Estimating the Applicability of NDVI and SIF to Gross Primary Productivity and Grain-Yield Monitoring in China Article Swipe
Zhaoqiang Zhou
,
Yibo Ding
,
Suning Liu
,
Yao Wang
,
Qiang Fu
,
Haiyun Shi
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3390/rs14133237
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3390/rs14133237
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.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/rs14133237
- https://www.mdpi.com/2072-4292/14/13/3237/pdf?version=1657080720
- OA Status
- gold
- Cited By
- 15
- References
- 59
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4286634238
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4286634238Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/rs14133237Digital Object Identifier
- Title
-
Estimating the Applicability of NDVI and SIF to Gross Primary Productivity and Grain-Yield Monitoring in ChinaWork title
- Type
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articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2022Year of publication
- Publication date
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2022-07-05Full publication date if available
- Authors
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Zhaoqiang Zhou, Yibo Ding, Suning Liu, Yao Wang, Qiang Fu, Haiyun ShiList of authors in order
- Landing page
-
https://doi.org/10.3390/rs14133237Publisher landing page
- PDF URL
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https://www.mdpi.com/2072-4292/14/13/3237/pdf?version=1657080720Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
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https://www.mdpi.com/2072-4292/14/13/3237/pdf?version=1657080720Direct OA link when available
- Concepts
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Normalized Difference Vegetation Index, Environmental science, Evergreen, Vegetation (pathology), Land cover, Remote sensing, Primary production, Evergreen forest, Physical geography, Atmospheric sciences, Leaf area index, Land use, Geography, Agronomy, Ecosystem, Geology, Ecology, Biology, Medicine, PathologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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15Total citation count in OpenAlex
- Citations by year (recent)
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2025: 6, 2024: 5, 2023: 4Per-year citation counts (last 5 years)
- References (count)
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59Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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