Correcting the influence of vegetation on surface soil moisture indices by using hyperspectral artificial 3D-canopy models

D. Spengler; T. Kuester; A. Frick; D. Scheffler; H. Kaufmann

doi:10.1117/12.2028496

16 October 2013 Correcting the influence of vegetation on surface soil moisture indices by using hyperspectral artificial 3D-canopy models

D. Spengler, T. Kuester, A. Frick, D. Scheffler, H. Kaufmann

Author Affiliations +

Proceedings Volume 8887, Remote Sensing for Agriculture, Ecosystems, and Hydrology XV; 88870Y (2013) https://doi.org/10.1117/12.2028496
Event: SPIE Remote Sensing, 2013, Dresden, Germany

Abstract

Surface soil moisture content is one of the key variables used for many applications especially in hydrology, meteorology and agriculture. Hyperspectral remote sensing provides effective methodologies for mapping soil moisture content over a broad area by different indices such as NSMI [1,2] and SMGM [3]. Both indices can achieve a high accuracy for non-vegetation influenced soil samples, but their accuracy is limited in case of the presence of vegetation. Since, the increase of the vegetation cover leads to non-linear variations of the indices. In this study a new methodology for moisture indices correcting the influence of vegetation is presented consisting of several processing steps. First, hyperspectral reflectance data are classified in terms of crop type and growth stage. Second, based on these parameters 3D plant models from a database used to simulate typical canopy reflectance considering variations in the canopy structure (e.g. plant density and distribution) and the soil moisture content for actual solar illumination and sensor viewing angles. Third, a vegetation correction function is developed, based on the calculated soil moisture indices and vegetation indices of the simulated canopy reflectance data. Finally this function is applied on hyperspectral image data. The method is tested on two hyperspectral image data sets of the AISA DUAL at the test site Fichtwald in Germany. The results show a significant improvements compared to solely use of NSMI index. Up to a vegetation cover of 75 % the correction function minimise the influences of vegetation cover significantly. If the vegetation is denser the method leads to inadequate quality to predict the soil moisture content. In summary it can be said that applying the method on weakly to moderately overgrown with vegetation locations enables a significant improvement in the quantification of soil moisture and thus greatly expands the scope of NSMI.

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D. Spengler, T. Kuester, A. Frick, D. Scheffler, and H. Kaufmann "Correcting the influence of vegetation on surface soil moisture indices by using hyperspectral artificial 3D-canopy models", Proc. SPIE 8887, Remote Sensing for Agriculture, Ecosystems, and Hydrology XV, 88870Y (16 October 2013); https://doi.org/10.1117/12.2028496

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