6533b7d2fe1ef96bd125df58
RESEARCH PRODUCT
Down-Scaling Modis Vegetation Products with Landsat GAP Filled Surface Reflectance in Google Earth Engine
Jordi Muñoz-maríNathaniel P. RobinsonManuel CamposNicholas ClintonJavier Garcia-haroEmma Izquierdo-verdiguierGustau Camps-vallsAdrian PerezSteven W. RunningJohn S. KimballÁLvaro Moreno-martínezJose E. AdsuaraMarco Monetasubject
010504 meteorology & atmospheric sciences0208 environmental biotechnology02 engineering and technologyDown scalingVegetationSeasonalitymedicine.disease01 natural sciencesReflectivity020801 environmental engineeringPhotosynthetically active radiationHigh spatial resolutionmedicineEnvironmental scienceLeaf area indexImage resolution0105 earth and related environmental sciencesRemote sensingdescription
High spatial resolution vegetation products are fundamental in different fields, such as improving the understanding of crop seasonality at regional scales. Here, two new vegetation products such as the Leaf Area Index (LAI) and the Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) are downscaled at continental scales. A novel HIghly Scalable Temporal Adaptive Reflectance Fusion Model (HIS-TARFM) is used to generate the gap-free time series of Landsat surface reflectance data by fusing MODIS and Landsat reflectance for the contiguous United States. An artificial neural network is trained to capture the relationship between the gap free Landsat surface reflectance and the MODIS LAI/FAPAR products and allows to predict both biophysical variables at 30 meters spatial resolution. The results confirm that both vegetation products largely agree with the test dataset, providing low error and high explained variance.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2020-09-26 | IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium |