Estimation and analysis of net primary productivity by integrating MODIS remote sensing data with a light use efficiency model

2013 ◽  
Vol 252 ◽  
pp. 3-10 ◽  
Author(s):  
Jia Li ◽  
Yaoping Cui ◽  
Jiyuan Liu ◽  
Wenjiao Shi ◽  
Yaochen Qin
Keyword(s):  
Remote Sensing ◽  
Primary Productivity ◽  
Net Primary Productivity ◽  
Remote Sensing Data ◽  
Light Use Efficiency ◽  
Sensing Data ◽  
Use Efficiency ◽  
Light Use Efficiency Model

scholarly journals LINKING PHOTOSYNTHETIC LIGHT USE EFFICIENCY AND OPTICAL VEGETATION ACTIVE INDICATORS: IMPLICATIONS FOR GROSS PRIMARY PRODUCTION ESTIMATION BY REMOTE SENSING

2020 ◽  
Vol V-3-2020 ◽  
pp. 571-578
Author(s):  
S. Wang ◽  
Z. Li ◽  
Y. Zhang ◽  
D. Yang ◽  
C. Ni
Keyword(s):  
Remote Sensing ◽  
Primary Productivity ◽  
Seasonal Changes ◽  
Gross Primary Production ◽  
Vegetation Indices ◽  
Canopy Structure ◽  
Remote Sensing Data ◽  
Growing Season ◽  
Light Use Efficiency ◽  
Use Efficiency

Abstract. Over the last 40 years, the light use efficiency (LUE) model has become a popular approach for gross primary productivity (GPP) estimation in the carbon and remote sensing communities. Despite the fact that the LUE model provides a simple but effective way to approximate GPP at ecosystem to global scales from remote sensing data, when implemented in real GPP modelling, however, the practical form of the model can vary. By reviewing different forms of LUE model and their performances at ecosystem to global scales, we conclude that the relationships between LUE and optical vegetation active indicators (OVAIs, including vegetation indices and sun-induced chlorophyll fluorescence-based products) across time and space are key for understanding and applying the LUE model. In this work, the relationships between LUE and OVAIs are investigated at flux-tower scale, using both remotely sensed and simulated datasets. We find that i) LUE-OVAI relationships during the season are highly site-dependent, which is complexed by seasonal changes of leaf pigment concentration, canopy structure, radiation and Vcmax; ii) LUE tends to converge during peak growing season, which enables applying pure OVAI-based LUE models without specifically parameterizing LUE and iii) Chlorophyll-sensitive OVAIs, especially machine-learning-based SIF-like signal, exhibits a potential to represent spatial variability of LUE during the peak growing season.We also show the power of time-series model simulations to improve the understanding of LUE-OVAI relationships at seasonal scale.

Spatio-temporal variation of net primary productivity in a rapidly expanding artificial woodland area based on remote-sensing data

Erdkunde ◽  
2021 ◽  
Vol 75 (3) ◽  
pp. 191-207
Author(s):  
Qi Yi ◽  
Yuting Gao ◽  
Hongrong Du ◽  
Junxu Chen ◽  
Liang Emlyn Yang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Primary Productivity ◽  
Net Primary Productivity ◽  
Meteorological Data ◽  
Arable Land ◽  
Remote Sensing Data ◽  
Stand Age ◽  
Sensing Data ◽  
Spatio Temporal

The expansion of artificial woodlands in China has contributed significantly to regional land-cover changes and changes in the regional net primary productivity (NPP). This study used Ximeng County in the Yunnan Province as a case study to investigate the overall changes, associated amplitude, and spatio-temporal distribution of NPP from 2000–2015.The Carnegie-Ames-Stanford approach was used in the rapidly expanding artificial woodland area based on MODIS-NDVI data, meteorological data, and Landsat 5 TM data to calculate the NPP. The results show that (1) artificial woodlands experience a 10fold increase and account for 93 % of the land cover transfer, which was mainly from woodland areas. (2) The NPP was 906.2×109 gC·yr-1 in 2000 and 972.0×109 gC·yr-1 in 2015, presenting a total increase of 65.8×109 gC·yr-1 and a mean increase of 52.4 gC·m-2·yr-1 in Ximeng County. (3) The most notable NPP changes take place in the central and the western border regions, with the increasing NPP of artificial woodlands and arable land offsetting the negative effects of the decrease in woodland NPP. (4) The total NPP in the study area kept increasing, primarily due to the growing area of artificial woodlands as well as the stand age of the woods, whereas the mean value change of the NPP is mostly related to the increasing stand age. (5) The artificial woodlands increase the NPP value more than natural woodlands. While protecting and promoting ecologically valuable natural forests at the same time, it seems quite advantageous to establish regional plantations and coordinate their development on a scientific basis with a view to increasing NPP, economic development, but also the ecological stability of this mountain region. Our study reveals the changes in NPP and its distribution in a rapidly expanding area of artificial woodland in southwest China based on remote-sensing data and the CASA model, providing a decision-making basis for rational land-use management, the optimal utilization of land resources, and a county-scale assessment approach.

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