390.
[8]FUNG A K,LI Z Q,CHEN K S.Back scattering from a Randomly Rough Dielectric surface[J].IEEE Trans On Geoscience and Remote sensing,1992,30(2):356-369.
[9]CHEN K S,WU T D,TSANG L,et al.Emission of rough surfaces calculated by the integral equation method with comparison to threedimensional moment method simulations[J].IEEE Transactions on Geoscience&Remote Sensing,2003,41(1):90-101.
[10]ATTEMA,Ulaby FT.Vegetation modeled as a water cloud[J].Radio Science,1978,13(2):357-364.
[11]ULABY FT,SARABANDI K,ME Dmald K.Michigan microwave canopy scattering model[J].International Journal of Remote Sensing,1990,11(7):1 223-1 253.
[12]JACKSON T J,CHEN D,COSH M,et al.Vegetation water content mapping using Landsat data derived normalized difference water index for corn and soybeans[J].Remote Sensing of Environment,2004,92(4):475-482.
[13]ZRIBI M,DECHAMBRE M.A new empirical model to retrieve soil moisture and roughness from radar data[J].Remote Sensing of Environment,2002,84(1):42-52.
[14]李震,廖静娟.合成孔径雷达地表参数反演模型与方法[M].北京:科学出版社,2011.