Estimating fractional vegetation cover and the vegetation index of bare soil and highly dense vegetation with a physically based method |
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| Institution: | 1. University of Bologna, Via Sant Alberto, 163, Scienze Ambientali, 48123, Ravenna, Italy;2. University of Cadiz, Campus de Puerto-Real, Puerto-Real, 11519, Cadiz, Spain;3. CIMA – Gambelas Campus, University of Algarve, Faro, 8005-139, Portugal;4. NILU – IMPEC, box 100, Kjeller, 2027, Norway;1. College of Resource and Environment Engineering, Ludong University, Yantai 264025, China;2. State Key Laboratory of Earth Surface Process and Resource Ecology, Beijing Normal University, Beijing 100875, China;1. Guangdong Provincial Key Laboratory of Urbanization and Geo-simulation, School of Geography and Planning, Sun Yat-sen University, Guangzhou, PR China;2. Department of Urban Planning and Design, The University of Hong Kong, Pokfulam Road, Hong Kong Special Administrative Region, PR China;3. School of Geographic Sciences, Key Lab. of Geographic Information Science (Ministry of Education), East China Normal University, 500 Dongchuan Rd, Shanghai 200241, PR China;1. State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China;2. INRA EMMAH UMR1114, Domaine Saint-Paul, Site Agroparc, Avignon 84914, France;3. Department of Physics, University of the West Indies, Mona, Jamaica;4. College of Geoexploration Science and Technology, Jilin University, Changchun 130061, China;5. China Academy of Launch Vehicle Technology, Beijing 100076, China |
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| Abstract: | Normalized difference vegetation index (NDVI) of highly dense vegetation (NDVIv) and bare soil (NDVIs), identified as the key parameters for Fractional Vegetation Cover (FVC) estimation, are usually obtained with empirical statistical methods However, it is often difficult to obtain reasonable values of NDVIv and NDVIs at a coarse resolution (e.g., 1 km), or in arid, semiarid, and evergreen areas. The uncertainty of estimated NDVIs and NDVIv can cause substantial errors in FVC estimations when a simple linear mixture model is used. To address this problem, this paper proposes a physically based method. The leaf area index (LAI) and directional NDVI are introduced in a gap fraction model and a linear mixture model for FVC estimation to calculate NDVIv and NDVIs. The model incorporates the Moderate Resolution Imaging Spectroradiometer (MODIS) Bidirectional Reflectance Distribution Function (BRDF) model parameters product (MCD43B1) and LAI product, which are convenient to acquire. Two types of evaluation experiments are designed 1) with data simulated by a canopy radiative transfer model and 2) with satellite observations. The root-mean-square deviation (RMSD) for simulated data is less than 0.117, depending on the type of noise added on the data. In the real data experiment, the RMSD for cropland is 0.127, for grassland is 0.075, and for forest is 0.107. The experimental areas respectively lack fully vegetated and non-vegetated pixels at 1 km resolution. Consequently, a relatively large uncertainty is found while using the statistical methods and the RMSD ranges from 0.110 to 0.363 based on the real data. The proposed method is convenient to produce NDVIv and NDVIs maps for FVC estimation on regional and global scales. |
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| Keywords: | Fractional vegetation cover (FVC) Linear mixture model Normalized difference vegetation index (NDVI) |
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