Comparison between Physical and Empirical Methods for Simulating Surface Brightness Temperature Time Series
"> Figure 1
<p>(<b>a</b>) Location of the study area of Heihe River Basin in China. Red triangles and circles represent three super stations and ten stations in the middle stream, respectively. (<b>b</b>–<b>d</b>) correspond to AR, DM, and SDQ stations, respectively.</p> "> Figure 2
<p>Architecture of LSTM with a forget gate.</p> "> Figure 3
<p>The concept map of the process of simulation and test for both empirical and physical methods. (<b>a</b>–<b>c</b>) corresponded to three different circumstances.</p> "> Figure 4
<p>Scattering plots between measured and SCOPE-simulated brightness temperatures. (<b>a</b>–<b>j</b>) correspond to cases M1–M10 in <a href="#remotesensing-14-03385-t001" class="html-table">Table 1</a>. (<b>k</b>) The boxplot of differences between SCOPE-simulated and measured brightness temperatures.</p> "> Figure 5
<p>The RMSEs of results predicted by RF regression method with different training and test data.</p> "> Figure 6
<p>The RMSEs of results predicted by LSTM regression method with different training and test data.</p> "> Figure 7
<p>Scattering plots between measured and SCOPE-simulated brightness temperatures at three super Scheme from 2013 to 2017. (<b>a</b>–<b>e</b>) correspond to cases at DM station. (<b>f</b>–<b>j</b>) correspond to cases at AR station. (<b>k</b>–<b>o</b>) correspond to cases at SDQ station. (<b>p</b>) The boxplots of differences between SCOPE-simulated and measured brightness temperatures.</p> "> Figure 8
<p>The RMSEs of predicted results by RF regression method using training and test data from same stations during different years. (<b>a</b>–<b>c</b>) correspond to DM, AR, and SDQ stations, respectively.</p> "> Figure 9
<p>The RMSEs of predicted results by RF regression method using training and test data from cross stations during different years. (a–c) correspond to RMSE, <math display="inline"><semantics> <mrow> <msup> <mi>R</mi> <mn>2</mn> </msup> </mrow> </semantics></math>, and bias, respectively.</p> "> Figure 10
<p>The RMSEs of predicted results by LSTM regression method using training and test data from same stations during different years. (<b>a</b>–<b>c</b>) correspond to DM, AR, and SDQ stations, respectively.</p> "> Figure 11
<p>The RMSEs of predicted results by LSTM regression method using training and test data from cross stations during different years. (<b>a</b>–<b>c</b>) correspond to RMSE, <math display="inline"><semantics> <mrow> <msup> <mi>R</mi> <mn>2</mn> </msup> </mrow> </semantics></math>, and bias, respectively.</p> "> Figure 12
<p>The contributions of inputs on predicting results by RF regression method for cases with (<b>a</b>) the same surface type, (<b>b</b>) different surface types, and (<b>c</b>) different time intervals. TA, DS, RH, LAI, DL, SW, and WS represent air temperature, downward shortwave radiation, relative humidity, leaf area index, downward longwave radiation, soil water content, and wind speed, respectively.</p> "> Figure A1
<p>The <math display="inline"><semantics> <mrow> <msup> <mi>R</mi> <mn>2</mn> </msup> </mrow> </semantics></math> and bias of predicted results by RF and LSTM regression method with different training data. (<b>a</b>,<b>b</b>) correspond to <math display="inline"><semantics> <mrow> <msup> <mi>R</mi> <mn>2</mn> </msup> </mrow> </semantics></math> and bias of RF, respectively. (<b>c</b>,<b>d</b>) correspond to <math display="inline"><semantics> <mrow> <msup> <mi>R</mi> <mn>2</mn> </msup> </mrow> </semantics></math> and bias of LSTM, respectively.</p> "> Figure A2
<p>The <math display="inline"><semantics> <mrow> <msup> <mi>R</mi> <mn>2</mn> </msup> </mrow> </semantics></math> and bias of predicted results by RF regression and LSTM method for three super stations with different years. (<b>a</b>–<b>f</b>) correspond to the RF method result, <math display="inline"><semantics> <mrow> <msup> <mi>R</mi> <mn>2</mn> </msup> </mrow> </semantics></math>, and bias of DM, AR, and SDQ stations, respectively. (<b>g</b>–<b>l</b>) correspond to the LSTM method result, <math display="inline"><semantics> <mrow> <msup> <mi>R</mi> <mn>2</mn> </msup> </mrow> </semantics></math>, and bias of DM, AR, and SDQ stations, respectively.</p> ">
Abstract
:1. Introduction
2. Dataset
2.1. Study Area and Experiment
2.2. Meteorological Station Data
3. Methods
3.1. Physical-Based SCOPE Method
3.2. Random Forest Regression Method
3.3. Long Short-Term Memory Method
3.4. Input and Output
4. Results
4.1. Evaluation within the Same Vegetation Type
4.1.1. Evaluation of SCOPE Model
4.1.2. Evaluation of the RF Method
4.1.3. Evaluation of the LSTM Method
4.2. Evaluation of Different Surface Types during Different Years
4.2.1. Evaluation of SCOPE Model
4.2.2. Evaluation of the RF Method
4.2.3. Evaluation of the LSTM Method
5. Discussion
5.1. The Effect of in SCOPE
5.2. The Contribution of Inputs
5.3. Intellectual Merits and Limitations
- The transferability of an empirical model after being trained is an important factor in evaluation. Therefore, the comparison was performed from different perspectives, i.e., within the same surface type, between different years, and between different climate types. To meet this need, only limited measurements were selected in this study; in particular, the comparison with different surface types is insufficient. Additional work is therefore required based on measurements for more surface types.
- Moreover, because the differences among methods were focused on simulating land surface temperature, the comparison was performed by a forward simulation type at a point scale. When they are used with remote sensing data in practical applications, there are some differences due to the uncertainty of remote sensing products and the introduction of other data, which also deserves further analysis in the future.
- In addition, the SCOPE and RF/LSTM models were selected for the physical and empirical methods, respectively. Even similar models have some differences, especially for machine learning models. The structure of LSTM itself is complex, so a series of simplifications have been made, leading to the proposal of gated recurrent units (GRUs) and minimal gate units (MGUs). Although some research shows that none of the variants can improve significantly upon the standard LSTM architecture, different structures have different performances according to different specific problems [45], which has not been discussed in this study.
6. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A
References
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Stations | Latitude (°)/ Longitude (°) | Altitude (m) | Year | DOY | Land Cover |
---|---|---|---|---|---|
M1 | 38.8869/ 100.3541 | 1559 | 2012 | 150–250 | Corn |
M2 | 38.8905/ 100.3763 | 1543 | 2012 | 150–250 | Corn |
M3 | 38.8757/ 100.3507 | 1567 | 2012 | 150–250 | Corn |
M4 | 38.8767/ 100.3652 | 1556 | 2012 | 150–250 | Corn |
M5 | 38.8725/ 100.3765 | 1550 | 2012 | 150–250 | Corn |
M6 | 38.8757/ 100.3957 | 1534 | 2012 | 150–250 | Corn |
M7 | 38.8652/ 100.3663 | 1559 | 2012 | 150–250 | Corn |
M8 | 38.8607/ 100.3785 | 1550 | 2012 | 150–250 | Corn |
M9 | 38.8587/ 100.331 | 1570 | 2012 | 150–250 | Corn |
M10 | 38.8555/ 100.3722 | 1556 | 2012 | 150–250 | Corn |
DM | 38.8555/ 100.3722 | 1556 | 2013–2017 | 150–250 | Corn |
AR | 38.0473/ 100.4643 | 3033 | 2013–2017 | 150–250 | Grass/ alpine meadow |
SDQ | 42.0012/ 101.1374 | 873 | 2013–2017 | 150–250 | Tamarix |
Corn | Grass | Tamarix | |
---|---|---|---|
Canopy structure information | |||
LAI | LAI2200/ MCD15A3H | MCD15A3H | MCD15A3H |
Canopy Height | 0–2.2 | 0.15 | 1.5 |
LADFa | −0.35 | −0.35 | −0.35 |
LADFb | −0.15 | −0.15 | −0.15 |
Component spectral information | |||
Leaf VNIR spectral | FLUSPECT model | ||
Leaf structure parameter (Ns) | 1.518 | 1.70 | 1.80 |
Chlorophyll a and b content (Cab) | 58 | 45 | 100 |
Dry matter content (Cm) | 0.0036 | 0.0030 | 0.0070 |
Equivalent water thickness (Cw) | 0.0131 | 0.0150 | 0.0038 |
Soil VNIR spectral | ASTER spectral library | ||
Leaf broad emissivity | 0.975 | 0.975 | 0.975 |
Soil broad emissivity | 0.955 | 0.955 | 0.955 |
Component biochemical information | |||
Vegetation Type | C4 | C3 | C3 |
Maximum carboxylation capacity (Vcmo) | 35 | 80 | 80 |
Ball–Berry slope (m) | 4 | 9 | 9 |
Respiration as fraction of Vcmo (Rdparam) | 0.025 | 0.015 | 0.015 |
Slope of cold temperature decline (slti) | 0.2 | 0.2 | 0.2 |
Slope of high temperature decline (shti) | 0.3 | 0.3 | 0.3 |
Temperature below which photosynthesis is lower than half that predicted by Q10 (Thl) | 288 | 281 | 278 |
Temperature above which photosynthesis is lower than half that predicted by Q10 (Thh) | 313 | 308 | 313 |
Temperature at which respiration is lower than half that predicted by Q10 (Trdm) | 328 | 328 | 328 |
Soil surface resistance (rss) | using soil water content [26] | ||
Surface meteorological information | |||
Air temperature (Ta) | Meteorological station | ||
Air humidity (RH) | |||
Wind speed (WS) | |||
Downward shortwave radiation (DS) | |||
Downward longwave radiation (DL) |
Unit | DM | AR | SDQ | |||||||
---|---|---|---|---|---|---|---|---|---|---|
Vcmo | 20 | 35 | 50 | 50 | 80 | 110 | 50 | 80 | 110 | |
RMSE | K | 2.03 | 1.83 | 1.85 | 2.16 | 2.25 | 2.06 | 2.07 | 2.12 | 2.24 |
Bias | K | 0.22 | −0.29 | −0.35 | 0.52 | −0.32 | 0.06 | 1.05 | 0.75 | 0.53 |
- | 0.91 | 0.92 | 0.92 | 0.94 | 0.92 | 0.93 | 0.97 | 0.97 | 0.97 |
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Bian, Z.; Lu, Y.; Du, Y.; Zhao, W.; Cao, B.; Hu, T.; Li, R.; Li, H.; Xiao, Q.; Liu, Q. Comparison between Physical and Empirical Methods for Simulating Surface Brightness Temperature Time Series. Remote Sens. 2022, 14, 3385. https://doi.org/10.3390/rs14143385
Bian Z, Lu Y, Du Y, Zhao W, Cao B, Hu T, Li R, Li H, Xiao Q, Liu Q. Comparison between Physical and Empirical Methods for Simulating Surface Brightness Temperature Time Series. Remote Sensing. 2022; 14(14):3385. https://doi.org/10.3390/rs14143385
Chicago/Turabian StyleBian, Zunjian, Yifan Lu, Yongming Du, Wei Zhao, Biao Cao, Tian Hu, Ruibo Li, Hua Li, Qing Xiao, and Qinhuo Liu. 2022. "Comparison between Physical and Empirical Methods for Simulating Surface Brightness Temperature Time Series" Remote Sensing 14, no. 14: 3385. https://doi.org/10.3390/rs14143385