How to extract river nets effectively is of great significance for water resources investigation,flood forecasting and environmental monitoring,etc.In the paper,combining with ant colony algorithm,a new approach of ex...How to extract river nets effectively is of great significance for water resources investigation,flood forecasting and environmental monitoring,etc.In the paper,combining with ant colony algorithm,a new approach of extracting river nets on moderate-resolution imaging spectroradiometer(MODIS)remote sensing images was proposed through analyzing two general extraction methods of river nets.The experiment results show that river nets can be optimized by ant colony algorithm efficiently,and difference ratio between the experimental vectorgraph and the data of National Fundamental Geographic Information System is down to 8.7%.The proposed algorithm can work for extracting river nets on MODIS remote sensing images effectively.展开更多
In this study,the Surface Energy Balance Algorithms for Land(SEBAL) model and Moderate Resolution Imaging Spectroradiometer(MODIS) products from Terra satellite were combined with meteorological data to estimate evapo...In this study,the Surface Energy Balance Algorithms for Land(SEBAL) model and Moderate Resolution Imaging Spectroradiometer(MODIS) products from Terra satellite were combined with meteorological data to estimate evapotranspiration(ET) over the Sanjiang Plain,Northeast China.Land cover/land use was classified by using a recursive partitioning and regression tree with MODIS Normalized Difference Vegetation Index(NDVI) time series data,which were reconstructed based on the Savitzky-Golay filtering approach.The MODIS product Quality Assessment Science Data Sets(QA-SDS) was analyzed and all scenes with valid data covering more than 75% of the Sanjiang Plain were selected for the SEBAL modeling.This provided 12 overpasses during 184-day growing season from May 1st to October 31st,2006.Daily ET estimated by the SEBAL model was misestimaed at the range of-11.29% to 27.57% compared with that measured by Eddy Covariance system(10.52% on average).The validation results show that seasonal ET from the SEBAL model is comparable to that from ground observation within 8.86% of deviation.Our results reveal that the time series daily ET of different land cover/use increases from vegetation on-going until June or July and then decreases as vegetation senesced.Seasonal ET is lower in dry farmland(average(Ave):491 mm) and paddy field(Ave:522 mm) and increases in wetlands to more than 586 mm.As expected,higher seasonal ET values are observed for the Xingkai Lake in the southeastern part of the Sanjiang Plain(Ave:823 mm),broadleaf forest(Ave:666 mm) and mixed wood(Ave:622 mm) in the southern/western Sanjiang Plain.The ET estimation with SEBAL using MODIS products can provide decision support for operational water management issues.展开更多
Water vapor plays a crucial role in atmospheric processes that act over a wide range of temporal and spatial scales, from global climate to micrometeorology. The determination of water vapor distribution in the atmosp...Water vapor plays a crucial role in atmospheric processes that act over a wide range of temporal and spatial scales, from global climate to micrometeorology. The determination of water vapor distribution in the atmosphere and its changing pattern is very important. Although atmospheric scientists have developed a variety of means to measure precipitable water vapor(PWV) using remote sensing data that have been widely used, there are some limitations in using one kind satellite measurements for PWV retrieval over land. In this paper, a new algorithm is proposed for retrieving PWV over land by combining different kinds of remote sensing data and it would work well under the cloud weather conditions. The PWV retrieval algorithm based on near infrared data is more suitable to clear sky conditions with high precision. The 23.5 GHz microwave remote sensing data is sensitive to water vapor and powerful in cloud-covered areas because of its longer wavelengths that permit viewing into and through the atmosphere. Therefore, the PWV retrieval results from near infrared data and the indices combined by microwave bands remote sensing data which are sensitive to water vapor will be regressed to generate the equation for PWV retrieval under cloud covered areas. The algorithm developed in this paper has the potential to detect PWV under all weather conditions and makes an excellent complement to PWV retrieved by near infrared data. Different types of surface exert different depolarization effects on surface emissions, which would increase the complexity of the algorithm. In this paper, MODIS surface classification data was used to consider this influence. Compared with the GPS results, the root mean square error of our algorithm is 8 mm for cloud covered area. Regional consistency was found between the results from MODIS and our algorithm. Our algorithm can yield reasonable results on the surfaces covered by cloud where MODIS cannot be used to retrieve PWV.展开更多
基金National High Technology Research and Development Program of China(No.2007AA120305)National ScienceFoundation of China(No.40771145)+2 种基金Special Project of Ministry of Science and Technology of China(No.GYHY20070628)Subtopics of Ministry of Land and Resources Project of China(No.KD081902-03)Scientific Research and Innovation Project of Graduate School of Shanghai University,China(No.SHUCX101033)
文摘How to extract river nets effectively is of great significance for water resources investigation,flood forecasting and environmental monitoring,etc.In the paper,combining with ant colony algorithm,a new approach of extracting river nets on moderate-resolution imaging spectroradiometer(MODIS)remote sensing images was proposed through analyzing two general extraction methods of river nets.The experiment results show that river nets can be optimized by ant colony algorithm efficiently,and difference ratio between the experimental vectorgraph and the data of National Fundamental Geographic Information System is down to 8.7%.The proposed algorithm can work for extracting river nets on MODIS remote sensing images effectively.
基金Under the auspices of National Basic Research Program of China (No. 2010CB951304-5)National Natural Science Foundation of China (No. 41101545,41030743)
文摘In this study,the Surface Energy Balance Algorithms for Land(SEBAL) model and Moderate Resolution Imaging Spectroradiometer(MODIS) products from Terra satellite were combined with meteorological data to estimate evapotranspiration(ET) over the Sanjiang Plain,Northeast China.Land cover/land use was classified by using a recursive partitioning and regression tree with MODIS Normalized Difference Vegetation Index(NDVI) time series data,which were reconstructed based on the Savitzky-Golay filtering approach.The MODIS product Quality Assessment Science Data Sets(QA-SDS) was analyzed and all scenes with valid data covering more than 75% of the Sanjiang Plain were selected for the SEBAL modeling.This provided 12 overpasses during 184-day growing season from May 1st to October 31st,2006.Daily ET estimated by the SEBAL model was misestimaed at the range of-11.29% to 27.57% compared with that measured by Eddy Covariance system(10.52% on average).The validation results show that seasonal ET from the SEBAL model is comparable to that from ground observation within 8.86% of deviation.Our results reveal that the time series daily ET of different land cover/use increases from vegetation on-going until June or July and then decreases as vegetation senesced.Seasonal ET is lower in dry farmland(average(Ave):491 mm) and paddy field(Ave:522 mm) and increases in wetlands to more than 586 mm.As expected,higher seasonal ET values are observed for the Xingkai Lake in the southeastern part of the Sanjiang Plain(Ave:823 mm),broadleaf forest(Ave:666 mm) and mixed wood(Ave:622 mm) in the southern/western Sanjiang Plain.The ET estimation with SEBAL using MODIS products can provide decision support for operational water management issues.
基金supported by the National Natural Science Foundation of China(Grant Nos.4147130541405036&41301653)+1 种基金the Sichuan Youth Science Foundation(Grant No.2015JQ0037)the Chongqing Meteorological Bureau Open Fund(Grant No.KFJJ-201402)
文摘Water vapor plays a crucial role in atmospheric processes that act over a wide range of temporal and spatial scales, from global climate to micrometeorology. The determination of water vapor distribution in the atmosphere and its changing pattern is very important. Although atmospheric scientists have developed a variety of means to measure precipitable water vapor(PWV) using remote sensing data that have been widely used, there are some limitations in using one kind satellite measurements for PWV retrieval over land. In this paper, a new algorithm is proposed for retrieving PWV over land by combining different kinds of remote sensing data and it would work well under the cloud weather conditions. The PWV retrieval algorithm based on near infrared data is more suitable to clear sky conditions with high precision. The 23.5 GHz microwave remote sensing data is sensitive to water vapor and powerful in cloud-covered areas because of its longer wavelengths that permit viewing into and through the atmosphere. Therefore, the PWV retrieval results from near infrared data and the indices combined by microwave bands remote sensing data which are sensitive to water vapor will be regressed to generate the equation for PWV retrieval under cloud covered areas. The algorithm developed in this paper has the potential to detect PWV under all weather conditions and makes an excellent complement to PWV retrieved by near infrared data. Different types of surface exert different depolarization effects on surface emissions, which would increase the complexity of the algorithm. In this paper, MODIS surface classification data was used to consider this influence. Compared with the GPS results, the root mean square error of our algorithm is 8 mm for cloud covered area. Regional consistency was found between the results from MODIS and our algorithm. Our algorithm can yield reasonable results on the surfaces covered by cloud where MODIS cannot be used to retrieve PWV.
