Inorganic carbon assimilation by planktonic community in Santos Basin, Southwestern Atlantic Ocean by Deborah S. Kutner, Jeff S. Bowman, Flávia M. P. Saldanha-Corrêa, Mateus G. Chuqui, Pedro M. Tura, Daniel L. Moreira, Frederico P. Brandini, Camila N. Signori

“…Rates were analyzed using statistical tests to verify spatial differences between groups of samples and generalized linear models to identify correlations with environmental variables (temperature, salinity, density, mixed layer depth, dissolved oxygen, nitrite, nitrate, silicate, phosphate, turbidity, CDOM, and phycoerythrin and chlorophyll-a concentrations). …”
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Climate change enhances soil fauna population and biomass in grasslands of the Loess Plateau by Xi Yang, Ming’an Shao, Tongchuan Li

“…Conservatively estimated, the number of soil fauna on the Loess Plateau exceeds 4 × 1015, with the estimated total biomass of 4 × 106 t, equivalent to 2 Mt carbon. In addition, model predictions indicate that soil fauna populations and biomass of grassland in the Loess Plateau increased 27% and 29%, from 2000 to 2022. …”
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Inorganic carbon assimilation by planktonic community in Santos Basin, Southwestern Atlantic Ocean by Deborah S. Kutner, Jeff S. Bowman, Flávia M. P. Saldanha-Corrêa, Mateus G. Chuqui, Pedro M. Tura, Daniel L. Moreira, Frederico P. Brandini, Camila N. Signori

“…Rates were analyzed using statistical tests to verify spatial differences between groups of samples and generalized linear models to identify correlations with environmental variables (temperature, salinity, density, mixed layer depth, dissolved oxygen, nitrite, nitrate, silicate, phosphate, turbidity, CDOM, and phycoerythrin and chlorophyll-a concentrations). …”
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MoHiPr-TB: A Monthly Gridded Multi-Source Merged Precipitation Dataset for the Tarim Basin Based on Machine Learning by Ping Chen, Junqiang Yao, Jing Chen, Mengying Yao, Liyun Ma, Weiyi Mao, Bo Sun

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Data fusion-based improvements in empirical regression and machine learning for global daily ∼ 8 km resolution sea surface nitrate estimation and interpretation by Aifen Zhong, Difeng Wang, Fang Gong, Jingjing Huang, Zhuoqi Zheng, Xianqiang He, Qing Zhang, Qiankun Zhu

“…Here we aim to enhance the accuracy and spatial resolution of SSN retrievals by developing improved regression and machine learning models, enabling the generation of global daily ∼ 8 km SSN products from satellite and model data. …”
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Weed Types and Dynamics Associations with Catena Landscape Positions: Smallholder Farmers’ Knowledge and Perception in Zimbabwe by Justin Chipomho, Simbarashe Tatsvarei, Cosmas Parwada, Arnold Bray Mashingaidze, Joyful T. Rugare, Stanford Mabasa, Regis Chikowo

“…The data were coded and processed using the CSPro software package, and then analysed using the SPSS program. Factors that predicted the spatial distribution of weeds were determined using a binary logistic model. …”
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Influence of Clouds and Aerosols on Solar Irradiance and Application of Climate Indices in Its Monthly Forecast over China by Shuting Zhang, Xiaochun Wang

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Estimating Geographical Variation in the Risk of Zoonotic Plasmodium knowlesi Infection in Countries Eliminating Malaria. by Freya M Shearer, Zhi Huang, Daniel J Weiss, Antoinette Wiebe, Harry S Gibson, Katherine E Battle, David M Pigott, Oliver J Brady, Chaturong Putaporntip, Somchai Jongwutiwes, Yee Ling Lau, Magnus Manske, Roberto Amato, Iqbal R F Elyazar, Indra Vythilingam, Samir Bhatt, Peter W Gething, Balbir Singh, Nick Golding, Simon I Hay, Catherine L Moyes

“…<h4>Methodology/principal findings</h4>A total of 439 records of P. knowlesi infections in humans, macaque reservoir and vector species were collated. To predict spatial variation in disease risk, a model was fitted using records from countries where the infection data coverage is high. …”
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Development of a Comprehensive Approach to Quality Control of Dermorphin Derivative—Representative of Synthetic Opioid Peptides with Non-Narcotic Type of Analgesia by Vasilisa A. Sukhanova, Elena V. Uspenskaya, Safdari Ainaz, Hoang Thi Ngoc Quynh, Aleksey A. Timofeev

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Mapping global leaf inclination angle (LIA) based on field measurement data by S. Li, S. Li, S. Li, H. Fang, H. Fang

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Mechanism of Irrigation Before Low-Temperature Exposure on Mitigating the Reduction in Yield Loss and Spikelet Abortion at the Jointing Stage of Wheat by Yangyang Wang, Mao Wang, Peipei Tian, Dechao Ren, Haiyan Zhang, Geng Ma, Jianzhao Duan, Chenyang Wang, Wei Feng

“…Furthermore, this study clarifies the spatial distribution of grain responses across different spike positions under low temperatures, providing insights into the physiological mechanisms by which irrigation mitigates grain loss. …”
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Estimation of nitrogen uptake, biomass, and nitrogen concentration, in cover crop monocultures and mixtures from optical UAV images by Paolo Dal Lago, Nikolaos Vavlas, Lammert Kooistra, Gerlinde B. De Deyn

“…This study provides a basis for spatially predicting key CC traits using UAV optical data.…”
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A spatiotemporal recurrent neural network for missing data imputation in tunnel monitoring by Junchen Ye, Yuhao Mao, Ke Cheng, Xuyan Tan, Bowen Du, Weizhong Chen

“…ST-RNN consists of two moduli: a temporal module employing recurrent neural network (RNN) to capture temporal dependencies, and a spatial module employing multilayer perceptron (MLP) to capture spatial correlations. …”
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Harnessing Multi-Source Data and Deep Learning for High-Resolution Land Surface Temperature Gap-Filling Supporting Climate Change Adaptation Activities by Katja Kustura, David Conti, Matthias Sammer, Michael Riffler

“…This method not only preserves ECOSTRESS’s native resolution but also fills data gaps and enhances spatial and temporal coverage. In non-gap areas validated against ground truth ECOSTRESS data, the model achieves LST predictions with at least 80% of all pixel errors falling within a ±3 °C range. …”
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Mapping Coastal Soil Salinity and Vegetation Dynamics Using Sentinel-1 and Sentinel-2 Data Fusion With Machine Learning Techniques by Wen Liu, Tiezhu Shi, Zhinian Zhao, Chao Yang

“…The analysis has been conducted for a coastal region in China, where derived features, such as normalized difference vegetation index (NDVI), salinity indices, and SAR-based soil moisture proxies, have been used as inputs to the CNN model. The model achieved an overall accuracy of 87% and a kappa coefficient of 0.82, outperforming traditional classification methods by leveraging spatial feature learning and data augmentation. …”
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Analysing factors underlying the reporting of established non-native species by Phillip J. Haubrock, Ismael Soto, Ross N. Cuthbert, Irmak Kurtul, Elizabeta Briski

“…We employ a series of broadscale national predictors classified into ‘research’, ‘economy’, ‘environment & culture’, and ‘land-use’ to predict successful establishment. Our null models, which assume the distribution of non-native species mirrors that of each predictor, accurately predicted non-native species numbers across European countries. …”
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Estimation of Relative Sea Level Change in Locations Without Tide Gauges Using Artificial Neural Networks by Heeryun Kim, Young Il Park, Wansik Ko, Taehyun Yoon, Jeong-Hwan Kim

“…Unlike conventional forecasting approaches focused on future time series prediction, the proposed model is designed to learn spatial distribution patterns and temporal rates of sea level change from a fusion of satellite altimetry and tide gauge data. …”
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Machine learning approaches for imputing missing meteorological data in Senegal by Mory Toure, Nana Ama Browne Klutse, Mamadou Adama Sarr, Md Abul Ehsan Bhuiyan, Annine Duclaire Kenne, Wassila Mamadou Thiaw, Daouda Badiane, Amadou Thierno Gaye, Ousmane Ndiaye, Cheikh Mbow

“…XGB consistently outperformed all methods across variables and scenarios, achieving the highest average predictive accuracy with R2 values up to [95 % CI: 0.82–0.88], along with lower Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). …”
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Physical forcing mechanisms controlling the variability of chlorophyll-a over the Royal-Charlotte and Abrolhos Banks-Eastern Brazilian Shelf. by Renato David Ghisolfi, Meyre Pereira da Silva, Felipe Thomaz dos Santos, Ricardo Nogueira Servino, Mauro Cirano, Fabiano Lopes Thompson

“…The present study investigates the seasonal and spatial distributions of chlorophyll-a (Chl-a) and water conditions by analyzing nine years (2003-2011) of level-3 Moderate-resolution Imaging Spectroradiometer (MODIS) derived Chl-a, National Centers for Environmental Prediction (NCEP)/ETA model-derived winds, NCEP model-derived heat fluxes, thermohaline and velocity results from the Hybrid Circulation Ocean Model (HYCOM) 1/12o assimilated simulation. …”
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Radar Echo Extrapolation Based on Translator Coding and Decoding Conditional Generation Adversarial Network by Xingang Mou, Yuan He, Wenfeng Li, Xiao Zhou

“…In response to the shortcomings of current spatiotemporal prediction models, which frequently encounter difficulties in temporal feature extraction and the forecasting of medium to high echo intensity regions over extended sequences, this study presents a novel model for radar echo extrapolation that combines a translator encoder-decoder architecture with a spatiotemporal dual-discriminator conditional generative adversarial network (STD-TranslatorNet). …”
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