Improving LST Downscaling Quality on Regional and Field-Scale by Parameterizing the DisTrad Method
<p>Location map describes the study area.</p> "> Figure 2
<p>NDVI derived from Landsat 8 18 January 2018.</p> "> Figure 3
<p>(<b>a</b>,<b>b</b>) Correlation between the NDVI and LST daily average for 10% and 25% methods, respectively.</p> "> Figure 4
<p>(<b>a</b>) LST Landsat10%, (<b>b</b>) LST Landsat 25%, and (<b>c</b>) LST Landsat native.</p> "> Figure 5
<p>(<b>a</b>–<b>c</b>) Scatter plot for the relation between NDVI and LST 10%, LST 25%, and LST native, respectively.</p> "> Figure 6
<p>(<b>a</b>–<b>c</b>) Scatter plot between native LST with LST10%, LST 25%, and LST25% polynomial, respectively.</p> "> Figure 7
<p>(<b>a</b>–<b>c</b>) Eta for native LST, LST 10%, and LST 25%, respectively.</p> "> Figure 8
<p>Correlation scatter plot of (<b>a</b>) ETa (LST native) against ETa (LST10%) and (<b>b</b>) ETa (LST native) against ETa (LST25%).</p> "> Figure 9
<p>(<b>a</b>) NDVI and LST25% correlation, (<b>b</b>) NDVI and LST10% correlation.</p> "> Figure 10
<p>ETa (<b>a</b>) derived from MODIS LST resampled and (<b>b</b>) downscaled, respectively. MODIS LST (<b>c</b>) resampled and (<b>d</b>) downscaled.</p> "> Figure 11
<p>Comparison between daily ETp (blue line) VS. ETa (red line) for 18 August 2017 to 24 February 2018.</p> "> Figure 12
<p>Correlation between ETp and ETa (mm·d<sup>−1</sup>).</p> ">
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. The DisTrad Downscaling Procedure for Radiometric Surface Temperature
2.2.1. DisTrad Modification
Modification Summary
- Use linear regression instead of polynomial regression by assuming that polynomial is more sensitive for outliers.
- Use 10% of the aggregated pixels instead of using 25% of the aggregated pixels assuming that based on the heterogeneity of the study area, the 10% of the aggregated pixels will give a stronger correlation between the NDVI and LST in the upper and lower tail in the distribution of the pixels.
The Validation
- LST from the Landsat 8 was aggregated to a coarser resolution (1000 m).
- NDVI from Landsat 8 was aggregated to a coarse resolution (1000 m).
- The modification was applied to LST1000m and NDVI1000M to downscale LST to fine resolution.
- LSTnative was used to validate LSTdown.
2.3. Evapotranspiration Estimation
2.3.1. The Surface Energy Balance System
2.3.2. Preparation of the Input Data for SEBS
Normalized Different Vegetation Index (NDVI)
Fraction of Vegetation Cover (FVC)
Emissivity
Albedo
Metrological Data
2.3.3. Retrieval of Actual Evapotranspiration in SEBS
2.3.4. Data and Processing
2.3.5. SEBS Validation
2.3.6. Statistical Justification
3. Results and Discussion
3.1. LST and NDVI Regression
3.2. Effects of LST Downscaling on Landsat 8 Image
3.3. Effects of Downscaling LST on ETa Estimation
3.4. Application of Downscaling Model on MODIS Data
3.5. Model Validation
4. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
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Data | Source | Spatial Resolution | Temporal Resolution |
---|---|---|---|
Landsat 8 | https://espa.cr.usgs.gov/ordering/new/ (23 March 2020) | 30 m | 16 days |
MODIS MOD11A1 V6 | https://earthexplorer.usgs.gov/ (23 March 2020) | 1 km | daily |
NDVI | https://espa.cr.usgs.gov/ordering/new/ (23 March 2020) | 30 m | 16 days |
Sunshine duration | https://apps.ecmwf.int/datasets/data/interim-full-daily/levtype=sfc/ (23 March 2020) | 80 km | Daily |
SRTM DEM | https://earthexplorer.usgs.gov/ (23 March 2020) | 30 m | - |
Other climatic data | https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5 (23 March 2020) | 9 km | Daily |
Method | Max ME | Min ME | Mean Error | RME |
---|---|---|---|---|
LST 25% | 9.37 | −5.12 | −0.011 | 0.89 |
LST 10% | 10.16 | −5.63 | −0.012 | 0.98 |
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Ibrahim, T.I.M.; Al-Maliki, S.; Salameh, O.; Waltner, I.; Vekerdy, Z. Improving LST Downscaling Quality on Regional and Field-Scale by Parameterizing the DisTrad Method. ISPRS Int. J. Geo-Inf. 2022, 11, 327. https://doi.org/10.3390/ijgi11060327
Ibrahim TIM, Al-Maliki S, Salameh O, Waltner I, Vekerdy Z. Improving LST Downscaling Quality on Regional and Field-Scale by Parameterizing the DisTrad Method. ISPRS International Journal of Geo-Information. 2022; 11(6):327. https://doi.org/10.3390/ijgi11060327
Chicago/Turabian StyleIbrahim, Taha I. M., Sadiq Al-Maliki, Omar Salameh, István Waltner, and Zoltán Vekerdy. 2022. "Improving LST Downscaling Quality on Regional and Field-Scale by Parameterizing the DisTrad Method" ISPRS International Journal of Geo-Information 11, no. 6: 327. https://doi.org/10.3390/ijgi11060327
APA StyleIbrahim, T. I. M., Al-Maliki, S., Salameh, O., Waltner, I., & Vekerdy, Z. (2022). Improving LST Downscaling Quality on Regional and Field-Scale by Parameterizing the DisTrad Method. ISPRS International Journal of Geo-Information, 11(6), 327. https://doi.org/10.3390/ijgi11060327