Combining Phenological Camera Photos and MODIS Reflectance Data to Predict GPP Daily Dynamics for Alpine Meadows on the Tibetan Plateau
Combining Phenological Camera Photos and MODIS Reflectance Data to Predict GPP Daily Dynamics for Alpine Meadows on the Tibetan Plateau
复制标题
结合物候相机照片和MODIS反射率数据预测青藏高原高寒草甸GPP日动态
DOI：
10.3390/rs12223735
复制
发表时间：
2020-11
期刊：
影响因子：
5
通讯作者：
Bai Xuejie
中科院分区：
文献类型：
--
作者：
Zhou Xuqiang;Wang Xufeng;Zhang Songlin;Zhang Yang;Bai Xuejie
总初级生产力(GPP)是单位空间和时间内碳的总体光合作用固定。由于云、雪、气溶胶和地形的不确定性，准确估计每日GPP是一项具有挑战性的任务。来自物候相机的每日数字照片记录了植被每日的绿色动态，几乎没有云或气溶胶干扰。它可以与卫星遥感数据融合，提高每日GPP的预测精度。在这项研究中，我们结合两种类型的数据集，以提高青藏高原高寒草甸GPP的估算精度。为了检验不同方法和植被指数(VIS)的性能，设计了三个实验。首先，利用基于物候相机的绿色色度坐标(GCC)和基于MODIS的植被指数建立的光能利用效率(LUE)模型估算了该地区的GPP。其次，利用GCC的物候仪数据和MODIS植被指数数据，采用反向传播神经网络机器学习算法估算了该地区的GPP。最后，以GCC和植被指数为输入，采用BNNA方法进行了GPP估算。与涡动协方差GPP相比，以GCC和植被指数为输入的BNNA方法对GPP的预测精度最高。结果表明，在LUE模型和BNNA方法中，当只使用一个植被指数数据时，GCC的精度高于NDVI和EVI。与传统LUE模型估算的GPP和涡度协方差估算的GPP相比，BNNA估算的GPP和涡度协方差估算的GPP的R2平均增加0.12，RMSE平均减少1.13gC·m−2·day−1，MAD平均减少0.87g C·m−2·day−1。本研究为提高青藏高原地区GPP的估算精度提供了一条新的途径。随着大量物候相机的出现，这种方法在受云雪影响较大的青藏高原上应用潜力巨大。
Gross primary production (GPP) is the overall photosynthetic fixation of carbon per unit space and time. Due to uncertainties resulting from clouds, snow, aerosol, and topography, it is a challenging task to accurately estimate daily GPP. Daily digital photos from a phenological camera record vegetation daily greenness dynamics with little cloud or aerosol disturbance. It can be fused with satellite remote sensing data to improve daily GPP prediction accuracy. In this study, we combine the two types of datasets to improve the estimation accuracy of GPP for alpine meadow on the Tibetan Plateau. To examine the performance of different methods and vegetation indices (VIs), three experiments were designed. First, GPP was estimated with the light use efficiency (LUE) model with the green chromatic coordinate (GCC) from the phenological camera and vegetation index from MODIS, respectively. Second, GPP was estimated with the Backpropagation neural network machine learning algorithm (BNNA) method with GCC from the phenological camera and vegetation index from MODIS, respectively. Finally, GPP was estimated with the BNNA method using GCC and vegetation index as inputs at the same time. Compared with eddy covariance GPP, GPP predicted by the BNNA method with GCC and vegetation indices as inputs at the same time showed the highest accuracy of all the experiments. The results indicated that GCC had a higher accuracy than NDVI and EVI when only one vegetation index data was used in the LUE model or the BNNA method. The R2 of GPP estimated by BNNA and GPP from eddy covariance increased by 0.12 on average, RMSE decreased by 1.13 g C·m−2·day−1 on average, and MAD decreased by 0.87 g C·m−2·day−1 on average compared with GPP estimated by the traditional LUE model and GPP from eddy covariance. This study puts forth a new way to improve the estimation accuracy of GPP on the Tibetan Plateau. With the emergence of a large number of phenological cameras, this method has great potential for use on the Tibetan Plateau, which is heavily affected by clouds and snow.
登录
查看更多内容
影响因子：
3.2
作者：
N. Delpierre;K. Soudani;D. Berveiller;E. Dufrene;G. Hmimina;G. Vincent
通讯作者：
N. Delpierre;K. Soudani;D. Berveiller;E. Dufrene;G. Hmimina;G. Vincent
DOI：
10.1007/s11430-006-8219-0
发表时间：
2006-11
期刊：
Science in China Series D: Earth Sciences
影响因子：
--
作者：
Lingling Xu;Xian-zhou Zhang;P. Shi;Guirui Yu
通讯作者：
Lingling Xu;Xian-zhou Zhang;P. Shi;Guirui Yu
影响因子：
3.5
作者：
Shaoqiang Wang;Kun Huang;Hao Yan;Huimin Yan;Lei Zhou;Huimin Wang;Jun-hui Zhang;Junhua Yan;Liang Zhao-;Yanfen Wang;P. Shi;F. Zhao;Leigang Sun
通讯作者：
Shaoqiang Wang;Kun Huang;Hao Yan;Huimin Yan;Lei Zhou;Huimin Wang;Jun-hui Zhang;Junhua Yan;Liang Zhao-;Yanfen Wang;P. Shi;F. Zhao;Leigang Sun
中国西南橡胶园数码相机图像的总初级生产力与冠层颜色指数的关系
影响因子：
3.7
作者：
Zhou Ruiwu;Zhang Yiping;Song Qinghai;Lin Youxing;Sha Liqing;Jin Yanqiang;Liu Yuntong;Fei Xuehai;Gao Jinbo;He Yunling;Li Tongyan;Wang Shusen
通讯作者：
Wang Shusen
影响因子：
13.5
作者：
Li, Zhengquan;Yu, Guirui;Fu, Yuling
通讯作者：
Fu, Yuling