A Robust Method for Generating High-Spatiotemporal-Resolution Surface Reflectance by Fusing MODIS and Landsat Data
Abstract
:1. Introduction
2. Methods
2.1. FCMSTRFM Logic
2.1.1. Class and Subclass Definition
2.1.2. Sensor-bias Adjustment
2.1.3. Class and Subclass Average Reflectance Calculation
2.1.4. Pixel Reflectance Calculation
2.2. Comparison with Other Fusion Methods
2.2.1. STDFA
2.2.2. ESTARFM
2.3. Evaluation Metrics
3. Experimental Data and Data Processing
3.1. Study Area
3.2. Data Preprocessing
4. Results
4.1. Evaluation of the FCMSTRFM
4.2. Comparison with Other Fusion Methods
5. Discussion
5.1. Uncertainties of the FCMSTRFM
5.1.1. Influence of Image Registration
5.1.2. Influence of the Accuracy of the Land Cover Map
5.1.3. Influence of Temporal and Spatial Heterogeneity
5.2. FCMSTRFM Improvements to Existing Models
5.3. The Application of FCMSTRFM
6. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
- Croft, H.; Anderson, K.; Kuhn, N.J. Evaluating the influence of surface soil moisture and soil surface roughness on optical directional reflectance factors. Eur. J. Soil Sci. 2014, 65, 605–612. [Google Scholar] [CrossRef]
- Cuppo, F.L.S.; Garcia-Valenzuela, A.; Olivares, J.A. Influence of surface roughness on the diffuse to near-normal viewing reflectance factor of coatings and its consequences on color measurements. Color Res. Appl. 2013, 38, 177–187. [Google Scholar] [CrossRef]
- Sun, Z.Q.; Wu, D.; Lv, Y.F.; Zhao, Y.S. Bidirectional Polarized Reflectance Factors of Vegetation Covers: Influence on the BRF Models Results. IEEE Trans. Geosci. Remote Sens. 2017, 55, 5687–5701. [Google Scholar] [CrossRef]
- Li, C.X.; Sun, Z.; Jiang, J.Y.; Liu, R.; Chen, W.L.; Xu, K.X. Typical ground object recognition based on principle component analysis and fuzzy clustering with near-infrared diffuse reflectance spectroscopy. Spectrosc. Spect. Anal. 2017, 37, 3386–3390. [Google Scholar]
- Zhong, L.H.; Hu, L.; Zhou, H.; Tao, X. Deep learning based winter wheat map** using statistical data as ground references in Kansas and northern Texas, US. Remote Sens. Environ. 2019, 233. [Google Scholar] [CrossRef]
- Lee, T.Y.; Kaufman, Y.J. Non-lambertian effects on remote-sensing of surface reflectance and vegetation index. IEEE Trans. Geosci. Remote Sens. 1986, 24, 699–708. [Google Scholar] [CrossRef]
- Wang, H.; Yang, L.K.; Zhao, M.R.; Du, W.B.; Liu, P.; Sun, X.B. The normalized difference vegetation index and angular variation of surface spectral polarized reflectance relationships: Improvements on aerosol remote sensing over land. Earth Space Sci. 2019, 6, 982–989. [Google Scholar] [CrossRef] [Green Version]
- Spanner, M.A.; Pierce, L.L.; Peterson, D.L.; Running, S.W. Remote-sensing of temperate coniferous forest leaf-area index—The influence of canopy closure, understory vegetation and background reflectance. Int. J. Remote Sens. 1990, 11, 95–111. [Google Scholar] [CrossRef]
- Zhai, H.; Huang, F.; Qi, H. Generating High Resolution LAI Based on a modified FSDAF model. Remote Sens. 2020, 12, 150. [Google Scholar] [CrossRef] [Green Version]
- Ma, R.; Zhang, L.; Tian, X.J.; Zhang, J.C.; Yuan, W.P.; Zheng, Y.; Zhao, X.; Kato, T. Assimilation of remotely-sensed leaf area index into a dynamic vegetation model for gross primary productivity estimation. Remote Sens. 2017, 9, 188. [Google Scholar] [CrossRef] [Green Version]
- Zheng, Y.; Zhang, L.; ** of forest disturbance based on Landsat and MODIS. Remote Sens. Environ. 2009, 113, 1613–1627. [Google Scholar] [CrossRef]
- Wang, T.; Tang, R.L.; Li, Z.L.; Jiang, Y.Z.; Liu, M.; Niu, L. An Improved spatio-temporal adaptive data fusion algorithm for evapotranspiration map**. Remote Sens. 2019, 11, 761. [Google Scholar] [CrossRef] [Green Version]
- **. IEEE Trans. Geosci. Remote Sens. 2011, 49, 4308–4317. [Google Scholar] [CrossRef]
- Comber, A.J.; Law, A.N.R.; Lishman, J.R. Application of knowledge for automated land cover change monitoring. Int. J. Remote Sens. 2004, 25, 3177–3192. [Google Scholar] [CrossRef] [Green Version]
- Hansen, M.C.; Defries, R.S.; Townshend, J.R.G.; Sohlberg, R. Global land cover classification at 1 km spatial resolution using a classification tree approach. Int. J. Remote Sens. 2000, 21, 1331–1364. [Google Scholar] [CrossRef]
- King, R.B. Land cover map** principles: A return to interpretation fundamentals. Int. J. Remote Sens. 2002, 23, 3525–3545. [Google Scholar] [CrossRef]
- Jokinen, J.; Raty, T.; Lintonen, T. Clustering structure analysis in time-series data with density-based clusterability measure. IEEE/CAA J. Autom. Sin. 2019, 6, 1332–1343. [Google Scholar] [CrossRef]
- Wang, Y.; Ru, Y.N.; Chai, J.P. Time series clustering based on sparse subspace clustering algorithm and its application to daily box-office data analysis. Neural Comput. Appl. 2019, 31, 4809–4818. [Google Scholar] [CrossRef]
- Zhang, L.; Weng, Q.H.; Shao, Z.F. An evaluation of monthly impervious surface dynamics by fusing Landsat and MODIS time series in the Pearl River Delta, China, from 2000 to 2015. Remote Sens. Environ. 2017, 201, 99–114. [Google Scholar] [CrossRef]
- Bezdek, J.C.; Ehrlich, R.; Full, W. FCM—The Fuzzy C-Means Clustering-Algorithm. Comput. Geosci. UK 1984, 10, 191–203. [Google Scholar] [CrossRef]
- Rodriguez, A.; Tomas, M.S.; Rubio-Martinez, J. A benchmark calculation for the fuzzy c-means clustering algorithm: Initial memberships. J. Math. Chem. 2012, 50, 2703–2715. [Google Scholar] [CrossRef]
- Saxena, A.; Prasad, M.; Gupta, A.; Bharill, N.; Patel, O.P.; Tiwari, A.; Er, M.J.; Ding, W.P.; Lin, C.T. A review of clustering techniques and developments. Neurocomputing 2017, 267, 664–681. [Google Scholar] [CrossRef] [Green Version]
- Garcia-Haro, F.J.; Sommer, S.; Kemper, T. A new tool for variable multiple endmember spectral mixture analysis (VMESMA). Int J. Remote Sens. 2005, 26, 2135–2162. [Google Scholar] [CrossRef] [Green Version]
- Yang, J.M.; Wu, Y.; Wei, Y.X.; Wang, B.; Ru, C.; Ma, Y.Y.; Zhang, Y. A model for the fusion of multi-source data to generate high temporal and spatial resolution VI data. J. Remote Sens. 2019, 23, 935–943. [Google Scholar] [CrossRef]
- **, X.; **, Y.X.; Yuan, D.H.; Mao, X.F. Effects of land-use data resolution on hydrologic modelling, a case study in the upper reach of the Heihe River, Northwest China. Ecol. Model. 2019, 404, 61–68. [Google Scholar] [CrossRef]
- Pokonieczny, K.; Moscicka, A. The Influence of the shape and size of the cell on develo** military passability maps. ISPRS Int. J. Geo-Inf. 2018, 7, 261. [Google Scholar] [CrossRef] [Green Version]
- Weigand, M.; Staab, J.; Wurm, M.; Taubenbock, H. Spatial and semantic effects of LUCAS samples on fully automated land use/land cover classification in high-resolution Sentinel-2 data. Int J. Appl. Earth Obs. Geoinf. 2020, 88. [Google Scholar] [CrossRef]
- Joshi, N.; Baumann, M.; Ehammer, A.; Fensholt, R.; Grogan, K.; Hostert, P.; Jepsen, M.R.; Kuemmerle, T.; Meyfroidt, P.; Mitchard, E.T.A.; et al. A review of the application of optical and radar remote sensing data fusion to land use map** and monitoring. Remote Sens. 2016, 8, 70. [Google Scholar] [CrossRef] [Green Version]
- Lei, G.B.; Li, A.N.; Bian, J.H.; Zhang, Z.J. The roles of criteria, data and classification methods in designing land cover classification systems: Evidence from existing land cover data sets. Int. J. Remote Sens. 2020, 41, 5062–5082. [Google Scholar] [CrossRef]
- Xu, G.; Zhang, H.R.; Chen, B.Z.; Zhang, H.F.; Yan, J.W.; Chen, J.; Che, M.L.; Lin, X.F.; Dou, X.M. A Bayesian based method to generate a synergetic land-cover map from existing land-cover products. Remote Sens. 2014, 6, 5589–5613. [Google Scholar] [CrossRef] [Green Version]
- Liu, Y.L.; Wang, X.M.; Liu, Q.L.; Chen, Y.Y.; Liu, L.L. An improved density-based time series clustering method based on image resampling: A case study of surface deformation pattern analysis. ISPRS Int. J. Geo-Inf. 2017, 6, 118. [Google Scholar] [CrossRef]
- Wei, Y.X.; Yang, J.M.; Wu, Y.; Wang, B.; Shaban, M.; Hou, J.X. Rice planting area extraction based on multi-source data fusion. Trans. Chin. Soc. Agric. Mach. 2018, 49, 300–306. [Google Scholar]
Image | Path/Row | Acquisition Date/DOY |
---|---|---|
Landsat | 117/027 | 11/04/2017,17/08/2017, 02/09/2017, 04/10/2017 25/02/2018, 29/03/2018, 30/04/2018,01/06/2018, 07/10/2018, 20/12/2018 |
MOD09GA | h25/v04 | 11/04/2017,17/08/2017, 02/09/2017, 04/10/2017 25/02/2018, 29/03/2018, 30/04/2018,01/06/2018, 07/10/2018, 20/12/2018 |
MOD13Q1 | h25/v04 | 11/04/2017,17/08/2017, 02/09/2017, 04/10/2017 |
Landsat8 OLI | MODIS | ||||
---|---|---|---|---|---|
Band | Bandwidth (μm) | Spatial Resolution (m) | Band | Bandwidth (μm) | Spatial Resolution (m) |
1 | 0.433–0.453 | 30 | 9 | 0.438–0.448 | 1000 |
2 | 0.450–0.515 | 30 | 3 | 0.459–0.479 | 500 |
3 | 0.525–0.600 | 30 | 4 | 0.545–0.565 | 500 |
4 | 0.630–0.680 | 30 | 1 | 0.620–0.670 | 250 |
5 | 0.845–0.885 | 30 | 2 | 0.841–0.876 | 250 |
6 | 1.560–1.660 | 30 | 6 | 1.628–1.652 | 500 |
7 | 2.100–2.300 | 30 | 7 | 2.105–2.155 | 500 |
8 | 0.500–0.680 | 15 | - | - | - |
9 | 1.360–1.390 | 30 | 26 | 1.360–1.390 | 1000 |
10 | 10.60–11.19 | 100 | 31 | 10.780–11.280 | 1000 |
11 | 11.50–12.51 | 100 | 32 | 11.770–12.270 | 1000 |
Time | Band | R | RMSE | MAD | ERGAS |
---|---|---|---|---|---|
30/4/2018 | Green | 0.7857 | 0.018 | 0.0129 | 1.7107 |
Red | 0.7914 | 0.0271 | 0.0214 | 2.0703 | |
NIR | 0.8432 | 0.0382 | 0.0282 | 1.7697 | |
1/6/2018 | Green | 0.8331 | 0.0163 | 0.0112 | 1.6398 |
Red | 0.8483 | 0.0227 | 0.0159 | 2.2883 | |
NIR | 0.9459 | 0.0385 | 0.0273 | 1.4883 |
Time | Band | STDFA | ESTARFM | ||||||
---|---|---|---|---|---|---|---|---|---|
R | RMSE | MAD | ERGAS | R | RMSE | MAD | ERGAS | ||
30/4/2018 | Green | 0.7357 | 0.0197 | 0.0138 | 2.873 | 0.8288 | 0.0241 | 0.0191 | 2.2861 |
Red | 0.748 | 0.0261 | 0.0191 | 2.994 | 0.7966 | 0.0378 | 0.028 | 2.8837 | |
NIR | 0.7993 | 0.0407 | 0.031 | 2.884 | 0.807 | 0.0617 | 0.0424 | 2.8592 | |
1/6/2018 | Green | 0.7158 | 0.0194 | 0.0129 | 1.9585 | 0.6778 | 0.0184 | 0.0138 | 1.8528 |
Red | 0.7094 | 0.0275 | 0.0193 | 2.7683 | 0.7513 | 0.025 | 0.0189 | 2.5191 | |
NIR | 0.8913 | 0.0512 | 0.0377 | 1.9769 | 0.6919 | 0.0738 | 0.0524 | 2.8495 |
Band | STDFA | FCM | FCMSTRFM |
---|---|---|---|
B3 | 0.0137 | 0.0067 | 0.0059 |
B4 | 0.0111 | 0.0078 | 0.0064 |
B5 | 0.0084 | 0.0035 | 0.0022 |
VI-DOY | R | RMSE | ERGAS | Variance |
---|---|---|---|---|
NDVI-229 | 0.9305 | 0.0607 | 1.7132 | 0.0037 |
NDVI-245 | 0.9028 | 0.0721 | 1.9655 | 0.0052 |
EVI-229 | 0.9154 | 0.0622 | 1.9547 | 0.0038 |
EVI-245 | 0.8744 | 0.0748 | 2.2849 | 0.0055 |
© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Share and Cite
Yang, J.; Yao, Y.; Wei, Y.; Zhang, Y.; Jia, K.; Zhang, X.; Shang, K.; Bei, X.; Guo, X. A Robust Method for Generating High-Spatiotemporal-Resolution Surface Reflectance by Fusing MODIS and Landsat Data. Remote Sens. 2020, 12, 2312. https://doi.org/10.3390/rs12142312
Yang J, Yao Y, Wei Y, Zhang Y, Jia K, Zhang X, Shang K, Bei X, Guo X. A Robust Method for Generating High-Spatiotemporal-Resolution Surface Reflectance by Fusing MODIS and Landsat Data. Remote Sensing. 2020; 12(14):2312. https://doi.org/10.3390/rs12142312
Chicago/Turabian StyleYang, Junming, Yunjun Yao, Yongxia Wei, Yuhu Zhang, Kun Jia, **aotong Zhang, Ke Shang, **angyi Bei, and **aozheng Guo. 2020. "A Robust Method for Generating High-Spatiotemporal-Resolution Surface Reflectance by Fusing MODIS and Landsat Data" Remote Sensing 12, no. 14: 2312. https://doi.org/10.3390/rs12142312