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A Revised Land Surface Parameterization (SiB2) for Atmospheric GCMS. Part II: The Generation of Global Fields of Terrestrial Biophysical Parameters from Satellite Data
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1996
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Earth ObservationEngineeringSource Ndvi DatasetGlobal Parameter FieldsClimate ModelingAtmospheric ModelLand CoverEarth System ScienceTerrestrial SensingEarth ScienceEarth SystemVegetation-atmosphere InteractionsAtmospheric ScienceForest MeteorologyClimate ChangeMeteorologyGeographyRadiation MeasurementEarth Observation DataEarth's ClimateClimate DynamicsClimatologyNdvi DatasetAtmospheric GcmsRemote SensingSatellite MeteorologyLand Surface ModelingGlobal FieldsLand Surface Parameterization
The revised Simple Biosphere Model (SiB2) employs global parameter fields. The model derives time‑varying vegetation parameters—FPAR, leaf area index, and canopy greenness—from monthly 1°×1° NDVI data, applying corrections for anomalies, solar angle, cloud‑related dropouts, and tropical cloud cover, and then computes land‑surface parameters using these corrected NDVI values and supplementary in‑situ observations. These datasets outperform existing land‑surface parameterizations by providing spatially and temporally resolved vegetation parameters directly from global observations rather than inferred from disparate land‑cover surveys. The other section reviews the global parameter fields used in SiB2.
The global parameter fields used in the revised Simple Biosphere Model (SiB2) of Sellers et al. are reviewed. The most important innovation over the earlier SiB1 parameter set of Dorman and Sellers is the use of satellite data to specify the time-varying phonological properties of FPAR, leaf area index. and canopy greenness fraction. This was done by processing a monthly 1° by 1° normalized difference vegetation index (NDVI) dataset obtained farm Advanced Very High Resolution Radiometer red and near-infrared data. Corrections were applied to the source NDVI dataset to account for (i) obvious anomalies in the data time series, (ii) the effect of variations in solar zenith angle, (iii) data dropouts in cold regions where a temperature threshold procedure designed to screen for clouds also eliminated cold land surface points, and (iv) persistent cloud cover in the Tropics. An outline of the procedures for calculating the land surface parameters from the corrected NDVI dataset is given, and a brief description is provided of source material, mainly derived from in situ observations, that was used in addition to the NDVI data. The datasets summarized in this paper should he superior to prescriptions currently used in most land surface parameterizations in that the spatial and temporal dynamics of key land surface parameters, in particular those related to vegetation, are obtained directly from a consistent set of global-scale observations instead of being inferred from a variety of survey-based land-cover classifications.