1. Installation

pip install pyxccd

Note: the installation has been cross-platform (Windows, Linux and MacOS), python >= 3.9. Contact the author (remotesensingsuy@gmail.com) if you have problems for installation

2. Using pyxccd for pixel-based processing

COLD:

from pyxccd import cold_detect
import pandas as pd
data = pd.read_csv('tutorial/datasets/1_hls_sc.csv')
dates, blues, greens, reds, nirs, swir1s, swir2s, thermals, qas, sensor = data.to_numpy().copy().T
cold_result = cold_detect(dates, blues, greens, reds, nirs, swir1s, swir2s, thermals, qas)

COLD algorithm for any combination of band inputs from any sensor:

from pyxccd import cold_detect_flex
# input a user-defined array instead of multiple lists
cold_result = cold_detect_flex(dates, np.stack((reds, nirs, swir1s), axis=1), qas, lambda=20,tmask_b1_index=1, tmask_b2_index=2)

S-CCD:

# require offline processing for the first time
from pyxccd import sccd_detect, sccd_update
sccd_pack = sccd_detect(dates, blues, greens, reds, nirs, swir1s, swir2s, qas)

# then use sccd_pack to do recursive and short-memory NRT update
sccd_pack_new = sccd_update(sccd_pack, dates, blues, greens, reds, nirs, swir1s, swir2s, qas)

S-CCD for outputting continuous seasonal and trend states:

# open state output (state_ensemble) by setting state_intervaldays as a non-zero value
sccd_result, state_ensemble = sccd_detect(dates, blues, greens, reds, nirs, swir1s, swir2s, qas, state_intervaldays=1)

3. Tutorials

Tutorial datasets: Github link, 夸克硬盘 (for China mainland)

4. GUI

We provided GUI to quickly test S-CCD or COLD algorithms using point-based time series formated in CSV or EXCEL:

夸克硬盘 (for China mainland)

Dropbox (for outside China mainland)

5. Documentation

API documents: readthedocs

Technical details and performance: pyxccd paper

6. Citations

If you make use of the algorithms in this repo (or to read more about them), please cite (/see) the relevant publications from the following list:

[PYXCCD] Ye, S., & Hu, Y. (2026). Pyxccd: An Efficient Python Package for Break-aware Time Series Analysis of Earth Observation Data.

[S-CCD] Ye, S., Rogan, J., Zhu, Z., & Eastman, J. R. (2021). A near-real-time approach for monitoring forest disturbance using Landsat time series: Stochastic continuous change detection. Remote Sensing of Environment, 252, 112167.

[COLD] Zhu, Z., Zhang, J., Yang, Z., Aljaddani, A. H., Cohen, W. B., Qiu, S., & Zhou, C. (2020). Continuous monitoring of land disturbance based on Landsat time series. Remote Sensing of Environment, 238, 111116.

7. Bibliography

The following publications have used pyxccd in their research.

Publications by the Development Team:

1. Liao, R., Guo, C., Chen, L., Jiang, Y., Tao, Y., Liao, J., … & Ye, S. (2026). Near-real-time mapping for causal agents of forest disturbances in China using harmonized Landsat and Sentinel-2 dataset. Remote Sensing of Environment, 342, 115461.

2. Ye, S., & Hu, Y. (2026). Pyxccd: An Efficient Python Package for Break-Aware Time Series Analysis of Earth Observation Data.

3. Ye, S., Zhu, Z., & Suh, J. W. (2024). Leveraging past information and machine learning to accelerate land disturbance monitoring. Remote Sensing of Environment, 305, 114071.

4. Ye, S., Zhu, Z., & Cao, G. (2023). Object-based continuous monitoring of land disturbances from dense Landsat time series. Remote Sensing of Environment, 287, 113462.

5. Ye, S., Rogan, J., Zhu, Z., Hawbaker, T. J., Hart, S. J., Andrus, R. A., … & Kulakowski, D. (2021). Detecting subtle change from dense Landsat time series: Case studies of mountain pine beetle and spruce beetle disturbance. Remote Sensing of Environment, 263, 112560.

6. Ye, S., Rogan, J., Zhu, Z., & Eastman, J. R. (2021). A near-real-time approach for monitoring forest disturbance using Landsat time series: Stochastic continuous change detection. Remote Sensing of Environment, 252, 112167.

Applications and Studies by Other Researchers:

7. Schiller, C., & Fassnacht, F. E. (2026). Comparative study of near real-time monitoring algorithms for early detection of bark beetle infestations in Germany with Sentinel-2. European Journal of Remote Sensing, 59(1), 2662660.

8. Schraik, D., Ursin, A., & Packalen, P. (2026). Detection of non-stand-replacing cuttings using satellite image time series with a Kalman filter. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

9. Wang, S., & Wang, L. (2026). Quantifying high spatiotemporal impervious surface change with time series PlanetScope imagery. Remote Sensing Applications: Society and Environment, 101890.

10. Hong, F., Hedges, S. B., Yang, Z., Suh, J. W., Qiu, S., Timyan, J., & Zhu, Z. (2025). Decoding primary forest changes in Haiti and the Dominican Republic using Landsat time series. Remote Sensing of Environment, 318, 114590.

11. Abdallah, M., Ali, E., Ding, X., & Wu, S. (2025). An improved COLD approach for monitoring construction dynamics using HLS and LULC data: A case study in the New Capital of Egypt. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 3–10.

12. Healey, N. C., Barber, C. P., Smith, K., Mital, R., Brown, J. F., & Robison, C. (2025). Resiliency of land change monitoring efforts to input data resampling. Frontiers in Remote Sensing, 6, 1570580.

13. Suh, J. W., Zhu, Z., & Zhao, Y. (2024). Monitoring construction changes using dense satellite time series and deep learning. Remote Sensing of Environment, 309, 114207.

14. Mota, F. B. D. S., Ferreira, K. R., & Escada, M. I. S. (2024). Evaluating forest disturbance detection methods based on satellite image time series for Amazon deforestation alerts. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 48, 357–364.

Last updated: October 31, 2026.

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