Volume 25, Issue 3 (2021)                   MJSP 2021, 25(3): 1-27 | Back to browse issues page

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Naderi M. Retrieving Parameters of Leaf Area Index, Chlorophyll Content and Fraction of Vegetation Cover Using an Empirical-Statistical Approach from Chris-Proba Satellite Hyperspectral Images over the Barrax area in Spain. MJSP 2021; 25 (3) :1-27
URL: http://hsmsp.modares.ac.ir/article-21-45407-en.html
Tarbiat Modares University , naderimahdi@modares.ac.ir
Abstract:   (2215 Views)
It’s Important to retrieve parameters of leaf area index, chlorophyll content and fraction of vegetation cover area used in a wide range of applications such as climate studies, photosynthesis rates, plant nutritional status and geochemical cycles. The knowledge of such parameters provides good insight into the vegetation health, growth stage and quality of vegetation and allows the study the long-term dynamics of vegetation. Generally, parameter retrieval approaches usually fall into two general groups: empirical-statistical approaches and approaches based on physical models. In this study, the retrieving parameters of Leaf Area Index (LAI), chlorophyll content and Fraction of Vegetation Cover (FVC) are presented using an empirical-statistical approach from CHRIS-Proba Satellite Images over the Barrax area in Spain. In this approach, field data and a satellite image of the study area is required to retrieve vegetation parameters. By Providing this data and establishing a relationship between them, the model is calibrated. Eventually, by using linear and nonlinear regression methods, vegetation parameters were retrieved. The results of this study showed that in the retrieving of leaf area index, chlorophyll content and fraction of vegetation cover, exponential GPR (RMSE= 0.78, R2=0.77, MAE=0.49), rational quadratic GPR (RMSE= 4.55, R2=0.36, MAE=3.61) and squared exponential GPR (RMSE= 0.11, R2=0.71, MAE=0.09) models provided the best estimation and fit with the field data. Respectively, analysis of retrieved maps of LAI, chlorophyll content and FVC Indices showed that Gaussian process models, which are nonlinear regression methods, performed better than linear regression methods and supported vector machine methods in retrieving the vegetation parameters. The retrieved maps showed that different Gaussian models have not only been successful in retrieving the shape of agricultural lands, but have also retrieved changes in LAI, chlorophyll content and FVC within agricultural lands with good accuracy
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Article Type: Original Research | Subject: planning models,techniques and methods
Received: 2020/08/22 | Accepted: 2021/07/22 | Published: 2021/10/2

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