Estimating the Applicability of NDVI and SIF to Gross Primary Productivity and Grain-Yield Monitoring in China
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 ve...
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| Format: | Article |
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MDPI AG
2022-07-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/14/13/3237 |
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| author | Zhaoqiang Zhou Yibo Ding Suning Liu Yao Wang Qiang Fu Haiyun Shi |
| author_facet | Zhaoqiang Zhou Yibo Ding Suning Liu Yao Wang Qiang Fu Haiyun Shi |
| author_sort | Zhaoqiang Zhou |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T10:25:49Z |
| format | Article |
| id | doaj.art-fa3b30425ae446a0be0a902d9d97c819 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T10:25:49Z |
| publishDate | 2022-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-fa3b30425ae446a0be0a902d9d97c8192023-12-01T21:41:10ZengMDPI AGRemote Sensing2072-42922022-07-011413323710.3390/rs14133237Estimating the Applicability of NDVI and SIF to Gross Primary Productivity and Grain-Yield Monitoring in ChinaZhaoqiang Zhou0Yibo Ding1Suning Liu2Yao Wang3Qiang Fu4Haiyun Shi5State 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, ChinaYellow River Engineering Consulting Co. Ltd., Zhengzhou 450003, ChinaCenter for Climate Physics, Institute for Basic Science, Busan 46241, KoreaState 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, ChinaSchool of Water Conservancy and Civil Engineering, Northeast Agricultural University, Harbin 150006, ChinaState 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, ChinaVegetation, 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.https://www.mdpi.com/2072-4292/14/13/3237Normalized Difference Vegetation Indexsolar-induced chlorophyll fluorescencegross primary productiongrain yieldChina |
| spellingShingle | Zhaoqiang Zhou Yibo Ding Suning Liu Yao Wang Qiang Fu Haiyun Shi Estimating the Applicability of NDVI and SIF to Gross Primary Productivity and Grain-Yield Monitoring in China Remote Sensing Normalized Difference Vegetation Index solar-induced chlorophyll fluorescence gross primary production grain yield China |
| title | Estimating the Applicability of NDVI and SIF to Gross Primary Productivity and Grain-Yield Monitoring in China |
| title_full | Estimating the Applicability of NDVI and SIF to Gross Primary Productivity and Grain-Yield Monitoring in China |
| title_fullStr | Estimating the Applicability of NDVI and SIF to Gross Primary Productivity and Grain-Yield Monitoring in China |
| title_full_unstemmed | Estimating the Applicability of NDVI and SIF to Gross Primary Productivity and Grain-Yield Monitoring in China |
| title_short | Estimating the Applicability of NDVI and SIF to Gross Primary Productivity and Grain-Yield Monitoring in China |
| title_sort | estimating the applicability of ndvi and sif to gross primary productivity and grain yield monitoring in china |
| topic | Normalized Difference Vegetation Index solar-induced chlorophyll fluorescence gross primary production grain yield China |
| url | https://www.mdpi.com/2072-4292/14/13/3237 |
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