Land Surface Condition-Driven Emissivity Variation and Its Impact on Diurnal Land Surface Temperature Retrieval Uncertainty
Abstract
1. Introduction
2. Materials and Methods
2.1. Observation Experiment and Data
- (1)
- Experimental design for observing diurnal variation in LSE: Diurnal variation experiments were primarily conducted at the DX, XCD, SDB, YCZ, SJZ, and BLG stations, encompassing three predominant underlying surface types: barren, cropland, and grassland. Diurnal variation monitoring experiments were performed under clear-sky conditions, with measurements conducted at 30-min intervals from 08:00 to 18:00 Beijing time, as depicted in Figure 1. The LSE measurements were carried out in the following order: cold blackbody; warm blackbody; gold plate; sample. Finally, the sample emissivity curve was obtained by automatically setting Planck’s function (setting ε = 1.0 at 7–7.5 μm provides the initial temperature estimate essential for subsequent emissivity spectral retrieval). The calibrated aperture lens (field of view angle is 4.8°) was selected and the temperatures of warm and cold blackbody were set to 50 °C and 10 °C, respectively. Each scan was set to 8 times of spectral superposition, and it was always ensured that the Dewar bottle was filled with liquid nitrogen. Reflectance was primarily measured using a portable field spectroradiometer (ASD), where the reflectance spectrum of a target object is determined by comparing measurements against a standard whiteboard. Each target’s reflectance spectrum was finalized by averaging five consecutive measurements. Shallow soil moisture and temperature parameters were acquired through a WET sensor, which rapidly obtains data via probe insertion. The LST was captured using a FLIR high-end infrared thermal imager through photographic observation, with final LST values calculated by inputting the measured emissivity. Both the LST and shallow soil temperature/moisture data were obtained by averaging three repeated observational measurements.
- (2)
- Experimental design for observing vegetation effects on LSE: The experimental investigation into the influence of vegetation on LSE was implemented within the field trials at the DX station. A total of 30 experimental plots (3 m × 3 m) were established (Figure 1), wherein water regulation was applied to induce gradients in vegetation growth status and fractional cover. LSE and hyperspectral measurements were systematically acquired across these plots to characterize thermal infrared spectral curves under varying vegetation coverage conditions. Simultaneously, hyperspectral and thermal imaging data were synchronously acquired across the study area using unmanned aerial vehicles (UAVs). The observation requirements are shown in Table 1.
- (3)
- Experimental design for observing soil moisture effects on LSE: At the DX station, two adjacent 3 m × 3 m bare soil plots were established, one under natural rainfall conditions and the other subjected to manual irrigation to maintain consistently high soil moisture. The diurnal LSE variations were monitored in both plots to comparatively assess soil moisture impacts on emissivity. This configuration ensured identical soil properties and atmospheric conditions during the measurements, minimizing biases from soil texture variations and atmospheric disturbances. Furthermore, vegetation influence was deliberately excluded to isolate and prioritize the effects of soil moisture and temperature dynamics on the LSE.
- (4)
- Experimental design for observing LSE of different underlying surfaces: LSE measurements were executed across distinct land-use categories in northwestern China, including croplands (DX, PL, and SJZ-cropland), barren (SJZ-barren, YCZ, XCD, and SDB), and grasslands (BLG). These measurements, illustrated in Figure 2, facilitated a comparative analysis of LSE variations among differing soil types. The primary observation sites and their brief information are shown in Table 2.
- (5)
- LST data from continuous observation of relatively homogeneous sites: The observation data of LST primarily originated from the ARZ, HMZ, and DSL sites in 2020, provided by the National Tibetan Plateau Data Center (http://data.tpdc.ac.cn/zh-hans/, accessed on 14 February 2023). The geographical locations of these sites are illustrated in Figure 2. The underlying surfaces at the ARZ and DSL sites are relatively uniform grasslands, while the underlying surface at the HMZ site is a desert. The LST was observed using an infrared radiometer (model SI-III: instrument accuracy ±0.2 °C @ −10–65 °C; ± 0.5 °C @ −40–70 °C), with measurements conducted at 1 h. The observation sites and their brief information are shown in Table 2.
- (6)
- The soil component data were sourced from the China Soil Dataset provided by the National Tibetan Plateau Scientific Data Center. The nearest-neighbor interpolation method was employed to obtain the relevant soil component values for each observation site.
2.2. Methods
2.2.1. NDVI Threshold Method
2.2.2. Diurnal Variation Emissivity Model
2.2.3. LST Inversion Algorithm
3. Results
3.1. Diurnal Emissivity Variation Patterns and Dominant Influencing Factors Across Heterogeneous Surfaces
3.2. Diurnal-Scale Characteristics of Emissivity Variations and Their Dominant Drivers
3.3. Quantifying Emissivity-Induced Uncertainties in Diurnal Land Surface Temperature Retrievals
4. Discussion
5. Conclusions
- (1)
- Thermal emissivity signatures demonstrate marked spectral stratification across surface cover classes. Bare soil substrates exhibit substantial emissivity disparities (8–11 µm), particularly in the dual absorption troughs spanning 8–9.5 µm where emissivity variations show composition-dependent patterns: strongly anti-correlated with sand fraction (R = −0.68, p < 0.1) versus positive correlations with clay (R = 0.77, p < 0.05) and organic content (R = 0.65, p < 0.1) across the 8–12 µm spectrum. Vegetation canopies present a diagnostic absorption trough at 9.6 µm, whereas senescent plant residues develop distinct spectral fingerprints featuring a primary trough at 9.1 µm accompanied by secondary absorption features through 10–12.5 µm. Post-senescence transformation processes, as observed in wheat stubble, progressively attenuate these spectral features in the 8–12 µm range, generating statistically significant emissivity contrasts relative to both bare soils and photo-synthetically active vegetation. These wavelength-specific emissivity differentials facilitate operational monitoring frameworks through land cover classification using absorption trough depth indices and crop phenology tracking via temporal emissivity trajectory analysis in thermal infrared bands.
- (2)
- Emissivity drivers exhibit fundamental divergences across land cover types. Beyond the universal regulation by soil mineralogy, bare surfaces demonstrate a pronounced anti-correlation with shallow soil moisture (R = −0.86, p < 0.01) and a strong thermal coupling with surface thermal regimes, whereas vegetated terrains manifest hybrid radiative contributions from both soil and canopy elements. Vegetation emissivity dynamics operate through dual-path modulation: while being partially constrained by subsurface hydrothermal conditions, they show tighter covariation with vegetation density and phenological status as quantified by seasonal leaf area index trajectories.
- (3)
- Incorporating emissivity diurnal dynamics substantially improves LST retrieval precision. On daily cycles, emissivity variations (Δε ≈ 0.025 amplitude) primarily stem from soil hydrothermal modulation, exhibiting phase coupling with insolation-driven thermal inertia transitions. Our retrieval framework establishes a baseline emissivity through soil constituent unmixing, with perturbation terms quantifying soil moisture–thermal gradient interactions, atmospheric path radiance effects, and vegetation-mediated emissivity depression. Spectral optimization reveals 8–12 µm broadband emissivity optimally captures substrate physical heterogeneity. Implementing this diurnal variation model in geostationary satellite processing reduces LST retrieval errors from 6.02 °C (conventional split-window algorithm) to 2.97 °C RMSE, achieving < 3.0 °C accuracy through considering the diurnal variation characteristics of emissivity.
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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ID | Instrument Name | Model | Measurement Elements | Requirements |
---|---|---|---|---|
1 | Portable Fourier Transform Thermal Infrared Spectrometer | 102F | 2–16 µm thermal infrared spectrum, spectral resolution 4 cm−1 | Clear and cloudless, with wind speed < 4 m·s−1. Set the UAV flight altitude to 100 m, the flight speed to 5 m s−1, and a “Z”—shaped flight path, with an overlap rate of 80% along both the heading and lateral directions. |
2 | Portable ground spectrometer | ASD | 400–2500 nm hyperspectral, spectral resolution 1 cm−1 | |
3 | FLIR high-end infrared thermal imager | FLIR T860 | Thermal imaging | |
4 | WET speed tester | Delta-T WET-2 | Moisture, temperature, and conductivity of surface soil | |
5 | Plant canopy analyzer | LAI2200 | LAI | |
6 | DJI drone | M600Pro | Carried GaiaSky-mini2 | |
7 | Airborne hyperspectral detector | GaiaSky-mini2 | 400–1000 nm hyperspectral, spectral resolution 1 cm−1 | |
8 | Airborne thermal imaging lens | XT2 | Global temperature measurement | |
9 | DJI drone | M210 | Carried XT2 | |
10 | DJI drone | Elf 4Pro V2.0 | Visible light imaging |
Abbreviation | Name | Geographic Location | Underlying Surface | Time | Purpose |
---|---|---|---|---|---|
DX | Dingxi | 104.58°E, 35.57°N | Croplands | 19 May 2017–16 August 2017, 24 May 2018–30 June 2018, 22 May 2019–5 June 2019, 9 June 2022–30 June 2022, 15 March 2023–28 October 2023 | Establishment and Verification of LSE Model |
PL | Pingliang | 106.96° E, 35.50° N | Croplands | 15 April 2019–20 April 2019 | |
XCD | Xiaochaidan | 95.07° E, 37.43° N | Barren | 6 August 2019–9 August 2019 | |
SDB | Shidaoban | 94.42° E, 38.68° N | Barren | 19 August 2019–10 August 2019 | |
YCZ | Yangchangzi | 96.22° E, 37.32° N | Barren | 5 August 2019 | |
SJZ | Shajinzhen | 100.28° E, 39.08° N (Barren) 100.25° E, 39.10° N (Cropland) | Barren and Croplands | 22 June 2024–26 June 2024, 9 August 2024–10 August 2024 | |
BLG | Boligou | 100.69° E, 38.42° N | Grasslands | 21 June 2024–24 June 2024, 11 August 2024–12 August 2024 | |
HMZ | Hhuangmozhan | 100.99° E, 42.11° N | Barren | 1 January 2020–31 December 2020 | Accuracy verification of LST inversion. |
ARZ | A’rouzhan | 100.46° E, 38.05° N | Grasslands | ||
DSL | Dashalong | 98.94° E, 38.84° N | Grasslands |
Parameter | a0/b0/c0 (Sensitivity, |Δε|/1) | a1/b1/c1 (Sensitivity, |Δε|/1) | a2/b2 (Sensitivity, |Δε|/1) | a3/b3 (Sensitivity, |Δε|/1) | a4/b4 (Sensitivity, |Δε|/1) | a5 (Sensitivity, |Δε|/1) | a6 (Sensitivity, |Δε|/1) | a7 (Sensitivity, |Δε|/1) | RMSE, R |
---|---|---|---|---|---|---|---|---|---|
Adjacent thermal infrared channels emissivity parameters | |||||||||
εdaily | 0.0130 (0.0098) | 0.0234 (1.0000) | 0.6844 (0.0104) | 0.0000 (0.0317) | −0.1339 (0.3235) | −0.2824 (0.0549) | −0.1252 (0.0089) | 0.4871 (0.0729) | 0.007, 0.59 * |
dεdaily | 0.0381 (0.0206) | 0.0396 (1.0000) | −0.296 (0.0185) | 0.1381 (0.0578) | −0.0778 (0.5853) | 0.2958 (0.0260) | 0.5459 (0.0053) | 0.3178 (0.0305) | 0.005, 0.45 * |
εsoil | 0.9706 (1.0000) | −0.0047 (0.5237) | 0.0201 (0.3464) | 0.0089 (0.1300) | 0.0717 (0.0102) | 0.0035, 0.78 * | |||
dεsoil | −0.0260 (1.0000) | 0.0000 (0.5237) | 0.0002 (0.3464) | 0.0921 (0.1300) | 0.5975 (0.0102) | 0.0040, 0.88 * | |||
εnew | 0.018 (0.1099) | 0.0000 (1.0000) | 0.008, 0.48 * | ||||||
dεnew | 0.027 (0.1099) | −0.0097 (1.0000) | 0.005, 0.40 * | ||||||
8–12 µm average emissivity | |||||||||
ε8_12,daily | 0.0040 (0.0004) | 0.0566 (1.0000) | 0.7257 (0.0128) | 0.0000 (0.0390) | −0.1472 (0.3912) | −0.3156 (0.057) | −0.0055 (0.010) | 0.5861 (0.074) | 0.006, 0.63 * |
ε8_12,soil | 0.8948 (1.0000) | −0.0151 (0.5237) | 0.0143 (0.3464) | 0.3796 (0.1300) | 0.0941 (0.0102) | 0.0050, 0.78 * | |||
ε8_12,new | 0.018 (0.1099) | 0.0266 (1.0000) | 0.009, 0.48 * |
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Wang, L.; Yue, P.; Yang, Y.; Sha, S.; Hu, D.; Ren, X.; Wang, X.; Han, H.; Jiang, X. Land Surface Condition-Driven Emissivity Variation and Its Impact on Diurnal Land Surface Temperature Retrieval Uncertainty. Remote Sens. 2025, 17, 2353. https://doi.org/10.3390/rs17142353
Wang L, Yue P, Yang Y, Sha S, Hu D, Ren X, Wang X, Han H, Jiang X. Land Surface Condition-Driven Emissivity Variation and Its Impact on Diurnal Land Surface Temperature Retrieval Uncertainty. Remote Sensing. 2025; 17(14):2353. https://doi.org/10.3390/rs17142353
Chicago/Turabian StyleWang, Lijuan, Ping Yue, Yang Yang, Sha Sha, Die Hu, Xueyuan Ren, Xiaoping Wang, Hui Han, and Xiaoyu Jiang. 2025. "Land Surface Condition-Driven Emissivity Variation and Its Impact on Diurnal Land Surface Temperature Retrieval Uncertainty" Remote Sensing 17, no. 14: 2353. https://doi.org/10.3390/rs17142353
APA StyleWang, L., Yue, P., Yang, Y., Sha, S., Hu, D., Ren, X., Wang, X., Han, H., & Jiang, X. (2025). Land Surface Condition-Driven Emissivity Variation and Its Impact on Diurnal Land Surface Temperature Retrieval Uncertainty. Remote Sensing, 17(14), 2353. https://doi.org/10.3390/rs17142353