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Remote Sensing Technique
Published in Ajai, Rimjhim Bhatnagar, Desertification and Land Degradation, 2022
The effect of the atmosphere on the data collected is to reduce the contrast of the features in the imagery it manifests as a loss of ‘sharpness'. Sometimes, the effect of the atmosphere is very clearly observed in the form of haze on the image which suppresses the clarity of the features. In presence of haze, discrimination of adjoining features becomes difficult. The path radiance also affects the radiometric fidelity of the measured data. Thus, for meaningful interpretation and analysis of remote sensing data, especially for quantitative analysis, corrections for atmospheric effects become mandatory, through some techniques. When the atmospheric influences are large, simple atmospheric correction techniques will not be sufficient and one has to adopt a radiative transfer model to retrieve actual reflectance from the atmospherically adulterated remote sensing data.
Drought Assessment and Management for Heat Waves Monitoring
Published in Saeid Eslamian, Faezeh Eslamian, Handbook of Drought and Water Scarcity, 2017
Nicolas R. Dalezios, Saeid Eslamian
New sensors and algorithms have constantly enabled the incorporation of improved remotely sensed information in drought characterization. New sensors have higher spatial resolution, a current shortcoming in drought indices [50]. Novel noise reduction algorithms and other atmospheric correction algorithms improve the thematic accuracy of remote sensing datasets. Remote sensing indices are diverse and new indices are frequently proposed. While NDVI has remained popular [7,56], other indices such as VegDRI, Vegetation Condition Index (VCI) [36], Temperature Condition index (TCI), and Vegetation Health Index (VHI) [35] are currently operationally used [49,51]. Traditionally used bands include near-infrared (NIR), red, and short-wavelength infrared (SWIR). The land surface temperature (LST) has been used as an additional source along with NDVI to improve drought characterization accuracy [9,37,57,59,77,78]. A comprehensive review of the performance of the large number of remote sensing drought indices for different configurations can be helpful.
Monitoring Ecosystem Toxins in a Water Body for Sustainable Development of a Lake Watershed
Published in Ni-Bin Chang, Kaixu Bai, Multisensor Data Fusion and Machine Learning for Environmental Remote Sensing, 2018
For the other two categories, atmospheric correction is required to remove the scattering effects of the atmosphere from the raw data, thereby producing surface reflectance instead of top of atmosphere radiance. The last category of processing for cropping out the land surrounding Lake Erie was performed on all relevant images. This step, with the purpose of masking the land pixel values, is required to prevent fusing land pixel values with surface water values during the data fusion process with the STARFM algorithm.
The effect of contaminated snow reflectance using hyperspectral remote sensing – a review
Published in International Journal of Image and Data Fusion, 2019
Arnab Saha, Pradeep Kumar Garg, Manti Patil
The first step in the processing chain, often referred to as pre-processing, involves radiometric and geometric corrections. Atmospheric correction methods used to remove atmospheric reduction are grouped under radiometric corrections. EO-1 product generation system has an in-orbit calibration plan for the Hyperion EO-1 hyperspectral payload. Four Welch Allyn quartz tungsten halogen lamps (Jarecke et al. 2001) uses for internal calibration source to illuminate back side cover of telescope in the closed position. The cover, located at the aperture stop of the telescope, is painted with diffuse, reflecting, white, silicone; thermal control paint (Jarecke et al. 2002). In-orbit procedure of calibration is used to radiometric calibration during when the parameters recorded. The method which is used for atmospheric correction is type of remote sensing data, nature of problem, the amount of in situ historical atmospheric information, and how accurate the bio-physical information is to be extracted from the remote sensing data (Jensen 1996). To process of hyperspectral remote sensing data sets, statistical based relative atmospheric correction methods and physics based absolute correction models are available.