2014 IEEE Geoscience and Remote Sensing Symposium最新文献_第6页

Application of statistical and machine learning models for grassland yield estimation based on a hypertemporal satellite remote sensing time series 统计与机器学习模型在高时相卫星遥感时间序列草地产量估算中的应用

2014 IEEE Geoscience and Remote Sensing Symposium Pub Date : 2014-07-13 DOI: 10.1109/IGARSS.2014.6947634

Iftikhar Ali, F. Cawkwell, S. Green, N. Dwyer

{"title":"Application of statistical and machine learning models for grassland yield estimation based on a hypertemporal satellite remote sensing time series","authors":"Iftikhar Ali, F. Cawkwell, S. Green, N. Dwyer","doi":"10.1109/IGARSS.2014.6947634","DOIUrl":"https://doi.org/10.1109/IGARSS.2014.6947634","url":null,"abstract":"More than 80% of agricultural land in Ireland is grassland, providing a major feed source for the pasture based dairy farming and livestock industry. Intensive grass based systems demand high levels of intervention by the farmer, with estimation of pasture cover (biomass) being the most important variable in land use management decisions, as well as playing a vital role in paddock and herd management. Many studies have been undertaken to estimate grassland biomass using satellite remote sensing data, but rarely in systems like Ire-lands intensively managed, small scale pastures, where grass is grazed as well as harvested for winter fodder. The objective of this study is to estimate grassland yield (kgDM/ha) from MODIS derived vegetation indices on a near weekly basis across the entire 300+ day growing season using three different methods (Multiple Linear Regression (MLR), Artificial Neural Networks (ANN) and Adaptive Neuro-Fuzzy Inference Systems (ANFIS)). The results show that ANFIS model produced best result (R2 = 0.86) as compare to the ANN (R2 = 0.57) and MLR (R2 = 0.31).","PeriodicalId":385645,"journal":{"name":"2014 IEEE Geoscience and Remote Sensing Symposium","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126938270","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 29

Orthorectification of Sich-2 satellite images using elastic models 基于弹性模型的ssi -2卫星图像正校正

2014 IEEE Geoscience and Remote Sensing Symposium Pub Date : 2014-07-13 DOI: 10.1109/IGARSS.2014.6946925

O. Kravchenko, M. Lavrenyuk, N. Kussul

引用次数: 2

Design of one-stop and on-demand remote sensing integrated services platform 一站式按需遥感综合服务平台设计

2014 IEEE Geoscience and Remote Sensing Symposium Pub Date : 2014-07-13 DOI: 10.1109/IGARSS.2014.6947488

Haizhen Zhang, Jian Jiao, Junxiao Hu, Dapeng Yan, Xin Li, Sheng Gao, Q. Zeng

引用次数: 0

KuROS: A new airborne Ku-band Doppler radar for observation of the ocean surface KuROS:一种用于观测海洋表面的新型机载ku波段多普勒雷达

2014 IEEE Geoscience and Remote Sensing Symposium Pub Date : 2014-07-13 DOI: 10.1109/IGARSS.2014.6946412

D. Hauser, G. Caudal, C. Gac, R. Valentin, L. Delaye, C. Tison

引用次数: 4

A bidirectional gradient prediction based method for hyperspectral data junk bands restoration 基于双向梯度预测的高光谱数据垃圾带恢复方法

2014 IEEE Geoscience and Remote Sensing Symposium Pub Date : 2014-07-13 DOI: 10.1109/IGARSS.2014.6947523

Yidan Teng, Ye Zhang, Yushi Chen

引用次数: 1

Spectral-based multi-level Morphological Active Contour algorithm for individual tree detection and crown delineation 基于光谱的树体检测和树冠圈定多级形态学主动轮廓算法

2014 IEEE Geoscience and Remote Sensing Symposium Pub Date : 2014-07-13 DOI: 10.1109/IGARSS.2014.6946820

Chao-Cheng Wu, Yi-Ling Chen, Jheng-De Wu, Chinsu Lin

引用次数: 3

Recovering land surface temperature under cloudy skies for potentially deriving surface emitted longwave radiation by fusing MODIS and AMSR-E measurements 通过融合MODIS和AMSR-E测量数据，恢复多云天气下地表温度，从而可能得到地表发射的长波辐射

2014 IEEE Geoscience and Remote Sensing Symposium Pub Date : 2014-07-13 DOI: 10.1109/IGARSS.2014.6946804

Tianxing Wang, Jiancheng Shi, G. Yan, T. Zhao, Dabin Ji, C. Xiong

{"title":"Recovering land surface temperature under cloudy skies for potentially deriving surface emitted longwave radiation by fusing MODIS and AMSR-E measurements","authors":"Tianxing Wang, Jiancheng Shi, G. Yan, T. Zhao, Dabin Ji, C. Xiong","doi":"10.1109/IGARSS.2014.6946804","DOIUrl":"https://doi.org/10.1109/IGARSS.2014.6946804","url":null,"abstract":"Longwave radiation is a key component of total energy that drives surface energy balance at the interface between the surface and atmosphere. To date, a number of algorithms have been developed toward accurately estimating surface longwave radiation from remotely sensed data. While most of these existing algorithms can only derive longwave radiation under clear-sky conditions due to the limited penetration of optical remote sensing thus leading to spatial incontinuity in derived radiation map. Wherein the land surface temperature (LST) play a key role in longwave radiation estimation, especially for surface emitted (upwelling) and net longwave flux. If LSTs under cloudy area can be recovered, the derivation of surface longwave ration under cloudy conditions would be straightforward. To this end, in this paper, a fusing strategy is proposed to combine the LST measurements from MODIS and AMSR-E. The results show that the proposed fusing strategy for combining microwave and optical space-based measurements in recovering surface LST under cloudy conditions is very effective. By fusion, the spatial coverage of valid LSTs over the globe is highly improved.","PeriodicalId":385645,"journal":{"name":"2014 IEEE Geoscience and Remote Sensing Symposium","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115495811","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 10

Potential of hyperspectral imagery for the spatial assessment of soil erosion stages in agricultural semi-arid Spain at different scales 高光谱影像在不同尺度西班牙农业半干旱地区土壤侵蚀阶段空间评价中的潜力

2014 IEEE Geoscience and Remote Sensing Symposium Pub Date : 2014-07-13 DOI: 10.1109/IGARSS.2014.6947087

S. Chabrillat, R. Milewski, T. Schmid, Manuel Rodríguez, P. Escribano, M. Pelayo, A. Palacios-Orueta

{"title":"Potential of hyperspectral imagery for the spatial assessment of soil erosion stages in agricultural semi-arid Spain at different scales","authors":"S. Chabrillat, R. Milewski, T. Schmid, Manuel Rodríguez, P. Escribano, M. Pelayo, A. Palacios-Orueta","doi":"10.1109/IGARSS.2014.6947087","DOIUrl":"https://doi.org/10.1109/IGARSS.2014.6947087","url":null,"abstract":"This research focuses on a semi-arid, agricultural area in Central Spain near Madrid, in which airborne hyperspectral images have been obtained. Small-scale soil erosion features are exposed at the surface as a consequence of human induced soil erosion derived mainly from tillage practice. Such features are associated with different soil horizons and rock outcrops with contrasted physical and chemical characteristics. Results show that the identification and mapping of different soil surface horizons linked to soil erosion and depositional stages can be achieved over selected test sites based on the spectroscopy data at high spatial resolution. Linked with field validation data and geomorphological analyses, the spatial mapping of the soil erosion and depositional stages is consistent with the soil erosion models implemented for the region. Preliminary multiscale analyses at 3 m, 6 m, and 30 m show the effect of increasing spatial mixing in the field-of-view of the sensor due to the variability at small scale of the different soil horizons representing the surface topsoil.","PeriodicalId":385645,"journal":{"name":"2014 IEEE Geoscience and Remote Sensing Symposium","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116562228","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 9

Potential atmospheric and terrestrial aplications of a geosynchronous radar 地球同步雷达在大气和地面的潜在应用

2014 IEEE Geoscience and Remote Sensing Symposium Pub Date : 2014-07-13 DOI: 10.1109/IGARSS.2014.6946582

G. Wadge, A. M. Guarnieri, S. Hobbs, D. Schulz

引用次数: 20

A ground network for SAR-derived soil moisture product calibration, validation and exploitation in Southern Italy 意大利南部基于sar的土壤水分产品校准、验证和开发的地面网络

2014 IEEE Geoscience and Remote Sensing Symposium Pub Date : 2014-07-13 DOI: 10.1109/IGARSS.2014.6947206

A. Balenzano, G. Satalino, V. Iacobellis, A. Gioia, S. Manfreda, M. Rinaldi, P. Vita, F. Miglietta, P. Toscano, G. Annicchiarico, F. Mattia

引用次数: 16