1. Software Tool for Recovering Missing Pixels of Remotely Sensed Image（updated on Nov. 3, 2016, download version 2.0）
    Reference: C. Zeng, H. Shen, L. Zhang, “Recovering missing pixels for Landsat ETM+ SLC-off imagery using multi-temporal regression analysis and a regularization method,” Remote Sensing of Environment, vol. 131, pp. 182-194, 2013.  (PDF)

2. Spatially Continuous and Daily Global Ozone Product
    We provide the spatially continuous and daily global level-3 (gridded) total ozone product OMTO3e (2004-2014) , acquired from Aura Ozone Monitoring Instrument (OMI), by employing our proposed algorithm.
    Click to download the ozone products：2004-2014 ozone products （updated on：Mar. 4, 2016，Ozone product descriptions）
    Click to download the flag files of ozone products：Flag （updated on：Mar. 4, 2016）
    Reference: X. Peng, H. Shen, L. Zhang, C. Zeng, G. Yang, and Z. He, “Spatially Continuous Mapping of Daily Global Ozone Distribution (2004-2014) with the Aura OMI Sensor: Spatially Continuous Ozone Product,” Journal of Geophysical Research Atmospheres, vol. 121, no. 21, 2016. (PDF)

3. Software Tool for Cloud and Cloud Shadow Detection in GF-1 WFV imagery（updated on Feb. 15, 2017, download version 1.0）
    Reference: Z. Li, H. Shen, H. Li, G. Xia, P. Gamba, and L. Zhang, “Multi-feature combined cloud and cloud shadow detection in GaoFen-1 wide field of view imagery,” Remote Sensing of Environment, vol. 191, pp. 342-358, 2017.  (PDF)
    Link: Multi-feature combined cloud and cloud shadow detection

4. Software Tool for Spatiotemporal Fusion of Multi-source Remotely Sensed Data（Updated on Apr. 27, 2018，download version 1.1）
    Reference: Q. Cheng, H. Liu, H. Shen, P. Wu, and L. Zhang, “A spatial and temporal nonlocal filter-based data fusion method.” IEEE Transactions on Geoscience and Remote Sensing, vol.55, no.8, pp. 4476-4488, 2017. (PDF)

5. Nonlinear Guided Filter for Polarimetric SAR Image Despeckling (download version 1.0）
    We propose a fully polarimetric SAR image despeckling method based on a guided filter with nonlinear weight kernels and adaptive windows.
    Reference: X. Ma, P. Wu, and H. Shen, “A Nonlinear Guided Filter for Polarimetric SAR Image Despeckling,” IEEE Transactions on Geoscience and Remote Sensing, 2018. DOI: 10.1109/TGRS.2018.2870188(PDF)

6. Multifrequency Polsar Nonlocal Means Filter Based on Space-Frequency Information Joint Covariance Matrix (download version 1.0）
    We propose a multifrequency fully polarimetric SAR image despeckling method by iterative nonlocal means based on a space-frequency information joint covariance matrix.
    Reference: X. Ma, P. Wu, and H. Shen, “Multifrequency Polarimetric SAR Image Despeckling by Iterative Nonlocal Means Based on a Space-Frequency Information Joint Covariance Matrix,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 12, no. 1, pp. 274-284, 2019.(PDF)

7. Multitemporal SAR Image Despeckling Based on a Polarimetric Covariance Matrix of Superpixel（Data）
    Reference: Ma X, Wu P. Multitemporal SAR Image Despeckling Based on a Scattering Covariance Matrix of Image Patch[J]. Sensors, 2019, 19(14): 3057.

8. Urban Water Extraction by Combining Deep Learning and Google Earth Engine（Code  List of data）
    Reference: Y. Wang, Z. Li, C. Zeng, G. Xia, and H. Shen, “Extracting urban water by combining deep learning and Google Earth Engine,” arXiv preprint arXiv:1912.10726, 2019.(PDF)

9. Long time-series NDVI reconstruction based on spatio-temporal tensor completion（Code）
    Reference: Chu, D., Shen, H., Guan, X., Chen, J.M., Li, X., Li, J., Zhang, L., 2021. Long time-series NDVI reconstruction in cloud-prone regions via spatio-temporal tensor completion. Remote Sens. Environ. 264, 112632. https://doi.org/https://doi.org/10.1016/j.rse.2021.112632.(PDF)