Quantifying the Impact of Solar Spectra on the Inter-Calibration of Satellite Instruments

In satellite-based remote sensing applications, the conversion of the sensor recorded top-of-atmosphere reflectance to radiance, or vice-versa, is carried out using a reference spectral solar irradiance (SSI) dataset. The choice of reference SSI spectrum has consistently changed over the past four decades with the increasing availability of more accurate SSI measurements with greater spectral coverage. Considerable differences (up to 15% at certain wavelengths) exist between the numerous SSI spectra that are currently being used in satellite ground processing systems. The aim of this study is to quantify the absolute differences between the most commonly used SSI datasets and investigate their impact in satellite inter-calibration and environmental retrievals. It was noted that if analogous SNPP and NOAA-20 VIIRS channel reflectances were perfectly inter-calibrated, the derived channel radiances can still differ by up to 3% due to the utilization of differing SSI datasets by the two VIIRS instruments. This paper also highlights a TSIS-1 SIM-based Hybrid Solar Reference Spectrum (HSRS) with an unprecedented absolute accuracy of 0.3% between 460 and 2365 nm, and recommends that the remote sensing community use it as a common reference SSI in satellite retrievals.

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A Systematic Review of Hardware-Accelerated Compression of Remotely Sensed Hyperspectral Images

Sensors ◽

10.3390/s22010263 ◽

2021 ◽

Vol 22 (1) ◽

pp. 263

Author(s):

Amal Altamimi ◽

Belgacem Ben Ben Youssef

Keyword(s):

Remote Sensing ◽

Systematic Review ◽

Hyperspectral Image ◽

Hyperspectral Data ◽

Hyperspectral Images ◽

Sensor Technology ◽

Future Research ◽

Sensing Applications ◽

Remote Sensing Applications ◽

The Impact

Hyperspectral imaging is an indispensable technology for many remote sensing applications, yet expensive in terms of computing resources. It requires significant processing power and large storage due to the immense size of hyperspectral data, especially in the aftermath of the recent advancements in sensor technology. Issues pertaining to bandwidth limitation also arise when seeking to transfer such data from airborne satellites to ground stations for postprocessing. This is particularly crucial for small satellite applications where the platform is confined to limited power, weight, and storage capacity. The availability of onboard data compression would help alleviate the impact of these issues while preserving the information contained in the hyperspectral image. We present herein a systematic review of hardware-accelerated compression of hyperspectral images targeting remote sensing applications. We reviewed a total of 101 papers published from 2000 to 2021. We present a comparative performance analysis of the synthesized results with an emphasis on metrics like power requirement, throughput, and compression ratio. Furthermore, we rank the best algorithms based on efficiency and elaborate on the major factors impacting the performance of hardware-accelerated compression. We conclude by highlighting some of the research gaps in the literature and recommend potential areas of future research.

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Physically Based Approach for Combined Atmospheric and Topographic Corrections

Remote Sensing ◽

10.3390/rs11101218 ◽

2019 ◽

Vol 11 (10) ◽

pp. 1218 ◽

Cited By ~ 3

Author(s):

Federico Santini ◽

Angelo Palombo

Keyword(s):

Remote Sensing ◽

Radiative Transfer ◽

Radiative Transfer Model ◽

Transfer Model ◽

Test Cases ◽

Remote Sensors ◽

Sensing Applications ◽

Physically Based ◽

Remote Sensing Applications ◽

The Impact

The enhanced spectral and spatial resolutions of the remote sensors have increased the need for highly performing preprocessing procedures. In this paper, a comprehensive approach, which simultaneously performs atmospheric and topographic corrections and includes second order corrections such as adjacency effects, was presented. The method, developed under the assumption of Lambertian surfaces, is physically based and uses MODTRAN 4 radiative transfer model. The use of MODTRAN 4 for the estimates of the radiative quantities was widely discussed in the paper and the impact on remote sensing applications was shown through a series of test cases.

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Editorial for the Special Issue “Advanced Machine Learning for Time Series Remote Sensing Data Analysis”

Remote Sensing ◽

10.3390/rs12172815 ◽

2020 ◽

Vol 12 (17) ◽

pp. 2815

Author(s):

Gwanggil Jeon ◽

Valerio Bellandi ◽

Abdellah Chehri

Keyword(s):

Machine Learning ◽

Remote Sensing ◽

Time Series ◽

Data Analysis ◽

Time Series Data ◽

Series Data ◽

Special Issue ◽

Sensing Applications ◽

Remote Sensing Applications ◽

The Impact

This Special Issue intended to probe the impact of the adoption of advanced machine learning methods in remote sensing applications including those considering recent big data analysis, compression, multichannel, sensor and prediction techniques. In principal, this edition of the Special Issue is focused on time series data processing for remote sensing applications with special emphasis on advanced machine learning platforms. This issue is intended to provide a highly recognized international forum to present recent advances in time series remote sensing. After review, a total of eight papers have been accepted for publication in this issue.

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