Long time-series NDVI reconstruction in cloud-prone regions via spatio-temporal tensor completion

The applications of Normalized Difference Vegetation Index (NDVI) time-series data are inevitably hampered by cloud-induced gaps and noise. Although numerous reconstruction methods have been developed, they have not effectively addressed the issues associated with large gaps in the time series over cloudy and rainy regions, due to the insufficient utilization of the spatial and temporal correlations. In this paper, an adaptive Spatio-Temporal Tensor Completion method (termed ST-Tensor) method is proposed to reconstruct long-term NDVI time series in cloud-prone regions, by making full use of the multi-dimensional spatio-temporal information simultaneously. For this purpose, a highly-correlated tensor is built by considering the correlations among the spatial neighbors, inter-annual variations, and periodic characteristics, 2 in order to reconstruct the missing information via an adaptive-weighted low-rank tensor completion model. An iterative 1 trend filtering method is then implemented to eliminate the residual temporal noise. This new method was tested using MODIS 16-day composite NDVI products from 2001 to 2018 obtained in the region of Mainland Southeast Asia, where the rainy climate commonly induces large gaps and noise in the data. The qualitative and quantitative results indicate that the ST-Tensor method is more effective than the five previous methods in addressing the different missing data problems, especially the temporally continuous gaps and spatio-temporally continuous gaps. It is also shown that the ST-Tensor method performs better than the other methods in tracking NDVI seasonal trajectories, and is therefore a superior option for generating high-quality long-term NDVI time series for cloud-prone regions.

[1]  Liang-pei Zhang,et al.  A long-term and comprehensive assessment of the urbanization-induced impacts on vegetation net primary productivity. , 2019, The Science of the total environment.

[2]  A. Belward,et al.  The Best Index Slope Extraction ( BISE): A method for reducing noise in NDVI time-series , 1992 .

[3]  Chunlin Huang,et al.  A Simplified Data Assimilation Method for Reconstructing Time-Series MODIS NDVI Data , 2008, IGARSS 2008 - 2008 IEEE International Geoscience and Remote Sensing Symposium.

[4]  Yang Liu,et al.  Global evaluation of gap-filling approaches for seasonal NDVI with considering vegetation growth trajectory, protection of key point, noise resistance and curve stability , 2017 .

[5]  R. Myneni,et al.  On the relationship between FAPAR and NDVI , 1994 .

[6]  José A. Sobrino,et al.  Comparison of cloud-reconstruction methods for time series of composite NDVI data , 2010 .

[7]  Massimo Menenti,et al.  Reconstruction of global MODIS NDVI time series: performance of harmonic analysis of time series (HANTS). , 2015 .

[8]  S. Dech,et al.  Comparison and enhancement of MODIS cloud mask products for Southeast Asia , 2013 .

[9]  Jin Chen,et al.  Thick cloud removal in Landsat images based on autoregression of Landsat time-series data , 2020 .

[10]  Xiaoliang Lu,et al.  Removal of Noise by Wavelet Method to Generate High Quality Temporal Data of Terrestrial MODIS Products , 2007 .

[11]  A. Huete,et al.  Overview of the radiometric and biophysical performance of the MODIS vegetation indices , 2002 .

[12]  Jin Chen,et al.  A simple method for reconstructing a high-quality NDVI time-series data set based on the Savitzky-Golay filter , 2004 .

[13]  Curtis E. Woodcock,et al.  Near real-time monitoring of tropical forest disturbance: New algorithms and assessment framework , 2019, Remote Sensing of Environment.

[14]  Tao Zhang,et al.  A Temporal-Spatial Iteration Method to Reconstruct NDVI Time Series Datasets , 2015, Remote. Sens..

[15]  P. Eilers.  A perfect smoother. , 2003, Analytical chemistry.

[16]  Tingzhu Huang,et al.  A non-convex tensor rank approximation for tensor completion , 2017 .

[17]  R. Lunetta,et al.  Land-cover change detection using multi-temporal MODIS NDVI data , 2006 .

[18]  Michael E. Schaepman,et al.  Predicting Missing Values in Spatio-Temporal Remote Sensing Data , 2018, IEEE Transactions on Geoscience and Remote Sensing.

[19]  Jinwei Dong,et al.  A comparison of forest cover maps in Mainland Southeast Asia from multiple sources: PALSAR, MERIS, MODIS and FRA , 2012 .

[20]  Ben Gorte,et al.  On the performance of remote sensing time series reconstruction methods – A spatial comparison , 2016 .

[21]  Jing M. Chen,et al.  Locally adjusted cubic-spline capping for reconstructing seasonal trajectories of a satellite-derived surface parameter , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[22]  Changsheng Li,et al.  Mapping paddy rice agriculture in South and Southeast Asia using multi-temporal MODIS images , 2006 .

[23]  Camilo Daleles Rennó,et al.  Window Regression: A Spatial-Temporal Analysis to Estimate Pixels Classified as Low-Quality in MODIS NDVI Time Series , 2014, Remote. Sens..

[24]  B. Recht,et al.  Tensor completion and low-n-rank tensor recovery via convex optimization , 2011 .

[25]  Stephen P. Boyd,et al.  1 Trend Filtering , 2009, SIAM Rev..

[26]  Ji Zhou,et al.  A simple method to improve the quality of NDVI time-series data by integrating spatiotemporal information with the Savitzky-Golay filter , 2018, Remote Sensing of Environment.

[27]  Per Jönsson,et al.  Seasonality extraction by function fitting to time-series of satellite sensor data , 2002, IEEE Trans. Geosci. Remote. Sens..

[28]  Sean Sloan,et al.  Remaining natural vegetation in the global biodiversity hotspots , 2014 .

[29]  A. Strahler,et al.  Monitoring vegetation phenology using MODIS , 2003 .

[30]  Gang Yang,et al.  Missing Information Reconstruction of Remote Sensing Data: A Technical Review , 2015, IEEE Geoscience and Remote Sensing Magazine.

[31]  María Amparo Gilabert,et al.  Noise Reduction and Gap Filling of fAPAR Time Series Using an Adapted Local Regression Filter , 2014, Remote. Sens..

[32]  P. Beck,et al.  Improved monitoring of vegetation dynamics at very high latitudes: A new method using MODIS NDVI , 2006 .

[33]  Xiaolin Zhu,et al.  Plant phenology and global climate change: Current progresses and challenges , 2019, Global change biology.

[34]  Jieping Ye,et al.  Tensor Completion for Estimating Missing Values in Visual Data , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[35]  Clement Atzberger,et al.  A time series for monitoring vegetation activity and phenology at 10-daily time steps covering large parts of South America , 2011, Int. J. Digit. Earth.

[36]  Frédéric Baret,et al.  The CACAO Method for Smoothing, Gap Filling, and Characterizing Seasonal Anomalies in Satellite Time Series , 2013, IEEE Transactions on Geoscience and Remote Sensing.

[37]  Liangpei Zhang,et al.  Climate Control on Net Primary Productivity in the Complicated Mountainous Area: A Case Study of Yunnan, China , 2018, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[38]  Stefan Dech,et al.  Characterisation of land surface phenology and land cover based on moderate resolution satellite data in cloud prone areas — A novel product for the Mekong Basin , 2013 .

[39]  Hao He,et al.  A Changing-Weight Filter Method for Reconstructing a High-Quality NDVI Time Series to Preserve the Integrity of Vegetation Phenology , 2012, IEEE Transactions on Geoscience and Remote Sensing.

[40]  Yang Shao,et al.  An evaluation of time-series smoothing algorithms for land-cover classifications using MODIS-NDVI multi-temporal data , 2016 .

[41]  C. Tucker,et al.  Satellite remote sensing of primary production , 1986 .

[42]  Yongqiang Zhang,et al.  A robust method for reconstructing global MODIS EVI time series on the Google Earth Engine , 2019, ISPRS Journal of Photogrammetry and Remote Sensing.

[43]  Jin Chen,et al.  An improved logistic method for detecting spring vegetation phenology in grasslands from MODIS EVI time-series data , 2015 .

[44]  Gang Yang,et al.  A Moving Weighted Harmonic Analysis Method for Reconstructing High-Quality SPOT VEGETATION NDVI Time-Series Data , 2015, IEEE Transactions on Geoscience and Remote Sensing.