Influence of atmospheric correction methods at the calculation of vegetation indices on hyperspectral images

To validate the iCOR atmospheric correction algorithm applied to the Sentinel-3 Ocean and Land Color Instrument (OLCI), Top-of-Atmosphere (TOA) observations over land, globally retrieved Aerosol Optical Thickness (AOT), Top-of-Canopy (TOC) reflectance, and Vegetation Indices (VIs) were intercompared with (i) AERONET AOT and AERONET-based TOC reflectance simulations, (ii) RadCalNet surface reflectance observations, and (iii) SYN Level 2 (L2) AOT, TOC reflectance, and VIs. The results reveal that, overall, iCOR’s statistical and temporal consistency is high. iCOR AOT retrievals overestimate relative to AERONET, but less than SYN L2. iCOR and SYN L2 TOC reflectances exhibit a negative bias of ~−0.01 and −0.02, respectively, in the Blue bands compared to the simulations. This diminishes for RED and NIR, except for a +0.02 bias for SYN L2 in the NIR. The intercomparison with RadCalNet shows relative differences < ±6%, except for bands Oa02 (Blue) and Oa21 (NIR), which is likely related to the reported OLCI “excess of brightness”. The intercomparison between iCOR and SYN L2 showed R2 = 0.80–0.93 and R2 = 0.92–0.96 for TOC reflectance and VIs, respectively. iCOR’s higher temporal smoothness compared to SYN L2 does not propagate into a significantly higher smoothness for TOC reflectance and VIs. Altogether, we conclude that iCOR is well suitable to retrieve statistically and temporally consistent AOT, TOC reflectance, and VIs over land surfaces from Sentinel-3/OLCI observations.

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Estimating the maize biomass by crop height and narrowband vegetation indices derived from UAV-based hyperspectral images

Ecological Indicators ◽

10.1016/j.ecolind.2021.107985 ◽

2021 ◽

Vol 129 ◽

pp. 107985

Author(s):

Yue Zhang ◽

Chenzhen Xia ◽

Xingyu Zhang ◽

Xianhe Cheng ◽

Guozhong Feng ◽

...

Keyword(s):

Vegetation Indices ◽

Hyperspectral Images ◽

Crop Height

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AT2ES: Simultaneous Atmospheric Transmittance-Temperature-Emissivity Separation Using Online Upper Midwave Infrared Hyperspectral Images

Remote Sensing ◽

10.3390/rs13071249 ◽

2021 ◽

Vol 13 (7) ◽

pp. 1249

Author(s):

Sungho Kim ◽

Jungsub Shin ◽

Sunho Kim

Keyword(s):

Air Temperature ◽

Atmospheric Correction ◽

Hyperspectral Data ◽

Hyperspectral Images ◽

Co2 Absorption ◽

Infrared Band ◽

Data Regression ◽

Midwave Infrared ◽

Novel Method ◽

Atmospheric Transmittance

This paper presents a novel method for atmospheric transmittance-temperature-emissivity separation (AT2ES) using online midwave infrared hyperspectral images. Conventionally, temperature and emissivity separation (TES) is a well-known problem in the remote sensing domain. However, previous approaches use the atmospheric correction process before TES using MODTRAN in the long wave infrared band. Simultaneous online atmospheric transmittance-temperature-emissivity separation starts with approximation of the radiative transfer equation in the upper midwave infrared band. The highest atmospheric band is used to estimate surface temperature, assuming high emissive materials. The lowest atmospheric band (CO2 absorption band) is used to estimate air temperature. Through onsite hyperspectral data regression, atmospheric transmittance is obtained from the y-intercept, and emissivity is separated using the observed radiance, the separated object temperature, the air temperature, and atmospheric transmittance. The advantage with the proposed method is from being the first attempt at simultaneous AT2ES and online separation without any prior knowledge and pre-processing. Midwave Fourier transform infrared (FTIR)-based outdoor experimental results validate the feasibility of the proposed AT2ES method.

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The effect of atmospheric correction methods on the relationship between vegetation indices and canopy cover (Case study: Marjan rangelands of Borujen)

Journal of Geospatial Information Technology ◽

10.29252/jgit.7.2.133 ◽

2019 ◽

Vol 7 (2) ◽

pp. 133-153

Author(s):

Fatemeh Pordel ◽

Ataollah Ebrahimi ◽

Zahra Azizi ◽

◽

...

Keyword(s):

Vegetation Indices ◽

Atmospheric Correction ◽

Canopy Cover ◽

The Relationship

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Scenarios of the Tropical Dry Forest of Purulia District West Bengal

Practice, Progress, and Proficiency in Sustainability - Handbook of Research on the Conservation and Restoration of Tropical Dry Forests ◽

10.4018/978-1-7998-0014-9.ch013 ◽

2020 ◽

pp. 254-267

Author(s):

Masuma Begum ◽

Niloy Pramanick ◽

Anirban Mukhopadhyay ◽

Sayani Datta Majumdar

Keyword(s):

Land Cover ◽

Forest Cover ◽

Vegetation Indices ◽

Atmospheric Correction ◽

Tropical Dry Forest ◽

Dry Forest ◽

Training Data ◽

Change Dynamics ◽

Land Cover Map ◽

Purulia District

In this chapter, satellite images of the years 1995, 2005, and 2015 of LANDSAT have been used. After pre-processing (geometric correction and atmospheric correction using FLAASH, LULC change dynamics have been assessed to estimate the changes in total forest cover in Purulia district through an unsupervised K-means classification scheme. To evaluate the health status, vegetation indices, namely NDVI, SAVI, and CVI, have been used. The increase in NDVI, SAVI, and CVI values was inferred as no significant degradation of Purulia forest cover. Moreover, future scenarios have been predicted by implementing a CA-MARKOV model. Using the land cover map of 1995 as the base map, and from 1995 to 2005 as training data, a land cover map of 2015 has been generated which in turn validated by the actual land cover of 2015. After validation, prediction of land cover was possible for the years 2035 and 2050. The prediction suggested that forest area will increase by approximately 4% from 2015 to 2035 and by 3% from 2035 to 2050.

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Investigating the Consistency of Uncalibrated Multispectral Lidar Vegetation Indices at Different Altitudes

Remote Sensing ◽

10.3390/rs11131531 ◽

2019 ◽

Vol 11 (13) ◽

pp. 1531 ◽

Cited By ~ 3

Author(s):

Maxim Okhrimenko ◽

Chris Hopkinson

Keyword(s):

Vegetation Indices ◽

Atmospheric Correction ◽

Spectral Ratios ◽

Additional Information ◽

Point Density ◽

Range Difference ◽

Spectral Vegetation Indices ◽

The Stability ◽

Considerable Range ◽

Different Altitudes

Multi-spectral (ms) airborne light detection and ranging (lidar) data are increasingly used for mapping purposes. Geometric data are enriched by intensity digital numbers (DNs) and, by utilizing this additional information either directly, or in the form of active spectral vegetation indices (SVIs), enhancements in land cover classification and change monitoring are possible. In the case of SVIs, the indices should be calculated from reflectance values derived from intensity DNs after rigorous calibration. In practice, such calibration is often not possible, and SVIs calculated from intensity DNs are used. However, the consistency of such active ms lidar products is poorly understood. In this study, the authors reported on an ms lidar mission at three different altitudes above ground to investigate SVI consistency. The stability of two families of indices—spectral ratios and normalized differences—was compared. The need for atmospheric correction in case of considerable range difference was established. It was demonstrated that by selecting single returns (provided sufficient point density), it was possible to derive stable SVI products. Finally, a criterion was proposed for comparing different lidar acquisitions over vegetated areas.

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