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Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
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TY - JOUR
T1 - Reconstruction of 500 m, 8-day Historical MODIS Fractional Vegetation Cover (FVC) Dataset (1982–2000) in China
AU - Ding, Xinyu
AU - Wang, Qunming
AU - Yang, Haoxuan
AU - Atkinson, Peter M.
PY - 2025/4/24
Y1 - 2025/4/24
N2 - Fractional vegetation cover (FVC) is a critical component of ecosystems, global climate change, and the carbon cycle. Several FVC products have been released, the most widely used of which are the GLASS FVC products (including the GLASS-Moderate Resolution Imaging Spectroradiometer (MODIS) and GLASS-AVHRR FVC products). Specifically, the GLASS-MODIS FVC product covers the period from 2000 to present with a 500-m spatial resolution, whereas the GLASS-AVHRR FVC product is available from 1982 to present with a coarser spatial resolution of 5-km. For local monitoring of patterns of change in vegetation, however, there is a great need for fine spatial resolution (e.g., 500-m in this article) and long-term time-series FVC datasets. To this end, we proposed to reconstruct a 500-m, 8-day historical MODIS FVC dataset (1982–2000) by making full use of the advantages of the existing GLASS-MODIS FVC (a fine spatial resolution of 500-m) and GLASS-AVHRR FVC (long-term coverage from 1982 to the present) products covering China in this article. The known GLASS-AVHRR FVC product was first used to fit the relationship between the FVC data after 2000 and before 2000, based on a random forest (RF) model. The trained relationship was migrated to the GLASS-MODIS FVC product, that is, predicting the MODIS FVC before 2000 based on the input of MODIS FVC after 2000. The validation using 64 scenes of Landsat FVC reference data revealed that the predicted historical MODIS FVC dataset has a reliable accuracy with a correlation coefficient (CC) value of 0.84, a root-mean-square error (RMSE) of 0.14, a Bias of 0.04, and an unbiased RMSE (ubRMSE) of 0.12. Moreover, an accuracy evaluation in seven different regions in 1999 suggested that the historical MODIS FVC is closer to the Landsat FVC than the GEOV2 FVC product. Overall, the 500-m, 8-day MODIS FVC dataset (1982–2000) in China can provide important historical data for long-term, local monitoring of vegetation, which has great potential in supporting studies in a range of application areas, including ecology, hydrology, and climatology. This dataset is available at https://doi.org/10.6084/m9.figshare.24616446.v1
AB - Fractional vegetation cover (FVC) is a critical component of ecosystems, global climate change, and the carbon cycle. Several FVC products have been released, the most widely used of which are the GLASS FVC products (including the GLASS-Moderate Resolution Imaging Spectroradiometer (MODIS) and GLASS-AVHRR FVC products). Specifically, the GLASS-MODIS FVC product covers the period from 2000 to present with a 500-m spatial resolution, whereas the GLASS-AVHRR FVC product is available from 1982 to present with a coarser spatial resolution of 5-km. For local monitoring of patterns of change in vegetation, however, there is a great need for fine spatial resolution (e.g., 500-m in this article) and long-term time-series FVC datasets. To this end, we proposed to reconstruct a 500-m, 8-day historical MODIS FVC dataset (1982–2000) by making full use of the advantages of the existing GLASS-MODIS FVC (a fine spatial resolution of 500-m) and GLASS-AVHRR FVC (long-term coverage from 1982 to the present) products covering China in this article. The known GLASS-AVHRR FVC product was first used to fit the relationship between the FVC data after 2000 and before 2000, based on a random forest (RF) model. The trained relationship was migrated to the GLASS-MODIS FVC product, that is, predicting the MODIS FVC before 2000 based on the input of MODIS FVC after 2000. The validation using 64 scenes of Landsat FVC reference data revealed that the predicted historical MODIS FVC dataset has a reliable accuracy with a correlation coefficient (CC) value of 0.84, a root-mean-square error (RMSE) of 0.14, a Bias of 0.04, and an unbiased RMSE (ubRMSE) of 0.12. Moreover, an accuracy evaluation in seven different regions in 1999 suggested that the historical MODIS FVC is closer to the Landsat FVC than the GEOV2 FVC product. Overall, the 500-m, 8-day MODIS FVC dataset (1982–2000) in China can provide important historical data for long-term, local monitoring of vegetation, which has great potential in supporting studies in a range of application areas, including ecology, hydrology, and climatology. This dataset is available at https://doi.org/10.6084/m9.figshare.24616446.v1
U2 - 10.1109/tgrs.2025.3564004
DO - 10.1109/tgrs.2025.3564004
M3 - Journal article
VL - 63
JO - IEEE Transactions on Geoscience and Remote Sensing
JF - IEEE Transactions on Geoscience and Remote Sensing
SN - 0196-2892
M1 - 4409619
ER -