A Reconstructed Global Daily Seamless SIF Product at 0.05 Degree Resolution Based on TROPOMI, MODIS and ERA5 Data
<p>Visualizations of the spatiotemporal limitations in original TROPOMI SIF including spatial resolution insufficiency (<b>a</b>), spatial gaps (<b>b</b>), and temporal discontinuities (<b>c</b>,<b>d</b>) at 0.05° resolution.</p> "> Figure 2
<p>The land cover map in 2019 from MCD12C1.</p> "> Figure 3
<p>The statistical metrics for the accuracy of SIF reconstruction models using different combinations of explanatory variables based on the testing samples at 0.1°, 8-day resolutions in 2019. (<b>a</b>) coefficient of determination (R<sup>2</sup>); (<b>b</b>) Root Mean Square Error (RMSE, mW/m<sup>2</sup>/nm/sr); (<b>c</b>) Mean Absolute Error (MAE, mW/m<sup>2</sup>/nm/sr). Ref1–4 and Ref1–7 refer to MODIS bands 1–4 and MODIS bands 1–7, respectively.</p> "> Figure 4
<p>Scatter diagrams between the TROPOMI SIF and the SIF predicted by RF models for the testing samples of three cross-validation experiments: first (<b>a</b>), second (<b>b</b>), and third (<b>c</b>) at 0.1°, 8-day resolutions in 2019. The density of points in logarithmic scale is represented by the colorbar. The black dash line represents the 1:1 line.</p> "> Figure 5
<p>The pixel-wise correlations between day-to-day SIF values from SDSIF and <math display="inline"><semantics> <mrow> <msubsup> <mrow> <mi>TROSIF</mi> </mrow> <mi>s</mi> <mrow> <mn>02</mn> </mrow> </msubsup> </mrow> </semantics></math> in 2019 at 0.2°, daily scales in terms of the coefficient of determination (R<sup>2</sup>) (<b>a</b>) and regression slope (<b>b</b>). All pixels in this figure achieved the significance level of 0.05.</p> "> Figure 6
<p>Spatial patterns of the 16-day, 0.1° re-aggregated SDSIF product (<b>leaf column</b>), as well as its residuals (<b>middle column</b>) and latitudinal averages (<b>right column</b>) compared with the original 16-day <math display="inline"><semantics> <mrow> <msubsup> <mrow> <mi>TROSIF</mi> </mrow> <mi>s</mi> <mrow> <mn>01</mn> </mrow> </msubsup> </mrow> </semantics></math> in January (<b>a</b>), March (<b>b</b>), July (<b>c</b>), and October (<b>d</b>) 2019. For each month, the first 16-day maps are shown here.</p> "> Figure 7
<p>Scatter diagrams between the re-aggregated SDSIF and the original <math display="inline"><semantics> <mrow> <msubsup> <mrow> <mi>TROSIF</mi> </mrow> <mi>s</mi> <mrow> <mn>01</mn> </mrow> </msubsup> </mrow> </semantics></math> at 16-day, 0.1° scales for the first 16 days in January (<b>a</b>), March (<b>b</b>), July (<b>c</b>), and October (<b>d</b>) 2019. The density of points in logarithmic scale is represented by the colorbar. The black dash line represents the 1:1 line.</p> "> Figure 8
<p>Comparison between the time series of tower-based SIF and the two satellite SIF products (SDSIF and original TROSIF<sup>005</sup>) at daily scale for (<b>a–e</b>) sites. All regressions in the right panel achieved the significance level of 0.05.</p> "> Figure 9
<p>Comparison between the time series of tower-based SIF and two satellite SIF products (SDSIF and original TROSIF<sup>005</sup>) at the 4-day scale for (<b>a</b>,<b>b</b>) sites. All regressions in the right panel achieved the significance level of 0.05. The blue hollow dots, hollow triangles and solid dots represent the 4-day averages with valid observations from no more than one or two days, three days and four days, respectively.</p> "> Figure 10
<p>Spatial patterns of annual mean (<b>a</b>) and maximum (90th percentile) (<b>b</b>) of re-aggregated SDSIF in 2019, as well as the spatial comparison between SDSIF (<b>c</b>) and TROSIF<sup>005</sup> (<b>d</b>) on 3 August 2019.</p> "> Figure 11
<p>Local enlarged images of the Mideastern United States region on 3 August 2019 in terms of different products: (<b>a</b>–<b>e</b>). All maps are at 0.05°, daily resolution.</p> "> Figure 12
<p>Comparison between the time series of tower-based GPP with SDSIF and original TROSIF<sup>005</sup> (<b>a</b>), as well as the corresponding correlations (<b>b</b>) at the 4-day scale for the DM site.</p> ">
Abstract
:1. Introduction
2. Materials and Methods
2.1. Datasets from Space and Ground
2.1.1. Satellite SIF Data from TROPOMI
2.1.2. MODIS and ERA5 Datasets
2.1.3. Tower-Based Datasets
2.2. Data-Driven Method for SIF Reconstruction
2.2.1. Explanatory Variable Selection
2.2.2. Model Development
2.2.3. Global-Scale SIF Reconstruction
2.3. Validation Approaches
3. Results
3.1. Performance of the SIF Reconstruction Models
3.2. Validation of SDSIF with Original TROPOMI SIF
3.3. Validation of SDSIF with Tower-Based SIF
3.4. Spatial Patterns of the Global SIF Product
4. Discussions
4.1. Benefits of the Reconstructed SDSIF
4.2. Reliability and Uncertainties in SIF Reconstruction Method
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Land Cover Type | Site Name | ID | Latitude | Longitude | Period | Height |
---|---|---|---|---|---|---|
CRO | HuaiLai | HL | 40.3489°N | 115.7882°E | May to October in 2018 | 4 m |
DaMan | DM | 38.8555°N | 100.3722°E | June to October in 2018 & 2019 | 25 m | |
GuCheng | GC | 39.1487°N | 115.7350°E | May to December in 2020 | 25 m | |
Aurora | - | 42.7228°N | 76.6628°W | July to October in 2018 | 7 m | |
GRA | Arou | AR | 38.0473°N | 100.4643°E | June to September in 2019 | 25 m |
Biome | Universal Model | Continent-Specific Model | Continent- and Monthly-Specific Model | ||||||
---|---|---|---|---|---|---|---|---|---|
R2 | RMSE | MAE | R2 | RMSE | MAE | R2 | RMSE | MAE | |
ENF | 0.804 | 0.0681 | 0.0502 | 0.819 | 0.0652 | 0.0481 | 0.829 | 0.0632 | 0.0463 |
EBF | 0.725 | 0.0851 | 0.0636 | 0.755 | 0.0800 | 0.0596 | 0.778 | 0.0760 | 0.0563 |
DNF | 0.886 | 0.0654 | 0.0486 | 0.889 | 0.0640 | 0.0476 | 0.892 | 0.0631 | 0.0468 |
DBF | 0.928 | 0.0735 | 0.0533 | 0.933 | 0.0709 | 0.0512 | 0.938 | 0.0685 | 0.0491 |
CSH | 0.864 | 0.0464 | 0.0329 | 0.879 | 0.0440 | 0.0309 | 0.886 | 0.0420 | 0.0296 |
OSH | 0.775 | 0.0491 | 0.0356 | 0.793 | 0.0470 | 0.0340 | 0.807 | 0.0454 | 0.0328 |
SAV | 0.892 | 0.0702 | 0.0517 | 0.902 | 0.0666 | 0.0488 | 0.911 | 0.0635 | 0.0464 |
GRA | 0.883 | 0.0577 | 0.0417 | 0.892 | 0.0554 | 0.0400 | 0.899 | 0.0535 | 0.0385 |
CRO | 0.937 | 0.0678 | 0.0493 | 0.943 | 0.0643 | 0.0468 | 0.948 | 0.0610 | 0.0441 |
All | 0.913 | 0.0653 | 0.0472 | 0.921 | 0.0622 | 0.0449 | 0.928 | 0.0596 | 0.0428 |
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Hu, J.; Jia, J.; Ma, Y.; Liu, L.; Yu, H. A Reconstructed Global Daily Seamless SIF Product at 0.05 Degree Resolution Based on TROPOMI, MODIS and ERA5 Data. Remote Sens. 2022, 14, 1504. https://doi.org/10.3390/rs14061504
Hu J, Jia J, Ma Y, Liu L, Yu H. A Reconstructed Global Daily Seamless SIF Product at 0.05 Degree Resolution Based on TROPOMI, MODIS and ERA5 Data. Remote Sensing. 2022; 14(6):1504. https://doi.org/10.3390/rs14061504
Chicago/Turabian StyleHu, Jiaochan, Jia Jia, Yan Ma, Liangyun Liu, and Haoyang Yu. 2022. "A Reconstructed Global Daily Seamless SIF Product at 0.05 Degree Resolution Based on TROPOMI, MODIS and ERA5 Data" Remote Sensing 14, no. 6: 1504. https://doi.org/10.3390/rs14061504
APA StyleHu, J., Jia, J., Ma, Y., Liu, L., & Yu, H. (2022). A Reconstructed Global Daily Seamless SIF Product at 0.05 Degree Resolution Based on TROPOMI, MODIS and ERA5 Data. Remote Sensing, 14(6), 1504. https://doi.org/10.3390/rs14061504