Global Daily Actual and Snow‐Free Blue‐Sky Land Surface Albedo Climatology From 20‐Year MODIS Products

Jia, Aolin; Wang, Dongdong; Liang, Shunlin; Peng, Jingjing; Yu, Yunyue

doi:10.1029/2021jd035987

Cited by 14 publications

(3 citation statements)

References 103 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The latitudinal average was obtained for every 30 • latitudinal band and the regional average was obtained based on different regions. Climatology (the monthly average values over 10-20 years) was used to report the systematic issues of different products, following ideas from [29]. Time-series anomalies of the products with a common base period of 2006-2009 were obtained by subtracting the corresponding average values from the absolute values.…”

Section: Data Processingmentioning

confidence: 99%

Evaluation of Five Global Top-of-Atmosphere Outgoing Longwave Radiation Products

Zhan

Yang

et al. 2023

Remote Sensing

View full text Add to dashboard Cite

Five global monthly top-of-atmosphere (TOA) outgoing longwave radiation (OLR) products are evaluated in this study, including the products derived from the High-Resolution Infrared Radiation Sounder (HIRS), Clouds and the Earth’s Radiant Energy System (CERES), Advanced Very High Resolution Radiometer (AVHRR), the CM SAF cLoud, Albedo and surface RAdiation dataset from AVHRR data (CLARA), and the Global Energy and Water Cycle EXchanges (GEWEX) project. Results show that overall there is good consistency among these five products. Larger differences are found between GEWEX and CERES (HIRS) after (before) 2000 (RMSE ~ 5 W/m2), particularly in the tropical regions. In terms of global mean values, GEWEX shows large differences with the other products from the year 1992 to 2002, and CLARA shows large differences from the year 1979 to 1981, which are more obvious in the global ocean values. Large discrepancies among these products exist at low latitudinal bands, particularly before the year 2000. Australia and Asia (mid–low latitude part) are two typical regions in which larger differences are found.

show abstract

Section: Data Processingmentioning

confidence: 99%

Evaluation of Five Global Top-of-Atmosphere Outgoing Longwave Radiation Products

Zhan

Yang

et al. 2023

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…Temporal-based methods are usually more reliable than those of spatial-based methods, particularly for large-area gap filling; temporal-based cloud removal methods have been studied more thoroughly [23,24]. Pixels missing in the area of interest can typically be compensated for through temporal interpolation by multiple years' average [25]. To enhance the reliability of predictions pertaining to pixel value changes within the covered area, the average values can be physically constrained by combining a priori knowledge, such as albedo phenology [26].…”

Section: Introductionmentioning

confidence: 99%

A Methodological Approach for Gap Filling of WFV Gaofen-1 Images from Spatial Autocorrelation and Enhanced Weighting

Chen,

Yu,

Zhang

et al. 2024

Atmosphere

View full text Add to dashboard Cite

Clouds and cloud shadow cover cause missing data in some images captured by the Gaofen-1 Wide Field of View (GF-1 WFV) cameras, limiting the extraction and analysis of the image information and further applications. Therefore, this study proposes a methodology to fill GF-1 WFV images using the spatial autocorrelation and improved weighting (SAIW) method. Specifically, the search window size is adaptively determined using Getis-Ord Gi* as a metric. The spatial and spectral weights of the pixels are computed using the Chebyshev distance and spectral angle mapper to better filter the suitable similar pixels. Each missing pixel is predicted using linear regression with similar pixels on the reference image and the corresponding similar pixel located in the non-missing region of the cloudy image. Simulation experiments showed that the average correlation coefficient of the proposed method in this study is 0.966 in heterogeneous areas, 0.983 in homogeneous farmland, and 0.948 in complex urban areas. It suggests that SAIW can reduce the spread of errors in the gap-filling process to significantly improve the accuracy of the filling results and can produce satisfactory qualitative and quantitative fill results in a wide range of typical land cover types and has extensive application potential.

show abstract

“…In these regions with high elevations, clear-sky LSTs with larger RMSEs were assimilated to the time-evolving model, thus affecting the cloudy-sky results. In addition, modeled temperature series include higher uncertainty in these regions, and a relevant 'cool bias' issue in highlands was found in regional simulation models and global reanalysis datasets (Jia et al, 2022b;Meng et al, 2018). Although the relationship between the site RMSE and site latitude was not statistically significant (Figure 7c and d), the GHA-LST data at higher latitudes produced higher RMSEs, especially under cloudy-sky conditions; thus, we inferred that high latitudes were frequently covered by clouds and fewer clear-sky LSTs could be used in the data assimilation.…”

mentioning

confidence: 99%

Global hourly, 5 km, all-sky land surface temperature data from 2011 to 2021 based on integrating geostationary and polar-orbiting satellite data

Jia

Liang²,

Wang³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

Abstract. Land surface temperature (LST) plays a dominant role in the surface energy budget (SEB) and hydrological cycling. Thermal infrared (TIR) remote sensing is the primary method of estimating LST globally. However, cloud cover leaves numerous data gaps in satellite LST products, which seriously restricts their applications. Efforts have been made to produce gap-free LST products from polar-orbiting satellites (e.g., Terra and Aqua); however, satellite data from limited overpasses are not suitable for characterizing the diurnal temperature cycle (DTC), which is directly related to heat waves, plant water stress, and soil moisture. Considering the high temporal variability of LST and the importance of the DTC, we refined the SEB-based cloudy-sky LST recovery method by improving its feasibility and efficiency and produced a global hourly, 5 km, all-sky land surface temperature (GHA-LST) dataset from 2011 to 2021. The GHA-LST product was generated using TIR LST products from geostationary and polar-orbiting satellite data from the Copernicus Global Land Service (CGLS) and Moderate Resolution Imaging Spectroradiometer (MODIS). Based on the ground measurements at the 197 global sites from the Surface Radiation Budget (SURFRAD), Baseline Surface Radiation Network (BSRN), Fluxnet, AmeriFlux, Heihe River Basin (HRB), and Tibet Plateau (TP) networks, the overall root mean square error (RMSE) of the hourly GHA-LST product is 3.38 K, with a bias of -0.53 K and R2 of 0.95, and it was more accurate than the clear-sky CGLS and MODIS MYD21C1 LST samples. The RMSE value of the daily mean LST was 1.67 K. Validation results at individual sites indicate that the GHA-LST dataset has relatively larger RMSEs for high elevation regions, which can be attributed to high surface heterogeneity and input data uncertainty. Temporal and spatial analyses suggested that GHA-LST has satisfactory spatiotemporal continuity and reasonable variation and matches the reference data well at hourly and daily scales. Furthermore, the regional comparison of GHA-LST with other gap-free hourly datasets (ERA5 and Global Land Data Assimilation System, GLDAS) demonstrated that GHA-LST can provide more spatial texture information. The monthly anomaly analysis suggests that GHA-LST couples well with global surface air temperature datasets and other LST datasets at daily mean and minimum temperature scales, whereas the maximum temperature and diurnal temperature range of LST and air temperature (AT) have different anomalous magnitudes. The GHA-LST dataset is the first global gap-free LST dataset at an hourly, 5 km scale with high accuracy, and it can be used to estimate global evapotranspiration, monitor extreme weather, and advance meteorological forecasting models. GHA-LST is freely available at https://doi.org/10.5281/zenodo.6981704 (Jia et al., 2022c) and glass.umd.edu/allsky_LST/GHA-LST.

show abstract

Global Daily Actual and Snow‐Free Blue‐Sky Land Surface Albedo Climatology From 20‐Year MODIS Products

Cited by 14 publications

References 103 publications

Evaluation of Five Global Top-of-Atmosphere Outgoing Longwave Radiation Products

Evaluation of Five Global Top-of-Atmosphere Outgoing Longwave Radiation Products

A Methodological Approach for Gap Filling of WFV Gaofen-1 Images from Spatial Autocorrelation and Enhanced Weighting

Global hourly, 5 km, all-sky land surface temperature data from 2011 to 2021 based on integrating geostationary and polar-orbiting satellite data

Contact Info

Product

Resources

About