[Objective] To extract desertification information of Hulun Buir region based on MODIS image data. [Method] Based on MODIS image data with the spatial res- olution of 1 km, 5 indicators which could reflect different d...[Objective] To extract desertification information of Hulun Buir region based on MODIS image data. [Method] Based on MODIS image data with the spatial res- olution of 1 km, 5 indicators which could reflect different desertification features were selected to conduct inversion. The desertification information of Hulun Buir region was extracted by decision tree classification. [Result] The desertification area of Hu- lun Buir region is 33 862 km2, accounting for 24% of the total area, and it is mainly dominated by sandiness desertification. Though field verification and mining point validation of high-resolution interpretation data, the overall accuracy of this evaluation is above 89%. [Conclusion] Evaluation method used in this study is not only effectively for large scale regional desertification monitoring but also has a better evaluation performance.展开更多
Snow cover plays a critical role in global climate regulation and hydrological processes.Accurate monitoring is essential for understanding snow distribution patterns,managing water resources,and assessing the impacts...Snow cover plays a critical role in global climate regulation and hydrological processes.Accurate monitoring is essential for understanding snow distribution patterns,managing water resources,and assessing the impacts of climate change.Remote sensing has become a vital tool for snow monitoring,with the widely used Moderate-resolution Imaging Spectroradiometer(MODIS)snow products from the Terra and Aqua satellites.However,cloud cover often interferes with snow detection,making cloud removal techniques crucial for reliable snow product generation.This study evaluated the accuracy of four MODIS snow cover datasets generated through different cloud removal algorithms.Using real-time field camera observations from four stations in the Tianshan Mountains,China,this study assessed the performance of these datasets during three distinct snow periods:the snow accumulation period(September-November),snowmelt period(March-June),and stable snow period(December-February in the following year).The findings showed that cloud-free snow products generated using the Hidden Markov Random Field(HMRF)algorithm consistently outperformed the others,particularly under cloud cover,while cloud-free snow products using near-day synthesis and the spatiotemporal adaptive fusion method with error correction(STAR)demonstrated varying performance depending on terrain complexity and cloud conditions.This study highlighted the importance of considering terrain features,land cover types,and snow dynamics when selecting cloud removal methods,particularly in areas with rapid snow accumulation and melting.The results suggested that future research should focus on improving cloud removal algorithms through the integration of machine learning,multi-source data fusion,and advanced remote sensing technologies.By expanding validation efforts and refining cloud removal strategies,more accurate and reliable snow products can be developed,contributing to enhanced snow monitoring and better management of water resources in alpine and arid areas.展开更多
China has a vast territory with abundant crops,and how to collect crop information in China timely,objectively and accurately,is of great significance to the scientific guidance of agricultural development.In this pap...China has a vast territory with abundant crops,and how to collect crop information in China timely,objectively and accurately,is of great significance to the scientific guidance of agricultural development.In this paper,by selecting moderateresolution imaging spectroradiometer(MODIS)data as the main information source,on the basis of spectral and biological characteristics mechanism of the crop,and using the freely available advantage of hyperspectral temporal MODIS data,conduct large scale agricultural remote sensing monitoring research,develop applicable model and algorithm,which can achieve large scale remote sensing extraction and yield estimation of major crop type information,and improve the accuracy of crop quantitative remote sensing.Moreover,the present situation of global crop remote sensing monitoring based on MODIS data is analyzed.Meanwhile,the climate and environment grid agriculture information system using large-scale agricultural condition remote sensing monitoring has been attempted preliminary.展开更多
The Yangtze River Delta（YRD） has experienced significant urban expansion in recent years, while the Meiyu belt of China has demonstrated a decadal northward shifting trend. Thus, it is of interest to assess how urba...The Yangtze River Delta（YRD） has experienced significant urban expansion in recent years, while the Meiyu belt of China has demonstrated a decadal northward shifting trend. Thus, it is of interest to assess how urban expansion affects Meiyu precipitation and hopefully to reveal the underlying physical mechanisms involved. In this study, the urban extents over the YRD in 2001 and 2010 are derived based on land use/land cover（LULC） category data and nighttime light image data. Two parallel groups of10-summer（2001-2010） numerical simulations are carried out with the urban extents over the YRD in2001 and 2010, respectively. The results show that the urban expansion in the YRD tends to result in increased（decreased） Meiyu precipitation over the Huaihe River（Yangtze River） basin with intensities of0.2-1.2 mm day-1. Further analysis indicates that the spatiotemporal pattern of the Meiyu precipitation change induced by the urban expansion resembles the third empirical orthogonal function（EOF） mode of the observed Meiyu precipitation. Analyses of the possible underlying physical mechanisms reveal that urban expansion in the YRD leads to changes in the surface energy balance and warming（cooling） of tropospheric（stratospheric） air temperature over eastern China. Anomalous upward（downward） motion and moisture convergence（divergence） over the Huaihe River（Yangtze River） basin occur, corresponding to the increases（decreases） of the Meiyu precipitation over the Huaihe River（Yangtze River） basin.展开更多
基金Supported by the Special Fundation of China Geological Survey(1212010911084)~~
文摘[Objective] To extract desertification information of Hulun Buir region based on MODIS image data. [Method] Based on MODIS image data with the spatial res- olution of 1 km, 5 indicators which could reflect different desertification features were selected to conduct inversion. The desertification information of Hulun Buir region was extracted by decision tree classification. [Result] The desertification area of Hu- lun Buir region is 33 862 km2, accounting for 24% of the total area, and it is mainly dominated by sandiness desertification. Though field verification and mining point validation of high-resolution interpretation data, the overall accuracy of this evaluation is above 89%. [Conclusion] Evaluation method used in this study is not only effectively for large scale regional desertification monitoring but also has a better evaluation performance.
基金funded by the Third Xinjiang Scientific Expedition Program(2021xjkk1400)the National Natural Science Foundation of China(42071049)+2 种基金the Natural Science Foundation of Xinjiang Uygur Autonomous Region(2019D01C022)the Xinjiang Uygur Autonomous Region Innovation Environment Construction Special Project&Science and Technology Innovation Base Construction Project(PT2107)the Tianshan Talent-Science and Technology Innovation Team(2022TSYCTD0006).
文摘Snow cover plays a critical role in global climate regulation and hydrological processes.Accurate monitoring is essential for understanding snow distribution patterns,managing water resources,and assessing the impacts of climate change.Remote sensing has become a vital tool for snow monitoring,with the widely used Moderate-resolution Imaging Spectroradiometer(MODIS)snow products from the Terra and Aqua satellites.However,cloud cover often interferes with snow detection,making cloud removal techniques crucial for reliable snow product generation.This study evaluated the accuracy of four MODIS snow cover datasets generated through different cloud removal algorithms.Using real-time field camera observations from four stations in the Tianshan Mountains,China,this study assessed the performance of these datasets during three distinct snow periods:the snow accumulation period(September-November),snowmelt period(March-June),and stable snow period(December-February in the following year).The findings showed that cloud-free snow products generated using the Hidden Markov Random Field(HMRF)algorithm consistently outperformed the others,particularly under cloud cover,while cloud-free snow products using near-day synthesis and the spatiotemporal adaptive fusion method with error correction(STAR)demonstrated varying performance depending on terrain complexity and cloud conditions.This study highlighted the importance of considering terrain features,land cover types,and snow dynamics when selecting cloud removal methods,particularly in areas with rapid snow accumulation and melting.The results suggested that future research should focus on improving cloud removal algorithms through the integration of machine learning,multi-source data fusion,and advanced remote sensing technologies.By expanding validation efforts and refining cloud removal strategies,more accurate and reliable snow products can be developed,contributing to enhanced snow monitoring and better management of water resources in alpine and arid areas.
文摘China has a vast territory with abundant crops,and how to collect crop information in China timely,objectively and accurately,is of great significance to the scientific guidance of agricultural development.In this paper,by selecting moderateresolution imaging spectroradiometer(MODIS)data as the main information source,on the basis of spectral and biological characteristics mechanism of the crop,and using the freely available advantage of hyperspectral temporal MODIS data,conduct large scale agricultural remote sensing monitoring research,develop applicable model and algorithm,which can achieve large scale remote sensing extraction and yield estimation of major crop type information,and improve the accuracy of crop quantitative remote sensing.Moreover,the present situation of global crop remote sensing monitoring based on MODIS data is analyzed.Meanwhile,the climate and environment grid agriculture information system using large-scale agricultural condition remote sensing monitoring has been attempted preliminary.
基金Supported by the National Basic Research and Development(973)Program of China(2011CB952002 and 2010CB428504)
文摘The Yangtze River Delta（YRD） has experienced significant urban expansion in recent years, while the Meiyu belt of China has demonstrated a decadal northward shifting trend. Thus, it is of interest to assess how urban expansion affects Meiyu precipitation and hopefully to reveal the underlying physical mechanisms involved. In this study, the urban extents over the YRD in 2001 and 2010 are derived based on land use/land cover（LULC） category data and nighttime light image data. Two parallel groups of10-summer（2001-2010） numerical simulations are carried out with the urban extents over the YRD in2001 and 2010, respectively. The results show that the urban expansion in the YRD tends to result in increased（decreased） Meiyu precipitation over the Huaihe River（Yangtze River） basin with intensities of0.2-1.2 mm day-1. Further analysis indicates that the spatiotemporal pattern of the Meiyu precipitation change induced by the urban expansion resembles the third empirical orthogonal function（EOF） mode of the observed Meiyu precipitation. Analyses of the possible underlying physical mechanisms reveal that urban expansion in the YRD leads to changes in the surface energy balance and warming（cooling） of tropospheric（stratospheric） air temperature over eastern China. Anomalous upward（downward） motion and moisture convergence（divergence） over the Huaihe River（Yangtze River） basin occur, corresponding to the increases（decreases） of the Meiyu precipitation over the Huaihe River（Yangtze River） basin.
