A 21-year dataset (2000–2020) of gap-free global daily surface soil moisture at 1-km grid resolution

Scientific Data, Journal Year: 2023, Volume and Issue: 10(1)

Published: March 15, 2023

Abstract Global soil moisture estimates from current satellite missions are suffering inherent discontinuous observations and coarse spatial resolution, which limit applications especially at the fine scale. This study developed a dataset of global gap-free surface (SSM) daily 1-km resolution 2000 to 2020. is achieved based on European Space Agency - Climate Change Initiative (ESA-CCI) SSM combined product 0.25° resolution. Firstly, an operational gap-filling method was fill missing data in ESA-CCI using ERA5 reanalysis dataset. Random Forest algorithm then adopted disaggregate coarse-resolution 1-km, with help International Soil Moisture Network in-situ other optical remote sensing datasets. The generated had good accuracy, high correlation coefficent (0.89) low unbiased Root Mean Square Error (0.045 m 3 /m ) by cross-validation. To best our knowledge, this currently only long-term far.

Language: Английский

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Vapor pressure deficit (VPD) downscaling based on multi-source remote sensing, in-situ observation, and machine learning in China

Journal of Hydrology Regional Studies, Journal Year: 2025, Volume and Issue: 57, P. 102192 - 102192

Published: Jan. 13, 2025

Language: Английский

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Investigating the Characteristics and Drivers of Slow Droughts and Flash Droughts: A Multi‐Temporal Scale Drought Identification Framework

Water Resources Research, Journal Year: 2025, Volume and Issue: 61(2)

Published: Feb. 1, 2025

Abstract Global climate change has altered the characteristics of conventional drought events, with an increasing number Slow droughts (SD) rapidly transitioning into Flash (FD). This study introduces a novel multi‐temporal scale identification framework (MTSDIF) that classifies historical agricultural events three types: SD, FD, and Slow‐to‐Flash Drought (SFD). Based on MTSDIF, GLDAS‐Noah root zone soil moisture dataset was used to analyze spatiotemporal characteristics, evolution, driving factors in China. Our confirms effectiveness proposed MTSDIF classifying different onset speeds (SD, SFD). The results indicate that, from 1980 2020, types China exhibited short‐term, medium‐term, long‐term periodic oscillations. Before 2000, SD were predominant type China, but post‐2000, areas affected by FD SFD have been continuously expanding. Compared key meteorological elements influencing show anomalies exceeding 0.5 times standard deviation. In southeastern regions human‐impacted soils, leached incept soils exhibit higher response frequency FD. Sea surface temperature indices, including interannual El Niño‐Southern Oscillation Pacific interdecadal variations such as +PDO −AMO, significantly influence occurrence monsoon ( p < 0.01). Together, highlight necessity understanding disparities consistencies land‐atmosphere‐ocean mechanisms behind varying speeds.

Language: Английский

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Remote sensing of root zone soil moisture: A review of methods and products

Journal of Hydrology, Journal Year: 2025, Volume and Issue: 656, P. 133002 - 133002

Published: March 5, 2025

Language: Английский

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Soil Emissions of Reactive Nitrogen Accelerate Summertime Surface Ozone Increases in the North China Plain

Environmental Science & Technology, Journal Year: 2023, Volume and Issue: 57(34), P. 12782 - 12793

Published: Aug. 19, 2023

Summertime surface ozone in China has been increasing since 2013 despite the policy-driven reduction fuel combustion emissions of nitrogen oxides (NOx). Here we examine role soil reactive (Nr, including NOx and nitrous acid (HONO)) 2013-2019 increase over North Plain (NCP), using GEOS-Chem chemical transport model simulations. We update add HONO based on observation-constrained parametrization schemes. The estimates significant daily maximum 8 h average (MDA8) enhancement from Nr 8.0 ppbv NCP 5.5 June-July 2019. identify a strong competing effect between sources production region. find that accelerate by 3.0 ppbv. contribution, however, is not primarily driven weather-induced increases emissions, but concurrent decreases which enhance efficiency pushing toward more NOx-sensitive regime. Our results reveal an important indirect emission trends highlighting necessity to consider interaction anthropogenic biogenic mitigation Plain.

Language: Английский

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Soil Moisture Dominates the Forest Productivity Decline During the 2022 China Compound Drought‐Heatwave Event

Geophysical Research Letters, Journal Year: 2023, Volume and Issue: 50(17)

Published: Sept. 7, 2023

Abstract Compound drought‐heatwave (CDHW) events threaten ecosystem productivity and are often characterized by low soil moisture (SM) high vapor pressure deficit (VPD). However, the relative roles of SM VPD in constraining forest during CDHWs remain controversial. In summer 2022, China experienced a record‐breaking CDHW event (DH2022). Here, we applied satellite remote‐sensing data meteorological data, machine‐learning techniques to quantify individual contributions variations investigate their interactions development DH2022. The results reveal that SM, rather than VPD, dominates decline We identified possible critical tipping point below which would quickly with decreasing SM. Furthermore, illuminated evolution evapotranspiration, productivity, throughout Our findings broaden understanding response extreme at scale.

Language: Английский

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Leveraging multisource data for accurate agricultural drought monitoring: A hybrid deep learning model

Agricultural Water Management, Journal Year: 2024, Volume and Issue: 293, P. 108692 - 108692

Published: Jan. 22, 2024

Accurate monitoring of agricultural droughts in data-scarce areas remains a challenge due to their intricate spatiotemporal patterns. Deep learning represents promising approach for developing efficient drought models. In this study, hybrid deep model, combining convolutional neural network and random forest (CNN-RF), is proposed monitor mountainous region located Southwest China. The model integrates multisource data obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite sensor, Global Land Data Assimilation System (GLDAS), Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) digital elevation (DEM) reproduce station-based 3-month Standardized Evapotranspiration Index (SPEI-3) during 2001–2020. Performance evaluation utilized an situ soil moisture dataset grain yields as benchmarks. results demonstrated superiority CNN-RF over both CNN RF models terms estimating SPEI-3 forecasting categories, quantified by lowest root mean square error (RMSE<0.4), highest correlation coefficient (CC>0.9) multi-class receiver operating characteristic (ROC) based area under curves (AUC) (AUC=0.86). successfully reproduced spatial heterogeneity pattern while maintaining temporal consistency actual conditions. Notably, strong was observed between simulated Soil Moisture (SSMI-3) (CC=0.42, p < 0.01). Moreover, model-estimated cropland winter early spring months exhibited significant summer harvest (CC<−0.45, 0.05). Another advantage its ability generalize well limited training samples. This study introduces scalable, simple, method reliably large leveraging freely available data, which can also be easily adapted other vegetated regions ground observations.

Language: Английский

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Response of blue-green water to climate and vegetation changes in the water source region of China's South-North water Diversion Project

Journal of Hydrology, Journal Year: 2024, Volume and Issue: 634, P. 131061 - 131061

Published: March 11, 2024

Language: Английский

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Spatial Distribution Prediction of Soil Heavy Metals Based on Random Forest Model

Sustainability, Journal Year: 2024, Volume and Issue: 16(11), P. 4358 - 4358

Published: May 22, 2024

Mastering the spatial distribution of soil heavy metal content and evaluating pollution status metals is great significance for ensuring agricultural production protecting human health. This study used a machine learning model to in coastal city eastern China. Having obtained six contents, including Cr, Cd, Pb, As, Hg, Ni, environmental variables such as precipitation, moisture, population density were selected. Random forest (RF) was content. The research findings indicate that RF demonstrates robust predictive capability discerning metals, factor can explain 60%, 52.3%, 53.5%, 63.1%, 61.2%, 51.2% Ni soil, respectively. Among chosen variables, precipitation exert notable influences on outcomes model. Specifically, exhibits most substantial impact Cr whereas emerges primary determinant Hg. prediction results show area are less affected by activities, while Hg more industrial production. Research has shown using models predicting distributions certain significance.

Language: Английский

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Using remote sensing and machine learning to generate 100-cm soil moisture at 30-m resolution for the black soil region of China: Implication for agricultural water management

Agricultural Water Management, Journal Year: 2025, Volume and Issue: 309, P. 109353 - 109353

Published: Feb. 2, 2025

Language: Английский

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