scholarly journals Atmospheric Correction of Multispectral VNIR Remote Sensing Data: Algorithm and Inter‐sensor Comparison of Aerosol and Surface Reflectance Products

2020 ◽  
Vol 7 (9) ◽  
Author(s):  
Manoj K. Mishra ◽  
Pradyuman S. Rathore ◽  
Arundhati Misra ◽  
Raj Kumar
Keyword(s):  
Remote Sensing ◽  
Atmospheric Correction ◽  
Remote Sensing Data ◽  
Surface Reflectance ◽  
Sensing Data

scholarly journals Mapping Forest Canopy Height in Mountainous Areas Using ZiYuan-3 Stereo Images and Landsat Data

Forests ◽  
2019 ◽  
Vol 10 (2) ◽  
pp. 105 ◽  
Author(s):  
Mingbo Liu ◽  
Chunxiang Cao ◽  
Yongfeng Dang ◽  
Xiliang Ni
Keyword(s):  
Remote Sensing ◽  
Forest Canopy ◽  
Remote Sensing Data ◽  
Canopy Height ◽  
Landsat 8 ◽  
Surface Reflectance ◽  
Landsat Data ◽  
Sensing Data ◽  
Forest Height ◽  
Reflectance Data

Forest canopy height is an important parameter for studying biodiversity and the carbon cycle. A variety of techniques for mapping forest height using remote sensing data have been successfully developed in recent years. However, the demands for forest height mapping in practical applications are often not met, due to the lack of corresponding remote sensing data. In such cases, it would be useful to exploit the latest, cheaper datasets and combine them with free datasets for the mapping of forest canopy height. In this study, we proposed a method that combined ZiYuan-3 (ZY-3) stereo images, Shuttle Radar Topography Mission global 1 arc second data (SRTMGL1), and Landsat 8 Operational Land Imager (OLI) surface reflectance data. The method consisted of three procedures: First, we extracted a digital surface model (DSM) from the ZY-3, using photogrammetry methods and subtracted the SRTMGL1 to obtain a crude canopy height model (CHM). Second, we refined the crude CHM and correlated it with the topographically corrected Landsat 8 surface reflectance data, the vegetation indices, and the forest types through a Random Forest model. Third, we extrapolated the model to the entire study area covered by the Landsat data, and obtained a wall-to-wall forest canopy height product with 30 m × 30 m spatial resolution. The performance of the model was evaluated by the Random Forest’s out-of-bag estimation, which yielded a coefficient of determination (R2) of 0.53 and a root mean square error (RMSE) of 3.28 m. We validated the predicted forest canopy height using the mean forest height measured in the field survey plots. The validation result showed an R2 of 0.62 and a RMSE of 2.64 m.

scholarly journals Improved Surface Reflectance from Remote Sensing Data with Sub-Pixel Topographic Information

2014 ◽  
Vol 6 (11) ◽  
pp. 10356-10374 ◽  
Author(s):  
Laure Roupioz ◽  
Francoise Nerry ◽  
Li Jia ◽  
Massimo Menenti
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Surface Reflectance ◽  
Sensing Data ◽  
Topographic Information

scholarly journals Agricultural Stress Monitoring using Remote Sensing Data

2020 ◽  
Vol 9 (3) ◽  
pp. 335-337
Keyword(s):  
Remote Sensing ◽  
Stress Level ◽  
Hyperspectral Image ◽  
Atmospheric Correction ◽  
Remote Sensing Data ◽  
Hyperspectral Remote Sensing ◽  
Hyperspectral Data ◽  
Stress Monitoring ◽  
Chlorophyll Contents ◽  
Sensing Data

This study consist of experiments on Hyperspectral remote sensing data for monitoring field stress using remote sensing tools. We have segmented Hyperspectral image and then calculated stress level using ENVI tool. EO-I hyperspectral remote sensing data from hyperion space born sensor has been used as the key input. QUACK (Quick Atmospheric Correction) algorithm has been used for atmospheric correction of hyperspectral data. EO-1, hyperion sensors data It has been observed that stress level depends on chlorophyll contents of a leaf. It has been observed that green field is with less stress and rock where no chlorophyll contents have most stress. We have also shown stress level in the scale of 1 to 9.

scholarly journals Aerosol Optical Retrieval and Surface Reflectance from Airborne Remote Sensing Data over Land

Sensors ◽  
2010 ◽  
Vol 10 (7) ◽  
pp. 6421-6438 ◽  
Author(s):  
Cristiana Bassani ◽  
Rosa Maria Cavalli ◽  
Stefano Pignatti
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Surface Reflectance ◽  
Airborne Remote Sensing ◽  
Sensing Data
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