Cloudy-sky land surface temperature from VIIRS and MODIS satellite data using a surface energy balance-based method

Jia, Aolin; Ma, Han; Liang, Shunlin; Wang, Dongdong

doi:10.1016/j.rse.2021.112566

Cited by 60 publications

(25 citation statements)

References 76 publications

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“…Established in 1993, SURFRAD was designed to support climate research with accurate, continuous, long‐term measurements of the surface radiation budget over the United States (Augustine et al., 2000), and has been widely used for the validation of surface radiation fluxes (Jia, Ma, et al., 2021; Li et al., 2013; Wang & Liang, 2009; Zhou et al., 2016). The BSRN is a network (Driemel et al., 2018) of globally distributed sites collecting data for different projects, including the Coordinated Energy and Water Cycle Observations Project (CEOP) and AmeriFlux, and is considered to have the longest duration and good quality owing to the strict maintenance (Wang & Dickinson, 2013).…”

Section: Methodsmentioning

confidence: 99%

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

et al. 2022

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Land surface albedo, which is defined as the ratio of the reflected shortwave radiation to the incoming solar radiation, represents the integrated surface hemispheric reflectivity over the solar spectrum (Liang et al., 2010;Lucht et al., 2000). It is a critical geographical parameter that directly controls the surface radiation budget between the surface and atmosphere, significantly affecting the energy budget, and atmospheric and water circulations (Boussetta et al., 2015;Liang et al., 2010). Therefore, it is listed as an Essential Climate Variable (GCOS, 2004) and basic climate forcing used in hydrological, and biogeochemical models, as well as in weather forecasting (

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Section: Methodsmentioning

confidence: 99%

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

et al. 2022

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“…Many research studies have focused on retrieving LST information as a function of brightness temperature values obtained from thermal bands data and ground surface emissivity values from vegetation indices such as NDVI and Fractional Vegetation Cover (FVC) under clear sky conditions for the coarse resolution data products such as MODIS and AVHRR data products (Sobrino et al, 2003;Katsiabani et al, 2009;Weng, 2009;Wang et al, 2019). Similarly, Sekertekin and Bonafoni (2020) retrieved LST values under clear sky conditions from Landsat 5, 7, and 8 images using different retrieval algorithms including LSE models and reported that MW window algorithm was comparatively better with root means square (RMSE) value of 2.39 °K when compared to other algorithms such as SW, SC, RTE, and LSE models (Arslan and Sekertekin, 2019;Mokhtari et al, 2021;Xie and Fan, 2021;Xu and Shen, 2013;Jia et al, 2021;L. Sun et al, 2017;Zhang et al, 2021;Zhao and Duan, 2020).…”

Section: Introductionmentioning

confidence: 99%

Reconstructing NDVI and land surface temperature from cloud cover pixels of Landsat-8 images and assessing vegetation health index in the Northeast region of Thailand

Mohanasundaram

Baghel

Thakur

et al. 2022

Preprint

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Critical applications of satellite data products include monitoring vegetation dynamics using vegetation indices such as Normalized Difference Vegetation Index (NDVI) and thermal characteristics such as Land Surface Temperature (LST) variations across different spatial and temporal scales. One of the major problems with passive remote sensing satellite data products is cloud and shadow cover that void the information from a specific image. The cloud cover problems further amplified during monsoon seasons as the formation of clouds are more frequent during these seasons. The present study proposes temporal aggregation of images and developing Harmonic Analysis of Time Series (HANTS) and Pixel-wise Multiple Linear Regression (PMLR) algorithms for retrieving cloud contaminated NDVI and LST information from Landsat-8 (L8) data products, respectively. The developed algorithms were applied in the northeastern part of Thailand to recover the missing NDVI and LST values from time series of L8 images acquired in 2018. The predicted NDVI and LST values at artificially clouded locations were compared with the corresponding clear pixel values. Additionally, the model predicted LST and NDVI values were also compared with MODIS LST and NDVI data sets. The calculated root mean square (RMSE) values were ranging from 0.004 to 0.05 and 1.05°C to 3.11°C for NDVI and LST variables, respectively. The validation statistics show that these models can be satisfactorily applied to retrieve NDVI and LST values from cloud-contaminated pixels of L8 images. Furthermore, a vegetation health index (VHI) calculated at province level show that most of the western provinces showed a healthy vegetation condition than other provinces in the northeast of Thailand.

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“…However, two inevitable flaws occur when using GEO satellites to observe diurnal LST variations glob-ally, namely data gaps caused by cloud cover and the limited spatial view fields of individual GEO satellites, which seriously limit the availability of hourly all-sky LST datasets at the global scale. Methods of recovering hourly LSTs have been developed and comprehensively reviewed by Jia et al (2022a). Currently available gap-free satellite-derived LST products are summarized in Table 1.…”

Section: Introductionmentioning

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

Liang

Wang

et al. 2023

Earth Syst. Sci. Data

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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 in 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 the Moderate Resolution Imaging Spectroradiometer (MODIS). Based on ground measurements at the 201 global sites from the Surface Radiation Budget (SURFRAD), Baseline Surface Radiation Network (BSRN), Fluxnet, AmeriFlux, Heihe River basin (HRB), and Tibetan Plateau (TP) networks, the overall root-mean-square error (RMSE) of the hourly GHA-LST product was 3.31 K, with a bias of −0.57 K and R2 of 0.95. Thus, this product was more accurate than the clear-sky CGLS and MODIS MYD21C1 LST samples. The RMSE value of the daily mean LST was 1.76 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.7487284 (Jia et al., 2022b) and http://glass.umd.edu/allsky_LST/GHA-LST (last access: 10 February 2023; Jia et al., 2022c).

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Cloudy-sky land surface temperature from VIIRS and MODIS satellite data using a surface energy balance-based method

Cited by 60 publications

References 76 publications

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

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

Reconstructing NDVI and land surface temperature from cloud cover pixels of Landsat-8 images and assessing vegetation health index in the Northeast region of Thailand

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

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