nyue Yu

Evaluation of Split-Window Land Surface Temperature Algorithms for Generating Climate Data Records

Land surface temperature (LST) is a key indicator of the Earths surface energy and is used in a range of hydrological, meteorological, and climatological applications. As needed for most modeling and climate analysis applications, LST products that are generated from polar-orbiting meteorological satellite sensors have spatial resolutions from several hundred meters to several kilometers and have (quasi) daily temporal resolution. These sensors include the National Oceanic and Atmospheric Administration advanced very high resolution radiometer (AVHRR), the earth observing system moderate resolution imaging spectroradiometer (MODIS), and the forthcoming visible/infrared imager radiometer suite (VIIRS) series, to be flown onboard the National Polar-Orbiting Operational Environmental Satellite System (VIIRS flights begin approximately 2009). Generally, split-window algorithms are used with these sensors to produce LST products. In this paper, we evaluated nine published LST algorithms (or, in some cases, their slight variants) to determine those that are most suitable for generating a consistent LST climate data record across these satellite sensors and platforms. A consistent set of moderate-resolution atmospheric transmission simulations were used in determining the appropriate coefficients for each algorithm and sensor (AVHRR, MODIS, and VIIRS) combination. Algorithm accuracy was evaluated over different view zenith angles, surface-atmosphere temperature combinations, and emissivity errors. Both simulated and actual remote sensing data were used in the evaluation. We found that the nine heritage algorithms can effectively be collapsed into three groups of highly similar performance. We also demonstrated the efficacy of an atmospheric path-length correction term that is added to the heritage algorithms. We conclude that the algorithms depending on both the mean and difference of band emissivities (Group 1 in our nomenclature) are most accurate and stable over a wide range of conditions, provided that the emissivity can be well estimated a priori . Where the emissivity cannot be well estimated, the Group 3 algorithms (which do not depend on the emissivity difference) modified with the path-length correction term perform better.

Evaluation of the Relationship between Air and Land Surface Temperature under Clear- and Cloudy-Sky Conditions

Abstract Clear and cloudy daytime comparisons of land surface temperature (LST) and air temperature (Tair) were made for 14 stations included in the U.S. Climate Reference Network (USCRN) of stations from observations made from 2003 through 2008. Generally, LST was greater than Tair for both the clear and cloudy conditions; however, the differences between LST and Tair were significantly less for the cloudy-sky conditions. In addition, the relationships between LST and Tair displayed less variability under the cloudy-sky conditions than under clear-sky conditions. Wind speed, time of the observation of Tair and LST, season, the occurrence of precipitation at the time of observation, and normalized difference vegetation index values were all considered in the evaluation of the relationship between Tair and LST. Mean differences between LST and Tair of less than 2°C were observed under cloudy conditions for the stations, as compared with a minimum difference of greater than 2°C (and as great as 7+°C) for th...

Developing Algorithm for Operational GOES-R Land Surface Temperature Product

The Geostationary Operational Environmental Satellite (GOES) program is developing the Advanced Baseline Imager (ABI), a new generation sensor to be carried onboard the GEOS-R satellite (launch expected in 2014). Compared to the current GOES Imager, ABI will have significant advantages for retrieving land surface temperature (LST) as well as providing qualitative and quantitative data for a wide range of applications. The infrared bands of the ABI sensor are designed to achieve a spatial resolution of 2 km at nadir and a noise equivalent temperature of 0.1 K. These improve the imager specifications and compare well with those of polar-orbiting sensors (e.g., Advanced Very High Resolution Radiometer and Moderate Resolution Imaging Spectroradiometer). In this paper, we discuss the development of a split window LST algorithm for the ABI sensor. First, we simulated ABI sensor data using the MODTRAN radiative transfer model and NOAA88 atmospheric profiles. To model land conditions, we developed emissivity data for 78 virtual surface types using the surface emissivity library from Snyder Using the simulation results, we performed regression analyses with the candidate LST algorithms. Algorithm coefficients were stratified for dry and moist atmospheres as well as for daytime and nighttime conditions. We estimated the accuracy and sensitivity of each algorithm for different sun-view geometries, emissivity errors, and atmospheric assessments. Finally, we evaluated the most promising algorithm using real data from the GOES-8 Imager and SURFace RADiation Network. The results indicate that the optimized LST algorithm meets the required accuracy (2.3 K) of the GOES-R mission.

Deriving Water Fraction and Flood Maps From MODIS Images Using a Decision Tree Approach

This study investigates how to derive water fraction and flood mapping from the Moderate-Resolution Imaging Spectroradiometer (MODIS) onboard the Earth Observing System (EOS) satellites using the linear mixture model and decision-tree approach. The recent floods in the Midwestern United States in June 2008 and in the New Orleans area in August 2005 were selected for this study. MODIS surface reflectance with the matched land cover data in the Midwest prior to the flooding events were used for the training dataset, with the split test mode of 50% for training and the remaining 50% for testing. The precision, or accuracy rate, of the water classification reaches over 90% from the test. Our results demonstrate that the reflectance difference (CH2-CH1) between the MODIS channel 2 (CH2) and channel 1 (CH1) is the most useful parameter to derive water fraction from the linear mixture model. Rules and threshold values from the decision tree training were applied to real applications on different dates (June 1, 17, and 19, 2008 for the Midwestern region of the U.S.) and at different locations (New Orleans in 2005) to identify standing water and to calculate water fraction. The derived water fraction maps were evaluated using higher resolution Thematic Mapper (TM) data from Landsat observations. It shows that the correlation between water fractions derived from the MODIS and TM data is 0.97, with difference or “bias” of 4.47%, standard deviation of 4.40%, and root mean square error (rmse) of 6.28%. Flood distributions in both space and time domains were generated using the differences in water fraction values before and after the flooding.

Validation of GOES-R Satellite Land Surface Temperature Algorithm Using SURFRAD Ground Measurements and Statistical Estimates of Error Properties

Validation of satellite land surface temperature (LST) is a challenge because of spectral, spatial, and temporal variabilities of land surface emissivity. Highly accurate in situ LST measurements are required for validating satellite LST products but are very hard to obtain, except at discrete points or for very short time periods (e.g., during field campaigns). To compare these field-measured point data with moderate-resolution (1 km) satellite products requires a scaling process that can introduce errors that ultimately exceed those in the satellite-derived LST products whose validation is sought. This paper presents a new method of validating the Geostationary Operational Environmental Satellite (GOES) R-Series (GOES-R) Advanced Baseline Imager (ABI) LST algorithm. It considers the error structures of both ground and satellite data sets. The method applies a linear fitting model to the satellite data and coregistered “match-up” ground data for estimating the precisions of both data sets. In this paper, GOES-8 Imager data were used as a proxy of the GOES-R ABI data for the satellite LST derivation. The in situ data set was obtained from the National Oceanic and Atmospheric Administrations SURFace RADiation (SURFRAD) budget network using a stringent match-up process. The data cover one year of GOES-8 Imager observations over six SURFRAD sites. For each site, more than 1000 cloud-free match-up data pairs were obtained for day and night to ensure statistical significance. The average precision over all six sites was found to be 1.58 K, as compared to the GOES-R LST required precision of 2.3 K. The least precise comparison at an individual SURFRAD site was 1.8 K. The conclusion is that, for these ground truth sites, the GOES-R LST algorithm meets the specifications and that an upper boundary on the precision of the satellite LSTs can be determined.

Greenland surface albedo changes in July 1981?2012 from satellite observations

Significant melting events over Greenland have been observed over the past few decades. This study presents an analysis of surface albedo change over Greenland using a 32-year consistent satellite albedo product from the global land surface satellite (GLASS) project together with ground measurements. Results show a general decreasing trend of surface albedo from 1981 to 2012 ( 0.009 0.002 decade 1 , p< 0:01). However, a large decrease has occurred since 2000 ( 0.028 0.008 decade 1 , p< 0:01) with most significant decreases at elevations between 1000 and 1500 m ( 0.055 decade 1 , p< 0:01) which may be associated with surface temperature increases. The surface radiative forcing from albedo changes is 2.73 W m 2 decade 1 and 3.06 W m 2 decade 1 under full-sky and clear-sky conditions, respectively, which indicates that surface albedo changes are likely to have a larger impact on the surface shortwave radiation budget than that caused by changes in the atmosphere over Greenland. A comparison made between satellite albedo products and data output from the Coupled Model Inter-comparison Project 5 (CMIP5) general circulation models (GCMs) shows that most of the CMIP5 models do not detect the significantly decreasing trends of albedo in recent decades. This suggests that more efforts are needed to improve our understanding and simulation of climate change at high latitudes.

Fusion of Satellite Land Surface Albedo Products Across Scales Using a Multiresolution Tree Method in the North Central United States

Land surface albedo is a key factor in climate change and land surface modeling studies, which affects the surface radiation budget. Many satellite albedo products have been generated during the last several decades. However, due to the problems resulting from the sensor characteristics (spectral bands, spatial and temporal resolutions, etc.) and/or the retrieving procedures, surface albedo estimations from different satellite sensors are inconsistent and often contain gaps, which limit their applications. Many approaches have been developed to generate the complete albedo data set; however, most of them suffer from either the persistent systematic bias of relying on only one data set or the problem of subpixel heterogeneity. In this paper, a data fusion method is prototyped using multiresolution tree (MRT) models to develop spatially and temporally continuous albedo maps from different satellite albedo/reflectance data sets. Data from the Multiangle Imaging Spectroradiometer (MISR), Moderate Resolution Imaging Spectroradiometer (MODIS), and Landsat Thematic Mapper/Enhanced Thematic Mapper Plus are used as examples, at a study area in the north central United States mostly covered by crop, grass, and forest, from June to September 2005. Results show that the MRT data fusion method is capable of integrating the three satellite data sets at different spatial resolutions to fill the gaps and to reduce the inconsistencies between different products. The validation results indicate that the uncertainties of the three satellite products have been reduced significantly through the data fusion procedure. Further efforts are needed to evaluate and improve the current algorithm over other locations, time periods, and land cover types.

A New Short-Wave Infrared (SWIR) Method for Quantitative Water Fraction Derivation and Evaluation With EOS/MODIS and Landsat/TM Data

A quantitative method is developed for deriving water fraction from coarse- to medium-resolution satellite data with visible to short-wave infrared (SWIR) channels based on the linear mixture theory. The method uses a SWIR channel (1.64 μm) by assuming that the water-surface-leaving radiance in this channel is insignificant and is thus less affected by water types and water depth than near-infrared (NIR) channels for inland water bodies. For a mixed water pixel, a dynamic nearest neighbor searching (DNNS) method is used to find the nearby land pixels to determine the average land reflectance. The nearby pure water pixels with a similar water type to the subpixel water portion of the mixed water pixel are found dynamically to derive the average water reflectance. The average reflectance in the SWIR channel from both pure land pixels and water pixels is used to calculate the water fraction from a linear mixture model. The developed method is applied to Moderate Resolution Imaging Spectroradiometer (MODIS) data and shows promising results. High-resolution satellite data from the Thematic Mapper (TM) are used to evaluate the water fraction derived from MODIS. During pixel-to-pixel water fraction evaluation, TM data are spatially aggregated to MODIS resolution. When evaluated against the high-resolution TM observations, water fractions derived from MODIS using the DNNS method with the SWIR channel show a bias of -0.021 with a standard deviation of 0.0338. Comparing lake areas between TM and MODIS data also shows consistent results with the pixel-to-pixel water fraction comparison. The DNNS method is also compared to the traditional histogram method both with SWIR channel and NIR channel. The results show that the DNNS method is more accurate than the histogram method and that the SWIR channel is better than the NIR channel to derive highly accurate water fraction from coarse- to medium-resolution satellite data.

Long-Term Detection of Global Vegetation Phenology from Satellite Instruments

Vegetation phenology is the expression of the seasonal cycles of plant processes and their connections to climate change (temperature and precipitation). The timing of phenological events can be used to document and evaluate the effects of climate change on both individual plant species and vegetation communities. Thus, vegetation phenology (including shifts in the timing of bud burst, leaf development, senescence, and growing season length) is considered as one of the simplest and most effective indicators of climate change (IPCC, 2007). Long-term observing and recording of changes in plant phenology support efforts to understand trends in regional and global climate changes, to reconstruct past climate variations, to explore the magnitude of climate change impacts on vegetation growth, and to predict biological responses to future climate scenarios.

Towards Operational Automatic Flood Detection Using EOS/MODIS Data

This study investigates how to derive water fraction and flood map from the Moderate-Resolution Imaging Spectroradiometer (MODIS) using a Regression Tree (RT) approach, which can integrate all predictors. The New Orleans, Louisiana floods in August 2005 were selected as a case study. MODIS surface reflectance with matched water fraction data were used for training. The tree-based regression models were obtained automatically through learning process. The tree structure reveals that near-infrared reflectance is more important than the difference and ratio between near-infrared and visible channels for water fraction estimate. Flood distributions were generated using the differences in water fraction values between after and before the flooding. The derived water fractions were evaluated against 30 m Thematic Mapper (TM) data from Landsat observations. Water fractions derived from the MODIS and TM data agree well (R 2 = 0.94, bias = 0.38 percent, and RMSE = 4.35 percent). The results show that the RT approach in dynamic monitoring of floods is acceptable.