Mapping Temperate Vegetation Climate Adaptation Variability Using Normalized Land Surface Phenology

An empirical method to account for climatic adaptation in plant phenology models

Phenological shifts in plant species are one of the most conspicuous signs of climate change impact on the biosphere. Modeling phenological variations of plant species over broad regions is challenging because of the varied climatic requirements of geographic populations due to local adaptation. In this study, we developed an empirical method to calibrate phenological models of temperate trees using latitude as a predictor to account for local adaptation of populations to a N-S temperature gradient. Fourteen widely distributed tree species in the eastern U.S.A. were investigated using data from the USA-National Phenology Network. We implemented the method in a basic thermal time bud break model to introduce the algorithm of the method and test its effectiveness. For each species, dates of breaking leaf buds were first predicted using a traditional non-spatial model and then with a spatial model that has the critical thermal forcing requirements calibrated for different populations at varied latitudes. As anticipated, non-spatial model predictions that assumed a uniform forcing requirement across latitudes showed consistent and systematic biases at both higher (overestimation-predictions being later) and lower (underestimation-predictions being earlier) latitudes. Spatial models that have been calibrated using our method removed the geographic biases and yielded latitudinal gradients that more closely matched those of the observations. The spatial models also reduced the overall prediction errors from an average root mean square error (RMSE) of 32.2 days to 20.4 days for the training dataset and an average root mean square error for prediction (RMSEP) of 32.2 days to 19.9 days for the testing dataset. This paper is focused on introducing the new calibration method as a preparatory step toward developing operational models that may potentially predict large-scale and range-wide phenological responses of various plant species to climatic changes with improved local accuracy.

Trends in land surface phenology across the conterminous United States (1982-2016) analyzed by NEON domains

Tracking phenological change in a regionally explicit context is a key to understanding ecosystem status and change. The current study investigated long-term trends of satellite-observed land surface phenology (LSP) in the 17 National Ecological Observatory Network (NEON) domains across the conterminous United States (CONUS). Characterization of LSP trends was based on a high temporal resolution (3-d) time series of the two-band enhanced vegetation index (EVI2) derived from a long-term data record (LTDR) of the Advanced Very High Resolution Radiometer (AVHRR) and the Moderate Resolution Imaging Spectroradiometer (MODIS). We identified significant trend patterns in LSP and their seasonal climate and land use/land cover drivers for each NEON domain. Key findings include (1) the start of season (SOS) predominantly shifted later in 13 out of 17 domains (24.3% of CONUS by area) due potentially to both a lack of spring warming in the eastern United States and changes in agronomic practices over agricultural lands; (2) the end of season (EOS) became predominantly later in nine domains dominated by natural vegetation (14.1% of CONUS by area) in response to widespread warming in autumn; (3) the EOS predominantly shifted earlier in three domains (10.6% of CONUS by area) over primarily agricultural lands as potentially affected by changes in crop growth cycles; and (4) earlier shift in the SOS was mostly found in the Northwest (3.6% of CONUS by area) and was predominant only in the moist Pacific Northwest (27.7% of the domain by area) in response to more pronounced spring warming in the region. The overall patterns of SOS and EOS trends across CONUS appeared constrained by continental-scale temperature trends as characterized by a west-east dipole and the distribution of the nation's agricultural lands. In addition, seasonal trend analysis revealed that most NEON domains (15/17) became predominantly greener in part of or throughout the growing season, potentially contributed by both climate change-induced growth increase and improved agricultural productivity. The domain-wide LSP trends with their underlying drivers identified here provide important contextual information for NEON science as well as for investigations within CONUS using other distributed observatories (e.g., LTER, LTAR, FLUXNET, USA-NPN, etc.).

Land surface phenology of Northeast China during 2000-2015: temporal changes and relationships with climate changes

As an important crop growing area, Northeast China (NEC) plays a vital role in China's food security, which has been severely affected by climate change in recent years. Vegetation phenology in this region is sensitive to climate change, and currently, the relationship between the phenology of NEC and climate change remains unclear. In this study, we used a satellite-derived normalized difference vegetation index (NDVI) to obtain the temporal patterns of the land surface phenology in NEC from 2000 to 2015 and validated the results using ground phenology observations. We then explored the relationships among land surface phenology, temperature, precipitation, and sunshine hours for relevant periods. Our results showed that the NEC experienced great phenological changes in terms of spatial heterogeneity during 2000-2015. The spatial patterns of land surface phenology mainly changed with altitude and land cover type. In most regions of NEC, the start date of land surface phenology had advanced by approximately 1.0 days year^-1, and the length of land surface phenology had been prolonged by approximately 1.0 days year^-1 except for the needle-leaf and cropland areas, due to the warm conditions. We found that a distinct inter-annual variation in land surface phenology related to climate variables, even if some areas presented non-significant trends. Land surface phenology was coupled with climate variables and distinct responses at different combinations of temperature, precipitation, sunshine hours, altitude, and anthropogenic influence. These findings suggest that remote sensing and our phenology extracting methods hold great potential for helping to understand how land surface phenology is sensitive to global climate change.