0000000000118090

AUTHOR

John S. Kimball

showing 3 related works from this author

Assessment of the SMAP Level-4 Surface and Root-Zone Soil Moisture Product Using In Situ Measurements

2017

International audience; The Soil Moisture Active Passive (SMAP) mission Level-4 Surface and Root-Zone Soil Moisture (L4_SM) data product is generated by assimilating SMAP L-band brightness temperature observations into the NASA Catchment land surface model. The L4_SM product is available from 31 March 2015 to present (within 3 days from real time) and provides 3-hourly, global, 9-km resolution estimates of surface (0-5 cm) and root-zone (0-100 cm) soil moisture and land surface conditions. This study presents an overview of the L4_SM algorithm, validation approach, and product assessment versus in situ measurements. Core validation sites provide spatially averaged surface (root zone) soil m…

Atmospheric Science010504 meteorology & atmospheric sciences0208 environmental biotechnologyDrainage basin[SDU.STU]Sciences of the Universe [physics]/Earth SciencesSoil science02 engineering and technologyLand cover01 natural sciencesStandard deviationITC-HYBRIDData assimilationSoil temperatureWater content0105 earth and related environmental sciencesgeographygeography.geographical_feature_category020801 environmental engineeringSatellite observations[SDU]Sciences of the Universe [physics]Brightness temperatureITC-ISI-JOURNAL-ARTICLEData assimilationDNS root zoneEnvironmental scienceSoil moistureLand surface modelScale (map)Kalman filtersJournal of hydrometeorology
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Down-Scaling Modis Vegetation Products with Landsat GAP Filled Surface Reflectance in Google Earth Engine

2020

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 MODI…

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 sensingIGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium
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Global Upscaling of the MODIS Land Cover with Google Earth Engine and Landsat Data

2021

Image classification has become one of the most common applications in remote sensing yielding to the creation of a variety of operational thematic maps at multiple spatio-temporal scales. The information contained in these maps summarizes key characteristics related with the physical environment and provides fundamental information of the Earth for vegetation monitoring or land use status over time. However, high spatial resolution land cover maps are usually only produced for specific small regions or in an image tile. We present a general methodology to obtain a high spatial resolution land cover maps using Landsat spectral information, the powerful Google Earth Engine platform, and oper…

Thematic mapContextual image classificationLand useComputer scienceRemote sensing (archaeology)ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONLand coverVegetationPlant functional typeImage resolutionRemote sensing2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS
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