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Xiaoli Ren
Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China

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Journal article
Published: 06 February 2021 in Ecological Informatics
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When researchers analyze data, it typically requires significant effort in data preparation to make the data analysis ready. This often involves cleaning, pre-processing, harmonizing, or integrating data from one or multiple sources and placing them into a computational environment in a form suitable for analysis. Research infrastructures and their data repositories host data and make them available to researchers, but rarely offer a computational environment for data analysis. Published data are often persistently identified, but such identifiers resolve onto landing pages that must be (manually) navigated to identify how data are accessed. This navigation is typically challenging or impossible for machines. This paper surveys existing approaches for improving environmental data access to facilitate more rapid data analyses in computational environments, and thus contribute to a more seamless integration of data and analysis. By analysing current state-of-the-art approaches and solutions being implemented by world‑leading environmental research infrastructures, we highlight the existing practices to interface data repositories with computational environments and the challenges moving forward. We found that while the level of standardization has improved during recent years, it still is challenging for machines to discover and access data based on persistent identifiers. This is problematic in regard to the emerging requirements for FAIR (Findable, Accessible, Interoperable, and Reusable) data, in general, and problematic for seamless integration of data and analysis, in particular. There are a number of promising approaches that would improve the state-of-the-art. A key approach presented here involves software libraries that streamline reading data and metadata into computational environments. We describe this approach in detail for two research infrastructures. We argue that the development and maintenance of specialized libraries for each RI and a range of programming languages used in data analysis does not scale well. Based on this observation, we propose a set of established standards and web practices that, if implemented by environmental research infrastructures, will enable the development of RI and programming language independent software libraries with much reduced effort required for library implementation and maintenance as well as considerably lower learning requirements on users. To catalyse such advancement, we propose a roadmap and key action points for technology harmonization among RIs that we argue will build the foundation for efficient and effective integration of data and analysis.

ACS Style

Robert Huber; Claudio D'Onofrio; Anusuriya Devaraju; Jens Klump; Henry W. Loescher; Stephan Kindermann; Siddeswara Guru; Mark Grant; Beryl Morris; Lesley Wyborn; Ben Evans; Doron Goldfarb; Melissa A. Genazzio; Xiaoli Ren; Barbara Magagna; Hannes Thiemann; Markus Stocker. Integrating data and analysis technologies within leading environmental research infrastructures: Challenges and approaches. Ecological Informatics 2021, 61, 101245 .

AMA Style

Robert Huber, Claudio D'Onofrio, Anusuriya Devaraju, Jens Klump, Henry W. Loescher, Stephan Kindermann, Siddeswara Guru, Mark Grant, Beryl Morris, Lesley Wyborn, Ben Evans, Doron Goldfarb, Melissa A. Genazzio, Xiaoli Ren, Barbara Magagna, Hannes Thiemann, Markus Stocker. Integrating data and analysis technologies within leading environmental research infrastructures: Challenges and approaches. Ecological Informatics. 2021; 61 ():101245.

Chicago/Turabian Style

Robert Huber; Claudio D'Onofrio; Anusuriya Devaraju; Jens Klump; Henry W. Loescher; Stephan Kindermann; Siddeswara Guru; Mark Grant; Beryl Morris; Lesley Wyborn; Ben Evans; Doron Goldfarb; Melissa A. Genazzio; Xiaoli Ren; Barbara Magagna; Hannes Thiemann; Markus Stocker. 2021. "Integrating data and analysis technologies within leading environmental research infrastructures: Challenges and approaches." Ecological Informatics 61, no. : 101245.

Journal article
Published: 26 March 2020 in Forests
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Process-based terrestrial ecosystem models are increasingly being used to predict carbon (C) cycling in forest ecosystems. Given the complexity of ecosystems, these models inevitably have certain deficiencies, and thus the model parameters and simulations can be highly uncertain. Through long-term direct observation of ecosystems, numerous different types of data have accumulated, providing valuable opportunities to determine which sources of data can most effectively reduce the uncertainty of simulation results, and thereby improve simulation accuracy. In this study, based on a long-term series of observations (biometric and flux data) of a subtropical Chinese fir plantation ecosystem, we use a model–data fusion framework to evaluate the effects of different constrained data on the parameter estimation and uncertainty of related variables, and systematically evaluate the uncertainty of parameters. We found that plant C pool observational data contributed to significant reductions in the uncertainty of parameter estimates and simulation, as these data provide information on C pool size. However, none of the data effectively constrained the foliage C pool, indicating that this pool should be a target for future observational activities. The assimilation of soil organic C observations was found to be important for reducing the uncertainty or bias in soil C pools. The key findings of this study are that the assimilation of multiple time scales and types of data stream are critical for model constraint and that the most accurate simulation results are obtained when all available biometric and flux data are used as constraints. Accordingly, our results highlight the importance of using multi-source data when seeking to constrain process-based terrestrial ecosystem models.

ACS Style

Longwei Hu; Honglin He; Yan Shen; Xiaoli Ren; Shao-Kui Yan; Wenhua Xiang; Rong Ge; Zhongen Niu; Qian Xu; Xiaobo Zhu. Modeling the Carbon Cycle of a Subtropical Chinese Fir Plantation Using a Multi-Source Data Fusion Approach. Forests 2020, 11, 369 .

AMA Style

Longwei Hu, Honglin He, Yan Shen, Xiaoli Ren, Shao-Kui Yan, Wenhua Xiang, Rong Ge, Zhongen Niu, Qian Xu, Xiaobo Zhu. Modeling the Carbon Cycle of a Subtropical Chinese Fir Plantation Using a Multi-Source Data Fusion Approach. Forests. 2020; 11 (4):369.

Chicago/Turabian Style

Longwei Hu; Honglin He; Yan Shen; Xiaoli Ren; Shao-Kui Yan; Wenhua Xiang; Rong Ge; Zhongen Niu; Qian Xu; Xiaobo Zhu. 2020. "Modeling the Carbon Cycle of a Subtropical Chinese Fir Plantation Using a Multi-Source Data Fusion Approach." Forests 11, no. 4: 369.

Journal article
Published: 09 March 2020 in Sustainability
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While a number of machine learning (ML) models have been used to estimate RE, systematic evaluation and comparison of these models are still limited. In this study, we developed three traditional ML models and a deep learning (DL) model, stacked autoencoders (SAE), to estimate RE in northern China’s grasslands. The four models were trained with two strategies: training for all of northern China’s grasslands and separate training for the alpine and temperate grasslands. Our results showed that all four ML models estimated RE in northern China’s grasslands fairly well, while the SAE model performed best (R2 = 0.858, RMSE = 0.472 gC m−2 d−1, MAE = 0.304 gC m−2 d−1). Models trained with the two strategies had almost identical performances. The enhanced vegetation index and soil organic carbon density (SOCD) were the two most important environmental variables for estimating RE in the grasslands of northern China. Air temperature (Ta) was more important than the growing season land surface water index (LSWI) in the alpine grasslands, while the LSWI was more important than Ta in the temperate grasslands. These findings may promote the application of DL models and the inclusion of SOCD for RE estimates with increased accuracy.

ACS Style

Xiaobo Zhu; Honglin He; Mingguo Ma; Xiaoli Ren; Li Zhang; Fawei Zhang; Yingnian Li; Peili Shi; Shiping Chen; Yanfen Wang; Xiaoping Xin; Yaoming Ma; Yu Zhang; Mingyuan Du; Rong Ge; Na Zeng; Pan Li; Zhongen Niu; Liyun Zhang; Yan Lv; Zengjing Song; Qing Gu. Estimating Ecosystem Respiration in the Grasslands of Northern China Using Machine Learning: Model Evaluation and Comparison. Sustainability 2020, 12, 2099 .

AMA Style

Xiaobo Zhu, Honglin He, Mingguo Ma, Xiaoli Ren, Li Zhang, Fawei Zhang, Yingnian Li, Peili Shi, Shiping Chen, Yanfen Wang, Xiaoping Xin, Yaoming Ma, Yu Zhang, Mingyuan Du, Rong Ge, Na Zeng, Pan Li, Zhongen Niu, Liyun Zhang, Yan Lv, Zengjing Song, Qing Gu. Estimating Ecosystem Respiration in the Grasslands of Northern China Using Machine Learning: Model Evaluation and Comparison. Sustainability. 2020; 12 (5):2099.

Chicago/Turabian Style

Xiaobo Zhu; Honglin He; Mingguo Ma; Xiaoli Ren; Li Zhang; Fawei Zhang; Yingnian Li; Peili Shi; Shiping Chen; Yanfen Wang; Xiaoping Xin; Yaoming Ma; Yu Zhang; Mingyuan Du; Rong Ge; Na Zeng; Pan Li; Zhongen Niu; Liyun Zhang; Yan Lv; Zengjing Song; Qing Gu. 2020. "Estimating Ecosystem Respiration in the Grasslands of Northern China Using Machine Learning: Model Evaluation and Comparison." Sustainability 12, no. 5: 2099.

Article
Published: 29 October 2019 in Journal of Geographical Sciences
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The ratio of transpiration to evapotranspiration (T/ET) is a key parameter for quantifying water use efficiency of ecosystems and understanding the interaction between ecosystem carbon uptake and water cycling in the context of global change. The estimation of T/ET has been paid increasing attention from the scientific community in recent years globally. In this paper, we used the Priestly-Taylor Jet Propulsion Laboratory Model (PT-JPL) driven by regional remote sensing data and gridded meteorological data, to simulate the T/ET in forest ecosystems along the North-South Transect of East China (NSTEC) during 2001–2010, and to analyze the spatial distribution and temporal variation of T/ET, as well as the factors influencing the variation in T/ET. The results showed that: (1) The PT-JPL model is suitable for the simulation of evapotranspiration and its components of forest ecosystems in Eastern China, and has relatively good stability and reliability. (2) Spatial distribution of T/ET in forest ecosystems along NSTEC was heterogeneous, i.e., T/ET was higher in the north and lower in the south, with an averaged value of 0.69; and the inter-annual variation of T/ET showed a significantly increasing trend, with an increment of 0.007/yr (p<0.01). (3) Seasonal and inter-annual variations of T/ET had different dominant factors. Temperature and EVI can explain around 90% (p<0.01) of the seasonal variation in T/ET, while the inter-annual variation in T/ET was mainly controlled by EVI (53%, p<0.05).

ACS Style

Xiaoli Ren; Qianqian Lu; Honglin He; Li Zhang; Zhongen Niu. Estimation and analysis of the ratio of transpiration to evapotranspiration in forest ecosystems along the North-South Transect of East China. Journal of Geographical Sciences 2019, 29, 1807 -1822.

AMA Style

Xiaoli Ren, Qianqian Lu, Honglin He, Li Zhang, Zhongen Niu. Estimation and analysis of the ratio of transpiration to evapotranspiration in forest ecosystems along the North-South Transect of East China. Journal of Geographical Sciences. 2019; 29 (11):1807-1822.

Chicago/Turabian Style

Xiaoli Ren; Qianqian Lu; Honglin He; Li Zhang; Zhongen Niu. 2019. "Estimation and analysis of the ratio of transpiration to evapotranspiration in forest ecosystems along the North-South Transect of East China." Journal of Geographical Sciences 29, no. 11: 1807-1822.

Journal article
Published: 24 August 2019 in Agricultural and Forest Meteorology
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The ratio of transpiration to total terrestrial evapotranspiration (T/ET) plays an important role in the hydrological cycle and in the energy budgets between the land and the atmosphere. Although China has experienced substantial climate warming and vegetation restoration (i.e., greening) over the past decades, the response of T/ET to the changing climate and environmental factors is poorly understood. Here, we apply a model-data fusion method that integrates the Priestly-Taylor Jet Propulsion Laboratory (PT-JPL) model with multivariate observational datasets (transpiration and evapotranspiration) to quantify the relative contributions of multiple factors to the T/ET trend for the terrestrial ecosystem of China from 1982 to 2015. Validation against the observational data indicates that the PT-JPL model performed well. The multi-year average T/ET was estimated to be 0.56 ± 0.05 in China. The T/ET of the forest ecosystems (0.65–0.72) was generally higher than that of the non-forest ecosystems (0.41–0.60). T/ET increased remarkably at a rate of 0.0019 yr−1 (P < 0.01) during the study period. Leaf area index increased significantly over the period, by 0.0031 m2 m−2 yr−1. It appears that greening and climate change were the most likely causes of the increasing T/ET in China, directly explaining 57.89% and 36.84% of the T/ET trend, respectively. Particularly, in the subtropical-tropical monsoonal region, greening directly contributed 24.43% to the T/ET trend whereas climate change contributed 60.95%. The influences of greening and climate change on T/ET trends are mutually reinforcing. Additionally, partial correlation analyses between the climate-driven T/ET and the climate variables indicate that warming (0.04 °C yr−1, P < 0.01) was the major driving force of the climate-induced interannual variability of T/ET across the whole study area (R = 0.84), especially in the subtropical-tropical monsoonal region (R = 0.89). Our results may help elucidate the interactions between terrestrial ecosystems and the atmosphere within the context of long-term global climate changes.

ACS Style

Zhongen Niu; Honglin He; Gaofeng Zhu; Xiaoli Ren; Li Zhang; Kun Zhang; Guirui Yu; Rong Ge; Pan Li; Na Zeng; Xiaobo Zhu. An increasing trend in the ratio of transpiration to total terrestrial evapotranspiration in China from 1982 to 2015 caused by greening and warming. Agricultural and Forest Meteorology 2019, 279, 107701 .

AMA Style

Zhongen Niu, Honglin He, Gaofeng Zhu, Xiaoli Ren, Li Zhang, Kun Zhang, Guirui Yu, Rong Ge, Pan Li, Na Zeng, Xiaobo Zhu. An increasing trend in the ratio of transpiration to total terrestrial evapotranspiration in China from 1982 to 2015 caused by greening and warming. Agricultural and Forest Meteorology. 2019; 279 ():107701.

Chicago/Turabian Style

Zhongen Niu; Honglin He; Gaofeng Zhu; Xiaoli Ren; Li Zhang; Kun Zhang; Guirui Yu; Rong Ge; Pan Li; Na Zeng; Xiaobo Zhu. 2019. "An increasing trend in the ratio of transpiration to total terrestrial evapotranspiration in China from 1982 to 2015 caused by greening and warming." Agricultural and Forest Meteorology 279, no. : 107701.

Journal article
Published: 07 March 2019 in Ecological Indicators
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Effective and accurate monitoring of grassland aboveground biomass (AGB) is necessary for improving our understanding of regional carbon cycle and pastoral agricultural management. In this study, we developed a suitable AGB estimation model for the Tibetan alpine grasslands based on the random forest algorithm, using 256 AGB observation data, remote sensing vegetation indices, meteorological data, and topographical data. We estimated the grassland AGB on the Tibetan Plateau during 2000–2014, analyzed its spatiotemporal changes, and further explored the response of AGB to the variation in climatic factors. The results indicated that (1) the RF model performed well in the AGB estimation, which can explain 86% of the variation of the observation data. (2) The grassland AGB decreased from the southeast to the northwest in this region, with an average value of 77.12 gm−2. (3) In the whole study area, the grassland AGB showed significantly positive correlation with temperature and precipitation. The correlation between grassland AGB and MAP was 0.54 (P < 0.05), much higher than that of MAT (R = 0.38, P < 0.05). (4) The inter-annual variation of AGB on the Tibetan Plateau was significantly and positively correlated with temperature (R2 = 0.45, P < 0.05). This study demonstrated that RF model can help improve our understanding of the spatiotemporal dynamics of the grassland AGB and the effects of climate variation.

ACS Style

Na Zeng; Xiaoli Ren; Honglin He; Li Zhang; Dan Zhao; Rong Ge; Pan Li; Zhongen Niu. Estimating grassland aboveground biomass on the Tibetan Plateau using a random forest algorithm. Ecological Indicators 2019, 102, 479 -487.

AMA Style

Na Zeng, Xiaoli Ren, Honglin He, Li Zhang, Dan Zhao, Rong Ge, Pan Li, Zhongen Niu. Estimating grassland aboveground biomass on the Tibetan Plateau using a random forest algorithm. Ecological Indicators. 2019; 102 ():479-487.

Chicago/Turabian Style

Na Zeng; Xiaoli Ren; Honglin He; Li Zhang; Dan Zhao; Rong Ge; Pan Li; Zhongen Niu. 2019. "Estimating grassland aboveground biomass on the Tibetan Plateau using a random forest algorithm." Ecological Indicators 102, no. : 479-487.

Journal article
Published: 19 January 2018 in Remote Sensing
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It is important to accurately evaluate ecosystem respiration (RE) in the alpine grasslands of the Tibetan Plateau and the temperate grasslands of the Inner Mongolian Plateau, as it serves as a sensitivity indicator of regional and global carbon cycles. Here, we combined flux measurements taken between 2003 and 2013 from 16 grassland sites across northern China and the corresponding MODIS land surface temperature (LST), enhanced vegetation index (EVI), and land surface water index (LSWI) to build a satellite-based model to estimate RE at a regional scale. First, the dependencies of both spatial and temporal variations of RE on these biotic and climatic factors were examined explicitly. We found that plant productivity and moisture, but not temperature, can best explain the spatial pattern of RE in northern China’s grasslands; while temperature plays a major role in regulating the temporal variability of RE in the alpine grasslands, and moisture is equally as important as temperature in the temperate grasslands. However, the moisture effect on RE and the explicit representation of spatial variation process are often lacking in most of the existing satellite-based RE models. On this basis, we developed a model by comprehensively considering moisture, temperature, and productivity effects on both temporal and spatial processes of RE, and then, we evaluated the model performance. Our results showed that the model well explained the observed RE in both the alpine (R2 = 0.79, RMSE = 0.77 g C m−2 day−1) and temperate grasslands (R2 = 0.75, RMSE = 0.60 g C m−2 day−1). The inclusion of the LSWI as the water-limiting factor substantially improved the model performance in arid and semi-arid ecosystems, and the spatialized basal respiration rate as an indicator for spatial variation largely determined the regional pattern of RE. Finally, the model accurately reproduced the seasonal and inter-annual variations and spatial variability of RE, and it avoided overestimating RE in water-limited regions compared to the popular process-based model. These findings provide a better understanding of the biotic and climatic controls over spatiotemporal patterns of RE for two typical grasslands and a new alternative up-scaling method for large-scale RE evaluation in grassland ecosystems.

ACS Style

Rong Ge; Honglin He; Xiaoli Ren; Li Zhang; Pan Li; Na Zeng; Guirui Yu; Liyun Zhang; Shi-Yong Yu; Fawei Zhang; Hongqin Li; Peili Shi; Shiping Chen; Yanfen Wang; Xiaoping Xin; Yaoming Ma; Mingguo Ma; Yu Zhang; Mingyuan Du. A Satellite-Based Model for Simulating Ecosystem Respiration in the Tibetan and Inner Mongolian Grasslands. Remote Sensing 2018, 10, 149 .

AMA Style

Rong Ge, Honglin He, Xiaoli Ren, Li Zhang, Pan Li, Na Zeng, Guirui Yu, Liyun Zhang, Shi-Yong Yu, Fawei Zhang, Hongqin Li, Peili Shi, Shiping Chen, Yanfen Wang, Xiaoping Xin, Yaoming Ma, Mingguo Ma, Yu Zhang, Mingyuan Du. A Satellite-Based Model for Simulating Ecosystem Respiration in the Tibetan and Inner Mongolian Grasslands. Remote Sensing. 2018; 10 (2):149.

Chicago/Turabian Style

Rong Ge; Honglin He; Xiaoli Ren; Li Zhang; Pan Li; Na Zeng; Guirui Yu; Liyun Zhang; Shi-Yong Yu; Fawei Zhang; Hongqin Li; Peili Shi; Shiping Chen; Yanfen Wang; Xiaoping Xin; Yaoming Ma; Mingguo Ma; Yu Zhang; Mingyuan Du. 2018. "A Satellite-Based Model for Simulating Ecosystem Respiration in the Tibetan and Inner Mongolian Grasslands." Remote Sensing 10, no. 2: 149.

Journal article
Published: 31 August 2015 in Remote Sensing
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Water use efficiency (WUE) is a useful indicator to illustrate the interaction of carbon and water cycles in terrestrial ecosystems. MODIS gross primary production (GPP) and evapotranspiration (ET) products have been used to analyze the spatial and temporal patterns of WUE and their relationships with environmental factors at regional and global scales. Although MODIS GPP and ET products have been evaluated using eddy covariance flux measurements, the accuracy of WUE estimated from MODIS products has not been well quantified. In this paper, we evaluated WUE estimated from MODIS GPP and ET products against eddy covariance measurements of GPP and ET during 2003–2008 at eight sites of the Chinese flux observation and research network (ChinaFLUX) and conducted sensitivity analysis to investigate the possible key contributors to the bias of MODIS products. Results show that MODIS products underestimate eight-day water use efficiency in four forest ecosystems and one cropland ecosystem with the bias from −0.36–−2.28 g·C·kg−1 H2O, while overestimating it in three grassland ecosystems with the bias from 0.26–1.11 g·C·kg−1 H2O. Mean annual WUE was underestimated by 14%–54% at four forest sites, 45% at one cropland site and 7% at an alpine grassland site, but overestimated by 66% and 9% at a temperate grassland site and an alpine meadow site, respectively. The underestimation of WUE by MODIS data results from underestimated GPP and overestimated ET at four forest sites, while MODIS WUE values are significantly overvalued mainly due to underestimated ET in the three grassland ecosystems. The maximum light use efficiency and fraction of photosynthetically-active radiation (FPAR) were the two most sensitive factors to the estimation of WUE derived from the MODIS GPP and ET algorithms. The error in meteorological data partly caused the overestimation of ET and accordingly underestimation in WUE in subtropical and tropical forests. The bias of MODIS-produced WUE was also derived from the uncertainties in eddy flux data due to gap-filling processes and unbalanced surface energy issue. Their contributions to the uncertainty in estimated WUE at both eight-day and annual scales still need to be further quantified.

ACS Style

Li Zhang; Jing Tian; Honglin He; Xiaoli Ren; Xiaomin Sun; Guirui Yu; Qianqian Lu; Linyu Lv. Evaluation of Water Use Efficiency Derived from MODIS Products against Eddy Variance Measurements in China. Remote Sensing 2015, 7, 11183 -11201.

AMA Style

Li Zhang, Jing Tian, Honglin He, Xiaoli Ren, Xiaomin Sun, Guirui Yu, Qianqian Lu, Linyu Lv. Evaluation of Water Use Efficiency Derived from MODIS Products against Eddy Variance Measurements in China. Remote Sensing. 2015; 7 (9):11183-11201.

Chicago/Turabian Style

Li Zhang; Jing Tian; Honglin He; Xiaoli Ren; Xiaomin Sun; Guirui Yu; Qianqian Lu; Linyu Lv. 2015. "Evaluation of Water Use Efficiency Derived from MODIS Products against Eddy Variance Measurements in China." Remote Sensing 7, no. 9: 11183-11201.