Soil moisture profile estimation under bare and vegetated soils using combined L-band and P-band radiometer observations: An incoherent modeling approach DOI Creative Commons
Foad Brakhasi, Jeffrey P. Walker, Jasmeet Judge

et al.

Remote Sensing of Environment, Journal Year: 2024, Volume and Issue: 307, P. 114148 - 114148

Published: April 11, 2024

Effective water management in agriculture requires a comprehensive understanding of the distribution content throughout soil profile to root zone. This knowledge empowers farmers and managers make informed decisions regarding irrigation timing quantity for optimizing crop growth. To estimate moisture profile, this study utilized combined L- P-band radiometry with four incoherent radiative transfer models, including three multi-layer models based on zero-order (IZ), first order (IF) solution (IS) approximation, uniform model (UM) model, as well stratified coherent Njoku (NM). The impact vegetation was considered through conventional tau-omega model. Linear (Li) second-order polynomial (Pn2) functions were used represent shape profile. Observations from tower-based experiment under various land cover conditions, bare, bare-weed, grass, wheat corn, used. mean square error (RMSE) calculated between observed estimated profiles. results revealed comparable RMSE values all five Pn2 function outperforming Li estimating deeper layers. Regardless employed utilizing employing yielded RMSEs 0.03 m3/m3, 0.08 0.1 m3/m3 over depths 0–5 cm, 0–30 0–60 respectively. A comparison indicated that latter slightly outperformed former dry bare exhibiting 0.003 lower at surface while nearly equal performance bottom Furthermore, provided only better than UM especially shallow layers, average entire being 0.002 lower. Consequently, complexity is not justified small gain performance. depth which reasonable ranged 1 cm (under wet corn) 39 bare), depended gradient These important findings pave way global scale using future satellite missions.

Language: Английский

Assessment of the urban waterlogging resilience and identification of its driving factors: A case study of Wuhan City, China DOI
Shuai Xiao, Lei Zou, Jun Xia

et al.

The Science of The Total Environment, Journal Year: 2023, Volume and Issue: 866, P. 161321 - 161321

Published: Jan. 2, 2023

Language: Английский

Citations

54
Spatial-temporal variability pattern of multi-depth soil moisture jointly driven by climatic and human factors in China DOI
Yangxiaoyue Liu,

Yaping Yang

Journal of Hydrology, Journal Year: 2023, Volume and Issue: 619, P. 129313 - 129313

Published: Feb. 26, 2023

Language: Английский

Citations

23
Past dynamics and future prediction of the impacts of land use cover change and climate change on landscape ecological risk across the Mongolian plateau DOI
Jingpeng Guo,

Beibei Shen,

Haoxin Li

et al.

Journal of Environmental Management, Journal Year: 2024, Volume and Issue: 355, P. 120365 - 120365

Published: March 1, 2024

Language: Английский

Citations

14
Spatiotemporal variability and dominant driving factors of satellite observed global soil moisture from 2001 to 2020 DOI

Yu-Xuan Li,

Pei Leng, Abba Aliyu Kasim

et al.

Journal of Hydrology, Journal Year: 2025, Volume and Issue: unknown, P. 132848 - 132848

Published: Feb. 1, 2025

Language: Английский

Citations

1
Quantifying the Contribution of Driving Factors on Distribution and Change of Net Primary Productivity of Vegetation in the Mongolian Plateau DOI Creative Commons
Chaohua Yin, Xiaoqi Chen, Min Luo

et al.

Remote Sensing, Journal Year: 2023, Volume and Issue: 15(8), P. 1986 - 1986

Published: April 9, 2023

In recent years, multiple disturbances have significantly altered terrestrial ecosystems in arid and semi-arid regions, particularly on the Mongolian Plateau (MP). Net primary productivity (NPP) of vegetation is an essential component surface carbon cycle. As such, it characterizes state variation reflects productive capacity natural vegetation. This study revealed complex relationship between environment NPP ecologically fragile sensitive MP. The modified Carnegie–Ames–Stanford Approach (CASA) model was used to simulate NPP. Further, contributions topography, vegetation, soils, climate NPP’s distribution spatiotemporal were explored using geographic detector (GDM) structural equation (SEM). study’s findings indicate following: (1) NPPs for different types MP order broad-leaved forest > meadow steppe coniferous cropland shrub typical sandy land alpine desert steppe. (2) showed increasing trend during growing seasons from 2000 2019, with forests providing larger stocks. It also maintained a more stable level productivity. (3) Vegetation cover, precipitation, soil moisture, solar radiation key factors affecting spatial distribution. primarily explained by normalized difference index, radiation, type, type (-statistics = 0.86, 0.71, 0.67, 0.57, respectively); contribution temperature small 0.26), topographic had least influence distribution, as their amounted less than 0.20. (4) A SEM constructed based index (NDVI), temperature, moisture 17% 65% MP’s variations. total effects variations absolute values NDVI (0.47) precipitation (0.33) (0.16) (0.14) (0.02), mechanisms responsible differed slightly among relevant types. Overall, this can help understand offer new perspective regional ecosystem management.

Language: Английский

Citations

21
Quantitative detection and attribution of soil moisture heterogeneity and variability in the Mongolian Plateau DOI
Min Luo, Fanhao Meng, Yunqian Wang

et al.

Journal of Hydrology, Journal Year: 2023, Volume and Issue: 621, P. 129673 - 129673

Published: May 15, 2023

Language: Английский

Citations

16
Spatial and temporal variation of economic resilience and its drivers: Evidence from Chinese cities DOI Creative Commons
Huang Jie, Qianqian Li, Minzhe Du

et al.

Frontiers in Environmental Science, Journal Year: 2023, Volume and Issue: 11

Published: Feb. 16, 2023

Based on panel data of 282 cities in China from 2005 to 2019, this paper constructs an economic resilience evaluation index system three dimensions and applies the entropy value method measure it. The two-stage nested Thiel index, kernel density estimation geographic detector methods are also used explore characteristics their spatial temporal divergence driving factors. We find that Chinese has increased rapidly over sample period, but with significant variation, intra-provincial variation being main source overall variation. Without considering conditions, a strong stability. In case factors have impact low resilience, not high resilience. Differences technological innovation capabilities key driver cities. interaction any two enhances respective effects differentiation above findings, should actively targeted differentiated ways improve based comparative advantages, accelerate construction collaborative improvement mechanism for urban support China. Our findings provide useful reference promoting concerted

Language: Английский

Citations

15
Spatial Pattern of Changing Vegetation Dynamics and Its Driving Factors across the Yangtze River Basin in Chongqing: A Geodetector-Based Study DOI Creative Commons
Bo Yao, Lei Ma,

Hongtao Si

et al.

Land, Journal Year: 2023, Volume and Issue: 12(2), P. 269 - 269

Published: Jan. 17, 2023

Revealing the spatial dynamics of vegetation change in Chongqing and their driving mechanisms is major value to regional ecological management conservation. Using several data sets, including SPOT Normalized Difference Vegetation Index (NDVI), meteorological, soil, digital elevation model (DEM), human population density others, combined with trend analysis, stability geographic detectors, we studied pattern temporal variation NDVI its across from 2000 2019, quantitatively analyzed relative contribution 18 drivers (natural or variables) that could influence dynamics. Over 20-year period, found region’s had an annual average 0.78, greater than 0.7 for 93.52% total area. Overall, increased at a rate 0.05/10 year, 81.67% areas undergoing significant expansion, primarily metropolitan Chongqing’s Three Gorges Reservoir Area (TGR) Wuling Mountain (WMA). The main factors influencing were activities, climate, topography, which most influential variables respectively night light brightness (NLB, 51.9%), air temperature (TEM, 47%), (ELE, 44.4%). Furthermore, interactions between differing types stronger those arising similar ones; all pairwise interaction tested, 92.9% them characterized by two-factor enhancement. three powerful detected NLB ∩ TEM (62.7%), atmospheric pressure (PRS, 62.7%), ELE (61.9%). Further, identified appropriate kind range key elements shaping development Altogether, our findings can serve as timely scientific foundation developing vegetative resource strategy Yangtze River basin duly takes into account local terrain, activity.

Language: Английский

Citations

14
Daily Streamflow Forecasting Using Networks of Real-Time Monitoring Stations and Hybrid Machine Learning Methods DOI Open Access
Yue Zhang, Zimo Zhou, Ying Deng

et al.

Water, Journal Year: 2024, Volume and Issue: 16(9), P. 1284 - 1284

Published: April 30, 2024

Considering the increased risk of urban flooding and drought due to global climate change rapid urbanization, imperative for more accurate methods streamflow forecasting has intensified. This study introduces a pioneering approach leveraging available network real-time monitoring stations advanced machine learning algorithms that can accurately simulate spatial–temporal problems. The Spatio-Temporal Attention Gated Recurrent Unit (STA-GRU) model is renowned its computational efficacy in events with forecast horizon 7 days. novel integration groundwater level, precipitation, river discharge as predictive variables offers holistic view hydrological cycle, enhancing model’s accuracy. Our findings reveal 7-day period, STA-GRU demonstrates superior performance, notable improvement mean absolute percentage error (MAPE) values R-square (R2) alongside reductions root squared (RMSE) (MAE) metrics, underscoring generalizability reliability. Comparative analysis seven conventional deep models, including Long Short-Term Memory (LSTM), Convolutional Neural Network LSTM (CNNLSTM), (ConvLSTM), (STA-LSTM), (GRU), GRU (CNNGRU), STA-GRU, confirms power STA-LSTM models when faced long-term prediction. research marks significant shift towards an integrated deep-learning forecasting, emphasizing importance spatially temporally encompassing variability within watershed’s stream network.

Language: Английский

Citations

5
Console-Based Mapping of Mongolia Using GMT Cartographic Scripting Toolset for Processing TerraClimate Data DOI Creative Commons
Polina Lemenkova

Geosciences, Journal Year: 2022, Volume and Issue: 12(3), P. 140 - 140

Published: March 21, 2022

This paper explores spatial variability of the ten climatic variables Mongolia in 2019: average minimal and maximal temperatures, wind speed, soil moisture, downward surface shortwave radiation (DSRAD), snow water equivalent (SWE), vapor pressure deficit (VPD), anomaly (VAP), monthly precipitation Palmer Drought Severity Index (PDSI). The PDSI demonstrates simplified balance estimating relative moisture conditions Mongolia. research presents mapping climate datasets derived from TerraClimate open source repository meteorological measurements NetCDF format. methodology presented compiled observations visualised by GMT coding approach using Generic Mapping Tools (GMT) cartographic scripting toolset. results present 10 new maps data over made automated techniques GMT. Spatial environmental analysis were conducted which determine distribution temperature extremes, speed DSRAD. DSRAD showed minimum at 40 Wm−2, maximum 113 Wm−2 Gobi Desert region, SWE (up to 491 mm), VAP VPD compared with landmass parameters represent powerful tools address complex regional issues Mongolia, a country contrasting topography, extreme unique setting.

Language: Английский

Citations

21