Estimating the Applicability of NDVI and SIF to Gross Primary Productivity and Grain-Yield Monitoring in China

Zhou，Zhaoqiang1,2; Ding，Yibo3; Liu，Suning4; Wang，Yao1,2; Fu，Qiang5; Shi，Haiyun1,2

2022-07-01

10.3390/rs14133237

REMOTE SENSING   影响因子和分区

2072-4292

Vegetation, a key intermediary linking water, the atmosphere, and the ground, performs extremely important functions in nature and for our existence. Although satellite-based remote-sensing technologies have become important for monitoring vegetation dynamics, selecting the correct remote-sensing vegetation indicator has become paramount for such investigations. This study investigated the consistencies between a photosynthetic activity index (the solar-induced chlorophyll fluorescence (SIF) indicator) and the traditional vegetation index (the Normalized Difference Vegetation Index (NDVI)) among different land-cover types and in different seasons and explored the applicability of NDVI and SIF in different cases by comparing their performances in gross primary production (GPP) and grain-yield-monitoring applications. The vegetation cover and photosynthesis showed decreasing trends, which were mainly concentrated in northern Xinjiang and part of the Qinghai-Tibet Plateau; a decreasing trend was also identified in a small part of Northeast China. The correlations between NDVI and SIF were strong for all land-cover types except evergreen needleleaf forests and evergreen broadleaf forests. Compared with NDVI, SIF had some advantages when monitoring the GPP and grain yields among different land-cover types. For example, SIF could capture the effects of drought on GPP and grain yields better than NDVI. To summarize, as the temporal extent of the available SIF data is extended, SIF will certainly perform increasingly wide applications in agricultural-management research that is closely related to GPP and grain-yield monitoring.

Normalized Difference Vegetation Index solar-induced chlorophyll fluorescence gross primary production grain yield China

SCI ; EI

英语

第一 ; 通讯

National Natural Science Foundation of China[51909117] ; Natural Science Foundation of Shenzhen[JCYJ20210324105014039] ; Guangdong Provincial Key Laboratory of Soil and Ground Water Pollution Control[2017B030301012]

Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology

Environmental Sciences ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology

WOS:000823918000001

MDPI

20222812357084

Chlorophyll ; Fluorescence ; Forestry ; Grain (agricultural product) ; Monitoring ; Photosynthesis ; Remote sensing

Light/Optics:741.1 ; Chemical Reactions:802.2 ; Organic Compounds:804.1 ; Agricultural Equipment and Methods; Vegetation and Pest Control:821 ; Agricultural Products:821.4

2-s2.0-85133890629

Web of Science

1.State Environmental Protection Key Laboratory of Integrated Surface Water-Groundwater Pollution Control,School of Environmental Science and Engineering,Southern University of Science and Technology,Shenzhen,518055,China
2.Guangdong Provincial Key Laboratory of Soil and Groundwater Pollution Control,School of Environmental Science and Engineering,Southern University of Science and Technology,Shenzhen,518055,China
3.Yellow River Engineering Consulting Co. Ltd.,Zhengzhou,450003,China
4.Center for Climate Physics,Institute for Basic Science,Busan,46241,South Korea
5.School of Water Conservancy and Civil Engineering,Northeast Agricultural University,Harbin,150006,China

Zhou，Zhaoqiang,Ding，Yibo,Liu，Suning,et al. Estimating the Applicability of NDVI and SIF to Gross Primary Productivity and Grain-Yield Monitoring in China[J]. REMOTE SENSING,2022,14(13).

Zhou，Zhaoqiang,Ding，Yibo,Liu，Suning,Wang，Yao,Fu，Qiang,&Shi，Haiyun.(2022).Estimating the Applicability of NDVI and SIF to Gross Primary Productivity and Grain-Yield Monitoring in China.REMOTE SENSING,14(13).

Zhou，Zhaoqiang,et al."Estimating the Applicability of NDVI and SIF to Gross Primary Productivity and Grain-Yield Monitoring in China".REMOTE SENSING 14.13(2022).