scholarly journals Radiometric Cross Calibration and Validation Using 4 Angle BRDF Model between Landsat 8 and Sentinel 2A

2020 ◽  
Vol 12 (5) ◽  
pp. 806 ◽  
Author(s):  
M M Farhad ◽  
Morakot Kaewmanee ◽  
Larry Leigh ◽  
Dennis Helder
Keyword(s):  
Spectral Band ◽  
Image Data ◽  
Calibration Procedure ◽  
Landsat 8 ◽  
Blue Band ◽  
Calibration Uncertainty ◽  
Significant Difference ◽  
The Cross ◽  
Sentinel 2A ◽  
Cross Calibration

This work describes a proposed radiometric cross calibration between the Landsat 8 Operational Land Imager (OLI) and Sentinel 2A Multispectral Instrument (MSI) sensors. The cross-calibration procedure involves (i) correction of the MSI data to account for spectral band differences with OLI and (ii) normalization of Bidirectional Reflectance Distribution Function (BRDF) effects in the data from both sensors using a new model accounting for the view zenith/azimuth angles in addition to the solar zenith/view angles. Following application of the spectral and BRDF normalization, standard least-squares linear regression is used to determine the cross-calibration gain and offset in each band. Uncertainties related to each step in the proposed process are determined, as is the overall uncertainty associated with the complete processing sequence. Validation of the proposed cross-calibration gains and offsets is performed on image data acquired over the Algodones Dunes site. The results of this work indicate that the blue band has the most significant offset, requiring use of the estimated cross-calibration offset in addition to the estimated gain. The highest difference was observed in the blue and red bands, which are 2.6% and 1.4%, respectively, while other bands shows no significant difference. Overall, the net uncertainty in the proposed process was estimated to be on the order of 6.76%, with the largest uncertainty component due to each sensor’s calibration uncertainty on the order of 5% and 3% for the MSI and OLI, respectively. Other significant contributions to the uncertainty include seasonal changes in solar zenith and azimuth angles, target site nonuniformity, variability in atmospheric water vapor, and/or aerosol concentration.

scholarly journals Radiometric Cross-Calibration of GF-1 PMS Sensor with a New BRDF Model

2019 ◽  
Vol 11 (6) ◽  
pp. 707 ◽  
Author(s):  
Qiyue Liu ◽  
Tao Yu ◽  
Hailiang Gao
Keyword(s):  
Calibration Method ◽  
Reference Image ◽  
Landsat 8 ◽  
Radiometric Calibration ◽  
Sensing Applications ◽  
Quantitative Remote Sensing ◽  
Calibration Accuracy ◽  
The Cross ◽  
One Year ◽  
Cross Calibration

On-orbit radiometric calibration of a space-borne sensor is of great importance for quantitative remote sensing applications. Cross-calibration is a common method with high calibration accuracy, and the core and emphasis of this method is to select the appropriate reference satellite sensor. As for the cross-calibration of high-spatial resolution and narrow-swath sensor, however, there are some scientific issues, such as large observation angles of reference image, and non-synchronization (or quasi-synchronization) between the imaging date of reference image and the date of sensor to be calibrated, which affects the accuracy of cross-calibration to a certain degree. Therefore, taking the GaoFen-1 (GF-1) Panchromatic and Multi-Spectral (PMS) sensor as an example in this research, an innovative radiometric cross-calibration method is proposed to overcome this bottleneck. Firstly, according a set of criteria, valid MODIS (Moderate Resolution Imagine Spectroradiometer) images of sunny day in one year over the Dunhuang radiometric calibration site in China are extracted, and a new and distinctive bidirectional reflectance distribution function (BRDF) model based on top-of-atmosphere (TOA) reflectance and imaging angles of the sunny day MODIS images is constructed. Subsequently, the cross-calibration of PMS sensor at Dunhuang and Golmud radiation calibration test sites is carried out by using the method presented in this paper, taking the MODIS image with large solar and observation angles and Landsat 8 Operational Land Imager (OLI) with different dates from PMS as reference. The validation results of the calibration coefficients indicate that our proposed method can acquire high calibration accuracy, and the total calibration uncertainties of PMS using MODIS as reference sensor are less than 6%.

scholarly journals Radiometric Cross-calibration of KOMPSAT-3A with Landsat-8

2016 ◽  
Vol XLI-B1 ◽  
pp. 379-382
Author(s):  
D. Y. Shin ◽  
H. Y. Ahn ◽  
S. G. Lee ◽  
C. U. Choi ◽  
J. S. Kim
Keyword(s):  
Optical Quality ◽  
Calibration Coefficient ◽  
Spectral Radiance ◽  
Color Filter ◽  
Landsat 8 ◽  
Radiometric Calibration ◽  
Atmospheric Conditions ◽  
Satellite Sensors ◽  
The Cross ◽  
Cross Calibration

In this study, Cross calibration was conducted at the Libya 4 PICS site on 2015 using Landsat-8 and KOMPSAT-3A. Ideally a cross calibration should be calculated for each individual scene pair because on any given date the TOA spectral profile is influenced by sun and satellite view geometry and the atmospheric conditions. However, using the near-simultaneous images minimizes this effect because the sensors are viewing the same atmosphere. For the cross calibration, the calibration coefficient was calculated by comparing the at sensor spectral radiance for the same location calculated using the Landsat-8 calibration parameters in metadata and the DN of KOMPSAT-3A for the regions of interest (ROI). Cross calibration can be conducted because the satellite sensors used for overpass have a similar bandwidth. However, not all satellites have the same color filter transmittance and sensor reactivity, even though the purpose is to observe the visible bands. Therefore, the differences in the RSR should be corrected. For the cross-calibration, a calibration coefficient was calculated using the TOA radiance and KOMPSAT-3 DN of the Landsat-8 OLI overpassed at the Libya 4 Site, As a result, the accuracy of the calibration coefficient at the site was assumed to be ± 1.0%. In terms of the results, the radiometric calibration coefficients suggested here are thought to be useful for maintaining the optical quality of the KOMPSAT-3A.

scholarly journals Empirical Absolute Calibration Model for Multiple Pseudo-Invariant Calibration Sites

2019 ◽  
Vol 11 (9) ◽  
pp. 1105 ◽  
Author(s):  
Bipin Raut ◽  
Morakot Kaewmanee ◽  
Amit Angal ◽  
Xiaoxiong Xiong ◽  
Dennis Helder
Keyword(s):  
Region Of Interest ◽  
Image Data ◽  
Sensor Calibration ◽  
Absolute Calibration ◽  
Landsat 8 ◽  
Calibration Model ◽  
General Applicability ◽  
Landsat 7 ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Sentinel 2A

This work extends an empirical absolute calibration model initially developed for the Libya 4 Pseudo-Invariant Calibration Site (PICS) to five additional Saharan Desert PICS (Egypt 1, Libya 1, Niger 1, Niger 2, and Sudan 1), and demonstrates the efficacy of the resulting models at predicting sensor top-of-atmosphere (TOA) reflectance. It attempts to generate absolute calibration models for these PICS that have an accuracy and precision comparable to or better than the current Libya 4 model, with the intent of providing additional opportunities for sensor calibration. In addition, this work attempts to validate the general applicability of the model to other sites. The method uses Terra Moderate Resolution Imaging Spectroradiometer (MODIS) as the reference radiometer and Earth Observing-1 (EO-1) Hyperion image data to provide a representative hyperspectral reflectance profile of the PICS. Data from a region of interest (ROI) in an “optimal region” of 3% temporal, spatial, and spectral stability within the PICS are used for developing the model. The developed models were used to simulate observations of the Landsat 7 (L7) Enhanced Thematic Mapper Plus (ETM+), Landsat 8 (L8) Operational Land Imager (OLI), Sentinel 2A (S2A) MultiSpectral Instrument (MSI) and Sentinel 2B (S2B) MultiSpectral Instrument (MSI) from their respective launch date through 2018. The models developed for the Egypt 1, Libya 1 and Sudan 1 PICS have an estimated accuracy of approximately 3% and precision of approximately 2% for the sensors used in the study, comparable to the current Libya 4 model. The models developed for the Niger 1 and Niger 2 sites are significantly less accurate with similar precision.

scholarly journals Radiometric Cross-calibration of KOMPSAT-3A with Landsat-8

2016 ◽  
Vol XLI-B1 ◽  
pp. 379-382
Author(s):  
D. Y. Shin ◽  
H. Y. Ahn ◽  
S. G. Lee ◽  
C. U. Choi ◽  
J. S. Kim
Keyword(s):  
Optical Quality ◽  
Calibration Coefficient ◽  
Spectral Radiance ◽  
Color Filter ◽  
Landsat 8 ◽  
Radiometric Calibration ◽  
Atmospheric Conditions ◽  
Satellite Sensors ◽  
The Cross ◽  
Cross Calibration

In this study, Cross calibration was conducted at the Libya 4 PICS site on 2015 using Landsat-8 and KOMPSAT-3A. Ideally a cross calibration should be calculated for each individual scene pair because on any given date the TOA spectral profile is influenced by sun and satellite view geometry and the atmospheric conditions. However, using the near-simultaneous images minimizes this effect because the sensors are viewing the same atmosphere. For the cross calibration, the calibration coefficient was calculated by comparing the at sensor spectral radiance for the same location calculated using the Landsat-8 calibration parameters in metadata and the DN of KOMPSAT-3A for the regions of interest (ROI). Cross calibration can be conducted because the satellite sensors used for overpass have a similar bandwidth. However, not all satellites have the same color filter transmittance and sensor reactivity, even though the purpose is to observe the visible bands. Therefore, the differences in the RSR should be corrected. For the cross-calibration, a calibration coefficient was calculated using the TOA radiance and KOMPSAT-3 DN of the Landsat-8 OLI overpassed at the Libya 4 Site, As a result, the accuracy of the calibration coefficient at the site was assumed to be ± 1.0%. In terms of the results, the radiometric calibration coefficients suggested here are thought to be useful for maintaining the optical quality of the KOMPSAT-3A.

scholarly journals IDENTIFICATION OF MANGROVE FORESTS USING MULTI-RESOLUTION SATELLITE IMAGERY

Jurnal Segara ◽  
2020 ◽  
Vol 16 (2) ◽  
Author(s):  
Anang Dwi Purwanto
Keyword(s):  
Remote Sensing ◽  
Mangrove Forest ◽  
Image Data ◽  
Field Measurements ◽  
Landsat 8 ◽  
Mangrove Forests ◽  
Remote Sensing Technology ◽  
Red Edge ◽  
Sensing Technology ◽  
Sentinel 2A

The development of remote sensing technology for identifying various of coastal and marine ecosystems which one of them is mangrove forest increasing rapidly. Identification of mangrove forests visually is constrained by much of combinations of RGB composite. The aims of this research is to determine the best combination of RGB composite for identifying mangrove forest in Segara Anakan, Cilacap using Optimum Index Factor (OIF) method. The image data used represents 3 levels of intermediate to high resolution spatial resolution including Landsat 8 imagery (30 m) acquisition on 30 May 2013, Sentinel 2A image (10 m) acquisition on 18 March 2018 and SPOT 6 image (6 m) acquisition on 10 January 2015. Data of mangrove distributions used were the results of field measurements in the period 2013-2015. The results showed that the band composites of 564 (NIR+SWIR+Red) of Landsat 8 image and the band composites of 8a114 (Vegetation Red Edge+SWIR+Red) of Sentinel 2A are the best RGB composites for identifying mangrove forest, while the band composites of 341 (Red+NIR+Blue) of SPOT 6 image is  also the best colour composites (R-G-B) for identifying mangrove forest in Segara Anakan, Cilacap. The RGB composites of images developed from Landsat 8 and Sentinel 2A image are able to distinguish objects of mangrove forest from surrounding objects more clearly, but image composites from SPOT 6 image still require additional of association elements to identify mangrove objects.The development of remote sensing technology for identifying various of coastal and marine ecosystems which one of them is mangrove forest increasing rapidly. Identification of mangrove forests visually is constrained by much of combinations of RGB composite. The aims of this research is to determine the best combination of RGB composite for identifying mangrove forest in Segara Anakan, Cilacap using Optimum Index Factor (OIF) method. The image data used represents 3 levels of intermediate to high resolution spatial resolution including Landsat 8 imagery (30 m) acquisition on 30 May 2013, Sentinel 2A image (10 m) acquisition on 18 March 2018 and SPOT 6 image (6 m) acquisition on 10 January 2015. Data of mangrove distributions used were the results of field measurements in the period 2013-2015.The results showed that the band composites of 564 (NIR+SWIR+Red) of Landsat 8 image and the band composites of 8a114 (Vegetation Red Edge+SWIR+Red) of Sentinel 2A are the best RGB composites for identifying mangrove forest, while the band composites of 341 (Red+NIR+Blue) of SPOT 6 image is  also the best colour composites(R-G-B) for identifying mangrove forest in Segara Anakan, Cilacap. The RGB composites of images developed from Landsat 8 and Sentinel 2A image are able to distinguish objects of mangrove forest from surrounding objects more clearly, but imagecomposites from SPOT 6 image still require additional of association elements to identify mangrove objects.

scholarly journals DETEKSI AWAL HABITAT PERAIRAN LAUT DANGKAL MENGGUNAKAN TEKNIK OPTIMUM INDEX FACTOR PADA CITRA SPOT 7 DAN LANDSAT 8

2019 ◽  
Vol 4 (2) ◽  
pp. 174-192
Author(s):  
Anang Dwi Purwanto ◽  
Kuncoro Teguh Setiawan
Keyword(s):  
Shallow Water ◽  
Image Interpretation ◽  
Image Data ◽  
Environmental Conservation ◽  
Landsat 8 ◽  
Blue Band ◽  
Composite Image ◽  
Sensing Technology ◽  
Optimum Index Factor

Informasi keberadaan habitat perairan laut dangkal semakin dibutuhkan terutama dalam kegiatan pelestarian lingkungan dan monitoring di wilayah pesisir. Komponen penyusun ekosistem habitat dasar perairan laut dangkal di antaranya terumbu karang dan lamun dimana lokasi keberadaan obyek habitat ini cenderung berdekatan. Dalam interpretasi ekosistem habitat dasar perairan laut dangkal terkendala oleh lokasi keberadaan ekosistem yang berasosiasi dengan obyek lainnya. Tujuan penelitian ini adalah menentukan kombinasi komposit kanal terbaik dalam mengidentifikasi obyek habitat dasar perairan laut dangkal di Pantai Pemuteran, Bali. Data citra satelit yang digunakan dalam penelitian ini adalah citra SPOT 7 akuisisi tanggal 11 April 2018 dan citra Landsat 8 akuisisi tanggal 14 April 2018, sedangkan data terkait informasi sebaran habitat dasar perairan laut dangkal diperoleh berdasarkan hasil survei lapangan yang telah dilakukan pada tanggal 7-13 April 2018 di Pantai Pemuteran, Bali. Data citra satelit diperoleh dari Pusat Teknologi dan Data LAPAN. Untuk menentukan kombinasi dari 3 (tiga) kanal terbaik dalam interpretasi habitat dasar perairan laut dangkal digunakan metode Optimum Index Factor (OIF) dimana metode ini menggunakan nilai standar deviasi dan koefisien korelasi dari kombinasi 3 (tiga) kanal citra yang digunakan. Hasil penelitian menunjukkan kombinasi komposit 2 (hijau), 3 (merah) dan 4 (NIR) mempunyai nilai OIF tertinggi untuk citra SPOT 7, sedangkan kombinasi komposit 2 (biru), 4 (merah) dan 6 (SWIR 1) Mempunyai nilai OIF tertinggi untuk citra Landsat 8. Interpretasi sebaran habitat dasar perairan laut dangkal dapat dilakukan secara efektif dengan menggunakan citra komposit RGB 423 untuk citra SPOT 7 dan RGB 642 untuk citra Landsat 8.DETECTION OF SHALLOW WATER HABITATS USING OPTIMUM INDEX FACTORS TECHNIQUE ON SPOT 7 AND LANDSAT 8 IMAGERY. Information of the existence of the shallow water habitat is required especially in environmental conservation and monitoring of activities in coastal areas. The component of the shallow water habitat including coral reefs and seagrass where the location of the existence of these relatively close together. Interpretation of the shallow water habitat is constrained by the location of ecosystem associated with other objects. The aim of study is to determine the best combination of band composites in identifying the shallow water habitat in Pemuteran Beach, Bali. The study used SPOT 7 imagery (acquisition on April 11, 2018) and Landsat 8 imagery (acquisition on April 14, 2018). The data of the shallow water habitat based on the result of field survey was conducted on 7-13 April 2018 at Pemuteran Beach, Bali. Image data obtained from Remote Sensing Technology and Data Center of LAPAN. Determination of combination of 3 (three) bands the shallow water habitat using Optimum Index Factor (OIF) method where this method used standard deviation value and correlation coefficient from combination of 3 (three) bands. The results show the composite combinations of band 2 (green), band 3 (red) and band 4 (NIR) have the highest OIF values for SPOT 7 image, while the composite combinations of band 2 (blue), band 4 (red) and band 6 (SWIR 1) have the highest OIF values for Landsat 8 image. Interpretation of distribution of shallow water habitat can be done effectively using RGB 423 composite image (SPOT 7) and RGB 642 composite image (Landsat 8).

scholarly journals Extended Pseudo Invariant Calibration Sites (EPICS) for the Cross-Calibration of Optical Satellite Sensors

2019 ◽  
Vol 11 (14) ◽  
pp. 1676 ◽  
Author(s):  
Mahesh Shrestha ◽  
Md. Nahid Hasan ◽  
Larry Leigh ◽  
Dennis Helder
Keyword(s):  
Region Of Interest ◽  
Image Data ◽  
Calibration Data ◽  
Gain Ratio ◽  
Time Period ◽  
Satellite Sensors ◽  
The Cross ◽  
Cross Calibration ◽  
Similar Accuracy ◽  
Shortwave Infrared

An increasing number of Earth-observing satellite sensors are being launched to meet the insatiable demand for timely and accurate data to aid the understanding of the Earth’s complex systems and to monitor significant changes to them. To make full use of the data from these sensors, it is mandatory to bring them to a common radiometric scale through a cross-calibration approach. Commonly, cross-calibration data were acquired from selected pseudo-invariant calibration sites (PICS), located primarily throughout the Saharan desert in North Africa, determined to be temporally, spatially, and spectrally stable. The major limitation to this approach is that long periods of time are required to assemble sufficiently sampled cloud-free cross-calibration datasets. Recently, Shrestha et al. identified extended, cluster-based sites potentially suitable for PICS-based cross-calibration and estimated representative hyperspectral profiles for them. This work investigates the performance of extended pseudo-invariant calibration sites (EPICS) in cross-calibration for one of Shrestha’s clusters, Cluster 13, by comparing its results to those obtained from a traditional PICS-based cross-calibration. The use of EPICS clusters can significantly increase the number of cross-calibration opportunities within a much shorter time period. The cross-calibration gain ratio estimated using a cluster-based approach had a similar accuracy to the cross-calibration gain derived from region of interest (ROI)-based approaches. The cluster-based cross-calibration gain ratio is consistent within approximately 2% of the ROI-based cross-calibration gain ratio for all bands except for the coastal and shortwave-infrared (SWIR) 2 bands. These results show that image data from any region within Cluster 13 can be used for sensor cross-calibration.

scholarly journals ANALYSIS OF CHANGES IN MANGROVE FOREST FERTILITY USING SATELLITE IMAGE DATA AND WILCOXON TEST

CI-TECH ◽  
2021 ◽  
Vol 2 (01) ◽  
pp. 25-29
Author(s):  
Siti Zainab ◽  
Hendrata Wibisana
Keyword(s):  
Regression Model ◽  
Satellite Imagery ◽  
Satellite Image ◽  
Image Data ◽  
Storm Surges ◽  
Wilcoxon Test ◽  
Landsat 8 ◽  
Significant Difference ◽  
Exponential Regression Model ◽  
The City

Gunung Anyar is one of the districts in the city of Surabaya. This district has a height of approximately 3 meters above sea level. Based on data from the Central Statistics Agency (BPS) for the City of Surabaya 2019, Gunung Anyar District has an area of ​​9.2 square kilometers and is divided into four sub-districts. These include the Kelurahan Rungkut Menanggal, Rungkut Tengah, Mount Anyar and Mount Anyar Tambak (AyoSurabaya.com by Rizma Riyandi). The mangrove's robust root system helps form a natural barrier against storm surges and flooding. River and land sediments are trapped by roots, which protect shorelines and slow erosion. This filtering process also prevents harmful sediments from reaching coral reefs and seagrass beds (Anugerah Ayu Sundari 2019). The method used by remote sensing with Landsat 8 satellite imagery was analyzed using SeaDAS software, it was obtained that the comparison value in each band 2,3,4 and band 5 had differences in each reflectance value. The 2015 satellite image map has the largest value in band_4 with the exponential regression model y = 125.06e-22.13x with R2 = 0.0732, while the 2019 satellite image map which has the largest value is band_4 with the logarithmic regression model y = 141.72ln (x) + 326.3 where R2 = 0.0281. Using the Wilcoxon H1 Test Statistics it is accepted that there is a significant difference between the diameter of mangroves from satellite imagery in 2015 and the diameter of mangroves from satellite images in 2019. Because the number of positive rankings from the diameter of mangrove satellite imagery in 2015 is greater than the diameter of mangroves from satellite imagery in 2019. , it can be concluded that the mangrove area of ​​Wonorejo Surabaya is experiencing fertility.

scholarly journals IDENTIFICATION OF MANGROVE FORESTS USING MULTISPECTRAL SATELLITE IMAGERIES

2019 ◽  
Vol 16 (1) ◽  
pp. 63
Author(s):  
Anang Dwi Purwanto ◽  
Wikanti Asriningrum
Keyword(s):  
Mangrove Forest ◽  
Near Infrared ◽  
Image Data ◽  
Short Wave ◽  
Landsat 8 ◽  
Mangrove Forests ◽  
Visual Identification ◽  
Red Edge ◽  
Swir Band ◽  
Sentinel 2A

The visual identification of mangrove forests is greatly constrained by combinations of RGB composite. This research aims to determine the best combination of RGB composite for identifying mangrove forest in Segara Anakan, Cilacap using the Optimum Index Factor (OIF) method. The OIF method uses the standard deviation value and correlation coefficient from a combination of three image bands. The image data comprise Landsat 8 imagery acquired on 30 May 2013, Sentinel 2A imagery acquired on 18 March 2018 and images from SPOT 6 acquired on 10 January 2015. The results show that the band composites of 564 (NIR+SWIR+Red) from Landsat 8 and 8a114 (Vegetation Red Edge+SWIR+Red) from Sentinel 2A are the best RGB composites for identifying mangrove forest, in addition to those of 341 (Red+NIR+Blue) from SPOT 6. The near-infrared (NIR) and short-wave infrared (SWIR) bands play an important role in determining mangrove forests. The properties of vegetation are reflected strongly at the NIR wavelength and the SWIR band is very sensitive to evaporation and the identification of wetlands.

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