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Zhang L.,Chinese Academy of Sciences | Zhang L.,Beijing Normal University | Zhang L.,Beijing Key Laboratory for Remote Sensing of Environment and Digital Cities | Sun R.,Chinese Academy of Sciences | And 9 more authors.
Chinese Journal of Ecology | Year: 2014

CO2 flux was measured continuously in a maize agroecosystem in Zhangye irrigation area during the growing season (June to September) using the eddy covariance technique to study the variation of CO2 flux and its response to key environmental factors. The results showed that there was a distinct diurnal variation of CO2 flux' with CO2 absorption in the daytime and emission at night. The maximum CO2 absorption occurred at filling stage and with a maximum value of -1. 426 mg • m-2 • s-1. Maize agroecosystem is characterized by carbon absorption during the growing season' and the ability to absorb CO2 is significantly different at different growth stages' which was ordered as filling stage > jointing stage > maturity stage > seedling stage. We also used the Michaelis-Menten equation and exponential curve fitting method to analyze the impact of photosynthetically active radiation (PAR) on daytime CO2 flux, and the impact of temperature on the ecosystem respiration at night. The results showed that CO2 absorbing intensity increased with PAR. PAR was the dominant factor to control photosynthesis under low solar radiation' and the carbon assimilation rate increased slowly under high solar radiation. The light quantum efficiency of maize ranged between 0. 00098 and 0. 0022 mg • μmol-1 during the observing period. The nighttime ecosystem respiration exponentially increased with temperature' and the dominant factor varied with growth stage. Soil temperature was the dominant factor of nighttime ecosystem respiration at the seedling stage, while air temperature was the dominant factor at the other growth stages. © 2014, Editorial Board of Chinese Journal of Ecology. All rights reserved. Source


Liu X.,Beijing Normal University | Liu X.,State Key Laboratory of Remote Sensing Science | Liu X.,Beijing Key Laboratory for Remote Sensing of Environment and Digital Cities | Sun R.,Beijing Normal University | And 7 more authors.
Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering | Year: 2010

The significance to agriculture, forestry production and environment monitoring is obvious that extraction of land cover information based on remote sensing data. So in this study, focusing on Henan Province, making use of MODIS 16-days composite EVI data at 2005, combining with crop phenology and other reference land cover data, the land-cover classification for Henan Province was performed. The raw EVI data was processed with cloud removing and smoothing, then the support vector machine (SVM) method was adopted for the classification. Refer to the classification result, compared with the statistics of crops acreage of Henan Province in 2005, the area accuracy of classification result was as following: for large-area planted crops, wheat got 81.47%, corn 94.87%, rice 82.43%; while for the economic crops, rape was 39.81%, soybean 93.65%, cotton 95.21%, peanut 74.27%. On the other hand, combining the classified land cover type into 5 types, farmland, woodland, grassland, water body, urban and built-up. The results were further compared with 1:100000 land cover map which was produced by using the Landsat ETM+ and TM data in 2000. The overall accuracy and Kappa coefficient were 78.07% and 0.66, respectively. It turns out that the feasibility of MODIS time-series VI data and classification strategies adopted to extract crops information. Source


Liu X.,Beijing Normal University | Liu X.,Beijing Key Laboratory for Remote Sensing of Environment and Digital Cities | Liu X.,Yunnan Normal University | Bo Y.,Beijing Normal University | And 6 more authors.
Remote Sensing | Year: 2015

The distribution of C3 and C4 vegetation plays an important role in the global carbon cycle and climate change. Knowledge of the distribution of C3 and C4 vegetation at a high spatial resolution over local or regional scales helps us to understand their ecological functions and climate dependencies. In this study, we classified C3 and C4 vegetation at a high resolution for spatially heterogeneous landscapes. First, we generated a high spatial and temporal land surface reflectance dataset by blending MODIS (Moderate Resolution Imaging Spectroradiometer) and ETM+ (Enhanced Thematic Mapper Plus) data. The blended data exhibited a high correlation (R2 = 0.88) with the satellite derived ETM+ data. The time-series NDVI (Normalized Difference Vegetation Index) data were then generated using the blended high spatio-temporal resolution data to capture the phenological differences between the C3 and C4 vegetation. The time-series NDVI revealed that the C3 vegetation turns green earlier in spring than the C4 vegetation, and senesces later in autumn than the C4 vegetation. C4 vegetation has a higher NDVI value than the C3 vegetation during summer time. Based on the distinguished characteristics, the time-series NDVI was used to extract the C3 and C4 classification features. Five features were selected from the 18 classification features according to the ground investigation data, and subsequently used for the C3 and C4 classification. The overall accuracy of the C3 and C4 vegetation classification was 85.75% with a kappa of 0.725 in our study area. © 2015 by the authors. Source


Cui T.,Beijing Normal University | Cui T.,Beijing Key Laboratory for Remote Sensing of Environment and Digital Cities | Wang Y.,Northwest Regional Climate Center | Wang Y.,Nanjing University | And 11 more authors.
PLoS ONE | Year: 2016

Estimating gross primary production (GPP) and net primary production (NPP) are significant important in studying carbon cycles. Using models driven by multi-source and multi-scale data is a promising approach to estimate GPP and NPP at regional and global scales. With a focus on data that are openly accessible, this paper presents a GPP and NPP model driven by remotely sensed data and meteorological data with spatial resolutions varying from 30 m to 0.25 degree and temporal resolutions ranging from 3 hours to 1 month, by integrating remote sensing techniques and eco-physiological process theories. Our model is also designed as part of the Multi-source data Synergized Quantitative (MuSyQ) Remote Sensing Production System. In the presented MuSyQ-NPP algorithm, daily GPP for a 10-day period was calculated as a product of incident photosynthetically active radiation (PAR) and its fraction absorbed by vegetation (FPAR) using a light use efficiency (LUE) model. The autotrophic respiration (Ra) was determined using eco-physiological process theories and the daily NPP was obtained as the balance between GPP and Ra. To test its feasibility at regional scales, our model was performed in an arid and semi-arid region of Heihe River Basin, China to generate daily GPP and NPP during the growing season of 2012. The results indicated that both GPP and NPP exhibit clear spatial and temporal patterns in their distribution over Heihe River Basin during the growing season due to the temperature, water and solar influx conditions. After validated against ground-based measurements, MODIS GPP product (MOD17A2H) and results reported in recent literature, we found the MuSyQ-NPP algorithm could yield an RMSE of 2.973 gC m-2 d-1 and an R of 0.842 when compared with ground-based GPP while an RMSE of 8.010 gC m-2 d-1 and an R of 0.682 can be achieved for MODIS GPP, the estimated NPP values were also well within the range of previous literature, which proved the reliability of our modelling results. This research suggested that the utilization of multi-source data with various scales would help to the establishment of an appropriate model for calculating GPP and NPP at regional scales with relatively high spatial and temporal resolution. © 2016 Cui et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Source


Zhang T.-L.,Beijing Normal University | Zhang T.-L.,Beijing Key Laboratory for Remote Sensing of Environment and Digital Cities | Sun R.,Beijing Normal University | Sun R.,Beijing Key Laboratory for Remote Sensing of Environment and Digital Cities | And 3 more authors.
Chinese Journal of Ecology | Year: 2011

Ecological process model based on defined mechanism can well simulate the dynamic behaviors and features of terrestrial ecosystem, but could become a bottleneck in application because of numerous parameters needed to be confirmed. In this paper, simulated annealing algorithm was used to optimize the physiological and ecological parameters of Biome-BGC model. The first step was to choose some of these parameters to optimize, and then, gradually optimized these parameters. By using the optimized parameters, the model simulation results were much more close to the observed data, and the parameter optimization could effectively reduce the uncertainty of model simulation. The parameter optimization method used in this paper could provide a case and an idea for the parameter identification and optimization of ecological process models, and also, help to expand the application area of the models. Source

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