Forecasting Spatio-Temporal Dynamics on the Land Surface Using Earth Observation Data—A Review

• Published in: Remote sensing (Basel, Switzerland) Vol. 12; no. 21; p. 3513
• Main Authors: Koehler, Jonas, Kuenzer, Claudia
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.11.2020
• Subjects: artificial intelligence; Autoregressive models; climate; Climate change; Cloud computing; Computer applications; Crop yield; data collection; Data processing; Datasets; decision making; dynamics; Earth Observation; Earth observations (from space); Economic forecasting; forecast; Forecasting; global change; information processing; issues and policy; land; Land cover; land surface; Land use; land use and land cover maps; Learning algorithms; literature; livelihood; Machine learning; Markov chain; Markov chains; multivariate analysis; Remote observing; Remote sensing; satellites; Sensors; spatial data; Surface dynamics; Time series; time series analysis; Trends; yield forecasting
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs12213513

Reliable forecasts on the impacts of global change on the land surface are vital to inform the actions of policy and decision makers to mitigate consequences and secure livelihoods. Geospatial Earth Observation (EO) data from remote sensing satellites has been collected continuously for 40 years and has the potential to facilitate the spatio-temporal forecasting of land surface dynamics. In this review we compiled 143 papers on EO-based forecasting of all aspects of the land surface published in 16 high-ranking remote sensing journals within the past decade. We analyzed the literature regarding research focus, the spatial scope of the study, the forecasting method applied, as well as the temporal and technical properties of the input data. We categorized the identified forecasting methods according to their temporal forecasting mechanism and the type of input data. Time-lagged regressions which are predominantly used for crop yield forecasting and approaches based on Markov Chains for future land use and land cover simulation are the most established methods. The use of external climate projections allows the forecasting of numerical land surface parameters up to one hundred years into the future, while auto-regressive time series modeling can account for intra-annual variances. Machine learning methods have been increasingly used in all categories and multivariate modeling that integrates multiple data sources appears to be more popular than univariate auto-regressive modeling despite the availability of continuously expanding time series data. Regardless of the method, reliable EO-based forecasting requires high-level remote sensing data products and the resulting computational demand appears to be the main reason that most forecasts are conducted only on a local scale. In the upcoming years, however, we expect this to change with further advances in the field of machine learning, the publication of new global datasets, and the further establishment of cloud computing for data processing.