Retrieval of net primary productivity in grassland of Inner Mongolia based on FY-3D/MERSI2 data

doi:10.11755/j.issn.1006-7639-2024-05-0734

Abstract

Abstract:

Net Primary Productivity (NPP) is not only the main parameter for estimating carbon sequestration and oxygen release in ecosystems and measuring terrestrial carbon cycle, but also the main index for evaluating ecosystem health. In view of the limited application of domestic satellites in remote sensing monitoring of grassland NPP, a set of NPP inversion model of grassland NPP in Inner Mongolia was constructed based on FY-3D/MERSI2 data. The NPP of grassland under clear sky condition in Inner Mongolia was obtained by using a strict cloud detection algorithm, which was driven by remote sensing data products and CLDAS meteorological assimilation data, combined with light energy utilization model and ecological process model. In this study, lattice meteorological data with high resolution are introduced, which greatly improves the precision of inversion results. At the same time, based on the observation data and MODIS products, multiple relationship models of above-ground biomass, the Fraction Photosynthetic Active Radiation Absorption Ratio (FPAR) and Normalized Difference Vegetation Index (NDVI) in different months (from May to August) of grassland growth period in Inner Mongolia were constructed, and process parameters such as Leaf Area Index (LAI) and FPAR could be directly estimated based on FY-3D data. By comparing several key ecological process parameters with MODIS products, it is found that they have good correlation and spatial consistency. Finally, the grass observation data of 18 ecological meteorological observation stations in June 2021 were compared with the estimated results, showing that there was a good consistency between them, with a correlation coefficient of 0.86. The NPP inversion using FY-3D/MERSI2 can fully present the general state of vegetation productivity in Inner Mongolia.

Key words: FY-3D, NPP, CLDAS, FPAR, NDVI, biomass, cloud detection

摘要：

净初级生产力（Net Primary Productivity，NPP）不仅是估算生态系统固碳释氧、衡量陆地碳循环的主要参数，也是评价生态系统健康状况的主要指标。针对目前国产卫星对草地净初级生产力遥感监测应用较少的情况，本文基于FY-3D/MERSI2资料构建一套内蒙古草地净初级生产力反演模型，结合光能利用率模型与生态过程模型，以遥感数据产品和中国气象局陆面数据同化系统（CMA Land Data Assimilation System，CLDAS）资料为驱动，通过较严格的云检测算法得到晴空条件下内蒙古草地NPP。研究中引入分辨率较高的格点化气象数据，提升了反演结果的精细化程度；同时还基于观测数据及MODIS产品构建了内蒙古草地生育期不同月份（5—8月）地上生物量及光合有效辐射吸收比率（Fraction Photosynthetic Active Radiation Absorption Ratio，FPAR）与归一化植被指数（Normalized Difference Vegetation Index，NDVI）的多种关系模型，基于FY-3D数据直接估算叶面积指数（Leaf Area Index，LAI）及FPAR等过程参数。将反演的关键生态过程参数与MODIS对应产品对比，发现二者具有较好相关性和空间一致性。最后利用2021年6月18个生态气象观测站牧草观测资料与估算结果进行对比验证，二者具有较好的一致性，相关系数为0.86。本研究利用FY-3D/MERSI2反演的NPP能够完整呈现内蒙古地区植被生产力的普遍状态。

关键词: FY-3D, 净初级生产力, CLDAS, FPAR, NDVI, 生物量, 云检测

CLC Number:

TP-79

LI Bin, SUN Xiaolong, LU Shiqing, WANG Yuchen, XIA Ningyue, HAN Fang. Retrieval of net primary productivity in grassland of Inner Mongolia based on FY-3D/MERSI2 data[J]. Journal of Arid Meteorology, 2024, 42(5): 734-743.

李彬, 孙小龙, 卢士庆, 王宇宸, 夏宁悦, 韩芳. 基于FY-3D/MERSI2的内蒙古草地净初级生产力反演[J]. 干旱气象, 2024, 42(5): 734-743.

Figures/Tables 11

Fig.1 The distribution of ecological stations in the study area

Fig.2 Flowchart of algorithm for NPP

Fig.3 Flowchart of estimation of ecological process parameters based on NDVI

Fig.4 The FY-3D true color image (a) and cloud mask (b) on May 16, 2021

Fig.5 Scatter plot of FY-3D photosynthetic effective radiation and MODIS/PAR

Fig.6 Fitting relationship between NDVI and biomass in May（a）， June（b）， July（c） and August（d） in grassland of Inner Mongolia

Fig.7 The distribution of FY-3D estimated LAI (a) and MODIS LAI (b) on July 5, 2021

Fig.8 The regression relationship of NDVI and FPAR in May（a）， June（b）， July（c） and August（d） in grassland of Inner Mongolia

Fig.9 The distribution of CWSI from July 1 to 10， 2021 in Inner Mongolia

Fig.10 The scatter plot between FY-3D estimated grassland NPP and station observation results in June 2021 in Inner Mongolia

Fig.11 The distribution of FY-3D estimated grassland NPP in Inner Mongolia in August 2021 （Unit： kgC·km-2）

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