Global 500 m seamless dataset (2000–2022) of land surface reflectance generated from MODIS products
The Moderate Resolution Imaging Spectroradiometer (MODIS) is widely utilized for retrieving land surface reflectance to reflect plant condition, detect ecosystem phenology, monitor forest fire, and constrain terrestrial energy budget. However, the state-of-art MODIS surface reflectance products suff...
| Main Authors: | , , , , |
|---|---|
| Format: | Text |
| Language: | English |
| Published: |
2023
|
| Subjects: | |
| Online Access: | https://doi.org/10.5194/essd-2023-314 https://essd.copernicus.org/preprints/essd-2023-314/ |
| Summary: | The Moderate Resolution Imaging Spectroradiometer (MODIS) is widely utilized for retrieving land surface reflectance to reflect plant condition, detect ecosystem phenology, monitor forest fire, and constrain terrestrial energy budget. However, the state-of-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 an urgent need for reconstructing spatiotemporally seamless (i.e. gap-filled) surface reflectance data from MODIS products. In general, there are two challenges in reconstructing seamless MODIS surface reflectance product. First, the intrinsic inconsistency of observations due to various sun/view geometry. Second, the prolonged missing values resulting from polar night or heavy cloud coverage, especially in monsoon season. To address these challenges, 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, 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 a RMSE of 0.056 and a bias of -0.0085 when compared with MODIS NBAR products, indicating the acceptable accuracy of both products. From a temporal perspective, the SDC500 eliminates abnormal fluctuations while retaining the useful localised feature of rapid disturbances. From a spatial perspective, the SDC500 shows satisfactory spatial continuity. In conclusion, the ... |
|---|