A Practical Method for Red-Edge Band Reconstruction for Landsat Image by Synergizing Sentinel-2 Data with Machine Learning Regression Algorithms
Abstract
:1. Introduction
2. Materials and Methodology
2.1. Remote Sensing Data
2.2. Land Surface Reflectance Conversion
2.3. Red-Edge Prediction Modeling and Performance Evaluation
3. Results
3.1. Consistency Analysis of OLI and MSI Spectral Bands
3.1.1. Comparison of TOA Reflectance with Different Resampling Algorithms
3.1.2. Comparison of BOA Reflectance with Different Atmospheric Correction Algorithms
3.2. The Simulated Red-Edge Bands and Consistency Assessment
3.3. The REIs Derived from Simulated LRE Bands and Consistency Assessment
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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ROI | Acquisition Date (dd/mm/yy) | Central Location (Latitude, Longitude) | Imaging Time (hh:mm:ss) | Dominant Land Covers | |
---|---|---|---|---|---|
Landsat-8 | Sentinel-2A | ||||
1 | 6 August 2018 | (47.440° N, 131.150° E) | 10:01:26 | 10:16:01 | Crop |
2 | 6 August 2018 | (47.265° N, 132.209° E) | 10:01:26 | 10:16:01 | Crop |
3 | 21 September 2023 | (37.507° N, 116.414° E) | 10:54:23 | 10:55:31 | Crop |
4 | 4 September 2020 | (33.802° N, 114.086° E) | 10:55:41 | 11:05:49 | Crop |
5 | 10 May 2022 | (37.839° S, 142.443° E) | 10:15:42 | 10:20:49 | Crop |
6 | 7 December 2021 | (27.782° N, 117.672° E) | 10:38:55 | 10:51:11 | Forest |
7 | 16 October 2023 | (25.724° N, 114.376° E) | 10:51:57 | 10:57:09 | Forest |
8 | 16 October 2023 | (24.778°N, 113.565° E) | 10:51:33 | 10:57:09 | Forest |
9 | 31 August 2023 | (46.154° N, 87.629° W) | 10:33:53 | 10:48:51 | Forest |
10 | 9 September 2023 | (55.544° N, 119.001° W) | 10:53:18 | 11:09:11 | Forest and grass |
11 | 9 September 2022 | (46.515° N, 129.484° E) | 10:08:51 | 10:15:39 | Forest and crop |
12 | 9 September 2022 | (41.773° N, 128.527° E) | 10:10:26 | 10:15:39 | Grass and forest |
13 | 21 June 2022 | (48.788°N,118.519° E) | 10:57:04 | 11:05:51 | Grass |
14 | 18 May 2024 | (44.084° N, 81.310° E) | 10:19:46 | 10:26:49 | Grass |
15 | 1 May 2023 | (52.808° N, 24.599° E) | 11:18:46 | 11:30:31 | Forest, crop, and grass |
16 | 14 August 2023 | (40.023° N, 44.863° E) | 10:37:42 | 10:46:19 | Forest, crop, and grass |
17 | 30 September 2023 | (39.130° N, 91.554° W) | 10:48:22 | 10:51:11 | Forest, crop, and grass |
18 | 25 October 2024 | (54.240° N, 113.522° W) | 10:28:59 | 10:53:59 | Forest, crop, and grass |
Band Name | CW (nm) | Bandwidth (nm) | Spatial Resolution (m) | ||||
---|---|---|---|---|---|---|---|
MSI | OLI | MSI | OLI | MSI | OLI | MSI | OLI |
B2: Blue | B2: Blue | 490 | 482 | 457–522 | 450–515 | 10 | 30 |
B3: Green | B2: Green | 560 | 563 | 543–578 | 525–600 | 10 | 30 |
B4: Red | B4: Red | 665 | 655 | 650–680 | 630–680 | 10 | 30 |
B5: Red edge1 | - | 705 | - | 698–713 | - | 20 | 30 |
B6: Red edge2 | - | 740 | - | 732–747 | - | 20 | 30 |
B7: Red edge3 | - | 783 | - | 773–793 | - | 20 | 30 |
B8a: Red edge4 | B5: NIR | 865 | 865 | 855–875 | 845–885 | 20 | 30 |
B11: SWIR1 | B6: SWIR1 | 1610 | 1605 | 1565–1655 | 1560–1651 | 20 | 30 |
B12: SWIR2 | B7: SWIR2 | 2190 | 2200 | 2100–2280 | 2100–2300 | 20 | 30 |
Red-Edge Index | Formula | References |
---|---|---|
NDREI | (RE1 − NIR)/(RE1 + NIR) | [52] |
CIre | NIR/RE1 − 1 | [53] |
IRECI | (RE3 − R)/(RE1/RE2) | [54] |
Algorithm | Statistical Measures | RE-1 (705 nm) | RE-2 (740 nm) | RE-3 (783 nm) |
---|---|---|---|---|
RR | R2 | 0.9771 | 0.9483 | 0.9735 |
RMSE | 0.0082 | 0.0132 | 0.0130 | |
rRMSE | 9.57% | 6.22% | 4.84% | |
GBRT | R2 | 0.9807 | 0.9651 | 0.9764 |
RMSE | 0.0076 | 0.0108 | 0.0122 | |
rRMSE | 8.86% | 5.09% | 4.54% | |
RFR | R2 | 0.9739 | 0.9646 | 0.9764 |
RMSE | 0.0088 | 0.0109 | 0.0127 | |
rRMSE | 10.27% | 5.14% | 6.41% |
RE/REI | Forest | Crop | Grass | Stat. Range |
---|---|---|---|---|
RE-1 (705 nm) | 99.99% | 99.69% | 99.64% | (−0.03, 0.03) |
RE-2 (740 nm) | 99.87% | 99.46% | 99.72% | (−0.03, 0.03) |
RE-3 (783 nm) | 99.49% | 99.50% | 98.71% | (−0.03, 0.03) |
REI | Forest | Crop | Grass | Stat. Range |
---|---|---|---|---|
NDREI | 99.81% | 98.76% | 98.72% | (−0.1, 0.1) |
CIre | 98.23% | 97.17% | 99.99% | (−1.0, 1.0) |
IRECI | 97.25% | 96.13% | 98.37% | (−0.2, 0.2) |
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Zhang, Y.; Fan, Z.; Yan, W.; Ge, C.; Sun, H. A Practical Method for Red-Edge Band Reconstruction for Landsat Image by Synergizing Sentinel-2 Data with Machine Learning Regression Algorithms. Sensors 2025, 25, 3570. https://doi.org/10.3390/s25113570
Zhang Y, Fan Z, Yan W, Ge C, Sun H. A Practical Method for Red-Edge Band Reconstruction for Landsat Image by Synergizing Sentinel-2 Data with Machine Learning Regression Algorithms. Sensors. 2025; 25(11):3570. https://doi.org/10.3390/s25113570
Chicago/Turabian StyleZhang, Yuan, Zhekui Fan, Wenjia Yan, Chentian Ge, and Huasheng Sun. 2025. "A Practical Method for Red-Edge Band Reconstruction for Landsat Image by Synergizing Sentinel-2 Data with Machine Learning Regression Algorithms" Sensors 25, no. 11: 3570. https://doi.org/10.3390/s25113570
APA StyleZhang, Y., Fan, Z., Yan, W., Ge, C., & Sun, H. (2025). A Practical Method for Red-Edge Band Reconstruction for Landsat Image by Synergizing Sentinel-2 Data with Machine Learning Regression Algorithms. Sensors, 25(11), 3570. https://doi.org/10.3390/s25113570