Net primary productivity of China's terrestrial ecosystems from a process model driven by remote sensing

Feng, Xiaohui; Liu, G.; Chen, J.M.; Chen, M.; Liu, Jing; Ju, Weimin; Sun, Rui; Zhou, Wen

doi:10.1016/j.jenvman.2006.09.021

Cited by 200 publications

(115 citation statements)

References 26 publications

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“…Many studies considered the uniform systematic error in input R se to study their impacts on terrestrial ecosystem carbon simulation (Feng et al, 2007;Yan et al, 2011). In reality, the error of R se varies greatly during individual months (even days) depending on the study area and cloud cover conditions.…”

Section: Discussionmentioning

confidence: 99%

Impact of estimated solar radiation on gross primary productivity simulation in subtropical plantation in southeast China

et al. 2015

Solar Energy

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Sunshine duration is widely used to estimate solar radiation, but this estimated inherently contains some uncertainties, limiting its applications. This study investigated the impacts of the estimated solar radiation on simulated gross primary productivity (GPP), which were obtained using ecosystem models -light use efficiency model (LUE) and process-based model -Boreal Ecosystem Productivity Simulator (BEPS) at an evergreen coniferous forest ecosystem in southeast China. The models for solar radiation and diffuse radiation estimation were calibrated through observation data from nearby meteorological stations. The results showed that the established model could be successfully used to estimate solar radiation with high coefficient of determination (0.92) and low root mean square error (2.18 MJ m À2 day À1 ), but the solar radiation was overestimated when the clearness index was less than 0.15 and underestimated when it was within the range of 0.2-0.35 or greater than 0.6. The estimated solar radiation has significant influence on the diffuse radiation estimation and GPP simulation comparing with using observations. The two ecosystem models reacted differently to the errors of estimated solar radiation. For the LUE model, the estimated solar radiation led to the underestimated GPP in growing season (May-October), and overestimated GPP during non-growing season (November-April) with the bias ranged from À11% to 10% depending on the month of a year. For the BEPS model, estimated solar radiation resulted in overestimated GPP in most months with the bias ranged from À6% to 20%. The difference between the simulated GPP based on these two sources of solar radiation could be counteracted to some extent at the annual scale, especially for LUE model.

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Section: Discussionmentioning

confidence: 99%

Impact of estimated solar radiation on gross primary productivity simulation in subtropical plantation in southeast China

et al. 2015

Solar Energy

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“…Climate covariates include annual average precipitation and annual average temperature with 1 km resolution, which were provided by Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research (IGSNRR), CAS. These climate data were produced by interpolating observation point data at 740 weather stations during the period of 1971-2000 in China (Feng et al, 2007;Yu et al, 2004) Geology Type Map was used to describe the parent material, the scale of which is 1:500,000 and they were provided by Institute of Soil Science, CAS. We understand that the spatial resolution (or scale) of the geology data layer is very coarse but it is the only geological data available for information on parent materials over this large area.…”

Section: Selection Of Environmental Covariatesmentioning

confidence: 99%

An heuristic uncertainty directed field sampling design for digital soil mapping

et al. 2016

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Legacy samples are a valuable data source for digital soil mapping. However, these sample sets are often small in size and ad hoc in spatial distribution. Constrained by the limited representativeness of such a sample set, the obtained soil maps are often incomplete in spatial coverage with "gaps" at the locations which cannot be well represented by these samples. The maps may also contain areas of high prediction uncertainty. In order to extend the predicted area and reduce prediction uncertainty, additional samples are needed. This paper presents a sampling design based on prediction uncertainty to select samples which will effectively complement the sparse and ad hoc samples, and maximize the spatial coverage of prediction and minimize prediction uncertainty. A case study in China shows that this sampling scheme was effective in achieving these goals. Compared with stratified random sampling scheme, when the number of additional samples is the same, the produced map using uncertainty directed samples has larger predicted area, and the accuracy of the produced map is higher than that of the maps using stratified random samples. The finding of this study suggests that prediction uncertainty is a useful indicator to aid field sample selection and to complement the legacy data. Furthermore, the mapping accuracy produced using this method can be quantitatively related to the number of additional samples needed which opens a new horizon for digital soil mapping.

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“…Methods have been developed to extract urban expansions from those interpreted land cover and land use maps in the 1990s, to understand the urban land use change processes, to summarize typical spatialtemporal urbanization patterns and to derive the differences in driving mechanisms of urban expansion [33]. Greenness indices derived from remotely sensed data were used in combination with ecosystem modeling and atmospheric inversion to derive the net primary productivity and carbon balance for China in 1980-1990s [34,35]. Regions of land degradation and improvement in 1981-2003 were identified through an analysis of greenness and NPP [36].…”

Section: Progress In Remote Sensing Of Environmental Change Over Chinamentioning

confidence: 99%

Remote sensing of environmental change over China: A review

Gong

2012

Chin. Sci. Bull.

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Net primary productivity of China's terrestrial ecosystems from a process model driven by remote sensing

Cited by 200 publications

References 26 publications

Impact of estimated solar radiation on gross primary productivity simulation in subtropical plantation in southeast China

Impact of estimated solar radiation on gross primary productivity simulation in subtropical plantation in southeast China

An heuristic uncertainty directed field sampling design for digital soil mapping

Remote sensing of environmental change over China: A review

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