Atmospheric and Radiometric Normalization of Satellite Images for Landscape-Level Environmental Monitoring: The Case of The Mediterranean Region
Yıl 2024,
, 620 - 633, 30.07.2024
Mehmet Ali Derse
,
Hakan Alphan
Öz
Ensuring atmospheric and radiometric consistency among the frameworks of satellite data used in regional studies is a critical requirement for change detection studies employed in regional planning monitoring. The purpose of this article is to provide a guide for the necessary atmospheric correction and radiometric normalization processes required in generating environmental data at the landscape level for physical planning. In this context, adjustments were made to remove atmospheric effects before merging multiple ASTER satellite image frames used in a project supported by TÜBİTAK, covering landscape-level environmental inventory and monitoring. The Dark Object Subtraction method with the Cos(t) model was utilized in the atmospheric correction process. Subsequently, separate regression relationships were computed for each band by considering overlapping areas on adjacent tracks of ASTER data, and radiometric normalization was performed based on these regression equations. Thus, differences between satellite images used in monitoring land changes and affecting multiple frames were minimized.
Etik Beyan
The article complies with national and international research and publication ethics.
Ethics Committee approval was not required for the study.
Destekleyen Kurum
TÜBİTAK
Teşekkür
This article was conducted within the scope of Project No. 107Y153 supported by the Scientific and Technological Research Council of Turkey (TÜBİTAK) under the 1001 Program. We would like to express our gratitude to TÜBİTAK for their financial support.
Kaynakça
- Abrams, M., Hook, S. & Ramachandran, B. (2008). Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) User Handbook, Version 2.
- Aghababaei, M., Ebrahimi, A., Naghipour, A.A., Asadi, E., Perez-Suay, A. & Morata, M. (2022). Introducing ARTMO's machine-learning classification algorithms toolbox: application to plant-type detection in a semi-steppe Iranian landscape, Remote Sens-Basel, 14, 4452.
- Akın, T. & Gül, A. (2020). Isparta-Atabey yöresinin ekoturizm potansiyeli ve turizm rotalarının belirlenmesi.
Journal of Architectural Sciences and Applications, 5(2), 221-240. https://doi.org/10.30785/mbud.793234
- Asam, S., Gessner, U., Gonzalez, R.A., Wenzl, M., Kriese, J. & Kuenzer, C. (2022). Mapping crop types of Germany by
combining temporal statistical metrics of Sentinel-1 and Sentinel-2 time series with LPIS data, Remote Sens-
Basel, 14(13), 2981, 2022.
- Barazzetti, L., Gianinetto, M. & Scaioni, M. (2016). Radiometric Normalization with Multi-image Pseudo-invariant
Features, Fourth International Conference on Remote Sensing and Geoinformation of the Environment
(Rscy2016), Paphos, Cyprus, Vol. 9688, 4-8 April 2016.
- Biday, S. G. & Bhosle, U. (2012). Relative radiometric correction of multitemporal Satellite Imagery using fourier
and wavelet transform, J. Indian Soc Remote, 40, 201-13.
- Boussadia-Omari, L., Ouillon, S., Hirche A., Salamani, M., Guettouche, M. S. & Ihaddaden, A. (2021). Contribution of
phytoecological data to spatialize soil erosion: Application of the RUSLE model in the Algerian atlas, Int Soil
Water Conse., 9, 502-19.
- Bujan, S., Guerra-Hernandez, J., Gonzalez-Ferreiro, E. & Miranda, D. (2021). Forest road detection using LiDAR
data and hybrid classification, Remote Sens-Basel, 13(3), 393.
- Chavez, P. S. (1988). An improved dark-object subtraction technique for atmospheric scattering correction of
multispectral data, Remote Sens Environ., 24, 459-79.
- Chavez, P. S. (1996). Image-based atmospheric corrections revisited and improved, Photogramm Eng Rem S.,
62, 1025-36.
- Chetia, S., Saikia, A., Basumatary, M. & Sahariah, D. (2020). When the heat is on: Urbanization and land surface
temperature in Guwahati, India, Acta Geophys, 68, 891-901, 2020.
- Du, Y., Teillet, P. M. & Cihlar, J. (2002). Radiometric normalization of multitemporal high-resolution satellite
images with quality control for land cover change detection, Remote Sensing of Environment, 82(1), 123-134,
ISSN 0034-4257. doi.org/10.1016/S0034-4257(02)00029-9.
- El Hajj, M., Begue, A., Lafrance, B., Hagolle, O., Dedieu, G. & Rumeau, M. (2008). Relative radiometric normalization
and atmospheric correction of a SPOT 5 time series, Sensors-Basel, 8, 2774-91.
- El Mortaji, N., Wahbi, M., Kazzi, M.A., Alaoui, O.Y., Boulaassal, H. & Maatouk, M. (2022). High resolution land cover
mapping and crop classification in the Loukkos watershed (Northern Morocco): An approach using SAR
Sentinel-1 time series, Rev Teledetec., 60, 47-69, 2022.
- Garcia-Pardo, K. A., Moreno-Rangel, D., Dominguez-Amarillo, S. & Garcia-Chavez, J. R. (2022). Remote sensing
for the assessment of ecosystem services provided by urban A review of the methods, Urban for Urban Gree.,
74, 127636, 2022.
- Gasparovic, M. & Dobrinic, D. (2021). Green ınfrastructure mapping in urban areas using sentinel-1 imagery,
Croat J. for Eng., 42, 336-55, 2021.
- Getachew, B. & Manjunatha, B. R. (2022). Impacts of land-use change on the hydrology of Lake Tana Basin,
Upper Blue Nile River Basin, Ethiopia, Glob Chall., 6, 2200041, 2022.
- Ghaseminik, F., Aghamohammadi, H. & Azadbakht, M. (2021). Land cover mapping of urban environments using
multispectral LiDAR data under data imbalance, Remote Sens Appl., 100449, 21, 2021
- Gu, Z. J., Shi, X. Z., Li, L., Yu, D. S., Liu, L. S. & Zhang, W. T. (2011). Using multiple radiometric correction images to
estimate leaf area index, In.t J. Remote Sens., 32, 9441-54.
- Islam, M. M., Borgqvist, H. & Kumar, L. (2019). Monitoring mangrove forest land cover changes in the coastline of
Bangladesh from 1976 to 2015. Geocarto Int., 34, 1458-76, 2019.
- İşler, B. & Aslan, Z. (2021). Bitki örtüsü ve mekânsal ve zamansal varyasyonların modellenmesi, Journal of the
Faculty of Engineering and Architecture of Gazi University, 36, 1863-74, 2021.
- Janzen, D. T., Fredeen, A.L. & Wheate, R.D. (2006). Radiometric correction techniques and accuracy assessment
for Landsat TM data in remote forested regions, Can. J. Remote Sens., 32, 330-40.
- Jenerowicz, A., Kaczynski, R., Siok, K. & Palkiewicz, K. (2019). Change detection of urban area based on multi-
sensor imagery, Remote Sensing Technologies and Applications in Urban Environments IV, 11157, 126-132.
- Karaca, A. C. & Güllü, M. K. (2019). Menderes ilçesindeki orman yangınının süperpiksel bölütleme temelli arama
yöntemiyle tespiti, Journal of the Faculty of Engineering and Architecture of Gazi University, 34, 1061-76.
- Khatami, R., Southworth, J., Muir, C., Caughlin, T., Ayana, A. N. & Brown, D. G. (2020). Operational large-area land-
cover mapping: An Ethiopia case study. Remote Sens.-Basel, 12, 954.
- Khorrami, B., Gunduz, O., Patel, N., Ghouzlane, S. & Najar, M. (2019). Land surface temperature anomalies in
response to changes in forest cover, Int. J. Eng. Geosci., 4, 149-56, 2019.
- Kiage, L. M., Liu, K. B., Walker, N. D., Lam, N. & Huh, O. K. (2007). Recent land-cover/use change associated with
land degradation in the Lake Baringo catchment, Kenya, East Africa: evidence from Landsat TM and ETM, Int. J.
Remote Sens., 28, 4285-309.
- Lelong, C. & Herimandimby, H. (2022). Land use / land cover map of Vavatenina region (Madagascar)
produced by object-based analysis of very high spatial resolution satellite images and geospatial reference
data, Data Brief, 44, 108517, 2022.
- Liu, S. H., Lin, C. W., Chen, Y. R. & Tseng, C. M. (2012). Automatic radiometric normalization with genetic algorithms
and a Kriging model, Comput Geosci-Uk, 43, 42-51.
- Liu, Y. K., Long, T. F., Jiao, W. L., He, G. J., Chen, B. & Huang, P. (2022). A General Relative Radiometric Correction
Method for Vignetting and Chromatic Aberration of Multiple CCDs: Take the Chinese Series of Gaofen Satellite
Level-0 Images for Example, Ieee T Geosci Remote, 60, 1-25.
- Lobo, F. L., Costa, M. P. F. & Novo, E. M. L. M. (2015). Time-series analysis of Landsat-MSS/TM/OLI images over
Amazonian waters impacted by gold mining activities, Remote Sens Environ., 157, 170-84.
- Lopez-Serrano, P. M., Corral-Rivas, J. J., Diaz-Varela, R. A. & Alvarez-Gonzalez, J. G., Lopez-Sanchez C. A. (2016).
Evaluation of Radiometric and Atmospheric Correction Algorithms for Aboveground Forest Biomass Estimation
Using Landsat 5 TM Data, Remote Sens-Basel, 8(5), 369.
- Luo, X., Tong, X. H., Hu, Z. W. & Wu, G. F. (2020). Improving Urban land cover/use mapping by ıntegrating a hybrid
convolutional neural network and an automatic training sample expanding strategy, Remote Sens-Basel,
12(14), 2292, 2020.
- MGM (2022). Meteoroloji Genel Müdürlüğü https://www.mgm.gov.tr/veridegerlendirme/il-ve-ilceler-
istatistik.aspx?k=A&m=MERSIN Erişim: 27.07.2022
- Prieto-Amparan, J. A., Villarreal-Guerrero, F., Martinez-Salvador, M., Manjarrez-Dominguez, C., Santellano-
Estrada, E. & Pinedo-Alvarez, A. (2018). Atmospheric and radiometric correction algorithms for the
multitemporal assessment of Grasslands productivity, Remote Sens-Basel, 10(2), 219.
- Pudale, S. R. & Bhosle, U. V. (2007). Comparative study of relative radiometric normalization techniques for
Resourcesat1 LISS III sensor images, Iccima: International Conference on Computational Intelligence and
Multimedia Applications, Sivakasi, Tamil Nadu, India, Vol III, 233-239, 13-15.
- Purwanto, A. D., Wikantika, K., Deliar, A. & Darmawan, S. (2023). Decision tree and random forest classification
algorithms for mangrove forest mapping in Sembilang National Park, Indonesia, Remote Sens-Basel, 15(1), 16,
2023.
- Rahman, M. M., Hay, G.J., Couloigner, I., Hemachandran, B. & Bailin, J. (2015). A comparison of four relative
radiometric normalization (RRN) techniques for mosaicing H-res multi-temporal thermal infrared (TIR) flight-
lines of a complex urban scene, Isprs J Photogramm., 106, 82-94.
- Rauf, U., Qureshi, W. S., Jabbar, H., Zeb, A., Mirza, A. & Alanazi, E. ( 2022). A new method for pixel classification for
rice variety identification using spectral and time series data from Sentinel-2 satellite imagery, Comput
Electron Agr., 193, 106731.
- Rostami, N. & Fathizad, H. (2022). Spatial and temporal changes of land uses and its relationship with surface
temperature in western Iran, Atmosfera, 35, 701-17, 2022.
- Ruiz, L. F. C., Dematte, J. A. M., Safanelli, J. L., Rizzo, R., Silvero, N. E. Q. & Rosin, N. A. (2022). Obtaining high-resolution
synthetic soil imagery for topsoil mapping, Remote Sens Lett., 13, 107-14, 2022.
- Sadeghi, V., Ahmadi, F. F. & Ebadi, H. (2017). A new automatic regression-based approach for relative
radiometric normalization of multitemporal satellite imagery, Comput Appl Math., 36, 825-42.
- Scheidt, S., Ramsey, M. & Lancaster, N. (2008). Radiometric normalization and image mosaic generation of
ASTER thermal infrared data: An application to extensive sand sheets and dune fields, Remote Sensing of
Environment, 112(3), 920-933, ISSN 0034-4257. doi.org/10.1016/j.rse.2007.06.020.
- Schroeder, T. A., Cohen, W. B., Song, C. H., Canty, M. J. & Yang, Z. Q. (2006). Radiometric correction of multi-
temporal Landsat data for characterization of early successional forest patterns in western Oregon, Remote
Sens Environ., 103, 16-26.
- Schott, J. R., Salvaggio, C. & Volchok, W. J. (1988). Radiometric Scene Normalization Using Pseudoinvariant
Features, Remote Sens Environ., 26 (1), 1-14.
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to tin mining activity in bangka regency using landsat ımagery, Sixth Geoinformation Science Symposium,
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classification and LULC mapping in the urban area of Belem, Eastern Brazilian Amazon, Sensors-Basel, 19(5),
1140, 2019.
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date satellite images, Photogramm Eng Rem S., 66, 967-80.
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Peyzaj Düzeyinde Çevresel İzleme İçin Uydu Verilerindeki Atmosferik ve Radyometrik Etkilerin Normalleştirilmesi: Akdeniz Bölgesi Örneği
Yıl 2024,
, 620 - 633, 30.07.2024
Mehmet Ali Derse
,
Hakan Alphan
Öz
Günümüzde onlarca faklı platform ve aygıttan çok bantlı ve yüksek yersel çözünürlüğe sahip uydu verileri sağlanmaktadır. Bölgesel çalışmalarda kullanılan uydu verilerinin çerçeveleri arasında atmosferik ve radyometrik uyumun sağlanması, bölgesel planlama çalışmalarının izlemede kullanılan değişim çalışmaları için önemli bir gereksinimdir. Bu makalenin amacı, fiziksel planlamaya peyzaj düzeyinde çevresel veri üretilmesi sürecinde gerekli olan atmosferik düzeltme ve radyometrik normalizasyon çalışması için bir rehber sunulmasıdır. Bu kapsamda TÜBİTAK tarafından desteklenen, peyzaj düzeyinde çevresel envanter ve izlemeyi kapsayan projede kullanılan birden fazla ASTER uydu görüntü çerçevesinin birleştirilmesi öncesinde, atmosferik etkilerin ortadan kaldırılması için düzeltmeler yapılmıştır. Atmosferik düzeltme işleminde Cos(t) modeli ile Koyu Obje Çıkarma (DOS) yöntemi kullanılmıştır. Daha sonra ASTER verilerinin komşu izleri üzerindeki çakışan bölgeler dikkate alınarak her bant için ayrı ayrı regresyon ilişkileri hesaplanmış, söz konusu regresyon eşitlikleri dikkate alınarak radyometrik normalizasyon yapılmıştır. Böylece arazi değişimlerinin izlemede kullanılan ve birçok çerçeveyi ilgilendiren uydu görüntüleri arasındaki farklılıklar minimuma indirilmiştir.
Kaynakça
- Abrams, M., Hook, S. & Ramachandran, B. (2008). Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) User Handbook, Version 2.
- Aghababaei, M., Ebrahimi, A., Naghipour, A.A., Asadi, E., Perez-Suay, A. & Morata, M. (2022). Introducing ARTMO's machine-learning classification algorithms toolbox: application to plant-type detection in a semi-steppe Iranian landscape, Remote Sens-Basel, 14, 4452.
- Akın, T. & Gül, A. (2020). Isparta-Atabey yöresinin ekoturizm potansiyeli ve turizm rotalarının belirlenmesi.
Journal of Architectural Sciences and Applications, 5(2), 221-240. https://doi.org/10.30785/mbud.793234
- Asam, S., Gessner, U., Gonzalez, R.A., Wenzl, M., Kriese, J. & Kuenzer, C. (2022). Mapping crop types of Germany by
combining temporal statistical metrics of Sentinel-1 and Sentinel-2 time series with LPIS data, Remote Sens-
Basel, 14(13), 2981, 2022.
- Barazzetti, L., Gianinetto, M. & Scaioni, M. (2016). Radiometric Normalization with Multi-image Pseudo-invariant
Features, Fourth International Conference on Remote Sensing and Geoinformation of the Environment
(Rscy2016), Paphos, Cyprus, Vol. 9688, 4-8 April 2016.
- Biday, S. G. & Bhosle, U. (2012). Relative radiometric correction of multitemporal Satellite Imagery using fourier
and wavelet transform, J. Indian Soc Remote, 40, 201-13.
- Boussadia-Omari, L., Ouillon, S., Hirche A., Salamani, M., Guettouche, M. S. & Ihaddaden, A. (2021). Contribution of
phytoecological data to spatialize soil erosion: Application of the RUSLE model in the Algerian atlas, Int Soil
Water Conse., 9, 502-19.
- Bujan, S., Guerra-Hernandez, J., Gonzalez-Ferreiro, E. & Miranda, D. (2021). Forest road detection using LiDAR
data and hybrid classification, Remote Sens-Basel, 13(3), 393.
- Chavez, P. S. (1988). An improved dark-object subtraction technique for atmospheric scattering correction of
multispectral data, Remote Sens Environ., 24, 459-79.
- Chavez, P. S. (1996). Image-based atmospheric corrections revisited and improved, Photogramm Eng Rem S.,
62, 1025-36.
- Chetia, S., Saikia, A., Basumatary, M. & Sahariah, D. (2020). When the heat is on: Urbanization and land surface
temperature in Guwahati, India, Acta Geophys, 68, 891-901, 2020.
- Du, Y., Teillet, P. M. & Cihlar, J. (2002). Radiometric normalization of multitemporal high-resolution satellite
images with quality control for land cover change detection, Remote Sensing of Environment, 82(1), 123-134,
ISSN 0034-4257. doi.org/10.1016/S0034-4257(02)00029-9.
- El Hajj, M., Begue, A., Lafrance, B., Hagolle, O., Dedieu, G. & Rumeau, M. (2008). Relative radiometric normalization
and atmospheric correction of a SPOT 5 time series, Sensors-Basel, 8, 2774-91.
- El Mortaji, N., Wahbi, M., Kazzi, M.A., Alaoui, O.Y., Boulaassal, H. & Maatouk, M. (2022). High resolution land cover
mapping and crop classification in the Loukkos watershed (Northern Morocco): An approach using SAR
Sentinel-1 time series, Rev Teledetec., 60, 47-69, 2022.
- Garcia-Pardo, K. A., Moreno-Rangel, D., Dominguez-Amarillo, S. & Garcia-Chavez, J. R. (2022). Remote sensing
for the assessment of ecosystem services provided by urban A review of the methods, Urban for Urban Gree.,
74, 127636, 2022.
- Gasparovic, M. & Dobrinic, D. (2021). Green ınfrastructure mapping in urban areas using sentinel-1 imagery,
Croat J. for Eng., 42, 336-55, 2021.
- Getachew, B. & Manjunatha, B. R. (2022). Impacts of land-use change on the hydrology of Lake Tana Basin,
Upper Blue Nile River Basin, Ethiopia, Glob Chall., 6, 2200041, 2022.
- Ghaseminik, F., Aghamohammadi, H. & Azadbakht, M. (2021). Land cover mapping of urban environments using
multispectral LiDAR data under data imbalance, Remote Sens Appl., 100449, 21, 2021
- Gu, Z. J., Shi, X. Z., Li, L., Yu, D. S., Liu, L. S. & Zhang, W. T. (2011). Using multiple radiometric correction images to
estimate leaf area index, In.t J. Remote Sens., 32, 9441-54.
- Islam, M. M., Borgqvist, H. & Kumar, L. (2019). Monitoring mangrove forest land cover changes in the coastline of
Bangladesh from 1976 to 2015. Geocarto Int., 34, 1458-76, 2019.
- İşler, B. & Aslan, Z. (2021). Bitki örtüsü ve mekânsal ve zamansal varyasyonların modellenmesi, Journal of the
Faculty of Engineering and Architecture of Gazi University, 36, 1863-74, 2021.
- Janzen, D. T., Fredeen, A.L. & Wheate, R.D. (2006). Radiometric correction techniques and accuracy assessment
for Landsat TM data in remote forested regions, Can. J. Remote Sens., 32, 330-40.
- Jenerowicz, A., Kaczynski, R., Siok, K. & Palkiewicz, K. (2019). Change detection of urban area based on multi-
sensor imagery, Remote Sensing Technologies and Applications in Urban Environments IV, 11157, 126-132.
- Karaca, A. C. & Güllü, M. K. (2019). Menderes ilçesindeki orman yangınının süperpiksel bölütleme temelli arama
yöntemiyle tespiti, Journal of the Faculty of Engineering and Architecture of Gazi University, 34, 1061-76.
- Khatami, R., Southworth, J., Muir, C., Caughlin, T., Ayana, A. N. & Brown, D. G. (2020). Operational large-area land-
cover mapping: An Ethiopia case study. Remote Sens.-Basel, 12, 954.
- Khorrami, B., Gunduz, O., Patel, N., Ghouzlane, S. & Najar, M. (2019). Land surface temperature anomalies in
response to changes in forest cover, Int. J. Eng. Geosci., 4, 149-56, 2019.
- Kiage, L. M., Liu, K. B., Walker, N. D., Lam, N. & Huh, O. K. (2007). Recent land-cover/use change associated with
land degradation in the Lake Baringo catchment, Kenya, East Africa: evidence from Landsat TM and ETM, Int. J.
Remote Sens., 28, 4285-309.
- Lelong, C. & Herimandimby, H. (2022). Land use / land cover map of Vavatenina region (Madagascar)
produced by object-based analysis of very high spatial resolution satellite images and geospatial reference
data, Data Brief, 44, 108517, 2022.
- Liu, S. H., Lin, C. W., Chen, Y. R. & Tseng, C. M. (2012). Automatic radiometric normalization with genetic algorithms
and a Kriging model, Comput Geosci-Uk, 43, 42-51.
- Liu, Y. K., Long, T. F., Jiao, W. L., He, G. J., Chen, B. & Huang, P. (2022). A General Relative Radiometric Correction
Method for Vignetting and Chromatic Aberration of Multiple CCDs: Take the Chinese Series of Gaofen Satellite
Level-0 Images for Example, Ieee T Geosci Remote, 60, 1-25.
- Lobo, F. L., Costa, M. P. F. & Novo, E. M. L. M. (2015). Time-series analysis of Landsat-MSS/TM/OLI images over
Amazonian waters impacted by gold mining activities, Remote Sens Environ., 157, 170-84.
- Lopez-Serrano, P. M., Corral-Rivas, J. J., Diaz-Varela, R. A. & Alvarez-Gonzalez, J. G., Lopez-Sanchez C. A. (2016).
Evaluation of Radiometric and Atmospheric Correction Algorithms for Aboveground Forest Biomass Estimation
Using Landsat 5 TM Data, Remote Sens-Basel, 8(5), 369.
- Luo, X., Tong, X. H., Hu, Z. W. & Wu, G. F. (2020). Improving Urban land cover/use mapping by ıntegrating a hybrid
convolutional neural network and an automatic training sample expanding strategy, Remote Sens-Basel,
12(14), 2292, 2020.
- MGM (2022). Meteoroloji Genel Müdürlüğü https://www.mgm.gov.tr/veridegerlendirme/il-ve-ilceler-
istatistik.aspx?k=A&m=MERSIN Erişim: 27.07.2022
- Prieto-Amparan, J. A., Villarreal-Guerrero, F., Martinez-Salvador, M., Manjarrez-Dominguez, C., Santellano-
Estrada, E. & Pinedo-Alvarez, A. (2018). Atmospheric and radiometric correction algorithms for the
multitemporal assessment of Grasslands productivity, Remote Sens-Basel, 10(2), 219.
- Pudale, S. R. & Bhosle, U. V. (2007). Comparative study of relative radiometric normalization techniques for
Resourcesat1 LISS III sensor images, Iccima: International Conference on Computational Intelligence and
Multimedia Applications, Sivakasi, Tamil Nadu, India, Vol III, 233-239, 13-15.
- Purwanto, A. D., Wikantika, K., Deliar, A. & Darmawan, S. (2023). Decision tree and random forest classification
algorithms for mangrove forest mapping in Sembilang National Park, Indonesia, Remote Sens-Basel, 15(1), 16,
2023.
- Rahman, M. M., Hay, G.J., Couloigner, I., Hemachandran, B. & Bailin, J. (2015). A comparison of four relative
radiometric normalization (RRN) techniques for mosaicing H-res multi-temporal thermal infrared (TIR) flight-
lines of a complex urban scene, Isprs J Photogramm., 106, 82-94.
- Rauf, U., Qureshi, W. S., Jabbar, H., Zeb, A., Mirza, A. & Alanazi, E. ( 2022). A new method for pixel classification for
rice variety identification using spectral and time series data from Sentinel-2 satellite imagery, Comput
Electron Agr., 193, 106731.
- Rostami, N. & Fathizad, H. (2022). Spatial and temporal changes of land uses and its relationship with surface
temperature in western Iran, Atmosfera, 35, 701-17, 2022.
- Ruiz, L. F. C., Dematte, J. A. M., Safanelli, J. L., Rizzo, R., Silvero, N. E. Q. & Rosin, N. A. (2022). Obtaining high-resolution
synthetic soil imagery for topsoil mapping, Remote Sens Lett., 13, 107-14, 2022.
- Sadeghi, V., Ahmadi, F. F. & Ebadi, H. (2017). A new automatic regression-based approach for relative
radiometric normalization of multitemporal satellite imagery, Comput Appl Math., 36, 825-42.
- Scheidt, S., Ramsey, M. & Lancaster, N. (2008). Radiometric normalization and image mosaic generation of
ASTER thermal infrared data: An application to extensive sand sheets and dune fields, Remote Sensing of
Environment, 112(3), 920-933, ISSN 0034-4257. doi.org/10.1016/j.rse.2007.06.020.
- Schroeder, T. A., Cohen, W. B., Song, C. H., Canty, M. J. & Yang, Z. Q. (2006). Radiometric correction of multi-
temporal Landsat data for characterization of early successional forest patterns in western Oregon, Remote
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