Brown algae invasions and bloom events need routine monitoring for effective adaptation

Brown algae blooms and invasions have affected 29% of the Earth’s coast, yet there is sparse evidence of the impacts and adaptations of these events. Through a systematic review of empirical literature on these blooms and invasions, we explore the prevalence of conventional analyses of environmental, economic, and social impacts, as well as opportunities for adaptation and valorisation. The study reveals crucial inconsistencies in the current evidence base on algae impacts: fragmented metrics for quantifying blooms and their effects; inconsistent application and testing of prevention measures (e.g. forecasting, early warning systems); reliance on removal as a management approach with limited evidence of associated costs; and scant evidence of the effectiveness of impact mitigation or adaptation strategies. With a focus on economic and societal dimensions of algae events, we introduce emerging opportunities within the blue economy for bloom utilization. The findings highlight the crucial need for harmonized monitoring protocols, robust cost-benefit analysis of management and adaptation options, and evidence of pathways to valorisation of algae biomass

Arctic lakes show strong decadal trend in earlier spring ice-out

The timing of the seasonal freeze-thaw cycle of arctic lakes affects ecological processes and land-atmosphere energy fluxes. We carried out detailed ice-phenology mapping of arctic lakes, based on daily surface-reflectance time series for 2000-2013 from MODIS at 250 m spatial resolution. We used over 13,300 lakes, area >1 km2, in five study areas distributed evenly across the circumpolar Arctic — the first such phenological dataset. All areas showed significant trends towards an earlier break-up, stronger than previously reported. The mean shift in break-up start ranged from -0.10 days/year (Northern Europe) to -1.05 days/year (central Siberia); the shift in break-up end was between -0.14 and -0.72 days/year. Finally, we explored the effect of temperature on break-up timing and compared results among study areas. The 0°C isotherm shows the strongest relationship (r = 0.56 – 0.81) in all study areas. If early break-up continues, rapidly changing ice phenology will likely generate significant, arctic-wide impacts

Validating satellite-derived vegetation phenology products

The phenology of terrestrial vegetation, i.e., the timing of events such as bud burst, leaf development, and senescence, plays an important role in the global climate system, biogeochemical cycles, and energy budget. Satellite-derived vegetation indices have long been used as proxies for representing the status of terrestrial vegetation, and hence, the time series of these data sets were used to derive key land surface phenological variables such as the start and end of the growing season. Despite an increase in effort toward characterization of vegetation phenology from satellite data, its validation with ground measurements is still challenging because of mismatches in both spatial and temporal scales between the two types of measurements, distribution of ground measurements, and spatial heterogeneity of vegetation types in a satellite sensor pixel

A systematic review of vegetation phenology in Africa

The study of vegetation phenology is important because it is a sensitive indicator of climate changes and it regulates carbon, energy and water fluxes between the land and atmosphere. Africa, which has 17% of the global forest cover, contributes significantly to the global carbon budget and has been identified as potentially highly vulnerable to climate change impacts. In spite of this, very little is known about vegetation phenology across Africa and the factors regulating vegetation growth and dynamics. Hence, this review aimed to provide a synthesis of studies of related Africa's vegetation phenology and classify them based on the methods and techniques used in order to identify major research gaps. Significant increases in the number of phenological studies in the last decade were observed, with over 70% of studies adopting a satellite-based remote sensing approach to monitor vegetation phenology. Whereas ground based studies that provides detailed characterisation of vegetation phenological development, occurred rarely in the continent. Similarly, less than 14% of satellite-based remote sensing studies evaluated vegetation phenology at the continental scale using coarse spatial resolution datasets. Even more evident was the lack of research focusing on the impacts of climate change on vegetation phenology. Consequently, given the importance and the uniqueness of both methods of phenological assessment, there is need for more ground-based studies to enable greater understanding of phenology at the species level. Likewise, finer spatial resolution satellite sensor data for regional phenological assessment is required, with a greater focus on the relationship between climate change and vegetation phenological changes. This would contribute greatly to debates over climate change impacts and, most importantly, climate change mitigation strategies

Europe Gross Primary Productivity (GPP) Dataset 2018 (.NetCDF)

Europe Gross Primary Productivity (GPP) Dataset 2018 (in .NetCDF Files) Product: Gross Primary Productivity (GPP) Year: 2018 Region: Europe Temporal Scale: 8 Days Spatial Resolution: 500 meters Method: Light-use-efficiency (LUE) approach. Referred Publication: https://www.sciencedirect.com/science/article/pii/S0048969718307149 https://onlinelibrary.wiley.com/doi/10.1111/gcb.12261</span

Australia Gross Primary Productivity (GPP) Dataset 2020 (Cleaned Updated .NC Files)

Australia Gross Primary Productivity (GPP) Dataset 2020 Product: Gross Primary Productivity (GPP) MODIS Tiles No: h30 v11 and h31v11 Year: 2020 Region: Australia Temporal Scale: 8 Days Spatial Resolution: 500 meters Format: NetCDF (.nc) Projection: Geographic Lat/Long Method: Light-use-efficiency (LUE) approach. Referred Publication: https://www.sciencedirect.com/science/article/pii/S0048969718307149 https://onlinelibrary.wiley.com/doi/10.1111/gcb.12261</span

Improving generalisability and transferability of machine-learning-based maize yield prediction model through domain adaptation

Modern problems in agricultural management require non-traditional solutions, one of which is by utilizing domain adaptive machine learning models for crop yield prediction which are able to perform reliably in different temporal or spatial domains. However, most studies have focused on the application of domain adaptation to classification tasks such as crop type identification, while the application to regression tasks such as crop yield prediction have been limited. In this study, we explore the generalisability and transferability of ordinary Deep Neural Network (DNN) and domain adaptive neural network models created using three domain adaptation algorithms, namely Discriminative Adversarial Neural Network (DANN), Kullback-Leibler Importance Estimation Procedure (KLIEP), and Regular Transfer Neural Network (RTNN). These three algorithms represent feature-based, instance-based, and parameter-based domain adaptations, respectively. Maize yield records, weather variables, and remotely sensed features from 11 states in the US corn belt acquired in 2006–2020 were compiled and segregated into classes according to temporal (year) and spatial characteristics (annual growing degree days [GDD], vapor pressure deficit [VPD], soil organic content [SOC], and green chlorophyll vegetation index/GCI). We found that models trained using datasets from temperate regions with medium-high GDD and moderate VPD perform well whereas SOC does not significantly affect the generalisability. It is not advisable to train models with datasets constrained by GCI as this feature correlates significantly with the maize yield, and adaptation between two domains that rarely intercept will not work well. We also demonstrate that Kullback-Leibler divergence computed using features from source and target domains can be used to justify the feasibility of domain adaptation. Based on the divergence, a model trained in the US (or another region with sufficient data) is expected to work reliably in other regions through domain adaptation, especially feature-based adaptation.<br/