Radiometric Cross-Calibration of HJ-2A/CCD3 Using the Random Forest Algorithm and a Spectral Interpolation Convolution Method with Sentinel-2/MSI
In the process of radiometric calibration, the corrections for bidirectional reflectance distribution functions (BRDFs) and spectral band adjustment factors (SBAFs) are crucial. Time-series MODIS images are commonly used to construct BRDFs by using the Ross–Li model in current research. However, the...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-11-01
|
| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/16/22/4337 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1846152519376437248 |
|---|---|
| author | Xiang Zhou Yidan Chen Yong Xie Jie Han Wen Shao |
| author_facet | Xiang Zhou Yidan Chen Yong Xie Jie Han Wen Shao |
| author_sort | Xiang Zhou |
| collection | DOAJ |
| description | In the process of radiometric calibration, the corrections for bidirectional reflectance distribution functions (BRDFs) and spectral band adjustment factors (SBAFs) are crucial. Time-series MODIS images are commonly used to construct BRDFs by using the Ross–Li model in current research. However, the Ross–Li BRDF model is based on the linear relationship between the kernel models and is unable to take into account the nonlinear relationship between them. Furthermore, when using SBAF to account for spectral difference, a radiative transfer model is often used, but it requires many parameters to be set, which may introduce more errors and reduce the calibration accuracy. To address these issues, the random forest algorithm and a spectral interpolation convolution method using the Sentinel-2/multispectral instrument (MSI) are proposed in this study, in which the HuanJing-2A (HJ-2A)/charge-coupled device (CCD3) sensor is taken as an example, and the Dunhuang radiometric calibration site (DRCS) is used as a radiometric delivery platform. Firstly, a BRDF model by using the random forest algorithm of the DRCS is constructed using time-series MODIS images, which corrects the viewing geometry difference. Secondly, the BRDF correction coefficients, MSI reflectance, and relative spectral responses (RSRs) of CCD3 are used to correct the spectral differences. Finally, with the validation results, the maximum relative error between the calibration results of the proposed method and the official calibration coefficients (OCCs) published by the China Centre for Resources Satellite Data and Application (CRESDA) is 3.38%. When tested using the Baotou sandy site, the proposed method is better than the OCCs of the average relative errors calculated for all the bands except for the near-infrared (NIR) band, which has a larger error. Additionally, the effects of the light-matching method and the radiative transfer method, different approaches to constructing the BRDF model, using SBAF to account for spectral differences, different BRDF sources, as well as the imprecise viewing geometrical parameters, spectral interpolation method, and geometric positioning error, on the calibration results are analyzed. Results indicate that the cross-calibration coefficients obtained using the random forest algorithm and the proposed spectral interpolation method are more applicable to the CCD3; thus, they also account for the nonlinear relationships between the kernel models and reduce the error due to the radiative transfer model. The total uncertainty of the proposed method in all bands is less than 5.16%. |
| format | Article |
| id | doaj-art-c44bc43637da409fa5c455b776e1b7d5 |
| institution | Kabale University |
| issn | 2072-4292 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-c44bc43637da409fa5c455b776e1b7d52024-11-26T18:20:28ZengMDPI AGRemote Sensing2072-42922024-11-011622433710.3390/rs16224337Radiometric Cross-Calibration of HJ-2A/CCD3 Using the Random Forest Algorithm and a Spectral Interpolation Convolution Method with Sentinel-2/MSIXiang Zhou0Yidan Chen1Yong Xie2Jie Han3Wen Shao4College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, ChinaSchool of Geographical Sciences, Nanjing University of Information Science and Technology, Nanjing 210044, ChinaSchool of Geographical Sciences, Nanjing University of Information Science and Technology, Nanjing 210044, ChinaSchool of Geography and Geomatics, Xuchang University, Xuchang 461000, ChinaSchool of Geographical Sciences, Nanjing University of Information Science and Technology, Nanjing 210044, ChinaIn the process of radiometric calibration, the corrections for bidirectional reflectance distribution functions (BRDFs) and spectral band adjustment factors (SBAFs) are crucial. Time-series MODIS images are commonly used to construct BRDFs by using the Ross–Li model in current research. However, the Ross–Li BRDF model is based on the linear relationship between the kernel models and is unable to take into account the nonlinear relationship between them. Furthermore, when using SBAF to account for spectral difference, a radiative transfer model is often used, but it requires many parameters to be set, which may introduce more errors and reduce the calibration accuracy. To address these issues, the random forest algorithm and a spectral interpolation convolution method using the Sentinel-2/multispectral instrument (MSI) are proposed in this study, in which the HuanJing-2A (HJ-2A)/charge-coupled device (CCD3) sensor is taken as an example, and the Dunhuang radiometric calibration site (DRCS) is used as a radiometric delivery platform. Firstly, a BRDF model by using the random forest algorithm of the DRCS is constructed using time-series MODIS images, which corrects the viewing geometry difference. Secondly, the BRDF correction coefficients, MSI reflectance, and relative spectral responses (RSRs) of CCD3 are used to correct the spectral differences. Finally, with the validation results, the maximum relative error between the calibration results of the proposed method and the official calibration coefficients (OCCs) published by the China Centre for Resources Satellite Data and Application (CRESDA) is 3.38%. When tested using the Baotou sandy site, the proposed method is better than the OCCs of the average relative errors calculated for all the bands except for the near-infrared (NIR) band, which has a larger error. Additionally, the effects of the light-matching method and the radiative transfer method, different approaches to constructing the BRDF model, using SBAF to account for spectral differences, different BRDF sources, as well as the imprecise viewing geometrical parameters, spectral interpolation method, and geometric positioning error, on the calibration results are analyzed. Results indicate that the cross-calibration coefficients obtained using the random forest algorithm and the proposed spectral interpolation method are more applicable to the CCD3; thus, they also account for the nonlinear relationships between the kernel models and reduce the error due to the radiative transfer model. The total uncertainty of the proposed method in all bands is less than 5.16%.https://www.mdpi.com/2072-4292/16/22/4337HuanJing-2A (HJ-2A)Sentinel-2cross-calibrationbidirectional reflectance distribution function (BRDF)random forest algorithmspectral difference |
| spellingShingle | Xiang Zhou Yidan Chen Yong Xie Jie Han Wen Shao Radiometric Cross-Calibration of HJ-2A/CCD3 Using the Random Forest Algorithm and a Spectral Interpolation Convolution Method with Sentinel-2/MSI Remote Sensing HuanJing-2A (HJ-2A) Sentinel-2 cross-calibration bidirectional reflectance distribution function (BRDF) random forest algorithm spectral difference |
| title | Radiometric Cross-Calibration of HJ-2A/CCD3 Using the Random Forest Algorithm and a Spectral Interpolation Convolution Method with Sentinel-2/MSI |
| title_full | Radiometric Cross-Calibration of HJ-2A/CCD3 Using the Random Forest Algorithm and a Spectral Interpolation Convolution Method with Sentinel-2/MSI |
| title_fullStr | Radiometric Cross-Calibration of HJ-2A/CCD3 Using the Random Forest Algorithm and a Spectral Interpolation Convolution Method with Sentinel-2/MSI |
| title_full_unstemmed | Radiometric Cross-Calibration of HJ-2A/CCD3 Using the Random Forest Algorithm and a Spectral Interpolation Convolution Method with Sentinel-2/MSI |
| title_short | Radiometric Cross-Calibration of HJ-2A/CCD3 Using the Random Forest Algorithm and a Spectral Interpolation Convolution Method with Sentinel-2/MSI |
| title_sort | radiometric cross calibration of hj 2a ccd3 using the random forest algorithm and a spectral interpolation convolution method with sentinel 2 msi |
| topic | HuanJing-2A (HJ-2A) Sentinel-2 cross-calibration bidirectional reflectance distribution function (BRDF) random forest algorithm spectral difference |
| url | https://www.mdpi.com/2072-4292/16/22/4337 |
| work_keys_str_mv | AT xiangzhou radiometriccrosscalibrationofhj2accd3usingtherandomforestalgorithmandaspectralinterpolationconvolutionmethodwithsentinel2msi AT yidanchen radiometriccrosscalibrationofhj2accd3usingtherandomforestalgorithmandaspectralinterpolationconvolutionmethodwithsentinel2msi AT yongxie radiometriccrosscalibrationofhj2accd3usingtherandomforestalgorithmandaspectralinterpolationconvolutionmethodwithsentinel2msi AT jiehan radiometriccrosscalibrationofhj2accd3usingtherandomforestalgorithmandaspectralinterpolationconvolutionmethodwithsentinel2msi AT wenshao radiometriccrosscalibrationofhj2accd3usingtherandomforestalgorithmandaspectralinterpolationconvolutionmethodwithsentinel2msi |