Journal Article10.1029/93JD03221
Methodology for the estimation of terrestrial net primary production from remotely sensed data
817
TL;DR: In this paper, the authors used the remote sensing of crop growth to estimate continental net primary productivity (NPP) as well as its seasonal and spatial variations, assuming a decomposition of NPP into independent parameters such as incident solar radiation, radiation absorption efficiency by canopies, and conversion efficiency of absorbed radiation into organic dry matter.
read more
Abstract: Kumar and Monteith's (1981) model for the remote sensing of crop growth has been used to estimate continental net primary productivity (NPP) as well as its seasonal and spatial variations. The model assumes a decomposition of NPP into independent parameters such as incident solar radiation (S0), radiation absorption efficiency by canopies (ƒ), and conversion efficiency of absorbed radiation into organic dry matter (e). The precision on some of the input parameters has been improved, compared to previous uses of this model at a global scale: remote sensing data used to derive ƒ have been calibrated, corrected of some atmospheric effects, and filtered; e has been considered as biome-dependent and derived from literature data. The resulting global NPP (approximatively 60 GtC per year) is within the range of values given in the literature. However, mean NPP estimates per biome do not agree with the literature (in particular, the estimation for tropical rain forests NPP is much lower and for cultivations much higher than field estimates), which results in zonal and seasonal variations of continental NPP giving more weight to the temperate northern hemisphere than to the equatorial zone.
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
The Frankfurt Biosphere Model (FBM): Regional Validation Using German Forest Yield Tables and Inventory Data and Extrapolation to a 2×CO2 Climate
G. Würth,Christof Häger,G. H. Kohlmaier +2 more
- 01 Jan 1998
TL;DR: The Frankfurt Biosphere Model (FBM) as discussed by the authors was developed to simulate global carbon exchange fluxes between terrestrial vegetation and the atmosphere with a spatial resolution of 0.5° × 0. 5°.
4
How to Simulate Carbon Sequestration Potential of Forest Vegetation? A Forest Carbon Sequestration Model across a Typical Mountain City in China
Dongjie Guan,Jialong Nie,Lilei Zhou,Qiongyao Chang,Jiameng Cao +4 more
TL;DR: The carbon sequestration potential of forest vegetation in Chongqing, China, was estimated using a forest carbon sequestration model. The average NPP of the forest vegetation in Chongqing from 2000 to 2020 was 797.95 g C/m2, and the carbon storage by 2060 was 269.94 Tg C.
4
Una herramienta para monitorear sequías en regiones áridas y semiáridas de Patagonia Norte
Marcos Horacio Easdale,Dardo Ruben Lopez,Emilio Bianchi,Emilio Bianchi,O. Bruzone,O. Bruzone,S. E. Villagra,Guillermo Lorenzo Siffredi,J. J. Gaitán,Fernando Umaña,Patricio Oricchio +10 more
- 01 Aug 2012
TL;DR: La variabilidad ambiental es una caracteristica de regiones pastoriles aridas and semiaridas, siendo la sequia uno de los principales problemas en sistemas as discussed by the authors.
4
Study on the spatial and temporal variations of maximum light use efficiency and possible driving factors of croplands in Jiangsu Province
TL;DR: This study proves that it is of importance to develop a parameterization scheme accounting for the temporal and spatial variations of e max for improving the calculation of productivity in croplands using light use efficiency models and remote sensing data.
4
Analysis of Spatiotemporal Evolution and Influencing Factors of Vegetation Net Primary Productivity in the Yellow River Basin from 2000 to 2022
Kunjun Tian,Xing Liu,Bingbing Zhang,Zhengtao Wang,Gong Xu,Kai Chang,Pengfei Xu,Baomin Han +7 more
TL;DR: The vegetation NPP in the YRB showed an increasing trend from 2000 to 2022, with the most significant changes occurring in the middle reaches of the YRB. The dominant factors influencing vegetation NPP are soil type, precipitation, and temperature. TWS has a significant negative impact on vegetation NPP, with a strong correlation of 39%.
4
References
Climate and the efficiency of crop production in Britain
TL;DR: The efficiency of crop production is defined in thermodynamic terms as the ratio of energy output (carbohydrate) to energy input (solar radiation). Temperature and water supply are the main climatic constraints on efficiency as mentioned in this paper.
3.7K
Characteristics of maximum-value composite images from temporal AVHRR data
TL;DR: In this paper, satellite data from the Advanced Very High Resolution Radiometer sensor have been processed over several days and combined to produce spatially continuous cloud-free imagery over large areas with sufficient temporal resolution to study green-vegetation dynamics.
3K
Solar radiation and productivity in tropical ecosystems
TL;DR: Conventional estimates of efficiency in terms of the amount of solar radiation incident at the earth's surface provide ecologists and agronomists with a method for comparing plant productivity under different systems of land use and management and in different * Opening paper read at IBP/UNESCO Meeting on Productivity of Tropical Ecosystems.
2.6K
Canopy reflectance, photosynthesis and transpiration
TL;DR: In this paper, a two-stream approximation model of radiative transfer was used to calculate values of hemispheric canopy reflectance in the visible and near-infrared wavelength intervals.
2.4K