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Tantray et al. (2026) Enhancing soil hydrothermal efficiency through spectral-selective mulching and drip irrigation in broccoli (Brassica oleracea var. italica L.)
This study evaluates the impact of various mulching materials on soil properties and broccoli yield, finding that silver plastic mulch optimizes productivity while organic mulch enhances soil health.
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Shen et al. (2026) SMFS-RF: a knowledge-guided machine-learning method for crop phenology extraction from fine-resolution vegetation index data
The study introduces SMFS-RF, a knowledge-guided machine learning framework that combines shape model fitting with random forest regression to accurately extract eight key rice phenological stages from fine-resolution NDVI data.
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Wang et al. (2026) Modulation of High‐Temperature Events in the Pearl River Delta by Tropical Cyclones With Different Track Types
This study examines how different tropical cyclone (TC) track types influence temperature events in the Pearl River Delta (PRD) and finds that global warming will likely weaken both TC-induced warming and cooling effects due to a decrease in TC frequency.
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Deng et al. (2026) Effects of aboveground biomass and soil moisture drought on VOD-based global isohydricity estimates
This study evaluates the robustness of two VOD-based isohydricity metrics ($\sigma$ and $R_{slope}$), concluding that $R_{slope}$ is significantly more stable and less influenced by biomass dynamics and dataset variations than $\sigma$.
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Park et al. (2026) Disentangling the relative importance of tree-cast shadows and transpiration in mitigating urban extreme heat
This study uses numerical simulations to quantify the cooling effects of street trees, finding that transpiration is significantly more effective than tree-cast shadows in mitigating urban extreme heat during the daytime.
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Shafiei et al. (2026) Downscaling SMAP Soil Moisture to 1 km with Machine Learning and MODIS Data for Agricultural Drought Assessment in Békés County, Hungary
This study compares three machine learning frameworks to downscale SMAP soil moisture data from 9 km to 1 km in Békés County, Hungary, concluding that Random Forest provides the most accurate high-resolution mapping.
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Yuan et al. (2026) Extreme Hydrological Events and Lake-Wetland Landscape Dynamics: Toward Adaptive Management in Large Freshwater Systems-Insights from Poyang Lake, China
This study analyzes the impact of compound drought and flood disturbances on Poyang Lake, identifying critical water level thresholds that trigger abrupt transitions in landscape connectivity and habitat stability.
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Fan et al. (2026) Three‐Dimensional Simulation of Tile Drainage System for Farmland: Quantifying Tile Drainage Impacts on Hydrological Cycle
This study investigates the impact of tile drainage design—specifically depth and spacing—on evapotranspiration (ET) and water budgeting in an Iowa corn field using an enhanced PFLOTRAN model. The results indicate that tile depth significantly influences ET and surface runoff, whereas tile spacing has a minimal effect.
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Schneider (2026) The satellite revolution for monitoring forest ecosystems needs knowledge integration
The paper argues that the primary challenge in forest monitoring has shifted from a lack of data to the need for integrating satellite observations with field data and models across scales to address biodiversity and climate crises.
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Jung et al. (2026) Development of a New Generic AI Model for Spatio‐Temporal Prediction of Soil Moisture and Soil Water Isotopes
The study implements a sequential AI approach combining LSTM and Random Forest models to simulate daily soil moisture and soil water isotopes in a mixed land use catchment, demonstrating superior performance over traditional process-based models.
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Cao et al. (2026) Anthropogenic dominance of water storage variability in the Yellow River Basin: A machine learning synthesis of multi-source data (1981–2031)
This study utilizes a machine learning synthesis of multi-source data to analyze water storage variability in the Yellow River Basin from 1981 to 2031, concluding that anthropogenic factors are the dominant driver.
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Feng et al. (2026) Reconstructing terrestrial water storage and quantifying drought evolution in Australia using LSTM networks
The study implements a Long Short-Term Memory (LSTM) framework to reconstruct high-resolution Terrestrial Water Storage (TWS) in Australia by fusing GNSS and GRACE data with meteorological inputs, effectively filling GRACE mission gaps and improving drought monitoring.
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Tao et al. (2026) Drought Propagates Asynchronously Across Hydrological Compartments: Stage Differences in Propagation Time and Implications for Drought Monitoring
This study examines the timing and characteristics of drought propagation across meteorological, soil, and groundwater compartments in China, revealing that the transition from soil to groundwater is significantly slower than the transition from meteorological to soil conditions.
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Çavuş et al. (2026) Budyko Curve Conformance Unravelling Hydrological Change in a Semi‐Arid Mediterranean‐Climate River Basin
This study utilizes a Budyko-based water balance framework to analyze long-term streamflow changes in the Gediz River Basin, concluding that climate variability—specifically declining precipitation—is a primary driver of streamflow reduction since a regime shift in 1984.
