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 (SA… Show more
“…Local climate and large-scale climatic conditions are the predictors of vegetation change [12,43]. EI Ninõ-Southern Oscillation (ENSO) has a significant impact on regional rainfall and temperature, which will affect the production of crops [44,45].…”
Based on remote sensing data of vegetation coverage, observation data of basic meteorological elements, and support vector machine (SVM) method, this study develops an analysis model of meteorological elements influence on vegetation coverage (MEVC). The variations for the vegetation coverage changes are identified utilizing five meteorological elements (temperature, precipitation, relative humidity, sunshine hour, and ground temperature) in the SVM model. The performance of the SVM model is also evaluated on simulating vegetation coverage anomaly change by comparing with statistical model multiple linear regression (MLR) and partial least squares (PLS)-based models. The symbol agreement rates (SAR) of simulations produced by MLR, PLS, and SVM models are 55%, 57%, and 66%, respectively. The SVM model shows obviously better performance than PLS and MLR models in simulating meteorological elements-related interannual variation of vegetation coverage in North China. Therefore, the introduction of the intelligent analysis method in term of SVM in model development has certain advantages in studying the internal impact of meteorological elements on regional vegetation coverage. It can also be further applied to predict the future vegetation anomaly change.
“…Local climate and large-scale climatic conditions are the predictors of vegetation change [12,43]. EI Ninõ-Southern Oscillation (ENSO) has a significant impact on regional rainfall and temperature, which will affect the production of crops [44,45].…”
Based on remote sensing data of vegetation coverage, observation data of basic meteorological elements, and support vector machine (SVM) method, this study develops an analysis model of meteorological elements influence on vegetation coverage (MEVC). The variations for the vegetation coverage changes are identified utilizing five meteorological elements (temperature, precipitation, relative humidity, sunshine hour, and ground temperature) in the SVM model. The performance of the SVM model is also evaluated on simulating vegetation coverage anomaly change by comparing with statistical model multiple linear regression (MLR) and partial least squares (PLS)-based models. The symbol agreement rates (SAR) of simulations produced by MLR, PLS, and SVM models are 55%, 57%, and 66%, respectively. The SVM model shows obviously better performance than PLS and MLR models in simulating meteorological elements-related interannual variation of vegetation coverage in North China. Therefore, the introduction of the intelligent analysis method in term of SVM in model development has certain advantages in studying the internal impact of meteorological elements on regional vegetation coverage. It can also be further applied to predict the future vegetation anomaly change.
“…This method has also been applied to the prediction of summer precipitation in China (Liu and Fan, 2014). It has also been tentatively used to forecast the spring Eurasian vegetation (Ji and Fan, 2019). However, only three factors concerning the winter SAT in China are adopted to establish the prediction model.…”
Two skilful 2‐month‐leading hybrid downscaling prediction schemes in October for winter surface air temperature (SAT) over China are proposed in this paper. The schemes are based on the year‐to‐year increment approach and the coupled climate patterns between the winter SAT over China and its predictors. Observed North Pacific sea surface temperature (SST) from the preceding July to September, Arctic sea ice concentration (SIC) in the preceding August and winter sea level pressure (SLP) over pan Eurasia from version 2 of the Climate Forecast System (CFSv2) are selected as the predictors based on the fundamental physics. Individual‐predictor schemes (IP‐schemes), that is, SLP‐scheme, SST‐scheme, SIC‐scheme, indicate that these predictors exhibit prediction skills in different regions. Multi‐predictor scheme I (MP‐schemeI) is developed by combining three predictors. However, MP‐schemeI shows limited skill in predicting SAT over Northeast China (NECTA), due to the limited skill of CFSv2 over the extratropics. Thus, MP‐schemeII is established, in which a hybrid downscaling model for NECTA is constructed. These two MP‐schemes have comparable prediction skill over China, but MP‐schemeII outperforms MP‐schemeI over NEC. The temporal (spatial) anomaly correlation coefficient (ACC) increases from 0.23 (0.15) in MP‐schemeI to 0.36 (0.21) in MP‐schemeII, and the ratio of the same sign of anomalous years (Anomalous‐RSS) improves from 39.1% to 56.5% over NEC. For the winter SAT over China, the MP‐schemes greatly enhance the prediction compared with CFSv2 outputs and the IP‐schemes. The temporal (spatial) ACC increases from 0.29 (0.05) in CFSv2 to 0.52 (0.29) in the MP‐schemes, and the station‐average root‐mean‐square error decreases by about 48.0% compared with CFSv2. Moreover, the RSS (Anomalous‐RSS) of the winter SAT over China is 55.9% (47.4%) in CFSv2 and 64.7% (63.2%) in the MP‐schemes. This indicates that the MP‐schemes perform better in predicting anomalous winters.
“…The correlation between the 16-day average NDVI gs and LST gs was high over vegetated areas (see Figure 3 for correlation coefficients of individual pixels). The NDVI in northern middle- and high latitudes is indeed more strongly related to temperature than precipitation (Ji and Fan, 2019; Los et al, 2001; Tucker et al, 2001), which is why our focus here is on the NDVI-temperature relationship.Figure 3.Correlation coefficients (indicated by colour) illustrating the significance of the relationship between growing season NDVI and growing season land surface temperatures for each pixel of the study area.…”
Northern high-alpine regions are currently experiencing rapid warming, which often results in the degradation of sub-surface permafrost and the upslope advancement of vegetation. The present study combines remotely sensed MODIS Land Surface Temperatures (LSTs) and the Normalised Difference Vegetation Index (NDVI) with observed air temperatures to model the thermal and vegetational dynamics in NE Jotunheimen (Norway) for the period 1957–2019. An altitudinal transect on the north-facing slope of Galdhøpiggen was used for ground truthing. Results indicate a substantial warming trend since the late 1950s, accompanied by increased NDVI. The spatial and temporal patterns of observed change were not uniform. Winter surface temperatures increased most rapidly, by 2.4–2.8°C at mid- and low altitudes (600–1500 m a.s.l.). The highest increases in NDVI (by ∼0.1) were detected during the growing season (April–September) and over the mid-range altitudes (1050–1500 m a.s.l.), that is, above the tree line on Galdhøpiggen. We attribute this to increased shrubification at these altitudes. Our results confirm that the surface temperatures near the previously estimated lower altitudinal limit of permafrost (∼1450 m a.s.l.) have continued to increase during the past decade, likely facilitating further permafrost degradation. Finally, we demonstrate that mapping remotely sensed mean growing season LSTs below 0°C can be used to identify areas suitable for continuous sub-surface permafrost, and mean June–September LSTs above 7°C can detect areas suitable for tree ( Betula pubescens) growth in NE Jotunheimen.
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