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Daily actual evapotranspiration (ET) and its components – soil evaporation (E) and vegetation transpiration (T) – play a key role in water resource management of irrigated areas. Nevertheless, due to large uncertainties in the parameterization of the resistances in irrigated areas, traditional ET models do not always provide accurate ET estimates, especially for its components. This uncertainty is mainly due to the difficulty of determining the empirical parameters accurately of soil and canopy resistances which is debated for a long time and the error in input variables. This paper proposed an optimized Shuttleworth-Wallace model (SW) using the particle swarm optimization (PSO) algorithm to integrate the original SW with the surface temperature-vegetation index (Ts-VI) triangle models (named Shuttleworth & Wallace_Temperature Vegetation Index model, SW_TVI).The performance of the SW_TVI model was significantly improved by optimizing the soil and canopy resistances in the original SW model using discontinuous regional ET estimates from the Ts-VI model under clear-sky conditions. Compared with the original SW model, the root mean squared error (RMSE) and mean absolute deviation (MAD) of the SW_TVI model were reduced by more than 30%, against in situ measurements in the Heihe River Basin. The Bias of the T/ET ratio was also significantly reduced from -22.3% for the original SW model to -5.5% for the SW_TVI model. The annual contributions of E and T to ET were about 20% and 80%, respectively, and had a strong seasonal variation in the typical irrigated area of China. In summary, the SW_TVI model shows three outstanding advantages: (1) it estimates daily continuous E, T, and total ET with high accuracy; (2) it is very robust and insensitive or slightly sensitive to most input variables and empirical parameters; (3) it is independent from ground data. This new SW_TVI model will benefit water resources management in irrigated areas, particularly in arid and semi-arid regions.
Yaokui Cui; Li Jia; Wenjie Fan. Estimation of actual evapotranspiration and its components in an irrigated area by integrating the Shuttleworth-Wallace and surface temperature-vegetation index schemes using the particle swarm optimization algorithm. Agricultural and Forest Meteorology 2021, 307, 108488 .
AMA StyleYaokui Cui, Li Jia, Wenjie Fan. Estimation of actual evapotranspiration and its components in an irrigated area by integrating the Shuttleworth-Wallace and surface temperature-vegetation index schemes using the particle swarm optimization algorithm. Agricultural and Forest Meteorology. 2021; 307 ():108488.
Chicago/Turabian StyleYaokui Cui; Li Jia; Wenjie Fan. 2021. "Estimation of actual evapotranspiration and its components in an irrigated area by integrating the Shuttleworth-Wallace and surface temperature-vegetation index schemes using the particle swarm optimization algorithm." Agricultural and Forest Meteorology 307, no. : 108488.
Gross primary productivity (GPP) represents total vegetation productivity and is crucial in regional or global carbon balance. The Northeast China (NEC), abundant in vegetation resources, has a relatively large vegetation productivity; however, under obvious climate change (especially warming), whether and how will the vegetation productivity and ecosystem function of this region changed in a long time period needs to be revealed. With the help of GPP products provided by the Global LAnd Surface Satellite (GLASS) program, this paper gives an overview of the regional feedback of vegetation productivity to the changing climate (including temperature, precipitation, and solar radiation) across the NEC from 1982 to 2015. Analyzing results show a slight positive response of vegetation productivities to warming across the NEC with an overall increasing trend of GPPGS (accumulated GPP within the growing season of each year) at 4.95 g C/m2. yr−2 over the last three decades. More specifically, the growth of crops, rather than forests, contributes more to the total increasing productivity, which is mainly induced by the agricultural technological progress as well as warming. As for GPP in forested area in the NEC, the slight increment of GPPGS in northern, high-latitude forested region of the NEC was caused by warming, while non-significant variation of GPPGS was found in southern, low-latitude forested region. In addition, an obvious greening trend, as reported in other regions, was also found in the NEC, but GPPGS of forests in southern NEC did not have significant variations, which indicated that vegetation productivity is not bound to increase simultaneously with greening, except for these high-latitude forested areas in the NEC. The regional feedback of vegetation productivity to climate change in the NEC can be an indicator for vegetations growing in higher latitudes in the future under continued climate change.
Ling Hu; Wenjie Fan; Wenping Yuan; Huazhong Ren; Yaokui Cui. Spatiotemporal Variation of Vegetation Productivity and Its Feedback to Climate Change in Northeast China over the Last 30 Years. Remote Sensing 2021, 13, 951 .
AMA StyleLing Hu, Wenjie Fan, Wenping Yuan, Huazhong Ren, Yaokui Cui. Spatiotemporal Variation of Vegetation Productivity and Its Feedback to Climate Change in Northeast China over the Last 30 Years. Remote Sensing. 2021; 13 (5):951.
Chicago/Turabian StyleLing Hu; Wenjie Fan; Wenping Yuan; Huazhong Ren; Yaokui Cui. 2021. "Spatiotemporal Variation of Vegetation Productivity and Its Feedback to Climate Change in Northeast China over the Last 30 Years." Remote Sensing 13, no. 5: 951.
Huazhong Ren; Jing Nie; Jiaji Dong; Rongyuan Liu; Wenzhe Fa; Ling Hu; Wenjie Fan. Lunar Surface Temperature and Emissivity Retrieval From Diviner Lunar Radiometer Experiment Sensor. Earth and Space Science 2020, 8, 1 .
AMA StyleHuazhong Ren, Jing Nie, Jiaji Dong, Rongyuan Liu, Wenzhe Fa, Ling Hu, Wenjie Fan. Lunar Surface Temperature and Emissivity Retrieval From Diviner Lunar Radiometer Experiment Sensor. Earth and Space Science. 2020; 8 (1):1.
Chicago/Turabian StyleHuazhong Ren; Jing Nie; Jiaji Dong; Rongyuan Liu; Wenzhe Fa; Ling Hu; Wenjie Fan. 2020. "Lunar Surface Temperature and Emissivity Retrieval From Diviner Lunar Radiometer Experiment Sensor." Earth and Space Science 8, no. 1: 1.
Spatio-temporally continuous and high-quality soil moisture (SM) is very important for assessing changes in the water cycle and climate, especially over the Tibetan Plateau (TP). Data fusion is an important method to improve the quality of SM product. However, limited observation overlaps between different satellite SM products, caused by inherent gaps, make it difficult to fuse them to create a continuous and high-quality product. In this study, a SM spatio-temporal continuity and quality simultaneously improving algorithm is proposed. The first step of the approach is obtaining spatio-temporally continuous reference data, including land surface temperature (LST), normalized difference vegetation index (NDVI), Albedo, and digital elevation model (DEM). The second step is training the general regression neural network (GRNN) model with all available Essential Climate Variables (ECV) and Fengyun (FY) SM. The last step is predicting the spatio-temporally continuous and high-quality SM using the trained GRNN derived by the spatio-temporal continuity reference data. An implementation of the algorithm on the TP showed that, compared with the original ECV and FY SM, both the continuity and quality of the fused SM product were largely improved in terms of coverage (72.5%), correlation (R=0.809), root mean square error (0.081 cm3 cm-3) and bias (0.050 cm3 cm-3). The algorithm showed a good performance in obtaining spatio-temporal variation fusion weights over the TP. This spatio-temporally continuous and high-quality SM of the TP will help advance our understanding of global and regional changes in water cycle and climate.
Yaokui Cui; Chao Zeng; Xi Chen; Wenjie Fan; Haijiang Liu; Yuan Liu; Wentao Xiong; Cong Sun; Zengliang Luo. A New Fusion Algorithm for Simultaneously Improving Spatio-Temporal Continuity and Quality of Remotely Sensed Soil Moisture Over the Tibetan Plateau. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2020, 14, 83 -91.
AMA StyleYaokui Cui, Chao Zeng, Xi Chen, Wenjie Fan, Haijiang Liu, Yuan Liu, Wentao Xiong, Cong Sun, Zengliang Luo. A New Fusion Algorithm for Simultaneously Improving Spatio-Temporal Continuity and Quality of Remotely Sensed Soil Moisture Over the Tibetan Plateau. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2020; 14 (99):83-91.
Chicago/Turabian StyleYaokui Cui; Chao Zeng; Xi Chen; Wenjie Fan; Haijiang Liu; Yuan Liu; Wentao Xiong; Cong Sun; Zengliang Luo. 2020. "A New Fusion Algorithm for Simultaneously Improving Spatio-Temporal Continuity and Quality of Remotely Sensed Soil Moisture Over the Tibetan Plateau." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 14, no. 99: 83-91.
Temporally continuous daily actual evapotranspiration (ET) data play a critical role in water resource management in arid areas. As a typical remotely sensed land surface temperature (LST)-based ET model, the surface temperature-vegetation index (Ts-VI) triangle model provides direct monitoring of ET, but these estimates are temporally discontinuous due to cloud contamination. In this work, we present a gap-filling algorithm (TSVI_DNN) using a deep neural network (DNN) with the Ts-VI triangle model to obtain temporally continuous daily actual ET at regional scale. The TSVI_DNN model is evaluated against in situ measurements in an arid area of China during 2009–2011 and shows good agreement with eddy covariance (EC) observations. The temporal coverage was improved from 16.1% with the original Ts-VI tringle model to 67.1% with the TSVI_DNN model. The correlation coefficient (R), root mean square error (RMSE), bias, and mean absolute difference (MAD) are 0.9, 0.86 mm d−1, −0.16 mm d−1, and 0.65 mm d−1, respectively. When compared with the National Aeronautics and Space Administration (NASA) official MOD16 version 6 ET product, estimates of ET using TSVI_DNN are improved by approximately 49.2%. The method presented here can potentially contribute to enhanced water resource management in arid areas, especially under climate change.
Yaokui Cui; Shihao Ma; Zhaoyuan Yao; Xi Chen; Zengliang Luo; Wenjie Fan; Yang Hong. Developing a Gap-Filling Algorithm Using DNN for the Ts-VI Triangle Model to Obtain Temporally Continuous Daily Actual Evapotranspiration in an Arid Area of China. Remote Sensing 2020, 12, 1121 .
AMA StyleYaokui Cui, Shihao Ma, Zhaoyuan Yao, Xi Chen, Zengliang Luo, Wenjie Fan, Yang Hong. Developing a Gap-Filling Algorithm Using DNN for the Ts-VI Triangle Model to Obtain Temporally Continuous Daily Actual Evapotranspiration in an Arid Area of China. Remote Sensing. 2020; 12 (7):1121.
Chicago/Turabian StyleYaokui Cui; Shihao Ma; Zhaoyuan Yao; Xi Chen; Zengliang Luo; Wenjie Fan; Yang Hong. 2020. "Developing a Gap-Filling Algorithm Using DNN for the Ts-VI Triangle Model to Obtain Temporally Continuous Daily Actual Evapotranspiration in an Arid Area of China." Remote Sensing 12, no. 7: 1121.
Surface soil moisture (SM) plays an essential role in the water and energy balance between the land surface and the atmosphere. Low spatio-temporal resolution, about 25–40 km and 2–3 days, of the commonly used global microwave SM products limits their application at regional scales. In this study, we developed an algorithm to improve the SM spatio-temporal resolution using multi-source remote sensing data and a machine-learning model named the General Regression Neural Network (GRNN). First, six high spatial resolution input variables, including Land Surface Temperature (LST), Normalized Difference Vegetation Index (NDVI), albedo, Digital Elevation Model (DEM), Longitude (Lon) and Latitude (Lat), were selected and gap-filled to obtain high spatio-temporal resolution inputs. Then, the GRNN was trained at a low spatio-temporal resolution to obtain the relationship between SM and input variables. Finally, the trained GRNN was driven by the high spatio-temporal resolution input variables to obtain high spatio-temporal resolution SM. We used the Fengyun-3B (FY-3B) SM over the Tibetan Plateau (TP) to test the algorithm. The results show that the algorithm could successfully improve the spatio-temporal resolution of FY-3B SM from 0.25° and 2–3 days to 0.05° and 1-day over the TP. The improved SM is consistent with the original product in terms of both spatial distribution and temporal variation. The high spatio-temporal resolution SM allows a better understanding of the diurnal and seasonal variations of SM at the regional scale, consequently enhancing ecological and hydrological applications, especially under climate change.
Yaokui Cui; Xi Chen; Wentao Xiong; Lian He; Feng Lv; Wenjie Fan; Zengliang Luo; Yang Hong. A Soil Moisture Spatial and Temporal Resolution Improving Algorithm Based on Multi-Source Remote Sensing Data and GRNN Model. Remote Sensing 2020, 12, 455 .
AMA StyleYaokui Cui, Xi Chen, Wentao Xiong, Lian He, Feng Lv, Wenjie Fan, Zengliang Luo, Yang Hong. A Soil Moisture Spatial and Temporal Resolution Improving Algorithm Based on Multi-Source Remote Sensing Data and GRNN Model. Remote Sensing. 2020; 12 (3):455.
Chicago/Turabian StyleYaokui Cui; Xi Chen; Wentao Xiong; Lian He; Feng Lv; Wenjie Fan; Zengliang Luo; Yang Hong. 2020. "A Soil Moisture Spatial and Temporal Resolution Improving Algorithm Based on Multi-Source Remote Sensing Data and GRNN Model." Remote Sensing 12, no. 3: 455.
Yuqi Bai; Jinshan Cao; Erxue Chen; Jun Chen; Jie Cheng; Robert E. Dickinson; Cuicui Dou; Jinyang Du; Wenjie Fan; Hongliang Fang; Yi Fang; Qiaoni Fu; Shuai Gao; Zhan Gao; Ruifang Guo; Tao He; Wenli Huang; Shunping Ji; Kun Jia; Bo Jiang; Lingmei Jiang; Zengyuan Li; Shunlin Liang; Ming Lin; Qiang Liu; Suhong Liu; Yaokai Liu; Yuanbo Liu; Yufu Liu; Qian Ma; Yuna Mao; Xiangcheng Meng; Xihan Mu; Wenjian Ni; Zheng Niu; Jinmei Pan; Yong Pang; Jingjing Peng; Ying Qu; Yonghua Qu; Jiancheng Shi; Jinling Song; Wanjuan Song; Guoqing Sun; Wanxiao Sun; Xin Tao; Xinpeng Tian; Dongdong Wang; Haoyu Wang; Jindi Wang; Kaicun Wang; Wenhui Wang; Zhigang Wang; Jianguang Wen; Guiping Wu; Zhiqiang Xiao; Chuan Xiong; Chunyan Yan; Guangjian Yan; Feng Yang; Wenping Yuan; Xiuxiao Yuan; Quan Zhang; Xiaotong Zhang; Zhiyu Zhang; Peisheng Zhao; Xiang Zhao; Xiaosong Zhao; Yi Zheng; Shugui Zhou; Xiufang Zhu. Contributors of the second edition. Advanced Remote Sensing 2019, 1 .
AMA StyleYuqi Bai, Jinshan Cao, Erxue Chen, Jun Chen, Jie Cheng, Robert E. Dickinson, Cuicui Dou, Jinyang Du, Wenjie Fan, Hongliang Fang, Yi Fang, Qiaoni Fu, Shuai Gao, Zhan Gao, Ruifang Guo, Tao He, Wenli Huang, Shunping Ji, Kun Jia, Bo Jiang, Lingmei Jiang, Zengyuan Li, Shunlin Liang, Ming Lin, Qiang Liu, Suhong Liu, Yaokai Liu, Yuanbo Liu, Yufu Liu, Qian Ma, Yuna Mao, Xiangcheng Meng, Xihan Mu, Wenjian Ni, Zheng Niu, Jinmei Pan, Yong Pang, Jingjing Peng, Ying Qu, Yonghua Qu, Jiancheng Shi, Jinling Song, Wanjuan Song, Guoqing Sun, Wanxiao Sun, Xin Tao, Xinpeng Tian, Dongdong Wang, Haoyu Wang, Jindi Wang, Kaicun Wang, Wenhui Wang, Zhigang Wang, Jianguang Wen, Guiping Wu, Zhiqiang Xiao, Chuan Xiong, Chunyan Yan, Guangjian Yan, Feng Yang, Wenping Yuan, Xiuxiao Yuan, Quan Zhang, Xiaotong Zhang, Zhiyu Zhang, Peisheng Zhao, Xiang Zhao, Xiaosong Zhao, Yi Zheng, Shugui Zhou, Xiufang Zhu. Contributors of the second edition. Advanced Remote Sensing. 2019; ():1.
Chicago/Turabian StyleYuqi Bai; Jinshan Cao; Erxue Chen; Jun Chen; Jie Cheng; Robert E. Dickinson; Cuicui Dou; Jinyang Du; Wenjie Fan; Hongliang Fang; Yi Fang; Qiaoni Fu; Shuai Gao; Zhan Gao; Ruifang Guo; Tao He; Wenli Huang; Shunping Ji; Kun Jia; Bo Jiang; Lingmei Jiang; Zengyuan Li; Shunlin Liang; Ming Lin; Qiang Liu; Suhong Liu; Yaokai Liu; Yuanbo Liu; Yufu Liu; Qian Ma; Yuna Mao; Xiangcheng Meng; Xihan Mu; Wenjian Ni; Zheng Niu; Jinmei Pan; Yong Pang; Jingjing Peng; Ying Qu; Yonghua Qu; Jiancheng Shi; Jinling Song; Wanjuan Song; Guoqing Sun; Wanxiao Sun; Xin Tao; Xinpeng Tian; Dongdong Wang; Haoyu Wang; Jindi Wang; Kaicun Wang; Wenhui Wang; Zhigang Wang; Jianguang Wen; Guiping Wu; Zhiqiang Xiao; Chuan Xiong; Chunyan Yan; Guangjian Yan; Feng Yang; Wenping Yuan; Xiuxiao Yuan; Quan Zhang; Xiaotong Zhang; Zhiyu Zhang; Peisheng Zhao; Xiang Zhao; Xiaosong Zhao; Yi Zheng; Shugui Zhou; Xiufang Zhu. 2019. "Contributors of the second edition." Advanced Remote Sensing , no. : 1.
The Clumping Index (Ω) was introduced to quantify the spatial distribution pattern of vegetation elements. It is crucial to improve the estimation accuracy of vital vegetation parameters, such as Leaf Area Index (LAI) and Gross Primary Production (GPP). Meanwhile, the parameterization of Ω is challenging partly due to the varying observations of canopy gaps from different view angles. Many previous studies have shown the increase of Ω with view zenith angle through samples of gap size distribution from in situ measurements. In contrast, remote sensing retrieval algorithms only assign a constant value for each biome type to roughly correct the clumping effect as a compromise between the accuracy and efficiency. In this paper, analytical models are proposed that estimate the directional clumping index (Ω(θ)) of crop and forest at canopy level. The angular variation trend and magnitude of crop Ω(θ) was analyzed within row structure where vegetation elements are randomly spaced along rows. The forest model predicts Ω(θ) with tree density, distribution pattern, crown shape, trunk size, and leaf area and angle distribution function. The models take into account the main directional characteristics of clumping index using easy-to-measure parameters. Test cases showed that Ω(θ) magnitude variation for black spruce forest was 102.3% of the hemispherical average clumping index ( Ω ˜ ), whereas the Larch forest had 48.7% variation, and row crop variation reached 32.4%. This study provided tools to assess Ω(θ) of discontinuous canopies.
Jingjing Peng; Wenjie Fan; Lizhao Wang; Xiru Xu; Jvcai Li; Beitong Zhang; Dingfang Tian. Modeling the Directional Clumping Index of Crop and Forest. Remote Sensing 2018, 10, 1576 .
AMA StyleJingjing Peng, Wenjie Fan, Lizhao Wang, Xiru Xu, Jvcai Li, Beitong Zhang, Dingfang Tian. Modeling the Directional Clumping Index of Crop and Forest. Remote Sensing. 2018; 10 (10):1576.
Chicago/Turabian StyleJingjing Peng; Wenjie Fan; Lizhao Wang; Xiru Xu; Jvcai Li; Beitong Zhang; Dingfang Tian. 2018. "Modeling the Directional Clumping Index of Crop and Forest." Remote Sensing 10, no. 10: 1576.
The leaf area index (LAI) is one of the most important parameters of vegetation canopy structure, which can represent the growth conditions of vegetation effectively. The accuracy, availability, and timeliness of LAI data can be improved greatly, which is of great importance to vegetation‐related research. There are various types of vegetation and terrain conditions in the Heihe River Basin, the second largest inland river basin in northwest China. It is not only helpful to evaluate the accuracy of LAI retrieval algorithms for the complex land surface but also useful to understand the fragile ecological status of the Heihe River Basin. In contrast to previous LAI inversion models, the bidirectional reflectance distribution function unified model can be applied for both continuous and discrete vegetation, and it is appropriate for analyzing heterogeneous vegetation distributions. In this work, we produced 30‐m LAI products once a month in the growing season of 2012. Results show that the algorithm can effectively retrieve LAIs. We verified the LAI product using field measurement data. The mean absolute errors in forest, farmland, and sparse grassland are 0.44, 0.56, and 0.38 respectively, and the R2 is 0.8736. Further analysis shows that main errors come from three parts: errors in the parameters, mistakes in the vegetation classification, and interval of the look‐up table. Mixed pixel is also a problem for this model. Despite this, high resolution and applicability means this algorithm can be a good approach for LAI retrieval.
Bo Ma; Jucai Li; Wenjie Fan; Huazhong Ren; Xiru Xu; Yaokui Cui; Jingjing Peng. Application of an LAI Inversion Algorithm Based on the Unified Model of Canopy Bidirectional Reflectance Distribution Function to the Heihe River Basin. Journal of Geophysical Research: Atmospheres 2018, 123, 10,671 -10,687.
AMA StyleBo Ma, Jucai Li, Wenjie Fan, Huazhong Ren, Xiru Xu, Yaokui Cui, Jingjing Peng. Application of an LAI Inversion Algorithm Based on the Unified Model of Canopy Bidirectional Reflectance Distribution Function to the Heihe River Basin. Journal of Geophysical Research: Atmospheres. 2018; 123 (18):10,671-10,687.
Chicago/Turabian StyleBo Ma; Jucai Li; Wenjie Fan; Huazhong Ren; Xiru Xu; Yaokui Cui; Jingjing Peng. 2018. "Application of an LAI Inversion Algorithm Based on the Unified Model of Canopy Bidirectional Reflectance Distribution Function to the Heihe River Basin." Journal of Geophysical Research: Atmospheres 123, no. 18: 10,671-10,687.
Gross primary productivity (GPP) is an important parameter that represents the productivity of vegetation and responses to various ecological environments. The Greater Khingan Mountain (GKM) is one of the most important state-owned forest bases, and boreal forests, including the largest primeval cold-temperature bright coniferous forest in China, are widely distributed in the GKM. This study aimed to reveal spatiotemporal vegetation variations in the GKM on the basis of GPP products that were generated by the Global LAnd Surface Satellite (GLASS) program from 1982 to 2015. First, we explored the spatiotemporal distribution of vegetation across the GKM. Then we analyzed the relationships between GPP variation and driving factors, including meteorological elements, growing season length (GSL), and Fraction of Photosynthetically Active Radiation (FPAR), to investigate the dominant factor for GPP dynamics. Results demonstrated that (1) the spatial distribution of accumulated GPP (AG) in spring, summer, autumn, and the growing season varied due to three main reasons: understory vegetation, altitude, and land cover; (2) interannual AG in summer, autumn, and the growing season significantly increased at the regional scale during the past 34 years under climate warming and drying; (3) interannual changes of accumulated GPP in the growing season (AGG) at the pixel scale displayed a rapid expansion in areas with a significant increasing trend (p < 0.05) during the period of 1982–2015 and this trend was caused by the natural forest protection project launched in 1998; and finally, (4) an analysis of driving factors showed that daily sunshine duration in summer was the most important factor for GPP in the GKM and this is different from previous studies, which reported that the GSL plays a crucial role in other areas.
Ling Hu; Wenjie Fan; Huazhong Ren; Suhong Liu; Yaokui Cui; Peng Zhao. Spatiotemporal Dynamics in Vegetation GPP over the Great Khingan Mountains Using GLASS Products from 1982 to 2015. Remote Sensing 2018, 10, 488 .
AMA StyleLing Hu, Wenjie Fan, Huazhong Ren, Suhong Liu, Yaokui Cui, Peng Zhao. Spatiotemporal Dynamics in Vegetation GPP over the Great Khingan Mountains Using GLASS Products from 1982 to 2015. Remote Sensing. 2018; 10 (3):488.
Chicago/Turabian StyleLing Hu; Wenjie Fan; Huazhong Ren; Suhong Liu; Yaokui Cui; Peng Zhao. 2018. "Spatiotemporal Dynamics in Vegetation GPP over the Great Khingan Mountains Using GLASS Products from 1982 to 2015." Remote Sensing 10, no. 3: 488.
After publication of the research paper [1], it was found that funding information was missing from the Acknowledgment part
Zunjian Bian; Biao Cao; Hua Li; Yongming Du; Lisheng Song; Wenjie Fan; Qing Xiao; Qinhuo Liu. Addendum: Bian, Z. et al. A Robust Inversion Algorithm for Surface Leaf and Soil Temperatures Using the Vegetation Clumping Index. Remote Sens. 2017, 9, 780. Remote Sensing 2017, 9, 1039 .
AMA StyleZunjian Bian, Biao Cao, Hua Li, Yongming Du, Lisheng Song, Wenjie Fan, Qing Xiao, Qinhuo Liu. Addendum: Bian, Z. et al. A Robust Inversion Algorithm for Surface Leaf and Soil Temperatures Using the Vegetation Clumping Index. Remote Sens. 2017, 9, 780. Remote Sensing. 2017; 9 (10):1039.
Chicago/Turabian StyleZunjian Bian; Biao Cao; Hua Li; Yongming Du; Lisheng Song; Wenjie Fan; Qing Xiao; Qinhuo Liu. 2017. "Addendum: Bian, Z. et al. A Robust Inversion Algorithm for Surface Leaf and Soil Temperatures Using the Vegetation Clumping Index. Remote Sens. 2017, 9, 780." Remote Sensing 9, no. 10: 1039.
The inversion of land surface component temperatures is an essential source of information for mapping heat fluxes and the angular normalization of thermal infrared (TIR) observations. Leaf and soil temperatures can be retrieved using multiple-view-angle TIR observations. In a satellite-scale pixel, the clumping effect of vegetation is usually present, but it is not completely considered during the inversion process. Therefore, we introduced a simple inversion procedure that uses gap frequency with a clumping index (GCI) for leaf and soil temperatures over both crop and forest canopies. Simulated datasets corresponding to turbid vegetation, regularly planted crops and randomly distributed forest were generated using a radiosity model and were used to test the proposed inversion algorithm. The results indicated that the GCI algorithm performed well for both crop and forest canopies, with root mean squared errors of less than 1.0 °C against simulated values. The proposed inversion algorithm was also validated using measured datasets over orchard, maize and wheat canopies. Similar results were achieved, demonstrating that using the clumping index can improve inversion results. In all evaluations, we recommend using the GCI algorithm as a foundation for future satellite-based applications due to its straightforward form and robust performance for both crop and forest canopies using the vegetation clumping index.
Zunjian Bian; Biao Cao; Hua Li; Yongming Du; Lisheng Song; Wenjie Fan; Qing Xiao; Qinhuo Liu. A Robust Inversion Algorithm for Surface Leaf and Soil Temperatures Using the Vegetation Clumping Index. Remote Sensing 2017, 9, 780 .
AMA StyleZunjian Bian, Biao Cao, Hua Li, Yongming Du, Lisheng Song, Wenjie Fan, Qing Xiao, Qinhuo Liu. A Robust Inversion Algorithm for Surface Leaf and Soil Temperatures Using the Vegetation Clumping Index. Remote Sensing. 2017; 9 (8):780.
Chicago/Turabian StyleZunjian Bian; Biao Cao; Hua Li; Yongming Du; Lisheng Song; Wenjie Fan; Qing Xiao; Qinhuo Liu. 2017. "A Robust Inversion Algorithm for Surface Leaf and Soil Temperatures Using the Vegetation Clumping Index." Remote Sensing 9, no. 8: 780.
Accurately quantifying the vegetation rainfall interception at a high resolution is critical for rainfall-runoff modeling and flood forecasting, and is also essential for understanding its further impact on local, regional, and even global water cycle dynamics. In this study, the Remote Sensing-based Gash model (RS-Gash model) is developed based on a modified Gash model for interception loss estimation using remote sensing observations at the regional scale, and has been applied and validated in the upper reach of the Heihe River Basin of China for different types of vegetation. To eliminate the scale error and the effect of mixed pixels, the RS-Gash model is applied at a fine scale of 30 m with the high resolution vegetation area index retrieved by using the unified model of bidirectional reflectance distribution function (BRDF-U) for the vegetation canopy. Field validation shows that the RMSE and R2 of the interception ratio are 3.7% and 0.9, respectively, indicating the model’s strong stability and reliability at fine scale. The temporal variation of vegetation rainfall interception and its relationship with precipitation are further investigated. In summary, the RS-Gash model has demonstrated its effectiveness and reliability in estimating vegetation rainfall interception. When compared to the coarse resolution results, the application of this model at 30-m fine resolution is necessary to resolve the scaling issues as shown in this study.
Yaokui Cui; Peng Zhao; Binyan Yan; Hongjie Xie; Pengtao Yu; Wei Wan; Wenjie Fan; Yang Hong. Developing the Remote Sensing-Gash Analytical Model for Estimating Vegetation Rainfall Interception at Very High Resolution: A Case Study in the Heihe River Basin. Remote Sensing 2017, 9, 661 .
AMA StyleYaokui Cui, Peng Zhao, Binyan Yan, Hongjie Xie, Pengtao Yu, Wei Wan, Wenjie Fan, Yang Hong. Developing the Remote Sensing-Gash Analytical Model for Estimating Vegetation Rainfall Interception at Very High Resolution: A Case Study in the Heihe River Basin. Remote Sensing. 2017; 9 (7):661.
Chicago/Turabian StyleYaokui Cui; Peng Zhao; Binyan Yan; Hongjie Xie; Pengtao Yu; Wei Wan; Wenjie Fan; Yang Hong. 2017. "Developing the Remote Sensing-Gash Analytical Model for Estimating Vegetation Rainfall Interception at Very High Resolution: A Case Study in the Heihe River Basin." Remote Sensing 9, no. 7: 661.
An accurate and operational bidirectional reflectance distribution function (BDRF) canopy model is the basis of quantitative vegetation remote sensing. The canopy reflectance should be approximated as the sum of the single scattering reflectance arising from the sun, ρ1, and the multiple scattering reflectance arising from the canopy, ρm, as their directional characteristics are dramatically different. Based on the existing BRDF model, we obtain a new analytical expression of ρ1 and ρm in this paper, which is suitable for different illumination conditions and different vegetation canopies. According to the geometrical optic model at the leaf scale, the anisotropy of ρ1 can be ascribed to the geometry of the object, sun and the sensor, multiple scale clumping, and the fraction of direct solar radiation and diffuse sky radiation. Then, we parameterize the area ratios of four components: the sunlit foliage, sunlit ground, shadow foliage and shadow ground based on a Poisson distribution, and develop a new approximate analytical single scattering reflectance model. Assuming G=0.5, a recollision probability theory based scattering model is developed which considers the effects of diffuse sky radiation, scattering inside the canopy and rebounds between the canopy and soil. Validation using ground measurements of maize and black spruce forest proves the reliability of the model.
Xiru Xu; Wenjie Fan; Jucai Li; Peng Zhao; Gaoxing Chen. A unified model of bidirectional reflectance distribution function for the vegetation canopy. Science China Earth Sciences 2017, 60, 463 -477.
AMA StyleXiru Xu, Wenjie Fan, Jucai Li, Peng Zhao, Gaoxing Chen. A unified model of bidirectional reflectance distribution function for the vegetation canopy. Science China Earth Sciences. 2017; 60 (3):463-477.
Chicago/Turabian StyleXiru Xu; Wenjie Fan; Jucai Li; Peng Zhao; Gaoxing Chen. 2017. "A unified model of bidirectional reflectance distribution function for the vegetation canopy." Science China Earth Sciences 60, no. 3: 463-477.
In contrast to herbaceous canopies and forests, savannas are grassland ecosystems with sparsely distributed individual trees, so the canopy is spatially heterogeneous and open, whereas the woody cover in savannas, e.g., tree cover, adversely affects ecosystem structures and functions. Studies have shown that the dynamics of canopy structure are related to available water, climate, and human activities in the form of porosity, leaf area index (LAI), and clumping index (CI). Therefore, it is important to identify the biophysical parameters of savanna ecosystems, and undertake practical actions for savanna conservation and management. The canopy openness presents a challenge for evaluating canopy LAI and other biophysical parameters, as most remotely sensed methods were developed for homogeneous and closed canopies. Clumping index is a key variable that can represent the clumping effect from spatial distribution patterns of components within a canopy. However, it is a difficult task to measure the clumping index of the moderate resolution savanna pixels directly using optical instruments, such as the Tracing Radiation and Architecture of Canopies, LAI-2000 Canopy Analyzer, or digital hemispherical photography. This paper proposed a new method using hemispherical photographs combined with high resolution remote sensing images to estimate the clumping index of savanna canopies. The effects of single tree LAI, crown density, and herbaceous layer on the clumping index of savanna pixels were also evaluated. The proposed method effectively calculated the clumping index of moderate resolution pixels. The clumping indices of two study regions located in Ejina Banner and Weichang were compared with the clumping index product over China’s landmass.
Jucai Li; Wenjie Fan; Yuan Liu; Gaolong Zhu; Jingjing Peng; Xiru Xu. Estimating Savanna Clumping Index Using Hemispherical Photographs Integrated with High Resolution Remote Sensing Images. Remote Sensing 2017, 9, 52 .
AMA StyleJucai Li, Wenjie Fan, Yuan Liu, Gaolong Zhu, Jingjing Peng, Xiru Xu. Estimating Savanna Clumping Index Using Hemispherical Photographs Integrated with High Resolution Remote Sensing Images. Remote Sensing. 2017; 9 (1):52.
Chicago/Turabian StyleJucai Li; Wenjie Fan; Yuan Liu; Gaolong Zhu; Jingjing Peng; Xiru Xu. 2017. "Estimating Savanna Clumping Index Using Hemispherical Photographs Integrated with High Resolution Remote Sensing Images." Remote Sensing 9, no. 1: 52.
The canopy bidirectional reflectance function (BRDF) characterizes the radiometric interface among vegetation, solar and the sensor, a stable, accurate and operational canopy BRDF model is the basis of retrieving canopy structure and biophysical parameters such as leaf area index and albedo by remote sensing method. The canopy reflectance can be divided into single scattering reflectance and multiple scattering reflectance. After the bidirectional gap fraction was derived by Poisson distribution, a new single scattering reflectance model was developed with Geometrical Optic model at leaf scale. The continuous canopy and discrete vegetation were unified by clumping index. So the area ratio of four components (sunlit crown, sunlit background, shadow crown and sunlit background) is parameterized. In order to simplify the expression of multiple scattering, a recollision probability based scattering model was used instead of the Radiative Transfer model. The validation using ground measurements has proved the reliability of the model.
Xiru Xu; Wenjie Fan; Jvcai Li; Peng Zhao. The unified model of BRDF for the vegetation canopy. 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) 2016, 3644 -3647.
AMA StyleXiru Xu, Wenjie Fan, Jvcai Li, Peng Zhao. The unified model of BRDF for the vegetation canopy. 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). 2016; ():3644-3647.
Chicago/Turabian StyleXiru Xu; Wenjie Fan; Jvcai Li; Peng Zhao. 2016. "The unified model of BRDF for the vegetation canopy." 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) , no. : 3644-3647.
Fraction of vegetation coverage (FVC), an important index to depict the conditions of land covered by vegetation, is increasingly used in monitoring grassland degradation in ecological researches. Hulunbuir Steppe is one of the best grasslands in China. So, in this study, we set Xinbaerhuyouqi, Xinbaerhuzuoqi, Chenbaerhuqi, and Ewenkezuzizhiqi as the study area. Pixel Decomposition Models was introduced to retrieve the vegetation coverage, and the time series of vegetation coverage was reconstructed. Then we analyzed the temporal-spatial changes FVC time series for study region over the 15-year period from 2000 to 2014. The results showed that the higher level vegetation coverage mainly distributed in the east of study area; on the contrary, the lower level of that mainly distributed in the west of study area. The vegetation coverage of whole study area was decreased in the first 10 years, while that was increased slowly in the latter 5 years. Additionally, the break points which occurred in green-up and green-end periods had much more significant correlation with temperature; the break points occurred from July to August correlated with precipitation.
Fei Peng; Wenjie Fan; Xiru Xu; Xing Liu. Analysis on temporal-spatial change of vegetation coverage in Hulunbuir Steppe (2000–2014). 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) 2016, 4514 -4517.
AMA StyleFei Peng, Wenjie Fan, Xiru Xu, Xing Liu. Analysis on temporal-spatial change of vegetation coverage in Hulunbuir Steppe (2000–2014). 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). 2016; ():4514-4517.
Chicago/Turabian StyleFei Peng; Wenjie Fan; Xiru Xu; Xing Liu. 2016. "Analysis on temporal-spatial change of vegetation coverage in Hulunbuir Steppe (2000–2014)." 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) , no. : 4514-4517.
Net Primary Productivity (NPP) is crucial in modelling global carbon cycle. There are a lot of studies focused on NPP evaluation using remote sensing method, resulting in different evaluation models. Most of the models are based on large spatial scale such as national or global, leading to retrieval errors in heterogeneous pixels and difficulties in field validation. This paper develops a new, remote sensing NPP evaluation method to estimate NPP on high spatial resolution. The model uses a newly improved Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) Model which is based on the recollision probability (FAPAR-P Model) to calculate the Absorbed Photosynthetic Active Radiation (APAR), which improves the accuracy of APAR estimation. The study area was the midstream of Heihe River Basin, located mostly in Zhangye, Gansu province, China.
Lu Wang; Wenjie Fan; Xiru Xu. Estimating crop net primary production using high spatial resolution remote sensing data. 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) 2016, 4410 -4413.
AMA StyleLu Wang, Wenjie Fan, Xiru Xu. Estimating crop net primary production using high spatial resolution remote sensing data. 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). 2016; ():4410-4413.
Chicago/Turabian StyleLu Wang; Wenjie Fan; Xiru Xu. 2016. "Estimating crop net primary production using high spatial resolution remote sensing data." 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) , no. : 4410-4413.
Remote sense values of the Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) suffer from the effect of ragged terrain. In this study, the effect of ragged terrain was internalized into the FAPAR model based on recollision probability (FAPAR-P), by improving FAPAR-P in two aspects: calculating the shielding factor to correct for the fraction of diffuse sky radiation to the total radiation, and correcting for the interception probability according to the slope and aspect of each pixel. Then, the new model FAPAR-PR (FAPAR-P Model for Ragged Terrain Area) was established. To validate the new model, we chose Saihanba National Forest Park of Hebei Province as the study area, and compared the FAPAR values derived from the models with FAPAR values measured in situ using photon flux sensors and the SunScan canopy analysis system (Delta-T Devices Ltd., UK). The validation result shows that the FAPAR-PR model is applicable to ragged terrain areas and achieves a high level of accuracy.
Peng Zhao; Wenjie Fan; Yuan Liu; Xiru Xu. Calculation of FAPAR over ragged terrains: A case study at Saihanba. 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) 2016, 3656 -3659.
AMA StylePeng Zhao, Wenjie Fan, Yuan Liu, Xiru Xu. Calculation of FAPAR over ragged terrains: A case study at Saihanba. 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). 2016; ():3656-3659.
Chicago/Turabian StylePeng Zhao; Wenjie Fan; Yuan Liu; Xiru Xu. 2016. "Calculation of FAPAR over ragged terrains: A case study at Saihanba." 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) , no. : 3656-3659.
Mountainous areas with rugged terrains are widely distributed around the world. Remotely sensed values of the fraction of absorbed photosynthetically active radiation (FAPAR) suffer from the effect of rugged terrain. In this study, the effect of rugged terrain was incorporated into the FAPAR model based on recollision probability (FAPAR-P), which was improved in two aspects: calculating the sky viewing factor to correct for the fraction of diffuse sky radiation to the total radiation, and correcting the interception probability according to the slope and aspect of each pixel. The newly developed model is called FAPAR-PR (FAPAR-P Model for Rugged Terrain Area). Two study areas were chosen to validate the proposed model: the Dayekou watershed in Gansu Province, and Weichang in Hebei Province, China. The FAPAR values derived from the models were compared with FAPAR values measured in situ using photon flux sensors and the SunScan canopy analysis system (Delta-T Devices Ltd., Cambridge, UK). The validation results show that the FAPAR-PR model is applicable to rugged terrain areas, and it achieves a high level of accuracy. The FAPAR retrieval at different scales was also conducted to estimate the effect of terrain on the FAPAR-P and FAPAR-PR models. In our chosen study area, the effect of rugged terrain was significant in fine resolution pixels, but it was not obvious at larger scales, as the effects of slope and aspect were partly eliminated by the upscaling of the digital elevation model.
Peng Zhao; Wenjie Fan; Yuan Liu; Xihan Mu; Xiru Xu; Jingjing Peng. Study of the Remote Sensing Model of FAPAR over Rugged Terrains. Remote Sensing 2016, 8, 309 .
AMA StylePeng Zhao, Wenjie Fan, Yuan Liu, Xihan Mu, Xiru Xu, Jingjing Peng. Study of the Remote Sensing Model of FAPAR over Rugged Terrains. Remote Sensing. 2016; 8 (4):309.
Chicago/Turabian StylePeng Zhao; Wenjie Fan; Yuan Liu; Xihan Mu; Xiru Xu; Jingjing Peng. 2016. "Study of the Remote Sensing Model of FAPAR over Rugged Terrains." Remote Sensing 8, no. 4: 309.