Global 500 m seamless dataset (2000–2022) of land surface reflectance generated from MODIS products
<p>The Moderate Resolution Imaging Spectroradiometer (MODIS) is widely utilized for retrieving land surface reflectance to reflect plant conditions, detect ecosystem phenology, monitor forest fires, and constrain terrestrial energy budgets. However, the state-of-the-art MODIS surface reflectan...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2024-01-01
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| Series: | Earth System Science Data |
| Online Access: | https://essd.copernicus.org/articles/16/177/2024/essd-16-177-2024.pdf |
| _version_ | 1797359317762965504 |
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| author | X. Liang Q. Liu J. Wang S. Chen P. Gong P. Gong |
| author_facet | X. Liang Q. Liu J. Wang S. Chen P. Gong P. Gong |
| author_sort | X. Liang |
| collection | DOAJ |
| description | <p>The Moderate Resolution Imaging Spectroradiometer (MODIS) is widely utilized for retrieving land surface reflectance to reflect plant conditions, detect ecosystem phenology, monitor forest fires, and constrain terrestrial energy budgets. However, the state-of-the-art MODIS surface reflectance products suffer from temporal and spatial gaps due to atmospheric conditions (e.g. clouds and aerosols), limiting their use in ecological, agricultural, and environmental studies. Therefore, there is a need for reconstructing spatiotemporally seamless (i.e. gap-filled) surface reflectance data from MODIS products, which is difficult due to the intrinsic inconsistency of observations resulting from various sun/view geometry and the prolonged missing values resulting from polar night or heavy cloud coverage, especially in monsoon season. We built a framework for generating the global 500 m daily seamless data cubes (SDC500) based on MODIS surface reflectance dataset, which contains the generation of a land-cover-based a priori database, bidirectional reflectance distribution function (BRDF) correction, outlier detection, gap filling, and smoothing. The first global spatiotemporally seamless land surface reflectance at 500 m resolution was produced, covering the period from 2000 to 2022. Preliminary evaluation of the dataset at 12 sites worldwide with different land cover demonstrated its robust performance. The quantitative assessment shows that the SDC500 gap-filling results have a root-mean-square error (RMSE) of 0.0496 and a mean absolute error (MAE) of 0.0430. The SDC500 BRDF correction results showed an RMSE of 0.056 and a bias of <span class="inline-formula">−</span>0.0085 when compared with MODIS nadir BRDF-adjusted reflectance (NBAR) products, indicating the acceptable accuracy of both products. From a temporal perspective, the SDC500 eliminates abnormal fluctuations while retaining the useful localized feature of rapid disturbances. From a spatial perspective, the SDC500 shows satisfactory spatial continuity. In conclusion, the SDC500 is a well-processed global daily surface reflectance product, which can serve as the fundamental input for large-scale ecological, agricultural, and environmental applications and quantitative remote sensing studies. The SDC500 is available at <span class="uri">http://data.starcloud.pcl.ac.cn/resource/27</span> (Liang et al., 2023b) or <a href="https://doi.org/10.12436/SDC500.27.20230701">https://doi.org/10.12436/SDC500.27.20230701</a> (Liang et al., 2023a).</p> |
| first_indexed | 2024-03-08T15:16:49Z |
| format | Article |
| id | doaj.art-fcf22e28f2ec4f90afcb9e92f347fc41 |
| institution | Directory Open Access Journal |
| issn | 1866-3508 1866-3516 |
| language | English |
| last_indexed | 2024-03-08T15:16:49Z |
| publishDate | 2024-01-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Earth System Science Data |
| spelling | doaj.art-fcf22e28f2ec4f90afcb9e92f347fc412024-01-10T13:00:10ZengCopernicus PublicationsEarth System Science Data1866-35081866-35162024-01-011617720010.5194/essd-16-177-2024Global 500 m seamless dataset (2000–2022) of land surface reflectance generated from MODIS productsX. Liang0Q. Liu1J. Wang2S. Chen3P. Gong4P. Gong5Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing 100084, ChinaPeng Cheng Laboratory, Shenzhen 518000, ChinaPeng Cheng Laboratory, Shenzhen 518000, ChinaDepartment of Geography, The University of Hong Kong, Hong Kong, ChinaDepartment of Geography, The University of Hong Kong, Hong Kong, ChinaInstitute for Climate and Carbon Neutrality and Department of Earth Sciences, The University of Hong Kong, Hong Kong, China<p>The Moderate Resolution Imaging Spectroradiometer (MODIS) is widely utilized for retrieving land surface reflectance to reflect plant conditions, detect ecosystem phenology, monitor forest fires, and constrain terrestrial energy budgets. However, the state-of-the-art MODIS surface reflectance products suffer from temporal and spatial gaps due to atmospheric conditions (e.g. clouds and aerosols), limiting their use in ecological, agricultural, and environmental studies. Therefore, there is a need for reconstructing spatiotemporally seamless (i.e. gap-filled) surface reflectance data from MODIS products, which is difficult due to the intrinsic inconsistency of observations resulting from various sun/view geometry and the prolonged missing values resulting from polar night or heavy cloud coverage, especially in monsoon season. We built a framework for generating the global 500 m daily seamless data cubes (SDC500) based on MODIS surface reflectance dataset, which contains the generation of a land-cover-based a priori database, bidirectional reflectance distribution function (BRDF) correction, outlier detection, gap filling, and smoothing. The first global spatiotemporally seamless land surface reflectance at 500 m resolution was produced, covering the period from 2000 to 2022. Preliminary evaluation of the dataset at 12 sites worldwide with different land cover demonstrated its robust performance. The quantitative assessment shows that the SDC500 gap-filling results have a root-mean-square error (RMSE) of 0.0496 and a mean absolute error (MAE) of 0.0430. The SDC500 BRDF correction results showed an RMSE of 0.056 and a bias of <span class="inline-formula">−</span>0.0085 when compared with MODIS nadir BRDF-adjusted reflectance (NBAR) products, indicating the acceptable accuracy of both products. From a temporal perspective, the SDC500 eliminates abnormal fluctuations while retaining the useful localized feature of rapid disturbances. From a spatial perspective, the SDC500 shows satisfactory spatial continuity. In conclusion, the SDC500 is a well-processed global daily surface reflectance product, which can serve as the fundamental input for large-scale ecological, agricultural, and environmental applications and quantitative remote sensing studies. The SDC500 is available at <span class="uri">http://data.starcloud.pcl.ac.cn/resource/27</span> (Liang et al., 2023b) or <a href="https://doi.org/10.12436/SDC500.27.20230701">https://doi.org/10.12436/SDC500.27.20230701</a> (Liang et al., 2023a).</p>https://essd.copernicus.org/articles/16/177/2024/essd-16-177-2024.pdf |
| spellingShingle | X. Liang Q. Liu J. Wang S. Chen P. Gong P. Gong Global 500 m seamless dataset (2000–2022) of land surface reflectance generated from MODIS products Earth System Science Data |
| title | Global 500 m seamless dataset (2000–2022) of land surface reflectance generated from MODIS products |
| title_full | Global 500 m seamless dataset (2000–2022) of land surface reflectance generated from MODIS products |
| title_fullStr | Global 500 m seamless dataset (2000–2022) of land surface reflectance generated from MODIS products |
| title_full_unstemmed | Global 500 m seamless dataset (2000–2022) of land surface reflectance generated from MODIS products |
| title_short | Global 500 m seamless dataset (2000–2022) of land surface reflectance generated from MODIS products |
| title_sort | global 500 thinsp m seamless dataset 2000 2022 of land surface reflectance generated from modis products |
| url | https://essd.copernicus.org/articles/16/177/2024/essd-16-177-2024.pdf |
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