Validation Of Satellite Retrievals Research Articles

A multilayer daily high‐resolution gridded homogenized soil temperature over continental China

AbstractThis paper describes the construction of a 0.25° × 0.25° daily soil temperature (TS) data set at six soil depths (i.e., 0, 5, 10, 15, 20, and 40 cm) based on a homogenized TS at more than 2,000 meteorological stations across mainland of China for 1960–2017. First, at each station, the observed TS is split into a daily climatology of 1981–2010 and a daily anomaly for 1960–2017. Then, they are respectively interpolated to 0.25° × 0.25° horizontal resolution by using the thin‐plain spline and the angular distance weight interpolation methods. Finally, a gridded multilayer TS data set is constructed from the sum of the above two gridded products. Latitude, longitude, and high‐resolution digital elevation models are explicitly incorporated into the interpolation processes. Selectivity tests indicate that the optimal interpolation distance for the TS anomaly field is 500 km, where the gridded data set may have maximum coverage of the entire Chinese mainland. The results show that the gridded TS climatology is slightly lower than the station observations. The differences between gridded and the station‐based TS varies ±3°C, with relatively large in SW and TIBET. For the gridded anomaly fields, the cross‐validation shows that the mean absolute errors between gridded and station‐based TS are generally less than 0.6°C in the YZ, SW, and SC, but they are above 1.0°C in the NW and TIBET due to the sparse station distribution and large topographic relief. From 1961 to 2016, the seasonal variability of the gridded TS is generally consistent with that of the surface air temperature. The gridded TS over the majority of land displays significantly positive trends in both summer and winter (p > 95%), indicating an overall warming land surface in China. The long‐term gridded TS data not only preserves the spatial features represented in the station observations, but also provides a spatio‐temporal continuous long‐term product of mainland China. Thus, it may be used in future studies, for example, evaluation of land surface model simulations, validation of satellite retrievals, land‐atmosphere interactions, and climate change.

Evaluation of in situ sea surface temperatures for use in the calibration and validation of satellite retrievals

In situ sea surface temperatures (SSTs) are used for calibration and validation of satellite retrievals. This study analyzes three in situ data sets from the National Centers for Environmental Prediction (NCEP) Global Telecommunication System (GTS), the International Comprehensive Ocean‐Atmosphere Data Set (ICOADS) release 2.4, and the U.S. Global Ocean Data Assimilation Experiment/Fleet Numerical Meteorology and Oceanography Center (FNMOC). Comparisons show that most reports in the ICOADS and FNMOC are of the same origin as NCEP GTS. Quality control (QC) information is either unavailable (NCEP), not well documented (FNMOC), or nonuniform (ICOADS, FNMOC). Preliminary QC was implemented in this study and uniformly applied to all data sets. All analyses are stratified by major types of in situ platforms including ships, drifters, and moored buoys, the latter being further subdivided into tropical and coastal. Ships overwhelmingly prevailed before 1990 but then declined, whereas the number of drifters significantly increased, as did their reporting density. Although both platforms sample the full SST range well, drifters cover the global ocean much more uniformly than ships. Statistical analyses are performed on the in situ SST anomalies with respect to daily Reynolds and daily Pathfinder. Different global mean biases are observed for different platform types (e.g., ∼+0.03 K for drifters and tropical moorings and ∼+0.15 K for ships, with respect to Reynolds SST), suggesting existence of cross‐platform biases that need to be reconciled. Root mean square (RMS) errors of the four types of in situ data have been estimated via three‐way analyses proposed in O'Carroll et al. (2008). The geographical distributions of RMS errors in Pathfinder, Reynolds, and in situ SSTs show distinct spatial patterns, which require further understanding and remediation.

The MODIS (Collection V005) BRDF/albedo product: Assessment of spatial representativeness over forested landscapes

A new methodology for establishing the spatial representativeness of tower albedo measurements that are routinely used in validation of satellite retrievals from global land surface albedo and reflectance anisotropy products is presented. This method brings together knowledge of the intrinsic biophysical properties of a measurement site, and the surrounding landscape to produce a number of geostatistical attributes that describe the overall variability, spatial extent, strength of the spatial correlation, and spatial structure of surface albedo patterns at separate seasonal periods throughout the year. Variogram functions extracted from Enhanced Thematic Mapper Plus (ETM+) retrievals of surface albedo using multiple spatial and temporal thresholds were used to assess the degree to which a given point (tower) measurement is able to capture the intrinsic variability of the immediate landscape extending to a satellite pixel. A validation scheme was implemented over a wide range of forested landscapes, looking at both deciduous and coniferous sites, from tropical to boreal ecosystems. The experiment focused on comparisons between tower measurements of surface albedo acquired at local solar noon and matching retrievals from the MODerate Resolution Imaging Spectroradiometer (MODIS) (Collection V005) Bidirectional Reflectance Distribution Function (BRDF)/albedo algorithm. Assessments over a select group of field stations with comparable landscape features and daily retrieval scenarios further demonstrate the ability of this technique to identify measurement sites that contain the intrinsic spatial and seasonal features of surface albedo over sufficiently large enough footprints for use in modeling and remote sensing studies. This approach, therefore, improves our understanding of product uncertainty both in terms of the representativeness of the field data and its relationship to the larger satellite pixel.