A fast radiometric correction method for Sentinel-2 satellite images

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Elahe Moradi ◽  
Alireza Sharifi
Keyword(s):  
Polynomial Equation ◽  
Correction Method ◽  
Landsat 8 ◽  
Radiometric Calibration ◽  
Landsat 8 Oli ◽  
Radiometric Correction ◽  
Content Type ◽  
Spectral Bands ◽  
Corrected Data ◽  
Sentinel 2A

Purpose Radiometric calibration is a method that estimates the reflection of the target from the measured input radiation. The purpose of this study is to radiometrically calibrate three spectral bands of Sentinel-2A, including green, red and infrared. For this purpose, Landsat-8 OLI data are used. Because they have bands with the same wavelength range and they have the same structure. As a result, Landsat-8 OLI is appropriate for relative radiometric calibration. Design/methodology/approach The method used in this study is radiometric calibration uncorrected data from a sensor with corrected data from another sensor. Also, another aim of this study is a comparison between radiometric correction data and data that, in addition to radiometric correction, has been sharpened with panchromatic data. In this method, both of them have been used for radiometric calibration. Calibration coefficients have been obtained using the first-order polynomial equation. Findings This study showed that the corrected data has more valid answers than corrected and sharpened data. This method studied three land-cover types, including soil, water and vegetation, which it obtained the most accurate coefficients of calibration for soil class because R-square in all three bands was above 88%, and the root mean square error in all three bands was below 0.01. In the case of water and vegetation classes, only results of red and infrared bands were suitable. Originality/value For validating this method, the radiometric correction module of SNAP software was used. According to the results, the coefficient of radiometric calibration of the Landsat-8 sensor was very close to the coefficients obtained from the corrected data by SNAP.

scholarly journals Radiometric Top-of-Atmosphere Reflectance Consistency Assessment for Landsat 8/OLI, Sentinel-2/MSI, PROBA-V, and DEIMOS-1 over Libya-4 and RadCalNet Calibration Sites

2019 ◽  
Vol 11 (19) ◽  
pp. 2253 ◽  
Author(s):  
Sindy Sterckx ◽  
Erwin Wolters
Keyword(s):  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Data Interoperability ◽  
Clear Trend ◽  
Spectral Bands ◽  
Satellite Sensors ◽  
Sentinel 2A ◽  
Sentinel 2 ◽  
Made In ◽  
Require Data

There is a clear trend toward the use of higher spatial resolution satellite sensors. Due to the low revisit time of these sensors and frequent cloud coverage, many applications require data from different sensors to be combined in order to have more frequent observations. This raises concerns regarding data interoperability and consistency. The initial pre-requisite is that there are no radiometric differences in top-of-atmosphere (TOA) observations. This paper aims to quantitatively assess differences in the TOA signal provided by PROBA-V, Sentinel-2A and Sentinel-2B, Landsat-8, and Deimos-1 by using observations over both the Libya-4 desert calibration site and the RadCalNet sites. The results obtained over the Libya-4 site indicate that for all sensors investigated, the inter-sensor deviations are negligible, i.e., within ±2% for comparable spectral bands, with the exception of the Deimos-1 Green band. Clear BRDF (bi-directional reflectance distribution function) effects were observed over the RadCalNet sites, thereby preventing consistent conclusions on inter-sensor deviations from being made. In order to fully explore the potential of the RadCalNet sites, it is recommended that BRDF characterizations be additionally incorporated into the RadCalNet simulations and made publicly available through the distribution portal.

scholarly journals On estimating the gross primary productivity of Mediterranean grasslands under different fertilization regimes using vegetation indices and hyperspectral reflectance

2018 ◽  
Vol 15 (17) ◽  
pp. 5455-5471 ◽  
Author(s):  
Sofia Cerasoli ◽  
Manuel Campagnolo ◽  
Joana Faria ◽  
Carla Nogueira ◽  
Maria da Conceição Caldeira
Keyword(s):  
Primary Productivity ◽  
Explanatory Power ◽  
Vegetation Indices ◽  
Gross Primary Productivity ◽  
Landsat 8 ◽  
Hyperspectral Reflectance ◽  
Empirical Modelling ◽  
Spectral Bands ◽  
Mediterranean Grasslands ◽  
Sentinel 2A

Abstract. We applied an empirical modelling approach for gross primary productivity (GPP) estimation from hyperspectral reflectance of Mediterranean grasslands undergoing different fertilization treatments. The objective of the study was to identify combinations of vegetation indices and bands that best represent GPP changes between the annual peak of growth and senescence dry out in Mediterranean grasslands. In situ hyperspectral reflectance of vegetation and CO2 gas exchange measurements were measured concurrently in unfertilized (C) and fertilized plots with added nitrogen (N), phosphorus (P) or the combination of N, P and potassium (NPK). Reflectance values were aggregated according to their similarity (r≥90 %) in 26 continuous wavelength intervals (Hyp). In addition, the same reflectance values were resampled by reproducing the spectral bands of both the Sentinel-2A Multispectral Instrument (S2) and Landsat 8 Operational Land Imager (L8) and simulating the signal that would be captured in ideal conditions by either Sentinel-2A or Landsat 8. An optimal procedure for selection of the best subset of predictor variables (LEAPS) was applied to identify the most effective set of vegetation indices or spectral bands for GPP estimation using Hyp, S2 or L8. LEAPS selected vegetation indices according to their explanatory power, showing their importance as indicators of the dynamic changes occurring in community vegetation properties such as canopy water content (NDWI) or chlorophyll and carotenoids ∕ chlorophyll ratio (MTCI, PSRI, GNDVI) and revealing their usefulness for grasslands GPP estimates. For Hyp and S2, bands performed as well as vegetation indices to estimate GPP. To identify spectral bands with a potential for improving GPP estimates based on vegetation indices, we applied a two-step procedure which clearly indicated the short-wave infrared region of the spectra as the most relevant for this purpose. A comparison between S2- and L8-based models showed similar explanatory powers for the two simulated satellite sensors when both vegetation indices and bands were included in the model. Altogether, our results describe the potential of sensors on board Sentinel-2 and Landsat 8 satellites for monitoring grassland phenology and improving GPP estimates in support of a sustainable agriculture management.

scholarly journals RADIOMETRIC CALIBRATION OF LANDSAT-8 OLI AND SENTINEL-2 MSI IMAGES FOR WATER QUALITY MODELLING OF LAGUNA LAKE, PHILIPPINES

2019 ◽  
Vol XLII-4/W19 ◽  
pp. 229-232
Author(s):  
E. V. Gubatanga Jr ◽  
A. C. Blanco ◽  
C. H. Lin ◽  
B. Y. Lin
Keyword(s):  
Remote Sensing ◽  
Water Quality ◽  
Reference Image ◽  
Time Interval ◽  
Landsat 8 ◽  
Radiometric Calibration ◽  
Landsat 8 Oli ◽  
Laguna Lake ◽  
Over Time ◽  
Sentinel 2

Abstract. Regular monitoring of water quality in Laguna Lake is important for it supports aquaculture and provides water supply for Metro Manila. Remote sensing makes it possible to monitor the spectral conditions of the lake on a regular time interval and with complete coverage except for the areas with cloud and shadow cover. Along with in-situ water quality measurements, bio-optical models can be developed to determine the relationship between spectral and bio-optical properties of the lake water and consequently enables the estimation of water quality through remote sensing. However, radiometric calibration is needed to minimize the effects of the changing atmospheric conditions over time and to account for the difference in sensors (e.g., Landsat-8 OLI, Sentinel-2 MSI) used for water quality assessment. Canonical correlation analysis is used to detect pseudo-invariant features (PIFs), which are ground objects that do not dramatically vary in spectral properties over time. Road surface and other large man-made infrastructures are the commonly detected PIFs. These PIFs are used to compute for the parameters used to normalize reflectance values of remotely-sensed images obtained on different dates and using different sensors. The normalization resulted to a reduction of difference in reflectance values between the reference image and the adjusted image, though not marginal. This is due to the use of a linear equation to adjust the image, which limits the ability of the reflectance values of the image to fit to the values of the reference image.

scholarly journals COMPARISON OF LANDSAT-8 AND SENTINEL-2 DATA FOR CLASSIFICATION OF RABI CROPS OVER KARNATAKA, INDIA

2019 ◽  
Vol XLII-3/W6 ◽  
pp. 579-584
Author(s):  
S. Paul ◽  
D. N. Kumar
Keyword(s):  
Vegetation Index ◽  
Support Vector ◽  
Growth Stages ◽  
Landsat 8 ◽  
Large Spatial Scale ◽  
Spectral Bands ◽  
Sentinel 2A ◽  
Rabi Crops ◽  
Sentinel 2

<p><strong>Abstract.</strong> Classification of crops is very important to study different growth stages and forecast yield. Remote sensing data plays a significant role in crop identification and condition assessment over a large spatial scale. Importance of Normalized Difference Indices (NDIs) along with surface reflectances of remotely sensed spectral bands have been evaluated for classification of eight types of Rabi crops utilizing the Landsat-8 and Sentinel-2 datasets and performances of both the satellites are compared. Landsat-8 and Sentinel-2A images are acquired for the location of crops and seven and nine spectral bands are utilized respectively for the classification. Experiments are carried out considering the different combinations of surface reflectances of spectral bands and optimal NDIs as features in support vector machine classifier. Optimal NDIs are selected from the set of <sup>7</sup>C<sub>2</sub> and <sup>9</sup>C<sub>2</sub> NDIs of Landsat-8 and Sentinel-2A datasets respectively using the partial informational correlation measure, a nonparametric feature selection approach. Few important vegetation indices (e.g. enhanced vegetation index) are also experimented in combination with the surface reflectances and NDIs to perform the crop classification. It has been observed that combination of surface reflectances and optimal NDIs can classify the crops more efficiently. The average overall accuracy of 80.96% and 88.16% are achieved using the Landsat-8 and Sentinel-2A datasets respectively. It has been observed that all the crop classes except Paddy and Cotton achieve producer accuracy and user accuracy of more than 75% and 85% respectively. This technique can be implemented for crop identification with adequate accessibility of crop information.</p>

scholarly journals Extraction of High-Precision Urban Impervious Surfaces from Sentinel-2 Multispectral Imagery via Modified Linear Spectral Mixture Analysis

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 2873 ◽  
Author(s):  
Rudong Xu ◽  
Jin Liu ◽  
Jianhui Xu
Keyword(s):  
High Precision ◽  
Impervious Surface ◽  
Spectral Mixture Analysis ◽  
Landsat 8 ◽  
Impervious Surfaces ◽  
Mixture Analysis ◽  
Spectral Bands ◽  
Linear Spectral Mixture Analysis ◽  
Sentinel 2A ◽  
Spectral Mixture

This study explores the performance of Sentinel-2A Multispectral Instrument (MSI) imagery for extracting urban impervious surface using a modified linear spectral mixture analysis (MLSMA) method. Sentinel-2A MSI provided 10 m red, green, blue, and near-infrared spectral bands, and 20 m shortwave infrared spectral bands, which were used to extract impervious surfaces. We aimed to extract urban impervious surfaces at a spatial resolution of 10 m in the main urban area of Guangzhou, China. In MLSMA, a built-up image was first extracted from the normalized difference built-up index (NDBI) using the Otsu’s method; the high-albedo, low-albedo, vegetation, and soil fractions were then estimated using conventional linear spectral mixture analysis (LSMA). The LSMA results were post-processed to extract high-precision impervious surface, vegetation, and soil fractions by integrating the built-up image and the normalized difference vegetation index (NDVI). The performance of MLSMA was evaluated using Landsat 8 Operational Land Imager (OLI) imagery. Experimental results revealed that MLSMA can extract the high-precision impervious surface fraction at 10 m with Sentinel-2A imagery. The 10 m impervious surface map of Sentinel-2A is capable of recovering more detail than the 30 m map of Landsat 8. In the Sentinel-2A impervious surface map, continuous roads and the boundaries of buildings in urban environments were clearly identified.

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