Affiliation:
1. Lamont-Doherty Earth Observatory, Columbia University, Palisades, NY 10964, USA
2. Department of Geography, San Diego State University, San Diego, CA 92182, USA
Abstract
Due to their transient nature, clouds represent anomalies relative to the underlying landscape of interest. Hence, the challenge of cloud identification can be considered a specific case in the more general problem of anomaly detection. The confounding effects of transient anomalies are particularly troublesome for spatiotemporal analysis of land surface processes. While spatiotemporal characterization provides a statistical basis to quantify the most significant temporal patterns and their spatial distributions without the need for a priori assumptions about the observed changes, the presence of transient anomalies can obscure the statistical properties of the spatiotemporal processes of interest. The objective of this study is to implement and evaluate a robust approach to distinguish clouds and other transient anomalies from diurnal and annual thermal cycles observed with time-lapse thermography. The approach uses Robust Principal Component Analysis (RPCA) to statistically distinguish low-rank (L) and sparse (S) components of the land surface temperature image time series, followed by a spatiotemporal characterization of its low rank component to quantify the dominant diurnal and annual thermal cycles in the study area. RPCA effectively segregates clouds, sensor anomalies, swath gaps, geospatial displacements and transient thermal anomalies into the sparse component time series. Spatiotemporal characterization of the low-rank component time series clearly resolves a variety of diurnal and annual thermal cycles for different land covers and water bodies while segregating transient anomalies potentially of interest.
Funder
USDA NIFA Sustainable Agroecosystems program
USDA AFRI Rapid Response to Extreme Weather Events Across Food and Agricultural Systems program
NASA Land-Cover/Land Use Change program
NASA Remote Sensing of Water Quality program
NASA Applications-Oriented Augmentations for Research and Analysis program
NASA Commercial Smallsat Data Analysis Program
NASA FireSense Airborne Science Program
California Climate Action Seed Award Program
endowment of the Lamont Doherty Earth Observatory of Columbia University
Reference32 articles.
1. Irish, R.R. (2000, January 24–28). Landsat 7 Automatic Cloud Cover Assessment. Proceedings of the AeroSense 2000, Orlando, FL, USA.
2. Improvement and Expansion of the Fmask Algorithm: Cloud, Cloud Shadow, and Snow Detection for Landsats 4–7, 8, and Sentinel 2 Images;Zhu;Remote Sens. Environ.,2015
3. Infrared Reflectance of High Altitude Clouds;Hovis;Appl. Opt.,1970
4. Spectral Reflectance of Clouds in the Near-Infrared: Comparison of Measurements and Calculations;Twomey;J. Meteorol. Soc. Japan Ser. II,1982
5. Diffuse Reflectance of Clouds: A Semiempirical Model;Young;Appl. Opt.,1979
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