The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.
Towards a local split window method over land surfaces
Abstract
The split window method is successfully being used to retrieve the temperature over sea surfaces from satellite radiances in clear sky and has the great advantage of simplicity. However, such a method does not work over land surfaces, mainly because the emissivity is not equal to 1 and depends on the channel. An extension of this method to apply to land surfaces requires one to take account of emissivity—such an extension is presented in this paper. First, using Lowtran 6, the accuracies of the various linearizations of the radiative transfer equation leading to the split window are checked. This implies that the retrieved surface temperature depends linearly on emissivities and brightness temperatures. Such behaviour has been checked on actual examples. Theoretical equations are then derived which show that the actual surface temperature can again be expressed as a linear combination of the brightness temperatures measured in two adjacent channels with coefficients depending on spectral emissivities but not on atmospheric conditions. Using Lowtran 6 these properties have been verified and the dependence of these coefficients has been explicitly computed leading to a local split window method for the NOAA-9 Advanced Very High Resolution Radiometer. Finally, we show that accurate surface temperatures can be retrieved using this local split window method once emissivities in two adjacent channels are known with sufficient accuracy.
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Cites
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Estimation of sea surface temperature from remote sensing in the 11- to 13-μm window region
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Cited by
New refinements and validation of the collection-6 MODIS land-surface temperature/emissivity product
Wan, Z. (2014) · Remote Sensing of Environment · doi:10.1016/j.rse.2013.08.027 · Cited by 1,075
Global Primary Production: A Remote Sensing Approach
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Algorithm for Automated Mapping of Land Surface Temperature Using LANDSAT 8 Satellite Data
Avdan, U., & Jovanovska, G. (2016) · Journal of Sensors · doi:10.1155/2016/1480307 · Cited by 787
Validation of Collection 6 MODIS land surface temperature product using in situ measurements
Duan, S.-B., Li, Z.-L., Li, H., Göttsche, F.-M., Wu, H., Zhao, W., Leng, P., Zhang, X., & Coll, C. (2019) · Remote Sensing of Environment · doi:10.1016/j.rse.2019.02.020 · Cited by 442
Self-citationLandsat 9: Empowering open science and applications through continuity
Masek, J. G., Wulder, M. A., Markham, B., McCorkel, J., Crawford, C. J., Storey, J., & Jenstrom, D. T. (2020) · Remote Sensing of Environment · doi:10.1016/j.rse.2020.111968 · Cited by 435
Metadata
- Authors
- BECKER, F., & LI, Z.-L.
- Venue
- International Journal of Remote Sensing
- Publisher
- Informa UK Limited
- Published
- 1990-03
- Volume
- 11
- Issue
- 3
- Pages
- 369-393
- ISSN
- 0143-1161 (ISSN-L)
- 1366-5901
- OpenAlex
- W1995282508
- Cited by
- 802
- References
- 9
- FWCI
- 31.95
- Type
- journal article
- Language
- en
- Open access
- closed
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.