کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6348441 | 1621803 | 2017 | 10 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
A radiative transfer model-based method for the estimation of grassland aboveground biomass
ترجمه فارسی عنوان
یک روش مبتنی بر مدل انتقال تابشی برای تخمینی زیست توده زمین بر روی زمین
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
علوم زمین و سیارات
کامپیوتر در علوم زمین
چکیده انگلیسی
This paper presents a novel method to derive grassland aboveground biomass (AGB) based on the PROSAILH (PROSPECTÂ +Â SAILH) radiative transfer model (RTM). Two variables, leaf area index (LAI, m2mâ2, defined as a one-side leaf area per unit of horizontal ground area) and dry matter content (DMC, gcmâ2, defined as the dry matter per leaf area), were retrieved using PROSAILH and reflectance data from Landsat 8 OLI product. The result of LAIÂ ÃÂ DMC was regarded as the estimated grassland AGB according to their definitions. The well-known ill-posed inversion problem when inverting PROSAILH was alleviated using ecological criteria to constrain the simulation scenario and therefore the number of simulated spectra. A case study of the presented method was applied to a plateau grassland in China to estimate its AGB. The results were compared to those obtained using an exponential regression, a partial least squares regression (PLSR) and an artificial neural networks (ANN). The RTM-based method offered higher accuracy (R2Â =Â 0.64 and RMSEÂ =Â 42.67Â gmâ2) than the exponential regression (R2Â =Â 0.48 and RMSEÂ =Â 41.65Â gmâ2) and the ANN (R2Â =Â 0.43 and RMSEÂ =Â 46.26Â gmâ2). However, the proposed method offered similar performance than PLSR as presented better determination coefficient than PLSR (R2Â =Â 0.55) but higher RMSE (RMSEÂ =Â 37.79Â gmâ2). Although it is still necessary to test these methodologies in other areas, the RTM-based method offers greater robustness and reproducibility to estimate grassland AGB at large scale without the need to collect field measurements and therefore is considered the most promising methodology.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: International Journal of Applied Earth Observation and Geoinformation - Volume 54, February 2017, Pages 159-168
Journal: International Journal of Applied Earth Observation and Geoinformation - Volume 54, February 2017, Pages 159-168
نویسندگان
Xingwen Quan, Binbin He, Marta Yebra, Changming Yin, Zhanmang Liao, Xueting Zhang, Xing Li,