A radiative transfer model-based method for the estimation of grassland aboveground biomass
A radiative transfer model-based method for the estimation of grassland aboveground biomass
复制标题
基于辐射传输模型的草地地上生物量估算方法
DOI：
10.1016/j.jag.2016.10.002
复制
发表时间：
2017
影响因子：
7.5
通讯作者：
Li Xing
中科院分区：
文献类型：
--
作者：
Quan Xingwen;He Binbin;Yebra Marta;Yin Changming;Liao Zhanmang;Zhang Xueting;Li Xing
提出了一种基于PROSAILH（前景+ SAILH）辐射传输模型（RTM）计算草地地上生物量的新方法。两个变量，叶面积指数（LAI，m2m−2，定义为每单位水平地面面积的单侧叶面积）和干物质含量（DMC，gcm−2，定义为每叶面积的干物质），使用PROSAILH和Landsat 8 OLI产品的反射率数据进行检索。根据LAI × DMC的定义，将其结果作为草地AGB的估算值。众所周知的不适定反演问题时，反演PROSAILH缓解使用生态标准来约束模拟场景，因此模拟光谱的数量。以中国某高原草地为例，应用所提出的方法估算了该草地的AGB。结果进行了比较，使用指数回归，偏最小二乘回归（PLSR）和人工神经网络（ANN）。基于RTM的方法提供了比指数回归（R2 = 0.48和RMSE = 41.65 gm−2）和ANN（R2= 0.43和RMSE = 46.26 gm − 2）更高的准确度（R2= 0.64和RMSE = 42.67 gm−2）。然而，所提出的方法提供了类似的性能比PLSR提供了更好的决定系数比PLSR（R2= 0.55），但更高的RMSE（RMSE = 37.79 gm−2）。虽然仍然有必要在其他地区测试这些方法，基于RTM的方法提供了更大的鲁棒性和再现性，以估计大规模的草地AGB，而不需要收集现场测量，因此被认为是最有前途的方法。
This paper presents a novel method to derive grassland aboveground biomass (AGB) based on the PROSAILH (PROSPECT + SAILH) radiative transfer model (RTM). Two variables, leaf area index (LAI, m2m−2, defined as a one-side leaf area per unit of horizontal ground area) and dry matter content (DMC, gcm−2, defined as the dry matter per leaf area), were retrieved using PROSAILH and reflectance data from Landsat 8 OLI product. The result of LAI × 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 (R2= 0.64 and RMSE = 42.67 gm−2) than the exponential regression (R2= 0.48 and RMSE = 41.65 gm−2) and the ANN (R2= 0.43 and RMSE = 46.26 gm−2). However, the proposed method offered similar performance than PLSR as presented better determination coefficient than PLSR (R2= 0.55) but higher RMSE (RMSE = 37.79 gm−2). 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.