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The accurate and timely estimation of regional crop biomass at different growth stages is of great importance in guiding crop management decision making. The recent availability of long time series of remote sensing data offers opportunities for crop monitoring. In this paper, four machine learning models, namely random forest (RF), support vector machine (SVM), artificial neural network (ANN), and extreme gradient boosting (XGBoost) were adopted to estimate the seasonal corn biomass based on field observation data and moderate resolution imaging spectroradiometer (MODIS) reflectance data from 2012 to 2019 in the middle reaches of the Heihe River basin, China. Nine variables were selected with the forward feature selection approach from among twenty-seven variables potentially influencing corn biomass: soil-adjusted total vegetation index (SATVI), green ratio vegetation index (GRVI), Nadir_B7 (2105–2155 nm), Nadir_B6 (1628–1652 nm), land surface water index (LSWI), normalized difference vegetation index (NDVI), Nadir_B4 (545–565 nm), and Nadir_B3 (459–479 nm). The results indicated that the corn biomass was suitably estimated (the coefficient of determination (R2) was between 0.72 and 0.78) with the four machine learning models. The XGBoost model performed better than the other three models (R2 = 0.78, root mean squared error (RMSE) = 2.86 t/ha and mean absolute error (MAE) = 1.86 t/ha). Moreover, the RF model was an effective method (R2 = 0.77, RMSE = 2.91 t/ha and MAE = 1.91 t/ha), with a performance comparable to that of the XGBoost model. This study provides a reference for estimating crop biomass from MOD43A4 datasets. In addition, the research demonstrates the potential of machine learning techniques to achieve a relatively accurate estimation of daily corn biomass at a large scale.
Liying Geng; Tao Che; Mingguo Ma; Junlei Tan; Haibo Wang. Corn Biomass Estimation by Integrating Remote Sensing and Long-Term Observation Data Based on Machine Learning Techniques. Remote Sensing 2021, 13, 2352 .
AMA StyleLiying Geng, Tao Che, Mingguo Ma, Junlei Tan, Haibo Wang. Corn Biomass Estimation by Integrating Remote Sensing and Long-Term Observation Data Based on Machine Learning Techniques. Remote Sensing. 2021; 13 (12):2352.
Chicago/Turabian StyleLiying Geng; Tao Che; Mingguo Ma; Junlei Tan; Haibo Wang. 2021. "Corn Biomass Estimation by Integrating Remote Sensing and Long-Term Observation Data Based on Machine Learning Techniques." Remote Sensing 13, no. 12: 2352.
Nitrogen dioxide (NO 2 ) as one of the key pollutants in the atmosphere, is an important trace gas of traffic emissions or industrial activities. The coronavirus disease (COVID-19) epidemic preventions have significantly reduced the emissions of NO 2 , which provided an opportunity to disentangling the contributions to global emissions in major epidemic regions. Here, we quantified the influences of lockdown measures on the NO 2 concentration at a global scale during the two waves of the COVID-19 epidemic, and disentangled the relative contributions of the major epidemic regions (i.e., China, Europe, and USA) on the global NO 2 densities due to the lockdown measures. Our results revealed that there was an evident decrease of global NO 2 in 2020, especially in China, Europe, and USA. The global mean NO 2 densities decreased approximately 2.1 and 3.9 μmol/m 2 in the first and second waves of the epidemic. While in the Northern Hemisphere, NO 2 densities decreased 6.6 μmol/m 2 on average during the first stage of the epidemic, and a slight increase (i.e., 1.7 μmol/m 2 ) in the second wave of the epidemic. The magnitudes and durations of the second wave were much smaller than the first wave of the coronavirus infections. The strict lockdown measures implemented in China significantly decreased the NO 2 concentration, which therefore the largest contributor in the first wave decreases of global average NO 2 densities. Intervention at an early stage would be beneficial to the preventions of epidemic situation. Our study could provide references for economic loss assessments and economic recovery strategies during the stages ofpost-pandemic.
Haibo Wang; Junlei Tan; Xin Li. Global NO2 Dynamics During the COVID-19 Pandemic: A Comparison Between Two Waves of the Coronavirus. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2021, 14, 4310 -4320.
AMA StyleHaibo Wang, Junlei Tan, Xin Li. Global NO2 Dynamics During the COVID-19 Pandemic: A Comparison Between Two Waves of the Coronavirus. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2021; 14 ():4310-4320.
Chicago/Turabian StyleHaibo Wang; Junlei Tan; Xin Li. 2021. "Global NO2 Dynamics During the COVID-19 Pandemic: A Comparison Between Two Waves of the Coronavirus." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 14, no. : 4310-4320.
Solar-induced chlorophyll fluorescence (SIF) measured from space has shed light on the diagnosis of gross primary production (GPP) and has emerged as a promising way to quantify plant photosynthesis. The SCOPE model can explicitly simulate SIF and GPP, while the uncertainty in key model parameters can lead to significant uncertainty in simulations. Previous work has constrained uncertain parameters in the SCOPE model using coarse-resolution SIF observations from satellites, while few studies have used finer resolution SIF measured from the Orbiting Carbon Observatory-2 (OCO-2) to improve the model. Here, we identified the sensitive parameters to SIF and GPP estimation, and improved the performance of SCOPE in simulating SIF and GPP for temperate forests by constraining the physiological parameters relating to SIF and GPP by combining satellite-based SIF measurements (e.g., OCO-2) with flux tower GPP data. Our study showed that SIF had weak capability in constraining maximum carboxylation capacity (Vcmax), while GPP could constrain this parameter well. The OCO-2 SIF data constrained fluorescence quantum efficiency (fqe) well and improved the performance of SCOPE in SIF simulation. However, the use of the OCO-2 SIF alone cannot significantly improve the GPP simulation. The use of both satellite SIF and flux tower GPP data as constraints improved the performance of the model for simulating SIF and GPP simultaneously. This analysis is useful for improving the capability of the SCOPE model, understanding the relationships between GPP and SIF, and improving the estimation of both SIIF and GPP by incorporating satellite SIF products and flux tower data.
Haibo Wang; Jingfeng Xiao. Improving the Capability of the SCOPE Model for Simulating Solar-Induced Fluorescence and Gross Primary Production Using Data from OCO-2 and Flux Towers. Remote Sensing 2021, 13, 794 .
AMA StyleHaibo Wang, Jingfeng Xiao. Improving the Capability of the SCOPE Model for Simulating Solar-Induced Fluorescence and Gross Primary Production Using Data from OCO-2 and Flux Towers. Remote Sensing. 2021; 13 (4):794.
Chicago/Turabian StyleHaibo Wang; Jingfeng Xiao. 2021. "Improving the Capability of the SCOPE Model for Simulating Solar-Induced Fluorescence and Gross Primary Production Using Data from OCO-2 and Flux Towers." Remote Sensing 13, no. 4: 794.
Water use efficiency (WUE), the ratio of carbon gain to water loss, is an important physiological indicator in assessing the interactions between the carbon and water cycles. Quantifying the spatiotemporal patterns of WUE at both canopy and ecosystem levels across main dryland ecosystems along climate gradients is vital for water resource management in water-limited regions. However, the patterns of WUE among natural and managed ecosystems in drylands are not well understood. We partitioned the evapotranspiration (ET) components and calculated WUE at both canopy and ecosystem levels across dryland ecosystems in an arid region in Northwest China using observations from a regional flux observation network. Our results showed divergent variations of canopy and ecosystem WUE across the main ecosystems along climate gradients in arid regions. The ecosystem WUE and canopy WUE ranged from 0.7 to 1.8 gC Kg−1 H2O and from 0.8 to 2.4 gC Kg−1 H2O, respectively. Climatic gradients were the dominant factors controlling the spatial patterns of WUE across dryland ecosystems. Divergences were also observed between oasis and natural ecosystems because of different environmental conditions and management practices. Climatic regulation of the spatial WUE patterns was dominated by water variability rather than temperature. The higher canopy WUE for desert ecosystems indicated that these ecosystems were adapted to the water-limited environment. The discrepancies of both canopy and ecosystem WUE between croplands in arid and hyper-arid climate zones were caused by the differences in agricultural management techniques for these crops. Human activities (e.g., irrigation and agriculture management) altered the distribution of water resource and water use strategies, which further affected the magnitude and patterns of WUE in drylands. This study provides insights into the spatiotemporal patterns of ET components and both canopy and ecosystem WUE over dryland ecosystems and can inform regional water resources management in water-limited regions.
Haibo Wang; Xin Li; Jingfeng Xiao; Mingguo Ma. Evapotranspiration components and water use efficiency from desert to alpine ecosystems in drylands. Agricultural and Forest Meteorology 2020, 298-299, 108283 .
AMA StyleHaibo Wang, Xin Li, Jingfeng Xiao, Mingguo Ma. Evapotranspiration components and water use efficiency from desert to alpine ecosystems in drylands. Agricultural and Forest Meteorology. 2020; 298-299 ():108283.
Chicago/Turabian StyleHaibo Wang; Xin Li; Jingfeng Xiao; Mingguo Ma. 2020. "Evapotranspiration components and water use efficiency from desert to alpine ecosystems in drylands." Agricultural and Forest Meteorology 298-299, no. : 108283.
The efficient use of limited water resources and improving the water use efficiency (WUE) of arid agricultural systems is becoming one of the greatest challenges in agriculture production and global food security because of the shortage of water resources and increasing demand for food in the world. In this study, we attempted to investigate the interannual trends of evapotranspiration and WUE and the responses of biophysical factors and water utilization strategies over a main cropland ecosystem (i.e., seeded maize, Zea mays L.) in arid regions of North-Western China based on continuous eddy-covariance measurements. This paper showed that ecosystem WUE and canopy WUE of the maize ecosystem were 1.90 ± 0.17 g C kg−1 H2O and 2.44 ± 0.21 g C kg−1 H2O over the observation period, respectively, with a clear variation due to a change of irrigation practice. Traditional flood irrigation generally results in over-irrigation, providing more water than actual crop requirements. Unlike flood irrigation, which can infiltrate into deep soil layers, drip irrigation can only influence the shallow soil moisture, which can lead to decreases of soil moisture of approximately 27–32% and 36–42% compared with flood irrigation for shallow and deep layers, respectively. Additionally, drip irrigation decreases evapotranspiration by 13% and transpiration by 11–14%, leading to increases in ecosystem and canopy WUE of 9–14% and 11%, respectively, compared to the traditional irrigation practice. Therefore, the drip irrigation strategy is an effective method to reduce irrigation water use and increase crop WUE in arid regions. Our study provides guidance to water-saving cultivation systems and has implications for sustainable water resources management and agriculture development in water-limited regions.
Haibo Wang; Xin Li; Junlei Tan. Interannual Variations of Evapotranspiration and Water Use Efficiency over an Oasis Cropland in Arid Regions of North-Western China. Water 2020, 12, 1239 .
AMA StyleHaibo Wang, Xin Li, Junlei Tan. Interannual Variations of Evapotranspiration and Water Use Efficiency over an Oasis Cropland in Arid Regions of North-Western China. Water. 2020; 12 (5):1239.
Chicago/Turabian StyleHaibo Wang; Xin Li; Junlei Tan. 2020. "Interannual Variations of Evapotranspiration and Water Use Efficiency over an Oasis Cropland in Arid Regions of North-Western China." Water 12, no. 5: 1239.
Dryland regions cover >40% of the Earth's land surface, making these ecosystems the largest biome in the world. Ecosystems in these areas play an important role in determining the interannual variability of the global terrestrial carbon sink. Examining carbon fluxes of various types of dryland ecosystems and their responses to climatic variability is essential for improving projections of the carbon cycle in these regions. In this study, we made use of observations from a regional flux tower observation network in a typical arid endorheic basin, the Heihe river basin (HRB). As a representative area of both the arid region of China and the entire region of central Asia, the HRB includes the main ecosystems in arid regions. We compared the spatial variations of carbon fluxes of five terrestrial ecosystems (i.e., grassland, cropland, desert, wetland, and forest ecosystems) and explored the responses of ecosystem carbon fluxes to climatic factors across different ecosystems. We found that our region exhibits a carbon sink ranging from 85.9 to 508.7 gC/m2/yr for different ecosystems, and the water availability is critical to the spatial variability of carbon fluxes in arid regions. Carbon fluxes across all sites exhibited weak correlations with temperature and precipitation. Marked differences in precipitation effects were observed between the sites within oases and those outside of oases. Irrigation and groundwater recharge were of great importance to the variations in carbon fluxes for the sites within oases. Evapotranspiration (ET) exhibited strong relationships with carbon fluxes, indicating that ET was a better metric of soil water availability than was precipitation in driving the spatial variability of carbon fluxes in arid regions. This study has implications for better understanding the carbon budget of terrestrial ecosystems and informing ecological management in dryland regions.
Haibo Wang; Xin Li; Jingfeng Xiao; Mingguo Ma; Junlei Tan; Xufeng Wang; Liying Geng. Carbon fluxes across alpine, oasis, and desert ecosystems in northwestern China: The importance of water availability. Science of The Total Environment 2019, 697, 133978 .
AMA StyleHaibo Wang, Xin Li, Jingfeng Xiao, Mingguo Ma, Junlei Tan, Xufeng Wang, Liying Geng. Carbon fluxes across alpine, oasis, and desert ecosystems in northwestern China: The importance of water availability. Science of The Total Environment. 2019; 697 ():133978.
Chicago/Turabian StyleHaibo Wang; Xin Li; Jingfeng Xiao; Mingguo Ma; Junlei Tan; Xufeng Wang; Liying Geng. 2019. "Carbon fluxes across alpine, oasis, and desert ecosystems in northwestern China: The importance of water availability." Science of The Total Environment 697, no. : 133978.
Accurate and continuous monitoring of the production of arid ecosystems is of great importance for global and regional carbon cycle estimation. However, the magnitude of carbon sequestration in arid regions and its contribution to the global carbon cycle is poorly understood due to the worldwide paucity of measurements of carbon exchange in arid ecosystems. The Moderate Resolution Imaging Spectroradiometer (MODIS) gross primary productivity (GPP) product provides worldwide high-frequency monitoring of terrestrial GPP. While there have been a large number of studies to validate the MODIS GPP product with ground-based measurements over a range of biome types. Few studies have comprehensively validated the performance of MODIS estimates in arid and semi-arid ecosystems, especially for the newly released Collection 6 GPP products, whose resolution have been improved from 1000 m to 500 m. Thus, this study examined the performance of MODIS-derived GPP by compared with eddy covariance (EC)-observed GPP at different timescales for the main ecosystems in arid and semi-arid regions of China. Meanwhile, we also improved the estimation of MODIS GPP by using in situ meteorological forcing data and optimization of biome-specific parameters with the Bayesian approach. Our results revealed that the current MOD17A2H GPP algorithm could, on the whole, capture the broad trends of GPP at eight-day time scales for the most investigated sites. However, GPP was underestimated in some ecosystems in the arid region, especially for the irrigated cropland and forest ecosystems (with R2 = 0.80, RMSE = 2.66 gC/m2/day and R2 = 0.53, RMSE = 2.12 gC/m2/day, respectively). At the eight-day time scale, the slope of the original MOD17A2H GPP relative to the EC-based GPP was only 0.49, which showed significant underestimation compared with tower-based GPP. However, after using in situ meteorological data to optimize the biome-based parameters of MODIS GPP algorithm, the model could explain 91% of the EC-observed GPP of the sites. Our study revealed that the current MODIS GPP model works well after improving the maximum light-use efficiency (εmax or LUEmax), as well as the temperature and water-constrained parameters of the main ecosystems in the arid region. Nevertheless, there are still large uncertainties surrounding GPP modelling in dryland ecosystems, especially for desert ecosystems. Further improvements in GPP simulation in dryland ecosystems are needed in future studies, for example, improvements of remote sensing products and the GPP estimation algorithm, implementation of data-driven methods, or physiology models.
Haibo Wang; Xin Li; Mingguo Ma; Liying Geng. Improving Estimation of Gross Primary Production in Dryland Ecosystems by a Model-Data Fusion Approach. Remote Sensing 2019, 11, 225 .
AMA StyleHaibo Wang, Xin Li, Mingguo Ma, Liying Geng. Improving Estimation of Gross Primary Production in Dryland Ecosystems by a Model-Data Fusion Approach. Remote Sensing. 2019; 11 (3):225.
Chicago/Turabian StyleHaibo Wang; Xin Li; Mingguo Ma; Liying Geng. 2019. "Improving Estimation of Gross Primary Production in Dryland Ecosystems by a Model-Data Fusion Approach." Remote Sensing 11, no. 3: 225.
The Qilian Mountain ecosystems play an irreplaceable role in maintaining ecological security in western China. Vegetation, as an important part of the ecosystem, has undergone considerable changes in recent decades in this area, but few studies have focused on the process of vegetation change. A long normalized difference vegetation index (NDVI) time series dataset based on remote sensing is an effective tool to investigate large-scale vegetation change dynamics. The MODerate resolution Imaging Spectroradiometer (MODIS) NDVI dataset has provided very detailed regional to global information on the state of vegetation since 2000. The aim of this study was to explore the spatial-temporal characteristics of abrupt vegetation changes and detect their potential drivers in the Qilian Mountain area using MODIS NDVI data with 1 km resolution from 2000 to 2017. The Breaks for Additive Season and Trend (BFAST) algorithm was adopted to detect vegetation breakpoint change times and magnitudes from satellite observations. Our results indicated that approximately 80.1% of vegetation areas experienced at least one abrupt change from 2000 to 2017, and most of these areas were distributed in the southern and northern parts of the study area, especially the area surrounding Qinghai Lake. The abrupt browning changes were much more widespread than the abrupt greening changes for most years of the study period. Environmental factors and anthropogenic activities mainly drove the abrupt vegetation changes. Long-term overgrazing is likely the main cause of the abrupt browning changes. In addition, our results indicate that national ecological protection policies have achieved positive effects in the study area.
Liying Geng; Tao Che; Xufeng Wang; Haibo Wang. Detecting Spatiotemporal Changes in Vegetation with the BFAST Model in the Qilian Mountain Region during 2000–2017. Remote Sensing 2019, 11, 103 .
AMA StyleLiying Geng, Tao Che, Xufeng Wang, Haibo Wang. Detecting Spatiotemporal Changes in Vegetation with the BFAST Model in the Qilian Mountain Region during 2000–2017. Remote Sensing. 2019; 11 (2):103.
Chicago/Turabian StyleLiying Geng; Tao Che; Xufeng Wang; Haibo Wang. 2019. "Detecting Spatiotemporal Changes in Vegetation with the BFAST Model in the Qilian Mountain Region during 2000–2017." Remote Sensing 11, no. 2: 103.
The Qinghai-Tibet (QT) Plateau Engineering Corridor is located in the hinterland of the QT Plateau, which is highly sensitive to global climate change. Climate change causes permafrost degradation, which subsequently affects vegetation growth. This study focused on the vegetation dynamics and their relationships with climate change and human activities in the region surrounding the QT Plateau Engineering Corridor. The vegetation changes were inferred by applying trend analysis, the Mann-Kendall trend test and abrupt change analysis. Six key regions, each containing 40 nested quadrats that ranged in size from 500 × 500 m to 20 × 20 km, were selected to determine the spatial scales of the impacts from different factors. Cumulative growing season integrated enhanced vegetation index (CGSIEVI) values were calculated for each of the nested quadrats of different sizes to indicate the overall vegetation state over the entire year at different spatial scales. The impacts from human activities, a sudden increase in precipitation and permafrost degradation were quantified at different spatial scales using the CGSIEVI values and meteorological data based on the double mass curve method. Three conclusions were derived. First, the vegetation displayed a significant increasing trend over 23.6% of the study area. The areas displaying increases were mainly distributed in the Hoh Xil. Of the area where the vegetation displayed a significant decreasing trend, 72.4% was made up of alpine meadows. Second, more vegetation, especially the alpine meadows, has begun to degenerate or experience more rapid degradation since 2007 due to permafrost degradation and overgrazing. Finally, an active layer depth of 3 m to 3.2 m represents a limiting depth for alpine meadows.
Yi Song; Long Jin; Haibo Wang. Vegetation Changes along the Qinghai-Tibet Plateau Engineering Corridor Since 2000 Induced by Climate Change and Human Activities. Remote Sensing 2018, 10, 95 .
AMA StyleYi Song, Long Jin, Haibo Wang. Vegetation Changes along the Qinghai-Tibet Plateau Engineering Corridor Since 2000 Induced by Climate Change and Human Activities. Remote Sensing. 2018; 10 (2):95.
Chicago/Turabian StyleYi Song; Long Jin; Haibo Wang. 2018. "Vegetation Changes along the Qinghai-Tibet Plateau Engineering Corridor Since 2000 Induced by Climate Change and Human Activities." Remote Sensing 10, no. 2: 95.
As an important part of the global ecosystem, wetlands and their dynamics greatly influence regional eco-environment systems. To understand the distributions, change processes and temporal-spatial characteristics of the wetlands of the inland river basin in an arid region (Heihe River Basin, HRB), this paper employed multi-source remote sensing data to facilitate multi-temporal monitoring of the HRB wetland using a wetland information extraction method. First, we performed monitoring of these wetlands for the years 2000, 2007, 2011 and 2014; then, we analyzed the variation characteristics of the spatial-temporal dynamics of the wetlands in the HRB over the last 15 years via the landscape dynamic change model and the transformation matrix. In addition, we studied the possible driving mechanisms of these changes. The research results showed that the total area of the HRB wetlands had decreased by 2959.13 hectares in the last 15 years (Since 2000), and the annual average loss was −1.09%. The dynamics characterizing the HRB wetlands generally presented a trend of slow increase after an initial decrease, which can be classified into three stages. From 2000 to 2007, the total wetland area rapidly decreased; from 2007 to 2011, the area slowly decreased; and from 2011 to 2014, the area gradually increased. The dynamic changing processes characterizing the wetland resources were ascribed to a combination of natural processes and human activities. The main driving mechanisms of wetland dynamic changes include climatic conditions, upper reach water inflows, population, water resources, cultivated area, and policy. The findings of this study can served as reference and support for the conservation and management of wetland resources in the HRB.
Haibo Wang; Mingguo Ma. Impacts of Climate Change and Anthropogenic Activities on the Ecological Restoration of Wetlands in the Arid Regions of China. Energies 2016, 9, 166 .
AMA StyleHaibo Wang, Mingguo Ma. Impacts of Climate Change and Anthropogenic Activities on the Ecological Restoration of Wetlands in the Arid Regions of China. Energies. 2016; 9 (3):166.
Chicago/Turabian StyleHaibo Wang; Mingguo Ma. 2016. "Impacts of Climate Change and Anthropogenic Activities on the Ecological Restoration of Wetlands in the Arid Regions of China." Energies 9, no. 3: 166.
In this study, a compound technique was developed using eight denoising techniques for reconstructing high-quality normalized difference vegetation index (NDVI) time series data. The new algorithm consists of two major procedures: 1) detecting noisy data according to variation in the modification rates of eight selected denoising techniques and 2) using the medians of the denoised values of the eight techniques to replace the noisy data. The eight techniques include the modified best index slope extraction (M-BISE) technique, the Savitzky-Golay (S-G) technique, the mean value iteration (MVI) filter, the asymmetric Gaussian (A-G) technique, the double logistic (D-L) technique, the changing-weight (CW) filter, the interpolation for data reconstruction (IDR) technique, and the Whittaker smoother (WS) technique. The technique was tested with moderate resolution imaging spectroradiometer (MODIS) NDVI time series data derived from MOD09GQ of the Heihe River Basin in China. In situ NDVI data were obtained during one nearly complete growing season for six land-use types in the study area. Analysis of the temporal and spatial characteristics of the reconstructed data revealed that the compound technique performs better than the other techniques. In addition, the lower root-mean-square error (RMSE) of the compound technique, which was calculated using ground measurements, demonstrated the improved performance of the new technique. The main advantage of the new technique is its ability to effectively denoise data and maintain fidelity such that it can be widely used for other NDVI time series data and for other study areas.
Liying Geng; Mingguo Ma; Haibo Wang. An Effective Compound Algorithm for Reconstructing MODIS NDVI Time Series Data and Its Validation Based on Ground Measurements. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2015, 9, 3588 -3597.
AMA StyleLiying Geng, Mingguo Ma, Haibo Wang. An Effective Compound Algorithm for Reconstructing MODIS NDVI Time Series Data and Its Validation Based on Ground Measurements. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2015; 9 (8):3588-3597.
Chicago/Turabian StyleLiying Geng; Mingguo Ma; Haibo Wang. 2015. "An Effective Compound Algorithm for Reconstructing MODIS NDVI Time Series Data and Its Validation Based on Ground Measurements." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 9, no. 8: 3588-3597.
As an important part of Qinghai Plateau, the Qinghai Lake is a sensitive and fragile zone for global change impacts. It is one of the most strongly desertified regions on the Qinghai Plateau. Based on remote sensing, a geographic information system and using Thematic Mapper imagery for the years 1987, 2000, 2009 and Landsat 8 images for the year 2014 as data sources, we extracted information regarding the dynamic changes of aeolian desertification in the study area over the last 28 years. The spatio-temporal evolutions of the landscape patterns of regional aeolian desertified land (ADL) are discussed. Our objective is to provide references for desertification control and eco-environmental restoration in the Qinghai Lake basin (QLB). Results elicit an aeolian desertified area which has increased by 96.74 km2 over the past 28 years. ADL mainly experienced processes of increasing stable to decreasing trends, before 2000, the area of aeolian desertification increased by 338.03 km2. After 2000, desertification remains stable, but as we speak desertification decreases and a moderate and slight ADL took the lead. The dynamics of aeolian desertification in QLB is mainly determined by climate change, human activities and management.
Haibo Wang; Mingguo Ma; Liying Geng. Monitoring the recent trend of aeolian desertification using Landsat TM and Landsat 8 imagery on the north-east Qinghai–Tibet Plateau in the Qinghai Lake basin. Natural Hazards 2015, 79, 1753 -1772.
AMA StyleHaibo Wang, Mingguo Ma, Liying Geng. Monitoring the recent trend of aeolian desertification using Landsat TM and Landsat 8 imagery on the north-east Qinghai–Tibet Plateau in the Qinghai Lake basin. Natural Hazards. 2015; 79 (3):1753-1772.
Chicago/Turabian StyleHaibo Wang; Mingguo Ma; Liying Geng. 2015. "Monitoring the recent trend of aeolian desertification using Landsat TM and Landsat 8 imagery on the north-east Qinghai–Tibet Plateau in the Qinghai Lake basin." Natural Hazards 79, no. 3: 1753-1772.
When we apply ecological models in environmental management, we must assess the accuracy of parameter estimation and its impact on model predictions. Parameters estimated by conventional techniques tend to be nonrobust and require excessive computational resources. However, optimization algorithms are highly robust and generally exhibit convergence of parameter estimation by inversion with nonlinear models. They can simultaneously generate a large number of parameter estimates using an entire data set. In this study, we tested four inversion algorithms (simulated annealing, shuffled complex evolution, particle swarm optimization, and the genetic algorithm) to optimize parameters in photosynthetic models depending on different temperatures. We investigated if parameter boundary values and control variables influenced the accuracy and efficiency of the various algorithms and models. We obtained optimal solutions with all of the inversion algorithms tested if the parameter bounds and control variables were constrained properly. However, the efficiency of processing time use varied with the control variables obtained. In addition, we investigated if temperature dependence formalization impacted optimally the parameter estimation process. We found that the model with a peaked temperature response provided the best fit to the data.
H. B. Wang; M. G. Ma; Y. M. Xie; X. F. Wang; J. Wang. Parameter inversion estimation in photosynthetic models: Impact of different simulation methods. Photosynthetica 2014, 52, 233 -246.
AMA StyleH. B. Wang, M. G. Ma, Y. M. Xie, X. F. Wang, J. Wang. Parameter inversion estimation in photosynthetic models: Impact of different simulation methods. Photosynthetica. 2014; 52 (2):233-246.
Chicago/Turabian StyleH. B. Wang; M. G. Ma; Y. M. Xie; X. F. Wang; J. Wang. 2014. "Parameter inversion estimation in photosynthetic models: Impact of different simulation methods." Photosynthetica 52, no. 2: 233-246.
Alpine meadow covers most of the Qinghai-Tibet Plateau where frozen soil is widely distributed. In order to correctly simulate the carbon, water and energy flux of an alpine meadow site at Qinghai-Tibet Plateau, a widely used carbon cycle model Biome-BGC and a cold region land surface model SHAW were coupled. The outputs of the coupled model were validated with the observed carbon fluxes (Gross Primary Productivity, Net Ecosystem Exchange, Ecosystem Respiration), energy fluxes (Latent heat flux, Sensible heat flux), water flux (Evapotranspiration), soil moisture and soil temperature at A’rou site which is located on the east edge of Qinghai-Tibet Plateau. The results indicate that the coupled model can correctly predict the interactions between alpine meadow ecosystem and atmosphere.
Xufeng Wang; Mingguo Ma; Yi Song; Junlei Tan; Haibo Wang. Coupling of a biogeochemical model with a simultaneous heat and water model and its evaluation at an alpine meadow site. Environmental Earth Sciences 2014, 72, 4085 -4096.
AMA StyleXufeng Wang, Mingguo Ma, Yi Song, Junlei Tan, Haibo Wang. Coupling of a biogeochemical model with a simultaneous heat and water model and its evaluation at an alpine meadow site. Environmental Earth Sciences. 2014; 72 (10):4085-4096.
Chicago/Turabian StyleXufeng Wang; Mingguo Ma; Yi Song; Junlei Tan; Haibo Wang. 2014. "Coupling of a biogeochemical model with a simultaneous heat and water model and its evaluation at an alpine meadow site." Environmental Earth Sciences 72, no. 10: 4085-4096.
More than 20 techniques have been developed to de-noise time-series vegetation index data from different satellite sensors to reconstruct long time-series data sets. Although many studies have compared Normalized Difference Vegetation Index (NDVI) noise-reduction techniques, few studies have compared these techniques systematically and comprehensively. This study tested eight techniques for smoothing different vegetation types using different types of multi-temporal NDVI data (Advanced Very High Resolution Radiometer (AVHRR) (Global Inventory Modeling and Map Studies (GIMMS) and Pathfinder AVHRR Land (PAL), Satellite Pour l’ Observation de la Terre (SPOT) VEGETATION (VGT), and Moderate Resolution Imaging Spectroradiometer (MODIS) (Terra)) with the ultimate purpose of determining the best reconstruction technique for each type of vegetation captured with four satellite sensors. These techniques include the modified best index slope extraction (M-BISE) technique, the Savitzky-Golay (S-G) technique, the mean value iteration filter (MVI) technique, the asymmetric Gaussian (A-G) technique, the double logistic (D-L) technique, the changing-weight filter (CW) technique, the interpolation for data reconstruction (IDR) technique, and the Whittaker smoother (WS) technique. These techniques were evaluated by calculating the root mean square error (RMSE), the Akaike Information Criterion (AIC), and the Bayesian Information Criterion (BIC). The results indicate that the S-G, CW, and WS techniques perform better than the other tested techniques, while the IDR, M-BISE, and MVI techniques performed worse than the other techniques. The best de-noise technique varies with different vegetation types and NDVI data sources. The S-G performs best in most situations. In addition, the CW and WS are effective techniques that were exceeded only by the S-G technique. The assessment results are consistent in terms of the three evaluation indexes for GIMMS, PAL, and SPOT data in the study area, but not for the MODIS data. The study will be very helpful for choosing reconstruction techniques for long time-series data sets.
Liying Geng; Mingguo Ma; Xufeng Wang; Wenping Yu; Shuzhen Jia; Haibo Wang. Comparison of Eight Techniques for Reconstructing Multi-Satellite Sensor Time-Series NDVI Data Sets in the Heihe River Basin, China. Remote Sensing 2014, 6, 2024 -2049.
AMA StyleLiying Geng, Mingguo Ma, Xufeng Wang, Wenping Yu, Shuzhen Jia, Haibo Wang. Comparison of Eight Techniques for Reconstructing Multi-Satellite Sensor Time-Series NDVI Data Sets in the Heihe River Basin, China. Remote Sensing. 2014; 6 (3):2024-2049.
Chicago/Turabian StyleLiying Geng; Mingguo Ma; Xufeng Wang; Wenping Yu; Shuzhen Jia; Haibo Wang. 2014. "Comparison of Eight Techniques for Reconstructing Multi-Satellite Sensor Time-Series NDVI Data Sets in the Heihe River Basin, China." Remote Sensing 6, no. 3: 2024-2049.
Phenology controls the seasonal activities of vegetation on land surfaces and thus plays a fundamental role in regulating photosynthesis and other ecosystem processes. Therefore, accurately simulating phenology and soil processes is critical to ecosystem and climate modeling. In this study, we present an integrated ecosystem model of plant productivity, plant phenology, and the soil freeze–thaw process to (1) improve the quality of simulations of soil thermal regimes and (2) estimate the seasonal variability of plant phenology and its effects on plant productivity in high-altitude seasonal frozen regions. We tested different model configurations and parameterizations, including a refined soil stratification scheme that included unfrozen water in frozen soil, a remotely sensed diagnostic phenology scheme, and a modified prognostic phenology scheme, to describe the seasonal variation in vegetation. After refined soil layering resolution and the inclusion of unfrozen water in frozen soil, the results show that the model adequately reproduced the soil thermal regimes and their interactions observed at the site. The inclusion of unfrozen water in frozen soil was found to have a significant effect on soil moisture simulation during the spring but only a small effect on soil temperature simulation at this site. Moreover, the performance of improved phenology schemes was good. The phenology model accurately predicted the start and end of phenology, and its precise prediction of phenology variation allows an improved simulation of vegetation production.
Haibo Wang; Mingguo Ma; Xufeng Wang; Wenping Yuan; Yi Song; Junlei Tan; Guanghui Huang. Seasonal variation of vegetation productivity over an alpine meadow in the Qinghai–Tibet Plateau in China: modeling the interactions of vegetation productivity, phenology, and the soil freeze–thaw process. Ecological Research 2012, 28, 271 -282.
AMA StyleHaibo Wang, Mingguo Ma, Xufeng Wang, Wenping Yuan, Yi Song, Junlei Tan, Guanghui Huang. Seasonal variation of vegetation productivity over an alpine meadow in the Qinghai–Tibet Plateau in China: modeling the interactions of vegetation productivity, phenology, and the soil freeze–thaw process. Ecological Research. 2012; 28 (2):271-282.
Chicago/Turabian StyleHaibo Wang; Mingguo Ma; Xufeng Wang; Wenping Yuan; Yi Song; Junlei Tan; Guanghui Huang. 2012. "Seasonal variation of vegetation productivity over an alpine meadow in the Qinghai–Tibet Plateau in China: modeling the interactions of vegetation productivity, phenology, and the soil freeze–thaw process." Ecological Research 28, no. 2: 271-282.
Stellera chamaejasme L.(Stellera) is a poisonous weed that widely distributed in grassland ecosystems of Western China. Field reflectance measurements were performed for the Stellera with varied coverage in Qilian county of China. The spectral characteristics of some main species were analyzed. The result indicated that the reflectance of the corolla of Stellera was greater than that of other species over the full range of wavelengths. The best time to distinguish Stellera is the full bloom period of the Stellera. Three groups of spectral measurements were performed for the Stellera with varied coverage. The first experiment indicated that the reflectance increased with the increased densities in near-infrared wavelengths, but no obvious regular pattern existed in visible bands. On the contrary, in the full bloom period of the Stellera, there is an obviously increasing trend of reflectance both in visible and near-infrared bands with the increased densities. No distinct trend was found in the third experiment that conducted after the full bloom period. A clear linear relationship exited in analyzing the correlation between Stellera with varied density and their spectral characteristics. Thus, the density of Stellera could be quantitatively estimated based on the spectral characteristics from hyperspectral remote sensing images.
Haibo Wang; Jinbo Qian; Mingguo Ma; Xufeng Wang. The spectral characteristics of Stellera chamaejasme L. with varied coverage in Qilian of China. SPIE Europe Remote Sensing 2009, 74721U -74721U-11.
AMA StyleHaibo Wang, Jinbo Qian, Mingguo Ma, Xufeng Wang. The spectral characteristics of Stellera chamaejasme L. with varied coverage in Qilian of China. SPIE Europe Remote Sensing. 2009; ():74721U-74721U-11.
Chicago/Turabian StyleHaibo Wang; Jinbo Qian; Mingguo Ma; Xufeng Wang. 2009. "The spectral characteristics of Stellera chamaejasme L. with varied coverage in Qilian of China." SPIE Europe Remote Sensing , no. : 74721U-74721U-11.