The DOI matches its Crossref record; 282 of the 282 references it cites were found in a registry or an index.
A review of earth surface thermal radiation directionality observing and modeling: Historical development, current status and perspectives
Open accessAbstract
The Earth surface thermal infrared (TIR) radiation shows conspicuously an anisotropic behavior just like the bi-directional reflectance of visible and near infrared spectral domains. The importance of thermal radiation directionality (TRD) is being more and more widely recognized in the applications because of the magnitude of the effects generated. The effects of TRD were originally evidenced through experiments in 1962, showing that two sensors simultaneously measuring temperature of the same scene may get significantly different values when the viewing geometry is different. Such effect limits inter-comparison of measurement datasets and land surface temperature (LST) products acquired at different view angles, while raising the question of measurement reliability when used to characterize land surface processes. These early experiments fostered the development of modeling approaches to quantify TRD with the aim of developing a correction for Earth surface TIR radiation. Initiatives for pushing the analysis of TIR data through modeling have been lasted since 1970s. They were initially aimed at mimicking the observed TIR radiance with consideration of canopy structure, component emissivities and temperatures, and Earth surface energy exchange processes. Presently, observing the Earth surface TRD effect is still a challenging task because the TIR status changes rapidly. Firstly, a brief theoretical background and the basic radiative transfer equation are presented. Then, this paper reviews the historical development and current status of observing TRD in the laboratory, in-situ, from airborne and space-borne platforms. Accordingly, the TRD model development, including radiative transfer models, geometric models, hybrid models, 3D models, and parametric models are reviewed for surfaces of water, ice and sea, snow, barren lands, vegetation and urban landscapes, respectively. Next, we introduce three potential applications, including normalizing the LST products, estimating the hemispheric upward longwave radiation using multi-angular TIR observations and separating surface component temperatures. Finally, we give hints and directions for future research work. The last section summarizes the study and stresses three main conclusions.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“In contrast, although satellite data are available at a resolution of 1 km, this pixel-level data have a relatively high degree of uncertainty, particularly for urban areas80.”
From Disproportionate exposure to urban heat island intensity across major US cities (2021) · cites this paper as [80]
Citations
Cites
Light Scattering by Small Particles
van de Hulst, H. C., & Twersky, V. (1957) · Physics Today · doi:10.1063/1.3060205 · Cited by 5,691
Light scattering by small particles
Snowdon, S. (1957) · Journal of the Franklin Institute · doi:10.1016/0016-0032(57)90988-2 · Cited by 3,763
Thermal remote sensing of urban climates
Voogt, J., & Oke, T. R. (2003) · Remote Sensing of Environment · doi:10.1016/s0034-4257(03)00079-8 · Cited by 3,422
Measurement of the Roughness of the Sea Surface from Photographs of the Sun’s Glitter
Cox, C., & Munk, W. H. (1954) · Journal of the Optical Society of America · doi:10.1364/josa.44.000838 · Cited by 2,583
Satellite-derived land surface temperature: Current status and perspectives
Li, Z.-L., Tang, B., Wu, H. B., Ren, H., Yan, G., Wan, Z., Trigo, I. F., & Sobrino, J. A. (2013) · Remote Sensing of Environment · doi:10.1016/j.rse.2012.12.008 · Cited by 2,296
Self-citationLight scattering by leaf layers with application to canopy reflectance modeling: The SAIL model
Verhoef, W. (1984) · Remote Sensing of Environment · doi:10.1016/0034-4257(84)90057-9 · Cited by 2,032
Bidirectional reflectance spectroscopy: 1. Theory
Hapke, B. W. (1981) · Journal of Geophysical Research Atmospheres · doi:10.1029/jb086ib04p03039 · Cited by 1,941
Theory of Reflectance and Emittance Spectroscopy
Hapke, B. W. (1993) · Cambridge University Press eBooks · doi:10.1017/cbo9780511524998 · Cited by 1,859
A generalized split-window algorithm for retrieving land-surface temperature from space
Wan, Z., & Dozier, J. (1996) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/36.508406 · Cited by 1,840
Norman, J. M., Kustas, W. P., & Humes, K. S. (1995) · Agricultural and Forest Meteorology · doi:10.1016/0168-1923(95)02265-y · Cited by 1,786
Research works printed but not linked: 2
- Fan, 2005, Integrative inversion of land surface component temperature. Science in China Ser, D Earth Sciences, 48, 2011
- Ma, W., 2009. A computer model for simulating the directional thermal radiance of urban targets (M.S. degree dissertation). Beijing Normal University, Beijing, China.
Cited by
Sorted by citations
Remote sensing for agricultural applications: A meta-review
Weiss, M., Jacob, F., & Duveiller, G. (2019) · Remote Sensing of Environment · doi:10.1016/j.rse.2019.111402 · Cited by 1,948
Self-citationDisproportionate exposure to urban heat island intensity across major US cities
Hsu, A., Sheriff, G., Chakraborty, T., & Manya, D. (2021) · Nature Communications · doi:10.1038/s41467-021-22799-5 · Cited by 846
Şekertekin, A., & Bonafoni, S. (2020) · Remote Sensing · doi:10.3390/rs12020294 · Cited by 464
Zhang, X., Zhou, J. P., Liang, S., & Wang, D. (2021) · Remote Sensing of Environment · doi:10.1016/j.rse.2021.112437 · Cited by 308
Time Evolution of the Surface Urban Heat Island
Stewart, I. D., Krayenhoff, E. S., Voogt, J., Lachapelle, J., Allen, M., & Broadbent, A. (2021) · Earth's Future · doi:10.1029/2021ef002178 · Cited by 135
Metadata
- Authors
- Cao, B., Liu, Q., Du, Y., Roujean, J.-L., Gastellu-Etchegorry, J.-P., Trigo, I. F., Zhan, W., Yu, Y., Cheng, J., Jacob, F., Lagouarde, J.-P., Bian, Z., Li, H., Hu, T., & Xiao, Q.
- Affiliations
- Chinese Academy of Sciences (CN)
- Centre National de la Recherche Scientifique (FR)
- Portuguese Sea and Atmosphere Institute (PT)
- Nanjing University (CN)
- National Oceanic and Atmospheric Administration (US)
- Beijing Normal University (CN)
- Université de Montpellier (FR)
- Interaction Sol Plante Atmosphère (FR)
- Griffith University (AU)
- Corresponding author
- Liu, Qinhuo
- ORCID
- Venue
- Remote Sensing of Environment
- Publisher
- Elsevier BV
- Published
- 2019-10
- Volume
- 232
- Pages
- 111304
- ISSN
- 0034-4257 (ISSN-L)
- OpenAlex
- W2963663407
- Cited by
- 196
- References
- 282
- Citations by year
- 2026: 18 · 2025: 43 · 2024: 40 · 2023: 26 · 2022: 12 · 2021: 31 · 2020: 17 · 2019: 8
- FWCI
- 12.50
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Remote Sensing of Environment (publishedVersion, cc-by)
- HAL (submittedVersion, cc-by)
- HAL (submittedVersion)
- NOAA Institutional Repository (submittedVersion)
- Griffith Research Online (submittedVersion, cc-by)
- Funders
- Chinese Academy of Sciences (XDA15012400)
- Institute of Remote Sensing and Digital Earth (Y5SJ1300CX)
- National Natural Science Foundation of China (41571359, 41671366, 41571357, 41501366, 41871258, 41571329)
- National Key Research and Development Program of China (2018YFA0605503)
- China Scholarship Council (201704910069, 2017VCA0034, 2019DC0020, 2017VCC0011)
- Topics
- Physical Sciences › Engineering › Aerospace Engineering › Calibration and Measurement Techniques
- Physical Sciences › Environmental Science › Ecology › Remote Sensing in Agriculture
- Physical Sciences › Engineering › Civil and Structural Engineering › Thermal Radiation and Cooling Technologies
- Keywords
- land surface temperature, radiative transfer modeling, thermal anisotropy, surface emissivity, snow and ice surfaces, vegetation surface, urban surfaces, barren land
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
