Correcting an Off-Nadir to a Nadir Land Surface Temperature Using a Multitemporal Thermal Infrared Kernel-Driven Model during Daytime
<p>(<b>a</b>) Locations of in situ sites; (<b>b</b>–<b>g</b>) photographs of USCRN sites; (<b>h</b>) boxplots of the STDs of 200 × 200 (downscaled to 10 m) LST values centered at each site. The site ID is shown in <a href="#remotesensing-16-01790-t001" class="html-table">Table 1</a>.</p> "> Figure 2
<p>The (<b>a</b>) Vinnikov kernel and (<b>b</b>) RL kernel in the solar principal plane (SPP).</p> "> Figure 3
<p>The flowchart of the TRD correction process in this study.</p> "> Figure 4
<p>The accuracy of all LST products, GOES-16 LST products, and MODIS LST products before (<b>a</b>–<b>c</b>) and after (<b>d</b>–<b>f</b>) TRD correction during the day.</p> "> Figure 5
<p>The angular distribution of <tt>∆</tt><span class="html-italic">LST</span> (i.e., MODIS LST minus in situ LST) during the daytime for (<b>a</b>) the off-nadir LST before TRD correction; (<b>b</b>) the corrected nadir LST.</p> "> Figure 6
<p>The off-nadir and nadir LST on (<b>a</b>) 2020/01/13; (<b>b</b>) 2020/04/15; (<b>c</b>) 2020/06/30; and (<b>d</b>) 2020/10/15 at the NM Las Cruces 20 N site.</p> "> Figure 7
<p>The temporal variation in <tt>∆</tt><span class="html-italic">LST</span> during the daytime at a (<b>a</b>) daily scale and (<b>b</b>) monthly scale.</p> "> Figure 8
<p>The spatial distribution of (<b>a</b>) MOD11A1 off-nadir LST; (<b>b</b>) the corrected nadir LST; (<b>c</b>) the LST difference, i.e., (<b>b</b>) minus (<b>a</b>); (<b>d</b>) the VZA; (<b>e</b>) the angle between the solar and view directions; and (<b>f</b>) the SZA, i.e., the angle between the solar and nadir directions.</p> "> Figure 9
<p>(<b>a</b>) The accuracy of nighttime LST products; (<b>b</b>) the angular dependence on the VZA for the MODIS nighttime off-nadir LST.</p> ">
Abstract
:1. Introduction
2. Materials
2.1. Remote Sensing LST Products
2.2. In Situ LST Data
3. Methods
3.1. Time-Evolved Kernel-Driven Model
3.2. Solving the TEKDM
3.3. Evaluation of the Nadir LST
4. Results
4.1. The Accuracy of Off-Nadir and Nadir LST
4.2. The Angular Dependence of MODIS Off-Nadir and Nadir LST
4.3. The Temporal Variation in Off-Nadir and Nadir LST
4.4. The Spatial Distribution of Off-Nadir and Nadir LST
5. Discussion
5.1. Comparison with Previous TRD Correction Method
5.2. The TRD of the Nighttime LST Product
5.3. The Applicability of the TEKDM
6. Conclusions
- The evaluation using the USCRN in situ nadir LST showed that the proposed TEKDM effectively reduced the RMSE (MBE) of off-nadir LST products from 3.29 K (−2.0 K) to 2.34 K (−0.02 K), resulting in an RMSE reduction of 0.95 K (29%) and a significant reduction in systematic bias.
- The TEKDM successfully eliminates the angular and temporal dependence when calculating the LST difference between the original off-nadir LST and the in situ nadir LST, which indicates that the TEKDM can depict the TRD patterns well and adapt to their temporal variations.
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
- Liang, S.; Wang, D.; He, T.; Yu, Y. Remote Sensing of Earth’s Energy Budget: Synthesis and Review. Int. J. Digit. Earth 2019, 12, 737–780. [Google Scholar] [CrossRef]
- Tajfar, E.; Bateni, S.M.; Lakshmi, V.; Ek, M. Estimation of Surface Heat Fluxes via Variational Assimilation of Land Surface Temperature, Air Temperature and Specific Humidity into a Coupled Land Surface-Atmospheric Boundary Layer Model. J. Hydrol. 2020, 583, 124577. [Google Scholar] [CrossRef]
- Wang, Y.-R.; Hessen, D.O.; Samset, B.H.; Stordal, F. Evaluating Global and Regional Land Warming Trends in the Past Decades with Both MODIS and ERA5-Land Land Surface Temperature Data. Remote Sens. Environ. 2022, 280, 113181. [Google Scholar] [CrossRef]
- Zhao, W.; Wen, F.; Wang, Q.; Sanchez, N.; Piles, M. Seamless Downscaling of the ESA CCI Soil Moisture Data at the Daily Scale with MODIS Land Products. J. Hydrol. 2021, 603, 126930. [Google Scholar] [CrossRef]
- Li, R.; Li, H.; Hu, T.; Bian, Z.; Liu, F.; Cao, B.; Du, Y.; Sun, L.; Liu, Q. Land Surface Temperature Retrieval From Sentinel-3A SLSTR Data: Comparison Among Split-Window, Dual-Window, Three-Channel, and Dual-Angle Algorithms. IEEE Trans. Geosci. Remote Sens. 2023, 61, 5003114. [Google Scholar] [CrossRef]
- Li, H.; Li, R.; Tu, H.; Cao, B.; Liu, F.; Bian, Z.; Hu, T.; Du, Y.; Sun, L.; Liu, Q. An Operational Split-Window Algorithm for Generating Long-Term Land Surface Temperature Products from Chinese Fengyun-3 Series Satellite Data. IEEE Trans. Geosci. Remote Sens. 2023, 61, 5004514. [Google Scholar] [CrossRef]
- Cao, B.; Liu, Q.; Du, Y.; Roujean, J.-L.; Gastellu-Etchegorry, J.-P.; Trigo, I.F.; Zhan, W.; Yu, Y.; Cheng, J.; Jacob, F.; et al. A Review of Earth Surface Thermal Radiation Directionality Observing and Modeling: Historical Development, Current Status and Perspectives. Remote Sens. Environ. 2019, 232, 111304. [Google Scholar] [CrossRef]
- Du, H.; Zhan, W.; Liu, Z.; Scott Krayenhoff, E.; Chakraborty, T.; Zhao, L.; Jiang, L.; Dong, P.; Li, L.; Huang, F.; et al. Global Mapping of Urban Thermal Anisotropy Reveals Substantial Potential Biases for Remotely Sensed Urban Climates. Sci. Bull. 2023, 68, 1809–1818. [Google Scholar] [CrossRef]
- Wang, D.; Chen, Y.; Hu, L.; Voogt, J.A.; Gastellu-Etchegorry, J.-P.; Krayenhoff, E.S. Modeling the Angular Effect of MODIS LST in Urban Areas: A Case Study of Toulouse, France. Remote Sens. Environ. 2021, 257, 112361. [Google Scholar] [CrossRef]
- Coll, C.; Galve, J.M.; Niclòs, R.; Valor, E.; Barberà, M.J. Angular Variations of Brightness Surface Temperatures Derived from Dual-View Measurements of the Advanced Along-Track Scanning Radiometer Using a New Single Band Atmospheric Correction Method. Remote Sens. Environ. 2019, 223, 274–290. [Google Scholar] [CrossRef]
- Liu, H.; He, B.; Gao, S.; Zhan, Q.; Yang, C. Influence of Non-Urban Reference Delineation on Trend Estimate of Surface Urban Heat Island Intensity: A Comparison of Seven Methods. Remote Sens. Environ. 2023, 296, 113735. [Google Scholar] [CrossRef]
- Zhao, Z.-Q.; He, B.-J.; Li, L.-G.; Wang, H.-B.; Darko, A. Profile and Concentric Zonal Analysis of Relationships between Land Use/Land Cover and Land Surface Temperature: Case Study of Shenyang, China. Energy Build. 2017, 155, 282–295. [Google Scholar] [CrossRef]
- Ermida, S.L.; Trigo, I.F.; DaCamara, C.C.; Roujean, J.-L. Assessing the Potential of Parametric Models to Correct Directional Effects on Local to Global Remotely Sensed LST. Remote Sens. Environ. 2018, 209, 410–422. [Google Scholar] [CrossRef]
- Wang, J.; Tang, R.; Jiang, Y.; Liu, M.; Li, Z.-L. A Practical Method for Angular Normalization of Global MODIS Land Surface Temperature over Vegetated Surfaces. ISPRS J. Photogramm. Remote Sens. 2023, 199, 289–304. [Google Scholar] [CrossRef]
- Cao, B.; Roujean, J.-L.; Gastellu-Etchegorry, J.-P.; Liu, Q.; Du, Y.; Lagouarde, J.-P.; Huang, H.; Li, H.; Bian, Z.; Hu, T.; et al. A General Framework of Kernel-Driven Modeling in the Thermal Infrared Domain. Remote Sens. Environ. 2021, 252, 112157. [Google Scholar] [CrossRef]
- Cao, B.; Bian, Z.; Du, Y.; Li, H.; Xiao, Q.; Liu, Q. Evaluation of Four Kernel Driven Models in the Thermal Infrared Band Using Airborne Measured Multi-Angle Datasets. In Proceedings of the IGARSS 2019–2019 IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan, 28 July–2 August 2019; pp. 6660–6663. [Google Scholar]
- Ermida, S.; Trigo, I.; DaCamara, C.; Pires, A. A Methodology to Simulate LST Directional Effects Based on Parametric Models and Landscape Properties. Remote Sens. 2018, 10, 1114. [Google Scholar] [CrossRef]
- Vinnikov, K.Y.; Yu, Y.; Goldberg, M.D.; Tarpley, D.; Romanov, P.; Laszlo, I.; Chen, M. Angular Anisotropy of Satellite Observations of Land Surface Temperature. Geophys. Res. Lett. 2012, 39, L23082. [Google Scholar] [CrossRef]
- Hu, T.; Roujean, J.-L.; Cao, B.; Mallick, K.; Boulet, G.; Li, H.; Xu, Z.; Du, Y.; Liu, Q. Correction for LST Directionality Impact on the Estimation of Surface Upwelling Longwave Radiation over Vegetated Surfaces at the Satellite Scale. Remote Sens. Environ. 2023, 295, 113649. [Google Scholar] [CrossRef]
- Liu, X.; Tang, B.-H.; Li, Z.-L. Evaluation of Three Parametric Models for Estimating Directional Thermal Radiation from Simulation, Airborne, and Satellite Data. Remote Sens. 2018, 10, 420. [Google Scholar] [CrossRef]
- Ermida, S.L.; DaCamara, C.C.; Trigo, I.F.; Pires, A.C.; Ghent, D.; Remedios, J. Modelling Directional Effects on Remotely Sensed Land Surface Temperature. Remote Sens. Environ. 2017, 190, 56–69. [Google Scholar] [CrossRef]
- Duffour, C.; Lagouarde, J.-P.; Roujean, J.-L. A Two Parameter Model to Simulate Thermal Infrared Directional Effects for Remote Sensing Applications. Remote Sens. Environ. 2016, 186, 250–261. [Google Scholar] [CrossRef]
- Qin, B.; Cao, B.; Roujean, J.-L.; Gastellu-Etchegorry, J.-P.; Ermida, S.L.; Bian, Z.; Du, Y.; Hu, T.; Li, H.; Xiao, Q.; et al. A Thermal Radiation Directionality Correction Method for the Surface Upward Longwave Radiation of Geostationary Satellite Based on a Time-Evolving Kernel-Driven Model. Remote Sens. Environ. 2023, 294, 113599. [Google Scholar] [CrossRef]
- Teng, Y.; Ren, H.; Zhu, J.; Jiang, C.; Ye, X.; Zeng, H. A Practical Method for Angular Normalization on Land Surface Temperature Using Space between Thermal Radiance and Fraction of Vegetation Cover. Remote Sens. Environ. 2023, 291, 113558. [Google Scholar] [CrossRef]
- Qin, B.; Cao, B.; Yu, Y.; Yu, P.; Na, Q.; Chen, S.; Li, D.; Jia, K.; Yang, Y.; Hu, T.; et al. Angular Normalization of GOES-16 and GOES-17 Land Surface Temperature over Overlapping Region Using an Extended Time-Evolving Kernel-Driven Model. Remote Sens. Environ. 2024; in progress. [Google Scholar]
- Liu, X.; Tang, B.-H.; Li, Z.-L.; Zhou, C.; Wu, W.; Rasmussen, M.O. An Improved Method for Separating Soil and Vegetation Component Temperatures Based on Diurnal Temperature Cycle Model and Spatial Correlation. Remote Sens. Environ. 2020, 248, 111979. [Google Scholar] [CrossRef]
- Yu, Y.; Tarpley, D.; Privette, J.L.; Goldberg, M.D.; Rama Varma Raja, M.K.; Vinnikov, K.Y.; Xu, H. Developing Algorithm for Operational GOES-R Land Surface Temperature Product. IEEE Trans. Geosci. Remote Sens. 2009, 47, 936–951. [Google Scholar] [CrossRef]
- Wan, Z.; Dozier, J. A Generalized Split-Window Algorithm for Retrieving Land-Surface Temperature from Space. IEEE Trans. Geosci. Remote Sens. 1996, 34, 892–905. [Google Scholar] [CrossRef]
- Guillevic, P.; Göttsche, F.; Hulley, J.; Ghent, G.; Román, J.; Camacho, M.; Guillevic, P.; Göttsche, F.-M.; Nickeson, J.; Hulley, G.; et al. Land Surface Temperature Product Validation Best Practice Protocol. Version 1.1; In Good Practices for Satellite-Derived Land Product Validation; Guillevic, P., Göttsche, F., Nickeson, J., Román, M., Eds.; Land Product Validation Subgroup (WGCV/CEOS): Stockholm, Sweden, 2018. [Google Scholar]
- Diamond, H.J.; Karl, T.R.; Palecki, M.A.; Baker, C.B.; Bell, J.E.; Leeper, R.D.; Easterling, D.R.; Lawrimore, J.H.; Meyers, T.P.; Helfert, M.R.; et al. U.S. Climate Reference Network after One Decade of Operations: Status and Assessment. Bull. Am. Meteorol. Soc. 2013, 94, 485–498. [Google Scholar] [CrossRef]
- Krishnan, P.; Kochendorfer, J.; Dumas, E.J.; Guillevic, P.C.; Bruce Baker, C.; Meyers, T.P.; Martos, B. Comparison of In-Situ, Aircraft, and Satellite Land Surface Temperature Measurements over a NOAA Climate Reference Network Site. Remote Sens. Environ. 2015, 165, 249–264. [Google Scholar] [CrossRef]
- Zhan, W.; Chen, Y.; Zhou, J.; Wang, J.; Liu, W.; Voogt, J.; Zhu, X.; Quan, J.; Li, J. Disaggregation of Remotely Sensed Land Surface Temperature: Literature Survey, Taxonomy, Issues, and Caveats. Remote Sens. Environ. 2013, 131, 119–139. [Google Scholar] [CrossRef]
- Zheng, L.; Cao, B.; Na, Q.; Qin, B.; Bai, J.; Du, Y.; Li, H.; Bian, Z.; Xiao, Q.; Liu, Q. Estimation and Evaluation of 15 Minute, 40 Meter Surface Upward Longwave Radiation Downscaled from the Geostationary FY-4B AGRI. Remote Sens. 2024, 16, 1158. [Google Scholar] [CrossRef]
- Nilson, T. A Theoretical Analysis of the Frequency of Gaps in Plant Stands. Agric. Meteorol. 1971, 8, 25–38. [Google Scholar] [CrossRef]
- Hong, F.; Zhan, W.; Göttsche, F.-M.; Liu, Z.; Zhou, J.; Huang, F.; Lai, J.; Li, M. Comprehensive Assessment of Four-Parameter Diurnal Land Surface Temperature Cycle Models under Clear-Sky. ISPRS J. Photogramm. Remote Sens. 2018, 142, 190–204. [Google Scholar] [CrossRef]
- Caselles, E.; Valor, E.; Abad, F.; Caselles, V. Automatic Classification-Based Generation of Thermal Infrared Land Surface Emissivity Maps Using AATSR Data over Europe. Remote Sens. Environ. 2012, 124, 321–333. [Google Scholar] [CrossRef]
- Li, H.; Liu, Q.; Yang, Y.; Li, R.; Wang, H.; Cao, B.; Bian, Z.; Hu, T.; Du, Y.; Sun, L. Comparison of the MuSyQ and MODIS Collection 6 Land Surface Temperature Products Over Barren Surfaces in the Heihe River Basin, China. IEEE Trans. Geosci. Remote Sens. 2019, 57, 8081–8094. [Google Scholar] [CrossRef]
- Pearson, R.K. Outliers in Process Modeling and Identification. IEEE Trans. Control Syst. Technol. 2002, 10, 55–63. [Google Scholar] [CrossRef] [PubMed]
- Duan, S.-B.; Li, Z.-L.; Li, H.; Göttsche, F.-M.; Wu, H.; Zhao, W.; Leng, P.; Zhang, X.; Coll, C. Validation of Collection 6 MODIS Land Surface Temperature Product Using in Situ Measurements. Remote Sens. Environ. 2019, 225, 16–29. [Google Scholar] [CrossRef]
- Göttsche, F.-M.; Olesen, F.-S.; Bork-Unkelbach, A. Validation of Land Surface Temperature Derived from MSG/SEVIRI with in Situ Measurements at Gobabeb, Namibia. Int. J. Remote Sens. 2013, 34, 3069–3083. [Google Scholar] [CrossRef]
- Li, Z.; Wu, H.; Duan, S.; Zhao, W.; Ren, H.; Liu, X.; Leng, P.; Tang, R.; Ye, X.; Zhu, J.; et al. Satellite Remote Sensing of Global Land Surface Temperature: Definition, Methods, Products, and Applications. Rev. Geophys. 2023, 61, e2022RG000777. [Google Scholar] [CrossRef]
- Stubenrauch, C.J.; Rossow, W.B.; Kinne, S.; Ackerman, S.; Cesana, G.; Chepfer, H.; Di Girolamo, L.; Getzewich, B.; Guignard, A.; Heidinger, A.; et al. Assessment of Global Cloud Datasets from Satellites: Project and Database Initiated by the GEWEX Radiation Panel. Bull. Am. Meteorol. Soc. 2013, 94, 1031–1049. [Google Scholar] [CrossRef]
- Wang, D.; Hu, L.; Voogt, J.A.; Chen, Y.; Zhou, J.; Chang, G.; Quan, J.; Zhan, W.; Kang, Z. Simulation of Urban Thermal Anisotropy at Remote Sensing Pixel Scales: Evaluating Three Schemes Using GUTA-T over Toulouse City. Remote Sens. Environ. 2024, 300, 113893. [Google Scholar] [CrossRef]
- Jiang, L.; Zhan, W.; Hu, L.; Huang, F.; Hong, F.; Liu, Z.; Lai, J.; Wang, C. Assessment of Different Kernel-Driven Models for Daytime Urban Thermal Radiation Directionality Simulation. Remote Sens. Environ. 2021, 263, 112562. [Google Scholar] [CrossRef]
Site ID | Site Name | Longitude | Latitude | Elevation | Land Cover |
---|---|---|---|---|---|
S1 | AZ Williams 35 NNW | 112.34° W | 35.76° W | 1809 m | GRA |
S2 | CO La Junta 17 WSW | 103.82° W | 37.86° W | 1342 m | GRA |
S3 | CO Nunn 7 NNE | 104.76° W | 40.81° W | 1633 m | GRA |
S4 | NM Socorro 20 N | 106.89° W | 34.36° W | 1479 m | BSV |
S5 | NM Las Cruces 20 N | 106.74° W | 32.61° W | 1325 m | OSH |
S6 | OR Riley 10 WSW | 119.69° W | 43.47° W | 1407 m | GRA |
Parameter | Initial Value | Lower Bound | Upper Bound |
---|---|---|---|
T0 | − 5 | + 5 | |
Ta | − 5 | + 5 | |
tm | − 1 | + 1 | |
ω | − 1 | + 1 | |
A | 0.015 | 0 | 0.03 |
B | −0.015 | −0.03 | 0 |
k | 0.5 | 0.0001 | 1 |
Product | Metric | Off-Nadir LST | TEKDM | Hu’s Method |
---|---|---|---|---|
All Data Points | RMSE (K) | 3.29 | 2.34 | 2.37 |
MBE (K) | −2.0 | −0.02 | −0.68 | |
GOES-16 | RMSE (K) | 3.44 | 2.33 | 2.41 |
MBE (K) | −2.22 | −0.09 | −0.82 | |
MODIS | RMSE (K) | 2.66 | 2.35 | 2.22 |
MBE (K) | −1.18 | 0.21 | −0.17 |
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Na, Q.; Cao, B.; Qin, B.; Mo, F.; Zheng, L.; Du, Y.; Li, H.; Bian, Z.; Xiao, Q.; Liu, Q. Correcting an Off-Nadir to a Nadir Land Surface Temperature Using a Multitemporal Thermal Infrared Kernel-Driven Model during Daytime. Remote Sens. 2024, 16, 1790. https://doi.org/10.3390/rs16101790
Na Q, Cao B, Qin B, Mo F, Zheng L, Du Y, Li H, Bian Z, Xiao Q, Liu Q. Correcting an Off-Nadir to a Nadir Land Surface Temperature Using a Multitemporal Thermal Infrared Kernel-Driven Model during Daytime. Remote Sensing. 2024; 16(10):1790. https://doi.org/10.3390/rs16101790
Chicago/Turabian StyleNa, Qiang, Biao Cao, Boxiong Qin, Fan Mo, Limeng Zheng, Yongming Du, Hua Li, Zunjian Bian, Qing Xiao, and Qinhuo Liu. 2024. "Correcting an Off-Nadir to a Nadir Land Surface Temperature Using a Multitemporal Thermal Infrared Kernel-Driven Model during Daytime" Remote Sensing 16, no. 10: 1790. https://doi.org/10.3390/rs16101790