Solar-Induced Chlorophyll Fluorescence-Based GPP Estimation and Analysis of Influencing Factors for Xinjiang Vegetation DOI Open Access
Cong Xue, Mei Zan, Yanlian Zhou

et al.

Forests, Journal Year: 2024, Volume and Issue: 15(12), P. 2100 - 2100

Published: Nov. 27, 2024

With climate change and the intensification of human activity, drought event frequency has increased, affecting Gross Primary Production (GPP) terrestrial ecosystems. Accurate estimation GPP in-depth exploration its response mechanisms to are essential for understanding ecosystem stability developing strategies adaptation. Combining remote sensing technology machine learning is currently mainstream method estimating in ecosystems, which can eliminate uncertainty model parameters errors input data. This study employed extreme gradient boosting, random forest (RF), light use efficiency models. Additionally, we integrated solar-induced chlorophyll fluorescence (SIF), near-infrared reflectance vegetation, leaf area index (LAI) construct various The standardised precipitation evapotranspiration (SPEI) was utilised at timescales analyse relationship between SPEI during dry years. Moreover, potential pathways coefficients environmental factors that influence were explored using structural equation modelling. Our key findings include following: (1) combining SIF RF algorithms exhibits higher accuracy applicability vegetation arid zone Xinjiang, with an overall (MODIS R2) 0.775; (2) Xinjiang had different characteristics drought, optimal timescale respond 9 months, a mean correlation coefficient 0.244 grass land SPEI09, indicating high sensitivity; (3) modelling, found temperature affect both directly indirectly through LAI. provides reliable tool methodology conclusions important references similar environments. In addition, this bridges research gap timescales, mechanism natural on scientific basis early warning management. Further validation longer time series required confirm robustness model.

Language: Английский

Forecasting land surface drought in urban environments based on machine learning model DOI
Junpai Chen, Hao Zheng

Sustainable Cities and Society, Journal Year: 2024, Volume and Issue: unknown, P. 106048 - 106048

Published: Dec. 1, 2024

Language: Английский

Citations

1
Assessment of Effectiveness and Suitability of Soil and Water Conservation Measures on Hillslopes of the Black Soil Region in Northeast China DOI Creative Commons
Haiou Shen, Wei Hu, Che Xiao-cui

et al.

Agronomy, Journal Year: 2024, Volume and Issue: 14(8), P. 1755 - 1755

Published: Aug. 10, 2024

There are four sizable black soil regions throughout the world, all of which valuable natural resources. The region in Northeast China is a major foundation for grain production. Serious risks erosion do exist, and they have an immediate impact on both country’s food security future ecological security. Many water conservation measures been put place to control erosion. However, how effective suitable these measures? Currently, systematic analyses assessments lacking. objective this study was assess effectiveness suitability hillslopes using comprehensive index method Pressure–State–Response model. categorization were similar methods: that is, very included no-tillage + straw mulch ridge belt or contour ridge. two methods validated one another. Thus, standard useful choosing best different regions.

Language: Английский

Citations

0
Erosion–Accumulative Soil Cover Patterns of Dry-Steppe Agrolandscape, Rostov Region DOI
N. B. Khitrov, Е. И. Кравченко, Д. И. Рухович

et al.

Eurasian Soil Science, Journal Year: 2024, Volume and Issue: 57(9), P. 1409 - 1432

Published: Sept. 1, 2024

Language: Английский

Citations

0
Solar-Induced Chlorophyll Fluorescence-Based GPP Estimation and Analysis of Influencing Factors for Xinjiang Vegetation DOI Open Access
Cong Xue, Mei Zan, Yanlian Zhou

et al.

Forests, Journal Year: 2024, Volume and Issue: 15(12), P. 2100 - 2100

Published: Nov. 27, 2024

With climate change and the intensification of human activity, drought event frequency has increased, affecting Gross Primary Production (GPP) terrestrial ecosystems. Accurate estimation GPP in-depth exploration its response mechanisms to are essential for understanding ecosystem stability developing strategies adaptation. Combining remote sensing technology machine learning is currently mainstream method estimating in ecosystems, which can eliminate uncertainty model parameters errors input data. This study employed extreme gradient boosting, random forest (RF), light use efficiency models. Additionally, we integrated solar-induced chlorophyll fluorescence (SIF), near-infrared reflectance vegetation, leaf area index (LAI) construct various The standardised precipitation evapotranspiration (SPEI) was utilised at timescales analyse relationship between SPEI during dry years. Moreover, potential pathways coefficients environmental factors that influence were explored using structural equation modelling. Our key findings include following: (1) combining SIF RF algorithms exhibits higher accuracy applicability vegetation arid zone Xinjiang, with an overall (MODIS R2) 0.775; (2) Xinjiang had different characteristics drought, optimal timescale respond 9 months, a mean correlation coefficient 0.244 grass land SPEI09, indicating high sensitivity; (3) modelling, found temperature affect both directly indirectly through LAI. provides reliable tool methodology conclusions important references similar environments. In addition, this bridges research gap timescales, mechanism natural on scientific basis early warning management. Further validation longer time series required confirm robustness model.

Language: Английский

Citations

0