Next Article in Journal
Modelling of Intra-Field Winter Wheat Crop Growth Variability Using in Situ Measurements, Unmanned Aerial Vehicle-Derived Vegetation Indices, Soil Properties, and Machine Learning Algorithms
Previous Article in Journal
Spatiotemporal Variations of Glacier Surface Facies (GSFs) in Svalbard: An Example of Midtre Lovénbreen
 
 
Please note that, as of 4 December 2024, Environmental Sciences Proceedings has been renamed to Environmental and Earth Sciences Proceedings and is now published here.
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Proceeding Paper

Split-Window Algorithm for Land Surface Temperature Retrieval from Joint Polar-Orbiting Satellite System JPSS-2/NOAA-21 †

by
Fatima Zahrae Rhziel
,
Mohammed Lahraoua
* and
Naoufal Raissouni
Remote Sensing, Systems and Telecommunications Research Unit, National School for Applied Sciences of Tetouan, University Abdelmalek Essaadi, Tetuan 93000, Morocco
*
Author to whom correspondence should be addressed.
Presented at the 5th International Electronic Conference on Remote Sensing, 7–21 November 2023; Available online: https://ecrs2023.sciforum.net/.
Environ. Sci. Proc. 2024, 29(1), 23; https://doi.org/10.3390/ECRS2023-16293
Published: 6 November 2023
(This article belongs to the Proceedings of ECRS 2023)

Abstract

:
Land surface temperature (LST) plays a pivotal role in the dynamic exchange of energy between the Earth’s surface and the atmosphere. This research centers on the assessment of LST from satellite data acquired by the Joint Polar-orbiting Satellite System (JPSS), specifically JPSS-2/NOAA-21, employing an innovative split-window algorithm (SWA). Atmospheric water vapor content (WVC) and surface emissivity are the two main input variables in the split-window technique. Therefore, the moderate resolution transmittance code, version 4.0 (MODTRAN 4.0), was used to simulate WVC and atmospheric transmittance. The performance of the SWA was rigorously assessed against standard atmospheric conditions, revealing its capacity to achieve an LST retrieval accuracy of 1.4 Kelvin (K), even in the presence of various errors. Moreover, the LST retrieval algorithm was validated using ground truth data sets from two Australian sites, and the RMSE value was 1.71 K. The achieved results demonstrate the algorithm’s capability to provide accurate LST estimation for NOAA-21 satellite data.

1. Introduction

Land surface temperature (LST) is a necessary parameter with a profound impact on the physical processes of land surfaces, influencing a range of phenomena from local to global scales. It drives the outgoing longwave radiation and turbulent heat fluxes at the interface between the Earth’s surface and the atmosphere. Consequently, LST is routinely applied in various fields such as evapotranspiration [1,2,3,4], the estimation of soil moisture [5,6,7,8], and environmental studies [9,10,11,12]. Furthermore, the International Geosphere and Biosphere Program (IGBP) [13] considers LST as one of the high-priority parameters.
According to one of the sustainable development goals (SDGs) promoted by the United Nations, the increase in the earth’s surface temperature is regarded as a major phenomenon [14]. Hence, it is crucial to monitor this dilemma in order to evaluate the rapid variations in LST spatially and temporally in the globe for vast geographic areas. The only way to measure LST on a worldwide scale is through remote sensing satellite data, which makes this conceivable [15].
The estimation of LSTs from TIR satellite data requires two primary parameters: emissivity and atmospheric corrections [16,17]. Over the course of several decades, researchers have dedicated their efforts to refining algorithms for deriving LST from TIR remote sensing data, using a range of approaches to deal with emissivity and atmospheric effects. Among these algorithms, the split-window (SW) technique stands out, as it directly mitigates atmospheric distortions by leveraging the brightness temperature (BT) from two adjacent TIR channels at the top of the atmosphere. This method is frequently employed for producing operational LST products [18,19,20,21]
The satellite NOAA-21, designated Joint Polar Satellite System JPSS-2 prior to launch [22], was launched on 10 November 2022 [22] by the National Oceanic and Atmospheric Administration (NOAA). Its primary objective is to furnish comprehensive global environmental data, encompassing insights into weather patterns, atmospheric dynamics, and various environmental indicators. A scanning radiometer sensor onboard JPSS-2/NOAA-21 called VIIRS gathers visible and infrared imagery, as well as radiometric measurements of the land, atmosphere, and oceans. Interestingly, two of the 22 spectral bands on VIIRS, which range in wavelength from 0.4 to 12.5 m, are thermal infrared channels that will be used for LST retrieval.
In this study, an SW algorithm was developed for JPSS-2; validation and comparison with ground-based measurements verified the algorithm’s efficacy in providing accurate and reliable land surface temperature estimates over diverse landscapes and climatic conditions.

2. Methodology

2.1. Split-Window Algorithm for LST Retrieval

The SW technique, which is based on the differential absorption in two neighboring infrared channels, was initially developed for calculating sea surface temperature (SST) from satellite observations. Then, it was expanded to estimating land surface temperature. In this study, emissivity and water vapor effects have been taken into consideration by using the SW-LST algorithm structure described by Sobrino and Raissouni [23] to retrieve LST from VIIRS NOAA-21 data. This algorithm is written as follows:
T S = T 15 + c 1 ( T 15 T 16 ) + c 2 ( T 15 T 16 ) 2 + c 0 + ( c 3 + c 4 W ) ( 1 ε ) + ( c 5 + c 6 W ) Δ ε
where TS is the earth surface temperature (in K), T15 and T16 are the at-sensor brightness temperatures (in K) of VIIRS NOAA-21, ε = (ε15 + ε16)/2 presents the mean effective emissivity, ∆ε = (ε15ε16) is the difference between the emissivities of VIIRS channels M15 and M16, W (g·cm2) is the total atmospheric water vapor column, and ck (k = 0, 1 … 6) are the SW algorithm coefficients.

2.2. VIIRS Sensor Characteristics

The Visible Infrared Imaging Radiometer Suite (VIIRS) is a whiskbroom radiometer designed for use onboard S-NPP, NOAA-20, NOAA-21, and future JPSS series satellites. The characteristics of the thermal infrared M15 and M16 bands that have been used in the SWA for LST retrieval are presented in Table 1 and Figure 1.

2.3. MODTRAN 4.0 Simulations

In order to determine the atmospheric parameters (downwelling and upwelling atmospheric radiances and atmospheric transmittance), the radiative transfer model (RTM) MODTRAN was used. The atmospheric profiles were extracted from the Thermodynamic Initial Guess Retrieval (TIGR) database [24]. To better characterize surface variations, the calculations were performed over a vast gradient of temperatures, T − 5, T, T + 5, T + 10, and T + 20 (taking into account the fact that T is the initial boundary layer temperature of the profiles). A total of 100 emissivities of various types of surfaces were taken from the Advanced Spaceborne Thermal Emission Reflection Radiometer (ASTER) spectral [25]. Furthermore, five different view angles (0°, 10°, 20°, 30° and 40°) and 54 atmospheric water vapor (W) values at nadir (varying between 0.15 g·cm−2 and 4.65 g·cm−2) were used in the simulation in order to consider the viewing angle and atmospheric water vapor effects. Therefore, 135,000 simulation data were composed.

2.4. Numerical Coefficients and Sensitivity Analysis

We conducted a sensitivity analysis based on the error theory to assess the performance of the SW algorithm and the impact of the possible errors in LST estimation. The sensitivity analysis is given by the following equation:
δ T o t a l ( T S ) = δ a lg 2 + δ N E Δ T 2 + δ ε 2 + δ W 2
where δalg is the algorithm’s standard deviation, and δNET, δW, and δε are the impacts on total error due to uncertainties of sensor temperatures, atmospheric water vapor, and land surface emissivity, respectively. δNET, δW, and δε are expressed by the following equations:
δ NE Δ T = ( T S T 15 ) e 2 ( T 15 ) + ( T S T 16 ) e 2 ( T 16 )
δ W = ( T S W ) e ( W )
δ ε = ( T S T 15 ) e 2 ( ε 15 ) + ( T S T 16 ) e 2 ( ε 16 )
Thus, we assume that both the brightness temperature errors of the M15 and M16 channels e(T15) = e(T16) = 0.05 K or 0.01 K, the emissivity errors in the VIIRS channels M15 and M16 e(ε15) = e(ε16) are 0.01 or 0.005 [26], and the atmospheric water vapor content can be considered as e(W) = 0.5 g·cm−2 [27].

3. Results and Discussion

3.1. Sensitivity Analysis

The SW coefficients (C0 to C6) of the developed SWA for LST estimation from the NOAA-21 satellite are presented in Table 2.
The results of the sensitivity analysis are shown in Table 3. Emissivity uncertainty is about 1.26 K and 0.63 K for e(ε15) = e(ε16) = 1% and e(ε15) = e(ε16) = 0.5%, successively. The total LST uncertainty, δTotal(Ts), is about 1.67 K, considering e(ε15) = e(ε16) = 1%, and it is less than 1.26 K for e(ε15) = e(ε16) = 0.5%. Therefore, the uncertainty in emissivity has an insignificant effect on the LST estimation. Thus, an accurate knowledge of the surface is required.

3.2. LST Validation

The accuracy of the proposed SWA for LST retrieval from VIIRS/NOAA-21 is also evaluated using ground truth data sets from the Hay and Walpeup sites [28].
Figure 2 presents the LST retrieved from the NOAA-21 satellite using the developed split-window algorithm and the in situ LST ground data from two Australian sites (Hay and Walpeup), as well as the correlation coefficient R, bias, standard deviation differences (SDV), and root-mean-square error (RMSE). The results show that the split-window algorithm onboard JPSS-2/NOAA-21 can estimate LST with a bias of 0.97 K, a standard deviation difference of 1.31 K, and an RMSE of less than 1.71 K for the Hay and Walpeup site measurements, confirming the algorithm’s accuracy in LST retrieval.

4. Conclusions

An alternate split-window technique for LST estimation from NOAA-21 satellite data was proposed in this study. The algorithm coefficients were obtained from the simulation dataset of atmospheric profiles. To assess the performance of the SW-LST method, a sensitivity analysis was performed.
The LST’s accuracy derived was validated using ground truth data sets from two Australian sites. The recovered LST shows a good fitting with the in situ LST at both sites. The bias and RMSE are, respectively, 0.97 and 1.71 K. This indicates that this algorithm offers an alternate and feasible method for retrieving LST using NOAA-21 satellite data. Nonetheless, more LST validation under various atmospheric conditions and surface types is required to adequately evaluate the efficacy of this approach.

Author Contributions

Conceptualization, F.Z.R.; methodology, all authors; formal analysis, M.L.; investigation, all authors; original draft preparation, F.Z.R.; writing review and editing, all authors; visualization, M.L.; supervision, N.R. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The VIIRS channels JPSS-2/NOAA-21 filters are obtained from the website https://www.star.nesdis.noaa.gov/icvs/status_N21_ATMS.php (accessed on 13 September 2023), while validation data are acquired from The Global Change Unit (GCU) of the University of Valencia in Spain.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Olioso, A.; Chauki, H.; Couraul, D.; Wigneron, J.P. Estimation of evapotran spiration and photosynthesis by assimilation of remote sensing data into SVAT models. Remote Sens. Environ. 1999, 68, 341–356. [Google Scholar] [CrossRef]
  2. Serafini, V.V. Estimation of the evapotranspiration using surface and satellite data. Int. J. Remote Sens. 1987, 8, 1547–1562. [Google Scholar] [CrossRef]
  3. Bussieres, N.; Louie, P.Y.T.; Hogg, W. Progress Report on the Implementation of an Algorithm to Estimate Regional Evapotranspiration Using Satellite Data. In Proceeding of the workshop on applications of remote sensing in hydrology, Saskaton, Saskatchewan, 13–14 February 1990. [Google Scholar]
  4. Zhang, L.; Lemeur, R.; Gouthorbe, J.P. A one-layer resistance model for estimating regional evapotranspiration using remote sensing data. Agric. For. Meteorol. 1995, 77, 241–261. [Google Scholar] [CrossRef]
  5. Schmugge, T.J. Remote sensing of surface soil moisture. J. Appl. Meteorol. 1978, 17, 1549–1557. [Google Scholar] [CrossRef]
  6. Price, J.C. The potential of Remotely Sensed Thermal Infrared data to Infer Surface Soil Moisture and Evaporation. Water Resour. 1990, 16, 787–795. [Google Scholar] [CrossRef]
  7. Uitdewilligen, D.C.A.; Kustas, W.P.; van Oevelen, P.J. Estimating surface soil moisture with the scanning low frequency microwave radiometer (SLFMR) during the Southern Great Plains 1997 (SGP97) hydrology experiment. Phys. Chem. Earth Parts A/B/C 2003, 28, 41–51. [Google Scholar] [CrossRef]
  8. Santanello, J.; Peters-Lidard, C.D.; Garcia, M.E.; Mocko, D.M.; Tischler, M.A.; Moran, M.S.; Thoma, D.P. Using remotely-sensed estimates of soil moisture to infer soil texture and hydraulic properties across a semi-arid watershed. Remote Sens. Environ. 2007, 110, 79–97. [Google Scholar] [CrossRef]
  9. Arnfield, A.J. Two decades of urban climate research: A review of turbulence, exchanges of energy and water, and the urban heat island. Int. J. Climatol. 2003, 23, 1–26. [Google Scholar] [CrossRef]
  10. Bastiaanssen, W.G.M.; Menenti, M.; Feddes, R.A.; Holtslag, A.A.M. A remote sensing surface energy balance algorithm for land (SEBAL). 1. Formulation. J. Hydrol. 1998, 212, 198–212. [Google Scholar] [CrossRef]
  11. Kogan, F.N. Operational space technology for global vegetation assessment. Bull. Am. Meteorol. Soc. 2001, 82, 1949–1964. [Google Scholar] [CrossRef]
  12. Kalma, J.D.; McVicar, T.R.; McCabe, M.F. Estimating land surface evaporation: A review of methods using remotely sensed surface temperature data. Surv. Geophys. 2008, 29, 421–469. [Google Scholar] [CrossRef]
  13. Townshend, J.R.G.; Justice, C.O.; Skole, D.; Malingreau, J.-P.; Cihlar, J.; Teillet, P.; Sadowski, F.; Ruttenberg, S. The 1 km resolution global data set: Needs of the International Geosphere Biosphere Programme. Int. J. Remote Sens. 1994, 15, 3417–3441. [Google Scholar] [CrossRef]
  14. United Nations, The Sustainable Development Goals Report 2019, New York. Available online: https://unstats.un.org/sdgs/report/2019/The-Sustainable-Development-Goals-Report-2019.pdf (accessed on 13 September 2023).
  15. Li, Z.L.; Tang, B.H.; Wu, H.; Ren, H.; Yan, G.; Wan, Z.; Trigo, I.F.; Sobrino, J.A. Satellite-derived land surface temperature: Current status and perspec tives. Remote Sens. Environ. 2013, 131, 14–37. [Google Scholar] [CrossRef]
  16. Li, Z.-L.; Becker, F. Feasibility of land surface temperature and emissivity determination from AVHRR data. Remote Sens. Environ. 1993, 43, 67–85. [Google Scholar] [CrossRef]
  17. Vidal, A. Atmospheric and emissivity correction of land surface temperature measured from satellite using ground measurements or satellite data. Int. J. Remote Sens. 1991, 12, 2449–2460. [Google Scholar] [CrossRef]
  18. Yu, Y.; Tarpley, D.; Privette, J.L.; Raja, M.K.R.V.; Vinnikov, K.; Xu, H. Developing algorithm for operational GOES-R land surface temperature product. IEEE Trans. Geosci. Remote Sens. 2009, 47, 936–951. [Google Scholar]
  19. Caselles, V.; Coll, C.; Valor, E. Land surface temperature determination in the whole Hapex Sahell area from AVHRR data. Int. J. Remote Sens. 1997, 18, 1009–1027. [Google Scholar] [CrossRef]
  20. Trigo, I.; Freitas, S.; Bioucas-Dias, J.; Barroso, C.; Monteiro, I.; Viterbo, P. Algorithm Theoretical Basis Document for Land Surface Temperature (LST) Products: LSA-001(MLST), LSA-050 (MLST-R). 2017. Available online: https://landsaf.ipma.pt/en/products/land-surface-temperature/lst/ (accessed on 20 September 2023).
  21. Wan, Z. MODIS Land-Surface Temperature Algorithm Basis Document (LST ATBD): Version 3.3. 1999. Available online: https://modis.gsfc.nasa.gov/data/atbd/atbd_mod11.pdf (accessed on 20 September 2023).
  22. Available online: https://www.nesdis.noaa.gov/news/jpss-2-has-new-name-noaa-21 (accessed on 22 September 2023).
  23. Sobrino, J.A.; Raissouni, N. Toward remote sensing methods for land cover dynamic monitoring. Application to Morocco. Int. J. Remote Sens. 2000, 20, 353–366. [Google Scholar] [CrossRef]
  24. Scott, N.A.; Chedin, A. A fast line by line method for atmospheric absorption computations: The authomatized atmospheric absorption atlas. J. Meteorol. 1981, 20, 802–812. [Google Scholar] [CrossRef]
  25. Hook, S.J. The ASTER Spectral Library. 1999. Available online: http://speclib.jpl.nasa.gov (accessed on 20 September 2023).
  26. Caselles, V.; Valor, E.; Coll, C.; Rubio, E. Thermal Band Selection for the PRISM Instrument. 1. Analysis of Emissivity-Temperature Separation Algorithms. J. Geophys. Res. 1997, 102, 11145–11164. [Google Scholar] [CrossRef]
  27. Vey, S.; Dietrich, R.; Rülke, A.; Fritsche, M.; Steigenberger, P.; Rothacher, M. Validation of Precipitable Water Vapor within the NCEP/DOE Reanalysis Using Global GPS Observations from One Decade. J. Clim. 2010, 23, 1675–1695. [Google Scholar] [CrossRef]
  28. Prata, A.J. Land surface temperatures derived from the advanced very high resolution radiometer and the along-track scanning radiometer 2: Experimental results and validation of AVHRR algorithms. J. Geophys. Res. 1994, 99, 13025–13058. [Google Scholar] [CrossRef]
Figure 1. Relative spectral response function of JPSS-2/NOAA-21 VIIRS M15 and M16 bands.
Figure 1. Relative spectral response function of JPSS-2/NOAA-21 VIIRS M15 and M16 bands.
Environsciproc 29 00023 g001
Figure 2. Validation of NOAA-21 split-window algorithm using the ground truth data set of [28].
Figure 2. Validation of NOAA-21 split-window algorithm using the ground truth data set of [28].
Environsciproc 29 00023 g002
Table 1. The characteristics of the JPSS-2/NOAA-21 VIIRS M15 and M16 bands.
Table 1. The characteristics of the JPSS-2/NOAA-21 VIIRS M15 and M16 bands.
JPSS-VIIRS BandWavelength (µm)Bandwidth (µm)Spatial Resolution (m)
M1510.76310.26–11.26750
M1612.01311.54–12.49750
Table 2. Split-window algorithm coefficients (C0 to C6) for JPSS-2/NOAA-21.
Table 2. Split-window algorithm coefficients (C0 to C6) for JPSS-2/NOAA-21.
SatelliteλieffλjeffC0C1C2C3C4C5C6
JPSS-2/NOAA-21 10.76312.013−0.161.330 0.230 58.1 −0.57 −1128.84
Table 3. The sensitivity analysis of the parameters influencing JPSS-2/NOAA-21 LST estimation.
Table 3. The sensitivity analysis of the parameters influencing JPSS-2/NOAA-21 LST estimation.
Satelliteλeff (µm)λeff
(µm)
Rδalg
(K)
δNE∆T
(K)
δε (1%)δε
(0.5%)
δW
(K)
δTotal(Ts)
(1%)
δTotal(Ts)
(0.5%)
JPSS-2/NOAA-2110.65411.9340.931.070.221.260.630.021.671.26
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Rhziel, F.Z.; Lahraoua, M.; Raissouni, N. Split-Window Algorithm for Land Surface Temperature Retrieval from Joint Polar-Orbiting Satellite System JPSS-2/NOAA-21. Environ. Sci. Proc. 2024, 29, 23. https://doi.org/10.3390/ECRS2023-16293

AMA Style

Rhziel FZ, Lahraoua M, Raissouni N. Split-Window Algorithm for Land Surface Temperature Retrieval from Joint Polar-Orbiting Satellite System JPSS-2/NOAA-21. Environmental Sciences Proceedings. 2024; 29(1):23. https://doi.org/10.3390/ECRS2023-16293

Chicago/Turabian Style

Rhziel, Fatima Zahrae, Mohammed Lahraoua, and Naoufal Raissouni. 2024. "Split-Window Algorithm for Land Surface Temperature Retrieval from Joint Polar-Orbiting Satellite System JPSS-2/NOAA-21" Environmental Sciences Proceedings 29, no. 1: 23. https://doi.org/10.3390/ECRS2023-16293

APA Style

Rhziel, F. Z., Lahraoua, M., & Raissouni, N. (2024). Split-Window Algorithm for Land Surface Temperature Retrieval from Joint Polar-Orbiting Satellite System JPSS-2/NOAA-21. Environmental Sciences Proceedings, 29(1), 23. https://doi.org/10.3390/ECRS2023-16293

Article Metrics

Back to TopTop