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Widespread changes in arctic and boreal Normalized Difference Vegetation Index (NDVI) values captured by satellite platforms indicate that northern ecosystems are experiencing rapid ecological change in response to climate warming. Increasing temperatures and altered hydrology are driving shifts in ecosystem biophysical properties that, observed by satellites, manifest as long-term changes in regional NDVI. In an effort to examine the underlying ecological drivers of these changes, we used field-scale remote sensing of NDVI to track peatland vegetation in experiments that manipulated hydrology, temperature, and carbon dioxide (CO 2 ) levels. In addition to NDVI, we measured percent cover by species and leaf area index (LAI). We monitored two peatland types broadly representative of the boreal region. One site was a rich fen located near Fairbanks, Alaska, at the Alaska Peatland Experiment (APEX), and the second site was a nutrient-poor bog located in Northern Minnesota within the Spruce and Peatland Responses Under Changing Environments (SPRUCE) experiment. We found that NDVI decreased with long-term reductions in soil moisture at the APEX site, coincident with a decrease in photosynthetic leaf area and the relative abundance of sedges. We observed increasing NDVI with elevated temperature at the SPRUCE site, associated with an increase in the relative abundance of shrubs and a decrease in forb cover. Warming treatments at the SPRUCE site also led to increases in the LAI of the shrub layer. We found no strong effects of elevated CO 2 on community composition. Our findings support recent studies suggesting that changes in NDVI observed from satellite platforms may be the result of changes in community composition and ecosystem structure in response to climate warming., (© 2018 John Wiley & Sons Ltd.)

Climate change threatens our ability to grow food for an ever-increasing population. There is a need for high-quality soil moisture predictions in under-monitored regions like Africa. However, it is unclear if soil moisture processes are globally similar enough to allow our models trained on available in situ data to maintain accuracy in unmonitored regions. We present a multitask long short-term memory (LSTM) model that learns simultaneously from global satellite-based data and in situ soil moisture data. This model is evaluated in both random spatial holdout mode and continental holdout mode (trained on some continents, tested on a different one). The model compared favorably to current land surface models, satellite products, and a candidate machine learning model, reaching a global median correlation of 0.792 for the random spatial holdout test. It behaved surprisingly well in Africa and Australia, showing high correlation even when we excluded their sites from the training set, but it performed relatively poorly in Alaska where rapid changes are occurring. In all but one continent (Asia), the multitask model in the worst-case scenario test performed better than the soil moisture active passive (SMAP) 9 km product. Factorial analysis has shown that the LSTM model's accuracy varies with terrain aspect, resulting in lower performance for dry and south-facing slopes or wet and north-facing slopes. This knowledge helps us apply the model while understanding its limitations. This model is being integrated into an operational agricultural assistance application which currently provides information to 13 million African farmers. [ABSTRACT FROM AUTHOR]

Climate change threatens our ability to grow food for a growing population. There are concurrent droughts and floods happening globally, with the greatest impacts felt in Africa. There is a need for high-quality soil moisture predictions in under-monitored regions like Africa. Yet it is unclear if soil moisture processes are globally similar enough to allow our models to maintain accuracy in unmonitored regions. We present a multitask long short-term memory (LSTM) model that learns simultaneously from global satellite-based and in-situ soil moisture data. This model is evaluated in both random spatial holdout mode and continental holdout mode (trained on some continents, tested on a different one). The model compared favorably to current land surface models, satellite products, and a candidate machine learning model, reaching a global median correlation of 0.792 for the random spatial holdout test. It behaved surprisingly well in Africa and Australia, showing high correlation even when we excluded their sites from the training set, but performed relatively poorly in Alaska where rapid changes are occurring. In all but one continent (Asia), the multitask model in the worst-case scenario test performed better than the soil moisture active passive (SMAP) 9-km product. Factorial analysis shows that the LSTM model’s accuracy with representing impacts of terrain aspect, resulting in lower performance for dry and south-facing slopes or wet and north-facing slopes. This knowledge helps us apply the model while understanding its limitations. This model is being integrated into an operational agricultural assistance application which currently provides information to 13 million African farmers. [ABSTRACT FROM AUTHOR]

We present discrete (2‐h resolution) multi‐year (2008–2017) in situ measurements of seasonal vegetation growth and soil biophysical properties from two sites on Alaska's North Slope, USA, representing dry and wet sedge tundra. We examine measurements of vertical active soil layer temperature and soil moisture profiles (freeze/thaw status), woody shrub vegetation physiological activity, and meteorological site data to assess interrelationships within (and between) these two study sites. Vegetation phenophases (cold de‐hardening start, physiological function start, stem growth start, stem growth end, physiological function end, cold hardening completion) were found to have greater interannual day of year (DOY) occurrence variability at the dry site compared with the wet site. At the dry site, vegetation activity begins on average ~7 days earlier and ends ~11 days earlier. The mean active stem growth window lasts ~54 days for the dry site and ~51 days for the wet site. Vegetation, in both tundra environments, began cold de‐hardening functions (warm season prep) prior to atmospheric temperatures warming above 0°C. Similar results were found related to the critical soil freeze/thaw/transition dates; the dry site had a DOY phenophase occurrence range that was 8 days larger than that of the wet site. A longer continuous summer thaw period was captured at the wet site by ~26 days throughout the active layer. In addition, the dry site was measured to have longer spring and fall soil isothermal conditions than the wet site by ~9 and 5 days throughout the active layer. These results show that the dry site's willow shrub vegetation physiology and soil condition phenology is more variable than the wet site. Alongside the in situ data, a remote sensing product from NASA's MEaSUREs program was utilized; our research indicates that the AMSR‐derived satellite product is more precise over the wet tundra site with critical date alignment between remote sensing observations and in situ measurements ranging from ~4 to 11 days. Furthermore, the AMSR product was shown to preemptively estimate land surface condition change during the spring transition for both tundra types while lagging during the fall transition and freeze‐up periods. [ABSTRACT FROM AUTHOR]

Soil moisture is an important driver of growth in boreal Alaska, but estimating soil hydraulic parameters can be challenging in this data‐sparse region. Parameter estimation is further complicated in regions with rapidly warming climate, where there is a need to minimize model error dependence on interannual climate variations. To better identify soil hydraulic parameters and quantify energy and water balance and soil moisture dynamics, we applied the physically based, one‐dimensional ecohydrological Simultaneous Heat and Water (SHAW) model, loosely coupled with the Geophysical Institute of Permafrost Laboratory (GIPL) model, to an upland deciduous forest stand in interior Alaska over a 13‐year period. Using a Generalized Likelihood Uncertainty Estimation parameterisation, SHAW reproduced interannual and vertical spatial variability of soil moisture during a five‐year validation period quite well, with root mean squared error (RMSE) of volumetric water content at 0.5 m as low as 0.020 cm3/cm3. Many parameter sets reproduced reasonable soil moisture dynamics, suggesting considerable equifinality. Model performance generally declined in the eight‐year validation period, indicating some overfitting and demonstrating the importance of interannual variability in model evaluation. We compared the performance of parameter sets selected based on traditional performance measures such as the RMSE that minimize error in soil moisture simulation, with one that is designed to minimize the dependence of model error on interannual climate variability using a new diagnostic approach we call CSMP, which stands for Climate Sensitivity of Model Performance. Use of the CSMP approach moderately decreases traditional model performance but may be more suitable for climate change applications, for which it is important that model error is independent from climate variability. These findings illustrate (1) that the SHAW model, coupled with GIPL, can adequately simulate soil moisture dynamics in this boreal deciduous region, (2) the importance of interannual variability in model parameterisation, and (3) a novel objective function for parameter selection to improve applicability in non‐stationary climates. [ABSTRACT FROM AUTHOR]

Arctic and boreal ecosystems play an important role in the global carbon (C) budget, and whether they act as a future net C sink or source depends on climate and environmental change. Here, we used complementary in situ measurements, model simulations, and satellite observations to investigate the net carbon dioxide (CO2) seasonal cycle and its climatic and environmental controls across Alaska and northwestern Canada during the anomalously warm winter to spring conditions of 2015 and 2016 (relative to 2010–2014). In the warm spring, we found that photosynthesis was enhanced more than respiration, leading to greater CO2 uptake. However, photosynthetic enhancement from spring warming was partially offset by greater ecosystem respiration during the preceding anomalously warm winter, resulting in nearly neutral effects on the annual net CO2 balance. Eddy covariance CO2 flux measurements showed that air temperature has a primary influence on net CO2 exchange in winter and spring, while soil moisture has a primary control on net CO2 exchange in the fall. The net CO2 exchange was generally more moisture limited in the boreal region than in the Arctic tundra. Our analysis indicates complex seasonal interactions of underlying C cycle processes in response to changing climate and hydrology that may not manifest in changes in net annual CO2 exchange. Therefore, a better understanding of the seasonal response of C cycle processes may provide important insights for predicting future carbon–climate feedbacks and their consequences on atmospheric CO2 dynamics in the northern high latitudes. [ABSTRACT FROM AUTHOR]

• Start of snowmelt predominates spring vegetation phenology in alaska. • Snowmelt-induced synergistic changes in soil temperature and moisture drive greenup. • Responses of greenup to snowmelt are associated with vegetation and climate feature. Snow is considered a vital component in the northern climate system, however, our understanding of the impacts of snowmelt on spring phenological shifts is still limited for high-latitude ecosystems. In this study, we analyzed the start of growing season (SOS) and its response to the process of snowmelt over 2000–2019 using multiple ground observations, satellite data products, and climate datasets in Alaska and part of Canada Yukon among five ecoregions. We found that the earlier snowmelt resulting from warming spring dominates the advanced spring vegetation phenology in the region. Spring vegetation greenup follows the start of snowmelt (SOM). During 2000–2019, the SOM and SOS advanced in a similar spatial pattern across the ecoregions, and SOS experienced further advancement than SOM. On average, SOS advanced about 0.49 days per day of SOM advancement. The impacts of snowmelt on vegetation phenology are largely driven by the snowmelt-induced synergistic changes in soil temperature and soil water content. As snowmelt starts, the infiltration of snowmelt water and insulation of the remaining snow cover stimulate the vegetation activities below the snow. The responses of SOS to the snowmelt phenology varied among the different ecoregions are associated with the varied climate backgrounds and vegetation types. Greater SOS and SOM advancements were found in the taiga-tundra ecotone, while lower changes were detected over the tundra. This study suggests that the snow cover and snowmelt phenology should be considered to better understand the vegetation seasonality under climate change in the high latitudes. [ABSTRACT FROM AUTHOR]