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Analysis of Vegetation Optical Depth and Soil Moisture Retrieved by SMOS Over Tropical Forests
摘要： In this letter, the results obtained with the last version (level 2, version 650) of SMOS retrieval algorithm are compared against independent measurements, over tropical forests. In particular, the climate research unit meteorological variables and data bases of forest height and forest biomass are considered. Comparisons with results obtained by AMSR-2 under similar conditions are also illustrated. Vegetation optical depth shows a generally good correlation with forest height and forest biomass, particularly in Africa and South America. Spatial and temporal trends of retrieved soil moisture follow trends of rainfall, particularly in regions of dry winter.
关键词： soil moisture (SM),SMOS,vegetation optical depth (VOD),tropical forests
更新于2025-09-04 15:30:14
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Multisensor Normalized Difference Vegetation Index Intercalibration: A Comprehensive Overview of the Causes of and Solutions for Multisensor Differences
摘要： Near-surface, airborne, and satellite platforms can provide multisensor, multiscale normalized difference vegetation index (NDVI) data for spatially and temporally continuous land-surface monitoring. This article presents a comprehensive literature review focused on consistency issues among these data, particularly in the spectral aspect. To begin, the main causes of multisensor NDVI inconsistencies are reviewed, and then current intercalibration studies are classified in terms of the way multiple NDVI data are combined. Most studies use linear models, although quadratic models are more effective to treat the dependence of multisensor NDVI differences on land cover, seasonality, and atmospheric condition. Radiative transfer-based models disclose causal factors of multisensor NDVI inconsistencies, yet accurate ancillary data are required. For each method, a distributed or lumped processing scheme can be used. The distributed scheme performs better for linear and quadratic models, while interscheme differences are minimal for complicated models. Finally, uncertainty issues are elucidated, and common practices are recommended for improved NDVI intercalibration.
关键词： vegetation index,NDVI,remote sensing,multisensor,intercalibration
更新于2025-09-04 15:30:14