Results 11 to 20 of about 35 (33)
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Earth Science Informatics, 2015
Snow cover characteristics play a vital role in hydrological and climatological analyses. Snow characteristics have been retrieved using different techniques but no study has been conducted hitherto to determine its relationship with snow cover indices. In the present study the relationship of snow cover characteristics i.e., snow grain size index (SGI)
Retinder Kour +2 more
exaly +2 more sources
Snow cover characteristics play a vital role in hydrological and climatological analyses. Snow characteristics have been retrieved using different techniques but no study has been conducted hitherto to determine its relationship with snow cover indices. In the present study the relationship of snow cover characteristics i.e., snow grain size index (SGI)
Retinder Kour +2 more
exaly +2 more sources
2009 IEEE International Geoscience and Remote Sensing Symposium, 2009
This paper describes evaluation of paddy yield and quality estimation methods using an airborne hyperspectral sensor, AISA. Estimation methods are based on various vegetation indices (VIs), Normalized Difference Spectral Index (NDSI), and Partial Least Squares (PLS). In the result of analysis, the paddy quality as measured by the crude protein of brown
Shinya Odagawa +6 more
openaire +1 more source
This paper describes evaluation of paddy yield and quality estimation methods using an airborne hyperspectral sensor, AISA. Estimation methods are based on various vegetation indices (VIs), Normalized Difference Spectral Index (NDSI), and Partial Least Squares (PLS). In the result of analysis, the paddy quality as measured by the crude protein of brown
Shinya Odagawa +6 more
openaire +1 more source
2011 IEEE International Geoscience and Remote Sensing Symposium, 2011
This paper describes to evaluate rice yield and protein estimation methods based on various vegetation indices (VIs), NDSI and PLS using an airborne hyperspectral sensor AISA in Shonai plane, northeast Japan. In several developing stages, which are the tillering stage (middle June), the maximum tiller number stage (early July) and the dough ripe stage (
Shinya Odagawa +6 more
openaire +1 more source
This paper describes to evaluate rice yield and protein estimation methods based on various vegetation indices (VIs), NDSI and PLS using an airborne hyperspectral sensor AISA in Shonai plane, northeast Japan. In several developing stages, which are the tillering stage (middle June), the maximum tiller number stage (early July) and the dough ripe stage (
Shinya Odagawa +6 more
openaire +1 more source
Sensitivity of Snow NDSI to Simulated Snow Grain Shape Characteristics
IEEE Geoscience and Remote Sensing Letters, 2023Gongxue Wang +2 more
exaly
Characterization of NDSI Variation: Implications for Snow Cover Mapping
IEEE Transactions on Geoscience and Remote Sensing, 2022Gongxue Wang +2 more
exaly
Snow cover analysis using NDSI and SWI indices in Pindari-Kafni Glacier valleys, Kumaon Himalaya
Applied GeomaticsPankaj Chauhan +5 more
openaire +1 more source
Spatial and Temporal Adaptive Gap-Filling Method Producing Daily Cloud-Free NDSI Time Series
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2020Siyong Chen, Xiaoyan Wang, Hui Guo
exaly
Combination of NDSI and NDFSI for snow cover mapping in a mountainous and forested region
Yaogan Xuebao/Journal of Remote Sensing, 2023exaly