A New Method for LAI Spatial Scaling Based on Gaussian Distribution Theory

Dong, Ying Ying; Wang, Ji Hua; Li, Cunjun; Xu, Xingang; Zhao, Jin Ling; Wang, Hui Fang; Huang, Wen Jiang

doi:10.4028/www.scientific.net/amr.356-360.2833

Cited by 1 publication

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“…Considered spatial heterogeneity of leaf area index (LAI) and non-linearity of LAI inversion models, a new statistical spatial scaling method is proposed to quantitatively analyse scale effects and reveal scaling rules of LAI with ground hyperspectral observations. Numerical results show the spatial consistency of multi-scale estimated LAI after processing with the new proposed scaling method [12] . Also, there are a lot of researches have been done to quantitatively analyse and correct the differences between multi-source and multi-scale spatial remote sensing observations and products [7] , [13] – [18] .…”

Section: Introductionmentioning

confidence: 93%

Analysing and Correcting the Differences between Multi-Source and Multi-Scale Spatial Remote Sensing Observations

et al. 2014

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Differences exist among analysis results of agriculture monitoring and crop production based on remote sensing observations, which are obtained at different spatial scales from multiple remote sensors in same time period, and processed by same algorithms, models or methods. These differences can be mainly quantitatively described from three aspects, i.e. multiple remote sensing observations, crop parameters estimation models, and spatial scale effects of surface parameters. Our research proposed a new method to analyse and correct the differences between multi-source and multi-scale spatial remote sensing surface reflectance datasets, aiming to provide references for further studies in agricultural application with multiple remotely sensed observations from different sources. The new method was constructed on the basis of physical and mathematical properties of multi-source and multi-scale reflectance datasets. Theories of statistics were involved to extract statistical characteristics of multiple surface reflectance datasets, and further quantitatively analyse spatial variations of these characteristics at multiple spatial scales. Then, taking the surface reflectance at small spatial scale as the baseline data, theories of Gaussian distribution were selected for multiple surface reflectance datasets correction based on the above obtained physical characteristics and mathematical distribution properties, and their spatial variations. This proposed method was verified by two sets of multiple satellite images, which were obtained in two experimental fields located in Inner Mongolia and Beijing, China with different degrees of homogeneity of underlying surfaces. Experimental results indicate that differences of surface reflectance datasets at multiple spatial scales could be effectively corrected over non-homogeneous underlying surfaces, which provide database for further multi-source and multi-scale crop growth monitoring and yield prediction, and their corresponding consistency analysis evaluation.

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Section: Introductionmentioning

confidence: 93%