Climate Prediction of Satellite-Based Spring Eurasian Vegetation Index (NDVI) using Coupled Singular Value Decomposition (SVD) Patterns

Liuqing Ji,Ke Fan

doi:10.3390/rs11182123

Abstract

Satellite-based normalized difference vegetation index (NDVI) data are widely used for estimating vegetation greenness. Seasonal climate predictions of spring (April–May–June) NDVI over Eurasia are explored by applying the year-to-year increment approach. The prediction models were developed based on the coupled modes of singular value decomposition (SVD) analyses between Eurasian NDVI and climate factors. One synchronous predictor, the spring surface air temperature from the NCEP’s Climate Forecast System (SAT-CFS), and three previous-season predictors (winter (December–January–February) sea-ice cover over the Barents Sea (SICBS), winter sea surface temperature over the equatorial Pacific (SSTP), and winter North Atlantic Oscillation (NAO) were chosen to develop four single-predictor schemes: the SAT-CFS scheme, SICBS scheme, SSTP scheme, and NAO scheme. Meanwhile, a statistical scheme that involves the three previous-season predictors (i.e., SICBS, SSTP, and NAO) and a hybrid scheme that includes all four predictors are also proposed. To evaluate the prediction skills of the schemes, one-year-out cross-validation and independent hindcast results are analyzed, revealing the hybrid scheme as having the best prediction skill. The results indicate that the temporal correlation coefficients at 92% of grid points over Eurasia are significant at the 5% significance level in the hybrid scheme, which is the best among all the schemes. Furthermore, spatial correlation coefficients (SCCs) of the six schemes are significant at the 1% significance level in most years during 1983–2015, with the averaged SCC of the hybrid scheme being the highest (0.60). The grid-averaged root-mean-square-error of the hybrid scheme is 0.04. By comparing the satellite-based NDVI value with the independent hindcast results during 2010–2015, it can be concluded that the hybrid scheme shows high prediction skill in terms of both the spatial pattern and the temporal variability of spring Eurasian NDVI.

Highlights

Unequivocal global warming since the mid-19th century has been revealed
The prediction models applying the coupled singular value decomposition (SVD) patterns based on the year-to-year increment method were developed for the spring Eurasian normalized difference vegetation index (NDVI)
Based on investigation and analyses of climate factors related to NDVIEA, one current-season predictor and three previous-season predictors were taken into consideration

Summary

Introduction

Unequivocal global warming since the mid-19th century has been revealed. In the Northern Hemisphere, the last three decades were the warmest of the last 1400 years [1]. The increased surface air temperature could prompt a longer growing season, increased photosynthetic activity and respiration, and an increase in productivity of terrestrial vegetation [2,3,4,5,6,7]. Terrestrial vegetation interacts with the climate through the surface albedo, hydrological processes, roughness, carbon exchange, and so on [8,9,10,11,12,13,14]. The changes in Eurasian vegetation have important effects on regional climate, such as the East Asian atmospheric circulation, Eurasian temperature and moisture etc., and have effects on global temperatures and the carbon cycle [17,18,19,20,21,22]. Seasonal climate predictions of spring vegetation over Eurasia are beneficial for the understanding of local and large-scale short-term climatic changes

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Results

Discussion

Conclusion