This page has only limited features, please log in for full access.
With the increasing population and continuation of climate change, an adequate food supply is vital to economic development and social stability. Winter crops are important crop types in China. Changes in winter crops planting areas not only have a direct impact on China’s production and economy, but also potentially affects China’s food security. Therefore, it is necessary to obtain information on the planting of winter crops. In this study, we use the time series data of individual pixels, calculate the temporal statistics of spectral bands and the vegetation indices of optical data based on the phenological characteristics of specific vegetation or crops and record them in the time series data, and apply decision trees and rule-based algorithms to generate annual maps of winter crops. First, we constructed a dataset combining all the available images from Landsat 7/8 and Sentinel-2A/B. Second, we generated an annual map of land cover types to obtain the cropland mask in 2019. Third, we generated a time series of a single cropland pixel, and calculated the phenological indicators for classification by extracting the differences in phenological characteristics of different crops: these phenological indicators include SOS (start of season), SDP (start date of peak), EOS (end of season), GUS (green-up speed) and GSL (growing-season length). Finally, we identified winter crops in 2019 based on their phenological characteristics. The main advantages of the phenology-based algorithm proposed in this study include: (1) Combining multiple sensor data to construct a high spatiotemporal resolution image collection. (2) By analyzing the whole growth season of winter crops, the planting area of winter crops can be extracted more accurately, and (3) the phenological indicators of different periods are extracted, which is conducive to monitoring winter crop planting information and seasonal dynamics. The results show that the algorithm constructed in this study can accurately extract the planting area of winter crops, with user, producer, overall accuracies and Kappa coefficients of 96.61%, 94.13%, 94.56% and 0.89, respectively, indicating that the phenology-based algorithm is reliable for large area crop classification. This research will provide a point of reference for crop area extraction and monitoring.
Li Pan; Haoming Xia; Xiaoyang Zhao; Yan Guo; Yaochen Qin. Mapping Winter Crops Using a Phenology Algorithm, Time-Series Sentinel-2 and Landsat-7/8 Images, and Google Earth Engine. Remote Sensing 2021, 13, 2510 .
AMA StyleLi Pan, Haoming Xia, Xiaoyang Zhao, Yan Guo, Yaochen Qin. Mapping Winter Crops Using a Phenology Algorithm, Time-Series Sentinel-2 and Landsat-7/8 Images, and Google Earth Engine. Remote Sensing. 2021; 13 (13):2510.
Chicago/Turabian StyleLi Pan; Haoming Xia; Xiaoyang Zhao; Yan Guo; Yaochen Qin. 2021. "Mapping Winter Crops Using a Phenology Algorithm, Time-Series Sentinel-2 and Landsat-7/8 Images, and Google Earth Engine." Remote Sensing 13, no. 13: 2510.
With the decline of cultivated land quality and area in recent decades, the intensification of land use plays an important role in meeting the growing demand for food. Cropping intensity refers to the number of crop planting cycles in one year, which is important for improving food production and safety at the local, regional and national scales. Therefore, it is necessary to develop an accurate high spatial resolution dataset of cropping intensity. The existing datasets of cropping intensity were generally developed based on MODIS or Landsat images, both of which have defects in spatial and temporal resolutions. In this paper, we improved the quality of the dataset on the Google Earth Engine (GEE) platform, and developed a new algorithm incorporating crop phenology. The algorithm was based on the Landsat 7/8 and Sentine-2A/B time series imageries to map the 30 m cropping intensity in the Huaihe basin in 2018 by extracting complete growth cycle. Results show that single cropping, double cropping and triple cropping in the Huaihe basin accounted for 41.6%, 57.7% and 0.7% of the total cultivated area in 2018, respectively, and the proportion of multiple cropping reached 58.4%. The accuracy of single cropping, double cropping and triple cropping are 92.93%, 91.39%, and 72.78% respectively. The overall accuracy is 91.38% and the kappa coefficient is 0.84. This algorithm accurately captures the seasonal dynamics of planting patterns in arable land, which can be used to produce cropping intensity products with high-resolution and provide a reference for large-scale regional vegetation monitoring.
Li Pan; Haoming Xia; Jia Yang; Wenhui Niu; Ruimeng Wang; Hongquan Song; Yan Guo; Yaochen Qin. Mapping cropping intensity in Huaihe basin using phenology algorithm, all Sentinel-2 and Landsat images in Google Earth Engine. International Journal of Applied Earth Observation and Geoinformation 2021, 102, 102376 .
AMA StyleLi Pan, Haoming Xia, Jia Yang, Wenhui Niu, Ruimeng Wang, Hongquan Song, Yan Guo, Yaochen Qin. Mapping cropping intensity in Huaihe basin using phenology algorithm, all Sentinel-2 and Landsat images in Google Earth Engine. International Journal of Applied Earth Observation and Geoinformation. 2021; 102 ():102376.
Chicago/Turabian StyleLi Pan; Haoming Xia; Jia Yang; Wenhui Niu; Ruimeng Wang; Hongquan Song; Yan Guo; Yaochen Qin. 2021. "Mapping cropping intensity in Huaihe basin using phenology algorithm, all Sentinel-2 and Landsat images in Google Earth Engine." International Journal of Applied Earth Observation and Geoinformation 102, no. : 102376.
As the land use issue, caused by urban shrinkage in China, is becoming more and more prominent, research on urban shrinkage and expansion has become particularly challenging and urgent. Based on the points of interest (POI) data, this paper redefines the scope, quantity, and area of natural cities by using threshold methods, which accurately identify the shrinkage and expansion of cities in the Yellow River affected area using night light data in 2013 and 2018. The results show that: (1) there are 3130 natural cities (48118.75 km2) in the Yellow River affected area, including 604 shrinking cities (8407.50 km2) and 2165 expanding cities (32972.75 km2). (2) The spatial distributions of shrinking and expanding cities are quite different. The shrinking cities are mainly located in the upper Yellow River affected area, except for the administrative cities of Lanzhou and Yinchuan; the expanding cities are mainly distributed in the middle and lower Yellow River affected area, and the administrative cities of Lanzhou and Yinchuan. (3) Shrinking and expanding cities are typically smaller cities. The research results provide a quick data supported approach for regional urban planning and land use management, for when regional and central governments formulate the outlines of urban development monitoring and regional planning.
Wenhui Niu; Haoming Xia; Ruimeng Wang; Li Pan; Qingmin Meng; Yaochen Qin; Rumeng Li; Xiaoyang Zhao; Xiqing Bian; Wei Zhao. Research on Large-Scale Urban Shrinkage and Expansion in the Yellow River Affected Area Using Night Light Data. ISPRS International Journal of Geo-Information 2020, 10, 5 .
AMA StyleWenhui Niu, Haoming Xia, Ruimeng Wang, Li Pan, Qingmin Meng, Yaochen Qin, Rumeng Li, Xiaoyang Zhao, Xiqing Bian, Wei Zhao. Research on Large-Scale Urban Shrinkage and Expansion in the Yellow River Affected Area Using Night Light Data. ISPRS International Journal of Geo-Information. 2020; 10 (1):5.
Chicago/Turabian StyleWenhui Niu; Haoming Xia; Ruimeng Wang; Li Pan; Qingmin Meng; Yaochen Qin; Rumeng Li; Xiaoyang Zhao; Xiqing Bian; Wei Zhao. 2020. "Research on Large-Scale Urban Shrinkage and Expansion in the Yellow River Affected Area Using Night Light Data." ISPRS International Journal of Geo-Information 10, no. 1: 5.
Hyperspectral images (HSIs) with rich spectral information have been widely used in many fields. Anomaly detection is one of the most interesting and important applications. In this article, a novel Gaussian mixture model (GMM)-based anomaly detection (GMMD) method for HSI is proposed. The main contributions of this article are a new GMM-based extraction approach for extracting the anomaly pixels and an effective GMM-based weighting approach for fusing the extracted anomaly results. Specifically, based on the fact that the spectral values of anomaly pixels in some bands are different from those of background pixels, we propose a GMM-based anomaly extraction approach in which the HSI is characterized by the GMM and the anomaly pixels are extracted by a range prescribed by the GMM parameters. In order to fuse the extracted anomaly results, the GMM-based weighting method is introduced to adaptively construct the detection map. The detection map is rectified by using a guided filter to obtain the final anomaly detection map. Experimental results conducted on four hyperspectral data sets demonstrate the superior performance of the proposed GMMD method.
Jiahui Qu; Qian Du; Yunsong Li; Long Tian; Haoming Xia. Anomaly Detection in Hyperspectral Imagery Based on Gaussian Mixture Model. IEEE Transactions on Geoscience and Remote Sensing 2020, PP, 1 -14.
AMA StyleJiahui Qu, Qian Du, Yunsong Li, Long Tian, Haoming Xia. Anomaly Detection in Hyperspectral Imagery Based on Gaussian Mixture Model. IEEE Transactions on Geoscience and Remote Sensing. 2020; PP (99):1-14.
Chicago/Turabian StyleJiahui Qu; Qian Du; Yunsong Li; Long Tian; Haoming Xia. 2020. "Anomaly Detection in Hyperspectral Imagery Based on Gaussian Mixture Model." IEEE Transactions on Geoscience and Remote Sensing PP, no. 99: 1-14.
The spatio-temporal change of the surface water is very important to agricultural, economic, and social development in the Hetao Plain, as well as the structure and function of the ecosystem. To understand the long-term changes of the surface water area in the Hetao Plain, we used all available Landsat images (7534 scenes) and adopted the modified Normalized Difference Water Index (mNDWI), Enhanced Vegetation Index (EVI), and Normalized Difference Vegetation Index (NDVI) to map the open-surface water from 1989 to 2019 in the Google Earth Engine (GEE) cloud platform. We further analyzed precipitation, temperature, and irrigated area, revealing the impact of climate change and human activities on long-term surface water changes. The results show the following. (1) In the last 31 years, the maximum, seasonal, and annual average water body area values in the Hetao Plain have exhibited a downward trend. Meanwhile, the number of maximum, seasonal, and permanent water bodies displayed a significant upward trend. (2) The variation of the surface water area in the Hetao Plain is mainly affected by the maximum water body area, while the variation of the water body number is mainly affected by the number of minimum water bodies. (3) Precipitation has statistically significant positive effects on the water body area and water body number, which has statistically significant negative effects with temperature and irrigation. The findings of this study can be used to help the policy-makers and farmers understand changing water resources and its driving mechanism and provide a reference for water resources management, agricultural irrigation, and ecological protection.
Ruimeng Wang; Haoming Xia; Yaochen Qin; Wenhui Niu; Li Pan; Rumeng Li; Xiaoyang Zhao; Xiqing Bian; Pinde Fu. Dynamic Monitoring of Surface Water Area during 1989–2019 in the Hetao Plain Using Landsat Data in Google Earth Engine. Water 2020, 12, 3010 .
AMA StyleRuimeng Wang, Haoming Xia, Yaochen Qin, Wenhui Niu, Li Pan, Rumeng Li, Xiaoyang Zhao, Xiqing Bian, Pinde Fu. Dynamic Monitoring of Surface Water Area during 1989–2019 in the Hetao Plain Using Landsat Data in Google Earth Engine. Water. 2020; 12 (11):3010.
Chicago/Turabian StyleRuimeng Wang; Haoming Xia; Yaochen Qin; Wenhui Niu; Li Pan; Rumeng Li; Xiaoyang Zhao; Xiqing Bian; Pinde Fu. 2020. "Dynamic Monitoring of Surface Water Area during 1989–2019 in the Hetao Plain Using Landsat Data in Google Earth Engine." Water 12, no. 11: 3010.
Aeolian dust can influence the climate, air quality, human health, and ecosystems. Dust events in Northern China are the main contributors to dust aerosols in the world, but the impacts of meteorological and anthropogenic factors and their interactions on dust events remain unclear. This study analyzed the spatial and temporal variations of dust event frequencies and quantitatively investigated the impacts of meteorological conditions, anthropogenic factors, and their interactions on dust events using the geographical detector model (GeoDetector) in Northern China. Results revealed that the dust event frequency significantly decreased by 0.006 times yr−1 per site during 1980–2007. At the regional scale, there were large seasonal variations in the effects of meteorological conditions and anthropogenic factors on dust events. Strong winds and soil surface conditions are main drivers of dust events in spring. In summer and autumn, anthropogenic factors have significant impacts on the occurrence of dust events, but the frozen period and relative humidity are major impacting factors in winter. Effects of natural and anthropogenic factors on dust events showed great spatial and seasonal disparities over different vegetation regions. Interactions between two factors enhanced their impacts on the occurrence of dust events. There are also large spatial and seasonal variations in the primary interactions on dust events over different vegetation regions. The findings could help us to better understand the relative importance of various factors on dust events, which has important implications for improving the prediction of dust emission models and developing desertification control strategies.
Xiaomeng Liu; Hongquan Song; Tianjie Lei; Pengfei Liu; Chengdong Xu; Dong Wang; Zhongling Yang; Haoming Xia; Tuanhui Wang; Haipeng Zhao. Effects of natural and anthropogenic factors and their interactions on dust events in Northern China. CATENA 2020, 196, 104919 .
AMA StyleXiaomeng Liu, Hongquan Song, Tianjie Lei, Pengfei Liu, Chengdong Xu, Dong Wang, Zhongling Yang, Haoming Xia, Tuanhui Wang, Haipeng Zhao. Effects of natural and anthropogenic factors and their interactions on dust events in Northern China. CATENA. 2020; 196 ():104919.
Chicago/Turabian StyleXiaomeng Liu; Hongquan Song; Tianjie Lei; Pengfei Liu; Chengdong Xu; Dong Wang; Zhongling Yang; Haoming Xia; Tuanhui Wang; Haipeng Zhao. 2020. "Effects of natural and anthropogenic factors and their interactions on dust events in Northern China." CATENA 196, no. : 104919.
Using remote sensing to study vegetation phenology faces a problem related to extraction methods. Phenology data from different methods vary greatly but there is a lack of widely recognized methods and evaluation approaches. In this study, based on the Leaf Area Index data from 2009 to 2015, we used two common fitting methods (Savitzky-Golay filter and Double Logistic function) and three phenological determining methods (Seasonal amplitude, Absolute value, and Seasonal Trend decomposition by Loess) to extract vegetation phenology in China. Using different thresholds, we obtained 18 extraction method combinations. Then the ground-based observations data from 31 phenology stations in relation to three key vegetation phenophases: Start Of growing Season (SOS), End Of growing Season (EOS) and Length Of growing Season (LOS) were used to evaluate the accuracy of these 18 method combinations. The results under five evaluation indicators showed that the suitable method combinations for SOS, EOS, and LOS were different. Compared with ground-based observations, SOS and EOS extracted by the suitable method combinations were delayed by 6.28 and 4.91 days, and the LOS was shorter. The potential difference of the suitable and unsuitable method combinations respectively reached −44.73, −35.79, and 37.38 days (for SOS, EOS, and LOS), clearly indicating the importance of selecting suitable method combination. The phenology dataset from Vegetation Index & Phenology (VIP) Lab. has also confirmed the reliability of our results. Furthermore, we explored the differences between remote sensing and ground-based phenology. Our study highlights the importance of using a suitable method combination to extract the vegetation phenology and provides a systematical assessment method for selecting a suitable method combination.
Xiaoxuan Zhang; Yaoping Cui; Yaochen Qin; Haoming Xia; Heli Lu; Sujie Liu; Nan Li; Yiming Fu. Evaluating the accuracy of and evaluating the potential errors in extracting vegetation phenology through remote sensing in China. International Journal of Remote Sensing 2020, 41, 3592 -3613.
AMA StyleXiaoxuan Zhang, Yaoping Cui, Yaochen Qin, Haoming Xia, Heli Lu, Sujie Liu, Nan Li, Yiming Fu. Evaluating the accuracy of and evaluating the potential errors in extracting vegetation phenology through remote sensing in China. International Journal of Remote Sensing. 2020; 41 (9):3592-3613.
Chicago/Turabian StyleXiaoxuan Zhang; Yaoping Cui; Yaochen Qin; Haoming Xia; Heli Lu; Sujie Liu; Nan Li; Yiming Fu. 2020. "Evaluating the accuracy of and evaluating the potential errors in extracting vegetation phenology through remote sensing in China." International Journal of Remote Sensing 41, no. 9: 3592-3613.
The forest stock volume (FSV) is one of the key indicators in forestry resource assessments on local, regional, and national scales. To date, scaling up in situ plot-scale measurements across landscapes is still a great challenge in the estimation of FSVs. In this study, Sentinel-2 imagery, the Google Earth Engine (GEE) cloud computing platform, three base station joint differential positioning technology (TBSJDPT), and three algorithms were used to build an FSV model for forests located in Hunan Province, southern China. The GEE cloud computing platform was used to extract the imagery variables from the Sentinel-2 imagery pixels. The TBSJDPT was put forward and used to provide high-precision positions of the sample plot data. The random forests (RF), support vector regression (SVR), and multiple linear regression (MLR) algorithms were used to estimate the FSV. For each pixel, 24 variables were extracted from the Sentinel-2 images taken in 2017 and 2018. The RF model performed the best in both the training phase (i.e., R2 = 0.91, RMSE = 35.13 m3 ha−1, n = 321) and in the test phase (i.e., R2 = 0.58, RMSE = 65.03 m3 ha−1, and n = 138). This model was followed by the SVR model (R2 = 0.54, RMSE = 65.60 m3 ha−1, n = 321 in training; R2 = 0.54, RMSE = 66.00 m3 ha−1, n = 138 in testing), which was slightly better than the MLR model (R2 = 0.38, RMSE = 75.74 m3 ha−1, and n = 321 in training; R2 = 0.49, RMSE = 70.22 m3 ha−1, and n = 138 in testing) in both the training phase and test phase. The best predictive band was Red-Edge 1 (B5), which performed well both in the machine learning methods and in the MLR method. The Blue band (B2), Green band (B3), Red band (B4), SWIR2 band (B12), and vegetation indices (TCW, NDVI_B5, and TCB) were used in the machine learning models, and only one vegetation index (MSI) was used in the MLR model. We mapped the FSV distribution in Hunan Province (3.50 × 108 m3) based on the RF model; it reached a total accuracy of 63.87% compared with the official forest report in 2017 (5.48 × 108 m3). The results from this study will help develop and improve satellite-based methods to estimate FSVs on local, regional and national scales.
Yang Hu; Xuelei Xu; Fayun Wu; Zhongqiu Sun; Haoming Xia; Qingmin Meng; Wenli Huang; Hua Zhou; Jinping Gao; Weitao Li; Daoli Peng; Xiangming Xiao. Estimating Forest Stock Volume in Hunan Province, China, by Integrating In Situ Plot Data, Sentinel-2 Images, and Linear and Machine Learning Regression Models. Remote Sensing 2020, 12, 186 .
AMA StyleYang Hu, Xuelei Xu, Fayun Wu, Zhongqiu Sun, Haoming Xia, Qingmin Meng, Wenli Huang, Hua Zhou, Jinping Gao, Weitao Li, Daoli Peng, Xiangming Xiao. Estimating Forest Stock Volume in Hunan Province, China, by Integrating In Situ Plot Data, Sentinel-2 Images, and Linear and Machine Learning Regression Models. Remote Sensing. 2020; 12 (1):186.
Chicago/Turabian StyleYang Hu; Xuelei Xu; Fayun Wu; Zhongqiu Sun; Haoming Xia; Qingmin Meng; Wenli Huang; Hua Zhou; Jinping Gao; Weitao Li; Daoli Peng; Xiangming Xiao. 2020. "Estimating Forest Stock Volume in Hunan Province, China, by Integrating In Situ Plot Data, Sentinel-2 Images, and Linear and Machine Learning Regression Models." Remote Sensing 12, no. 1: 186.
Monitoring and mapping the spatial distribution of winter wheat accurately is important for crop management, damage assessment and yield prediction. In this study, northern and central Anhui province were selected as study areas, and Sentinel-2 imagery was employed to map winter wheat distribution and the results were verified with Planet imagery in the 2017–2018 growing season. The Sentinel-2 imagery at the heading stage was identified as the optimum period for winter wheat area extraction after analyzing the images from different growth stages using the Jeffries–Matusita distance method. Therefore, ten spectral bands, seven vegetation indices (VI), water index and building index generated from the image at the heading stage were used to classify winter wheat areas by a random forest (RF) algorithm. The result showed that the accuracy was from 93% to 97%, with a Kappa above 0.82 and a percentage error lower than 5% in northern Anhui, and an accuracy of about 80% with Kappa ranging from 0.70 to 0.78 and a percentage error of about 20% in central Anhui. Northern Anhui has a large planting scale of winter wheat and flat terrain while central Anhui grows relatively small winter wheat areas and a high degree of surface fragmentation, which makes the extraction effect in central Anhui inferior to that in northern Anhui. Further, an optimum subset data was obtained from VIs, water index, building index and spectral bands using an RF algorithm. The result of using the optimum subset data showed a high accuracy of classification with a great advantage in data volume and processing time. This study provides a perspective for winter wheat mapping under various climatic and complicated land surface conditions and is of great significance for crop monitoring and agricultural decision-making.
Dongyan Zhang; Shengmei Fang; Bao She; Huihui Zhang; Ning Jin; Haoming Xia; Yuying Yang; Yang Ding. Winter Wheat Mapping Based on Sentinel-2 Data in Heterogeneous Planting Conditions. Remote Sensing 2019, 11, 2647 .
AMA StyleDongyan Zhang, Shengmei Fang, Bao She, Huihui Zhang, Ning Jin, Haoming Xia, Yuying Yang, Yang Ding. Winter Wheat Mapping Based on Sentinel-2 Data in Heterogeneous Planting Conditions. Remote Sensing. 2019; 11 (22):2647.
Chicago/Turabian StyleDongyan Zhang; Shengmei Fang; Bao She; Huihui Zhang; Ning Jin; Haoming Xia; Yuying Yang; Yang Ding. 2019. "Winter Wheat Mapping Based on Sentinel-2 Data in Heterogeneous Planting Conditions." Remote Sensing 11, no. 22: 2647.
Forest ecosystems in an ecotone and their dynamics to climate change are growing ecological and environmental concerns. Phenology is one of the most critical biological indicators of climate change impacts on forest dynamics. In this study, we estimated and visualized the spatiotemporal patterns of forest phenology from 2001 to 2017 in the Qinling Mountains (QMs) based on the enhanced vegetation index (EVI) from MODerate-resolution Imaging Spectroradiometer (MODIS). We further analyzed this data to reveal the impacts of climate change and topography on the start of the growing season (SOS), end of the growing season (EOS), and the length of growing season (LOS). Our results showed that forest phenology metrics were very sensitive to changes in elevation, with a 2.4 days delayed SOS, 1.4 days advanced EOS, and 3.8 days shortened LOS for every 100 m increase in altitude. During the study period, on average, SOS advanced by 0.13 days year−1, EOS was delayed by 0.22 days year−1, and LOS increased by 0.35 day year−1. The phenological advanced and delayed speed across different elevation is not consistent. The speed of elevation-induced advanced SOS increased slightly with elevation, and the speed of elevation-induced delayed EOS shift reached a maximum value of 1500 m from 2001 to 2017. The sensitivity of SOS and EOS to preseason temperature displays that an increase of 1 °C in the regionally averaged preseason temperature would advance the average SOS by 1.23 days and delay the average EOS by 0.72 days, respectively. This study improved our understanding of the recent variability of forest phenology in mountain ecotones and explored the correlation between forest phenology and climate variables in the context of the ongoing climate warming.
Haoming Xia; Yaochen Qin; Gary Feng; Qingmin Meng; Yaoping Cui; Hongquan Song; Ying Ouyang; Gangjun Liu. Forest Phenology Dynamics to Climate Change and Topography in a Geographic and Climate Transition Zone: The Qinling Mountains in Central China. Forests 2019, 10, 1007 .
AMA StyleHaoming Xia, Yaochen Qin, Gary Feng, Qingmin Meng, Yaoping Cui, Hongquan Song, Ying Ouyang, Gangjun Liu. Forest Phenology Dynamics to Climate Change and Topography in a Geographic and Climate Transition Zone: The Qinling Mountains in Central China. Forests. 2019; 10 (11):1007.
Chicago/Turabian StyleHaoming Xia; Yaochen Qin; Gary Feng; Qingmin Meng; Yaoping Cui; Hongquan Song; Ying Ouyang; Gangjun Liu. 2019. "Forest Phenology Dynamics to Climate Change and Topography in a Geographic and Climate Transition Zone: The Qinling Mountains in Central China." Forests 10, no. 11: 1007.
As a key parameter that represents the structural characteristics and biophysical changes of crop canopy, the leaf area index (LAI) plays a significant role in monitoring crop growth and mapping yield. A considerable amount of farmland is dispersed with strong spatial heterogeneity. The existing time series satellite LAI products fail to capture spatial distributions and growth changes of crops due to coarse spatial resolutions and spatio-temporal discontinuities. Therefore, it becomes crucial for fine resolution LAI mapping in time series over crop areas. A two-stage data assimilation scheme was developed for dense time series LAI mapping in this study. A LAI dynamic model was first constructed using multi-year MODIS LAI data. This model coupled with the PROSAIL radiative transfer model, and MOD09A1 reflectance data were used to retrieve temporal LAI profiles at the 500 m resolution with the assistance of the very fast simulated annealing (VFSA) algorithm. Then, the LAI dynamics at the 500 m scale were incorporated as prior information into the Landsat 8 OLI reflectance data for time series LAI mapping at the 30 m resolution. Finally, the spatio-temporal continuities and retrieval accuracies of assimilated LAI values were assessed at the 500 m and 30 m resolutions respectively, using the MODIS LAI product, fine resolution LAI reference map and field measurements. The results indicated that the assimilated the LAI estimations at the 500 m scale effectively eliminated the spatio-temporal discontinuities of the MODIS LAI product and displayed reasonable temporal profiles and spatial integrity of LAI. Moreover, the 30 m resolution LAI retrievals showed more abundant spatial details and reasonable temporal profiles than the counterparts at the 500 m scale. The determination coefficient R2 between the estimated and field LAI values was 0.76 with a root mean square error (RMSE) value of 0.71 at the 30 m scale. The developed method not only improves the spatio-temporal continuities of the LAI at the 500 m scale, but also obtains 30 m resolution LAI maps with fine spatial and temporal consistencies, which can be expected to meet the needs of analysis on crop dynamic changes and yield mapping in fragmented and highly heterogeneous areas.
Huaan Jin; Weixing Xu; Ainong Li; Xinyao Xie; Zhengjian Zhang; Haoming Xia. Spatially and Temporally Continuous Leaf Area Index Mapping for Crops through Assimilation of Multi-resolution Satellite Data. Remote Sensing 2019, 11, 2517 .
AMA StyleHuaan Jin, Weixing Xu, Ainong Li, Xinyao Xie, Zhengjian Zhang, Haoming Xia. Spatially and Temporally Continuous Leaf Area Index Mapping for Crops through Assimilation of Multi-resolution Satellite Data. Remote Sensing. 2019; 11 (21):2517.
Chicago/Turabian StyleHuaan Jin; Weixing Xu; Ainong Li; Xinyao Xie; Zhengjian Zhang; Haoming Xia. 2019. "Spatially and Temporally Continuous Leaf Area Index Mapping for Crops through Assimilation of Multi-resolution Satellite Data." Remote Sensing 11, no. 21: 2517.
The dynamics of surface water play a crucial role in the hydrological cycle and are sensitive to climate change and anthropogenic activities, especially for the agricultural zone. As one of the most populous areas in China’s river basins, the surface water in the Huai River Basin has significant impacts on agricultural plants, ecological balance, and socioeconomic development. However, it is unclear how water areas responded to climate change and anthropogenic water exploitation in the past decades. To understand the changes in water surface areas in the Huai River Basin, this study used the available 16,760 scenes Landsat TM, ETM+, and OLI images in this region from 1989 to 2017 and processed the data on the Google Earth Engine (GEE) platform. The vegetation index and water index were used to quantify the spatiotemporal variability of the surface water area changes over the years. The major results include: (1) The maximum area, the average area, and the seasonal variation of surface water in the Huai River Basin showed a downward trend in the past 29 years, and the year-long surface water areas showed a slight upward trend; (2) the surface water area was positively correlated with precipitation (p < 0.05), but was negatively correlated with the temperature and evapotranspiration; (3) the changes of the total area of water bodies were mainly determined by the 216 larger water bodies (>10 km2). Understanding the variations in water body areas and the controlling factors could support the designation and implementation of sustainable water management practices in agricultural, industrial, and domestic usages.
Haoming Xia; Jinyu Zhao; Yaochen Qin; Jia Yang; Yaoping Cui; Hongquan Song; Liqun Ma; Ning Jin; Qingmin Meng. Changes in Water Surface Area during 1989–2017 in the Huai River Basin using Landsat Data and Google Earth Engine. Remote Sensing 2019, 11, 1824 .
AMA StyleHaoming Xia, Jinyu Zhao, Yaochen Qin, Jia Yang, Yaoping Cui, Hongquan Song, Liqun Ma, Ning Jin, Qingmin Meng. Changes in Water Surface Area during 1989–2017 in the Huai River Basin using Landsat Data and Google Earth Engine. Remote Sensing. 2019; 11 (15):1824.
Chicago/Turabian StyleHaoming Xia; Jinyu Zhao; Yaochen Qin; Jia Yang; Yaoping Cui; Hongquan Song; Liqun Ma; Ning Jin; Qingmin Meng. 2019. "Changes in Water Surface Area during 1989–2017 in the Huai River Basin using Landsat Data and Google Earth Engine." Remote Sensing 11, no. 15: 1824.
Both cropland and climate change over time, but the potential effects of climate change on cropland is currently not well understood. Here, we combined temporally and spatially explicit dynamics of cropland with air temperature, precipitation, and solar radiation datasets. China’s cropland showed a clear northward-shifting trend from 1990 to 2015. The cropland decreased south of the break line at 38° N, whereas it increased from the break line to northern regions. Correspondingly, the temperature showed a significant warming trend in the early part of the study period, which slowed down in later years. During the whole study period, both precipitation and solar radiation decreased over time, showed no significant linear characteristics, and the annual fluctuations were very large. The cropland areas in China showed a displacement characteristic with the increasing temperature, precipitation, and radiation. Overall, the cropland was shifting towards the high-temperature, low-precipitation, and low-radiation areas. The cropland dynamics indicate that they are likely to face severe drought and radiation pressure. Our findings imply that more resources such as irrigation may be needed for cropland, which will undoubtedly aggravate the agricultural water use in most northern regions, and the potential impacts on food security will further emerge in the future.
Yiming Fu; Yaoping Cui; Yaochen Qin; Nan Li; Liangyu Chen; Haoming Xia. Continued Hydrothermal and Radiative Pressure on Changed Cropland in China. Sustainability 2019, 11, 3762 .
AMA StyleYiming Fu, Yaoping Cui, Yaochen Qin, Nan Li, Liangyu Chen, Haoming Xia. Continued Hydrothermal and Radiative Pressure on Changed Cropland in China. Sustainability. 2019; 11 (14):3762.
Chicago/Turabian StyleYiming Fu; Yaoping Cui; Yaochen Qin; Nan Li; Liangyu Chen; Haoming Xia. 2019. "Continued Hydrothermal and Radiative Pressure on Changed Cropland in China." Sustainability 11, no. 14: 3762.
Over the period 1982–2015, temperatures have exhibited an asymmetric warming pattern diurnally, as well as seasonally across the Loess Plateau. However, very limited research has studied the implications and effects of such seasonally heterogeneous warming across the Loess Plateau. In this study, we also analyzed the time series trends and seasonal spatial patterns of the maximum (Tmax) and minimum (Tmin) temperatures and evaluated how different vegetation responded to daytime and nighttime warming in the Loess Plateau from 1982 to 2015 based on the NDVI and meteorological parameters (precipitation or temperature). We found that Tmax and Tmin significantly increased throughout the years except for Tmax in autumn, and the diurnal asymmetric warming showed some striking seasonal differences. For example, the increasing rates of Tmin in spring, summer, autumn, and winter were 0.75, 1.20, 1.88, and 1.10 times larger than that of Tmax, respectively. NDVI showed significantly positive correlation with Tmax and Tmin in spring and winter, while NDVI presented significantly positive correlation with Tmin in summer and Tmax in autumn across entire Loess Plateau. Furthermore, we also discovered diverse seasonal responses in terms of vegetation types to daytime and nighttime warming. For instance, Spring NDVI showed significantly positive partial correlations with Tmax and Tmin. In summer, grasslands and wetlands merely displayed significantly positive partial correlations with Tmin. Cultivated land presented significantly positive partial correlation between the NDVI and Tmax (Tmin) in autumn. In winter, cultivated land, forest, and grassland exhibited significantly positive partial correlation with Tmax and Tmin, while only wetland showed a significantly positive partial correlation with Tmax. Our results demonstrated responses of vegetation to climate extremes and enhance a better understanding of the seasonally different responses of vegetation under global climate change at different scale.
Liqun Ma; Fen Qin; Hao Wang; Yaochen Qin; Haoming Xia. Asymmetric seasonal daytime and nighttime warming and its effects on vegetation in the Loess Plateau. PLOS ONE 2019, 14, e0218480 .
AMA StyleLiqun Ma, Fen Qin, Hao Wang, Yaochen Qin, Haoming Xia. Asymmetric seasonal daytime and nighttime warming and its effects on vegetation in the Loess Plateau. PLOS ONE. 2019; 14 (6):e0218480.
Chicago/Turabian StyleLiqun Ma; Fen Qin; Hao Wang; Yaochen Qin; Haoming Xia. 2019. "Asymmetric seasonal daytime and nighttime warming and its effects on vegetation in the Loess Plateau." PLOS ONE 14, no. 6: e0218480.
Temperatures from 1982 to 2015 have exhibited an asymmetric warming pattern between day and night throughout the Yellow River Basin. The response to this asymmetric warming can be linked to vegetation growth as quantified by the NDVI (Normalized Difference Vegetation Index). In this study, the time series trends of the maximum temperature (Tmax) and the minimum temperature (Tmin) and their spatial patterns in the growing season (April-October) of the Yellow River Basin from 1982 to 2015 were analyzed. We evaluated how vegetation NDVI had responded to daytime and night-time warming, based on NDVI and meteorological parameters (precipitation and temperature) over the period 1982-2015. We found: (1) a persistent increase in the growing season Tmax and Tmin in 1982-2015 as confirmed by using the Mann-Kendall (M-K) non-parametric test method (p < 0.01), where the rate of increase of Tmin was 1.25 times that of Tmax, and thus the diurnal warming was asymmetric during 1982-2015; (2) the partial correlation between Tmax and NDVI was significantly positive only for cultivated plants, shrubs, and desert, which means daytime warming may increase arid and semi-arid vegetation's growth and coverage, and cultivated plants' growth and yield. The partial correlation between Tmin and NDVI of all vegetation types except broadleaf forest is very significant (p < 0.01) and, therefore, it has more impacts vegetation across the whole basin. This study demonstrates a methodogy for studying regional responses of vegetation to climate extremes under global climate change.
Liqun Ma; Haoming Xia; Qingmin Meng. Spatiotemporal Variability of Asymmetric Daytime and Night-Time Warming and Its Effects on Vegetation in the Yellow River Basin from 1982 to 2015. Sensors 2019, 19, 1832 .
AMA StyleLiqun Ma, Haoming Xia, Qingmin Meng. Spatiotemporal Variability of Asymmetric Daytime and Night-Time Warming and Its Effects on Vegetation in the Yellow River Basin from 1982 to 2015. Sensors. 2019; 19 (8):1832.
Chicago/Turabian StyleLiqun Ma; Haoming Xia; Qingmin Meng. 2019. "Spatiotemporal Variability of Asymmetric Daytime and Night-Time Warming and Its Effects on Vegetation in the Yellow River Basin from 1982 to 2015." Sensors 19, no. 8: 1832.
It is crucial to assess the effects of urban expansion on croplands to allow sustainable urbanization and cropland supply. However, owing to the complexity of land conversion and various land policies in China, it is difficult to quantify the cropland dynamics and implications of urban expansion throughout the whole accelerated stage of urbanization. This study was based on land use data from 1990 to 2015 and urban expansion data from 2000 to 2030, analyzing urban expansion and predicting its impact on croplands. We found that urban area would continue to increase and croplands would contribute more than 70% of the urban expansion area. The urban area in China will likely reach 71.6–87.0 thousand km2 or more by 2030. Although the overall area of croplands may remain at a similar magnitude in future decades, our findings imply that croplands will tend to shift northward, resulting in some potential challenges owing to resource limitations in northern regions. Our study provides a new perspective in terms of assessing future cropland dynamics and the effects of urban expansion and highlights the significance of ensuring a realistic land policy in the future.
Yaoping Cui; Jiyuan Liu; Xinliang Xu; Jinwei Dong; Nan Li; Yiming Fu; Siqi Lu; Haoming Xia; Bo Si; Xiangming Xiao. Accelerating Cities in an Unsustainable Landscape: Urban Expansion and Cropland Occupation in China, 1990–2030. Sustainability 2019, 11, 2283 .
AMA StyleYaoping Cui, Jiyuan Liu, Xinliang Xu, Jinwei Dong, Nan Li, Yiming Fu, Siqi Lu, Haoming Xia, Bo Si, Xiangming Xiao. Accelerating Cities in an Unsustainable Landscape: Urban Expansion and Cropland Occupation in China, 1990–2030. Sustainability. 2019; 11 (8):2283.
Chicago/Turabian StyleYaoping Cui; Jiyuan Liu; Xinliang Xu; Jinwei Dong; Nan Li; Yiming Fu; Siqi Lu; Haoming Xia; Bo Si; Xiangming Xiao. 2019. "Accelerating Cities in an Unsustainable Landscape: Urban Expansion and Cropland Occupation in China, 1990–2030." Sustainability 11, no. 8: 2283.
The Loess Plateau is located at the transition zone between agriculture and livestock farming; its spatial and temporal pattern of drought is the key for an appropriate adaptation to climate change. This study investigated monthly meteorological observation data of 79 meteorological stations from 1955 to 2014 to calculate the standardized precipitation evapotranspiration index at different time scales. The spatial and temporal characteristics and persistence of drought were analyzed. The results showed the following: (i) The drought trend is most apparent in spring (0.096/10a) and lower in summer (0.036/10a) and autumn (0.009/10a). (ii) A higher drought level indicates a lower frequency of droughts occurrence and vice versa. The frequency of light drought was highest (11.36%), while that of extreme drought was lowest (0.12%). (iii) The mean drought intensity was highest in summer, followed by spring, autumn, and winter. The drought intensity was mainly light, showing a pattern of severe drought in the northwest and light drought in the southeast. (iv) The Loess Plateau will continue a trend of drought in the future, but the season of the continuous intensity will differ. Droughts in spring and summer are highly persistent, autumn drought trends continue but may slow, and winter droughts become random events.
Yang Li; Zhixiang Xie; Yaochen Qin; Haoming Xia; Zhicheng Zheng; Lijun Zhang; Ziwu Pan; Zhenzhen Liu. Drought Under Global Warming and Climate Change: An Empirical Study of the Loess Plateau. Sustainability 2019, 11, 1281 .
AMA StyleYang Li, Zhixiang Xie, Yaochen Qin, Haoming Xia, Zhicheng Zheng, Lijun Zhang, Ziwu Pan, Zhenzhen Liu. Drought Under Global Warming and Climate Change: An Empirical Study of the Loess Plateau. Sustainability. 2019; 11 (5):1281.
Chicago/Turabian StyleYang Li; Zhixiang Xie; Yaochen Qin; Haoming Xia; Zhicheng Zheng; Lijun Zhang; Ziwu Pan; Zhenzhen Liu. 2019. "Drought Under Global Warming and Climate Change: An Empirical Study of the Loess Plateau." Sustainability 11, no. 5: 1281.
The accessibility of hospital facilities is of great importance not only for maintaining social stability, but also for protecting the basic human right to health care. Traditional accessibility research often lacks consideration of the dynamic changes in transport costs and does not reflect the actual travel time of urban residents, which is critical to time-sensitive hospital services. To avoid these defects, this study considered the city of Kaifeng, China, as an empirical case, and directly acquired travel time data for two travel modes to the hospital in different time periods through web mapping API (Application Program Interface). Further, based on travel time calculations, we compared five baseline indicators. For the last indicator, we used the optimal weighted accessibility model to measure hospital accessibility for each residential area. The study discovered significant differences in the frequency and spatial distribution of hospital accessibility using public transit and self-driving modes of transportation. In addition, there is an imbalance between accessibility travel times in the study area and the number of arrivals at hospitals. In particular, different modes of transportation and different travel periods also have a certain impact on accessibility of medical treatment. The research results shed new light on the accessibility of urban public facilities and provide a scientific basis with which local governments can optimize the spatial structure of hospital resources.
Zhicheng Zheng; Haoming Xia; Shrinidhi Ambinakudige; Yaochen Qin; Yang Li; Zhixiang Xie; Lijun Zhang; Haibin Gu. Spatial Accessibility to Hospitals Based on Web Mapping API: An Empirical Study in Kaifeng, China. Sustainability 2019, 11, 1160 .
AMA StyleZhicheng Zheng, Haoming Xia, Shrinidhi Ambinakudige, Yaochen Qin, Yang Li, Zhixiang Xie, Lijun Zhang, Haibin Gu. Spatial Accessibility to Hospitals Based on Web Mapping API: An Empirical Study in Kaifeng, China. Sustainability. 2019; 11 (4):1160.
Chicago/Turabian StyleZhicheng Zheng; Haoming Xia; Shrinidhi Ambinakudige; Yaochen Qin; Yang Li; Zhixiang Xie; Lijun Zhang; Haibin Gu. 2019. "Spatial Accessibility to Hospitals Based on Web Mapping API: An Empirical Study in Kaifeng, China." Sustainability 11, no. 4: 1160.
As the second largest economy in the world, China experiences severe particulate matter (PM) pollution in many of its cities. Meteorological factors are critical in determining both areal and temporal variations in PM pollution levels; understanding these factors and their interactions is critical for accurate forecasting, comprehensive analysis, and effective reduction of this pollution. This study analyzed areal and temporal variations in concentrations of PM2.5, PM10, and PMcoarse (PM10 - PM2.5) and PM2.5 to PM10 ratios (PM2.5/PM10) and their relationships with meteorological conditions in 366 Chinese cities from January 1, 2015 to December 31, 2017. On the national scale, PM2.5 and PM10 decreased from 48 to 42 μg m−³ and from 88 to 84 μg m−³, respectively, and the annual mean concentrations were 45 μg m−³ (PM2.5) and 84 μg m−³ (PM10) during the time period (2015–2017). In most regions, largest PM concentrations occurred in winter. However, in northern China, in spring PMcoarse concentrations were highest due to dust. The PM2.5/PM10 ratio was higher in southern than in northern China. There were large regional disparities in PM diurnal variations. Generally, PM concentrations were negatively correlated with precipitation, relative humidity, air temperature, and wind speed, but were positively correlated with surface pressure. The sunshine duration showed negative and positive impacts on PM in northern and southern cities, respectively. Meteorological factors impacted particulates of different size differently in different regions and over different periods of time.
Xiaoyang Li; Hongquan Song; Shiyan Zhai; Siqi Lu; Yunfeng Kong; Haoming Xia; Haipeng Zhao. Particulate matter pollution in Chinese cities: Areal-temporal variations and their relationships with meteorological conditions (2015–2017). Environmental Pollution 2018, 246, 11 -18.
AMA StyleXiaoyang Li, Hongquan Song, Shiyan Zhai, Siqi Lu, Yunfeng Kong, Haoming Xia, Haipeng Zhao. Particulate matter pollution in Chinese cities: Areal-temporal variations and their relationships with meteorological conditions (2015–2017). Environmental Pollution. 2018; 246 ():11-18.
Chicago/Turabian StyleXiaoyang Li; Hongquan Song; Shiyan Zhai; Siqi Lu; Yunfeng Kong; Haoming Xia; Haipeng Zhao. 2018. "Particulate matter pollution in Chinese cities: Areal-temporal variations and their relationships with meteorological conditions (2015–2017)." Environmental Pollution 246, no. : 11-18.
The Yellow River Basin has been affected by global climate change. Studying the spatial–temporal variability of the hydrothermal climate conditions in the Yellow River Basin is of vital importance for the development of technologies and policies related to ecological, environmental, and agricultural adaptation in this region. This study selected temperature and precipitation data observed from 118 meteorological stations distributed in the Yellow River Basin over the period of 1957–2015, and used the Mann–Kendall, Pettitt, and Hurst indices to investigate the spatial–temporal variability of the hydrothermal climate conditions in this area. The results indicated: (1) the annual maximum, minimum, and average temperatures have increased. The seasonal maximum, minimum, and average temperatures for the spring, summer, autumn, and winter have also increased, and this trend is statistically significant (p < 0.01) between 1957–2015. The rate of increase in the minimum temperature exceeded that of the maximum temperature, and diurnal warming was asymmetric. Annual precipitation and the total spring, summer, and autumn precipitations declined, while the total winter precipitation increased, although the trend was non-significant (p > 0.05). (2) Based on the very restrictive assumption that future changes will be similar to past changes, according to the Hurst index experiment, the future trends of temperature and precipitation in the Yellow River Basin are expected to stay the same as in the past. There will be a long-term correlation between the two trends: the temperature will continue to rise, while the precipitation will continue to decline (except in the winter). However, over the late stage of the study period, the trends slowed down to some extent.
Liqun Ma; Haoming Xia; Jiulin Sun; Hao Wang; Gary Feng; Fen Qin. Spatial–Temporal Variability of Hydrothermal Climate Conditions in the Yellow River Basin from 1957 to 2015. Atmosphere 2018, 9, 433 .
AMA StyleLiqun Ma, Haoming Xia, Jiulin Sun, Hao Wang, Gary Feng, Fen Qin. Spatial–Temporal Variability of Hydrothermal Climate Conditions in the Yellow River Basin from 1957 to 2015. Atmosphere. 2018; 9 (11):433.
Chicago/Turabian StyleLiqun Ma; Haoming Xia; Jiulin Sun; Hao Wang; Gary Feng; Fen Qin. 2018. "Spatial–Temporal Variability of Hydrothermal Climate Conditions in the Yellow River Basin from 1957 to 2015." Atmosphere 9, no. 11: 433.