Jianli Ding
Biographic Data
| ID | 7513480 |
|---|---|
| NAME | Jianli Ding |
| GIVEN NAMES | Jianli |
| FAMILY NAME | Ding |
| SIGNATURE | DING J |
| AFFILIATIONS | Xinjiang Institute of Technology Aksu China |
| ORCID | 0000-0002-5024-931X |
| VERIFIED | Yes |
| TOTAL WORKS | 16 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 16 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2015 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 0 |
Assessing Natural Resource Carrying Capacity in Xinjiang Using Remote Sensing
Xinjiang has undergone rapid oasis expansion, leading to significant changes in land use and, consequently, to unsustainable issues regarding natural resources. Most studies on natural resource carrying capacity (NRCC) focus on single resource elements and rarely examine patterns at the pixel scale. Although remote sensing technologies and cloud platforms offer significant advantages in NRCC assessment, this potential remains underutilized. Meanw…
Application of the Process‐Oriented and Machine Learning Combined Model in Mapping of Soil Organic Carbon of Non‐Waterlogged Mineral Soils at National Scale
Integrating process‐oriented (PO) and machine learning (ML) models is effective for obtaining dynamic spatial information on soil organic carbon (SOC) stocks. However, PO‐ML integration, particularly at large scales, has received insufficient attention. This gap limits our understanding of and predictive capabilities regarding SOC dynamics. To explore the adaptability and effectiveness of PO‐ML integration on a large scale, we constructed a natio…
Fractional Order Differentiation Preprocessing Based Fusion of Vis– NIR and pXRF
Soil contamination by heavy metals has become a significant issue threatening the ecological security of global agriculture, particularly in arid regions, where accurate monitoring of low‐concentration heavy metals remains a technical challenge. This study proposes a proximal sensing method based on the fusion of visible–near infrared (Vis–NIR) spectroscopy and portable X‐ray fluorescence (pXRF) sensors, aiming to address the limitations of tradi…
Unveiling the Advantages of UV −Vis/ NIR − pXRF Data Fusion for Precise Estimation of Soil Heavy Metals in Farmland
Heavy metal contamination in agricultural soils threatens ecosystem stability and food safety. Rapid and accurate estimation of arsenic (As), cadmium (Cd), and lead (Pb) is therefore essential for environmental protection and soil remediation. Near‐field sensing technologies provide a fast and cost‐efficient alternative to laboratory analysis, yet single‐spectrum approaches often suffer from limited information coverage and reduced prediction acc…
Hybrid Modeling of Physics and Machine Learning for Soil Moisture Inversion in Arid Regions on Google Earth Engine
Soil moisture content (SMC) is vital for agriculture and water management, but accurate monitoring remains challenging. Current methods rely on standalone physical models (e.g., Water Cloud Model) or machine learning (ML), but physical models lack accuracy in complex environments, while ML lacks physical constraints. We hypothesize that hybrid models can improve SMC estimation. Using 90 soil samples from arid oasis ecosystems, we compared four ap…
Advanced Dynamic Monitoring and Precision Analysis of Soil Salinity in Cotton Fields Using CNN ‐Attention and UAV Multispectral Imaging Integration
Accurate and timely estimates of crop exposure to salt stress are essential for monitoring crop growth and implementing effective management practices. However, most contemporary research has focused on single‐period soil salinity estimations and relied on traditional machine learning methods, which struggle to account for the temporal dynamics of soil salinity. This study proposed a modeling framework that combined multi‐temporal UAV multispectr…
Assessing Surface Water Hydrological Connectivity and Spatiotemporal Evolution in Xinjiang (2000–2020)
Assessing hydrological connectivity is crucial for maintaining the health and integrity of wetland and river‐lake ecosystems in arid regions as it plays a key role in watershed ecological balance and sustainable development. We utilized the Joint Research Center's global surface water dataset. We combined these data with connectivity indices and circuit theory to analyze the hydrological connectivity and spatiotemporal evolution of surface water …
Soil Organic Carbon Sequestration Potential, Storage, and Influencing Mechanisms in China
The soil organic carbon sequestration potential (SOC sp ) has important implications for the global carbon cycle and responses to climate change. However, there is a dearth of spatial information specifically for China within this field, and our knowledge regarding the factors influencing SOC sp remains somewhat limited. To solve this problem, this study utilized legacy soil data collected in the 1980s (1979–1984s), combined with climatic landsca…
Ameliorating saline‐sodic soils
As the demand for food continues to rise, soil salinization and sodification pose an increasingly pressing challenge. Currently, there is a knowledge gap regarding how to effectively improve saline‐sodic soils to support sustainable agricultural production, especially the lack of systematic analysis on the effects of different amendments at a global scale. To address this gap, this study aims to explore the feasibility of using exogenous amendmen…
Future changes in soil salinization across Central Asia under CMIP6 forcing scenarios
Soil salinization is a critical environmental and socio‐economic concern with global implications, and its severity is expected to amplify under changing climate. The impact of climate change on salinization in Central Asia is still not fully understood. This study addresses this gap by employing a digital soil mapping (DSM) framework. Cubist, random forest (RF), and quantile regression forests (QRF) are utilized to project variations in soil sur…
Soil salinity dynamics in arid oases during irrigated and non‐irrigated seasons
Preventing soil salinization is key to the healthy development of agroecosystems in arid regions. Notably, long‐term anthropogenic irrigation accelerates secondary salinity accumulation in arid areas; however, the regional‐scale spatial patterns of soil salinity pre‐ and post‐irrigation season remain unclear. Accordingly, field observations over two periods ( n = 182 samples), along with environmental covariates and the randomForest algorithm, we…
Soil salinity estimation
The microwave dielectric constant is a key bridge in establishing the relationship between microwave remote sensing and soil salinity (electrical conductivity, EC). However, the response between microwave dielectric spectrum type, frequency, and soil salinity is still unclear. The purpose of this study is to reveal the dielectric spectrum and frequency range closely related to soil salinity. In this study, 129 surface soil samples were collected,…
Revealing the scale‐ and location‐specific variation and control factors of soil salinity using bi‐dimensional empirical modal decomposition
The key to saline land management and soil salinity control is to clarify the dynamics of soil salinity and the characteristics of environmental factors. However, due to the high heterogeneity of soil salinity, the influence of environmental factors on soil salinity variation at specific scales and locations is still unclear. The purpose of this study is to reveal the variability characteristics and controlling factors of soil salinity in the Wei…
Changes in soil organic carbon stocks from 1980‐1990 and 2010‐2020 in the northwest arid zone of China
Soil is the largest carbon reservoir in terrestrial ecosystems, and thus minor changes in it can dramatically affect atmospheric CO 2 concentrations. In the northwestern arid zone of China, the prediction of soil organic carbon (SOC) changes is often limited by the scarcity of soil samples and the scale and depth of research, which limit the understanding of carbon cycling processes in arid zone terrestrial ecosystems. Therefore, this study produ…
Positive Effects of Organic Substitution in Reduced-Fertilizer Regimes on Bacterial Diversity and N-Cycling Functionality in Greenhouse Ecosystem
Conventional fertilization in the greenhouses of North China used excessive amounts of chemical and organic fertilizer, resulting in soil degradation and severe agricultural non-point source pollution. A nine-year study was conducted on a loamy clay soil in Shijiazhuang, Hebei province, to investigate the effects of reduced-fertilizer input regimes on soil property, bacterial diversity, nitrogen (N) cycling and their interactions. There were four…
Land-use conversion and its attribution in the Kaidu–Kongqi River Basin, China
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Land-use conversion and its attribution in the Kaidu–Kongqi River Basin, China
Revealing the scale‐ and location‐specific variation and control factors of soil salinity using bi‐dimensional empirical modal decomposition
The key to saline land management and soil salinity control is to clarify the dynamics of soil salinity and the characteristics of environmental factors. However, due to the high heterogeneity of soil salinity, the influence of environmental factors on soil salinity variation at specific scales and locations is still unclear. The purpose of this study is to reveal the variability characteristics and controlling factors of soil salinity in the Wei…
Changes in soil organic carbon stocks from 1980‐1990 and 2010‐2020 in the northwest arid zone of China
Soil is the largest carbon reservoir in terrestrial ecosystems, and thus minor changes in it can dramatically affect atmospheric CO 2 concentrations. In the northwestern arid zone of China, the prediction of soil organic carbon (SOC) changes is often limited by the scarcity of soil samples and the scale and depth of research, which limit the understanding of carbon cycling processes in arid zone terrestrial ecosystems. Therefore, this study produ…
Positive Effects of Organic Substitution in Reduced-Fertilizer Regimes on Bacterial Diversity and N-Cycling Functionality in Greenhouse Ecosystem
Conventional fertilization in the greenhouses of North China used excessive amounts of chemical and organic fertilizer, resulting in soil degradation and severe agricultural non-point source pollution. A nine-year study was conducted on a loamy clay soil in Shijiazhuang, Hebei province, to investigate the effects of reduced-fertilizer input regimes on soil property, bacterial diversity, nitrogen (N) cycling and their interactions. There were four…
Soil salinity dynamics in arid oases during irrigated and non‐irrigated seasons
Preventing soil salinization is key to the healthy development of agroecosystems in arid regions. Notably, long‐term anthropogenic irrigation accelerates secondary salinity accumulation in arid areas; however, the regional‐scale spatial patterns of soil salinity pre‐ and post‐irrigation season remain unclear. Accordingly, field observations over two periods ( n = 182 samples), along with environmental covariates and the randomForest algorithm, we…
Soil salinity estimation
The microwave dielectric constant is a key bridge in establishing the relationship between microwave remote sensing and soil salinity (electrical conductivity, EC). However, the response between microwave dielectric spectrum type, frequency, and soil salinity is still unclear. The purpose of this study is to reveal the dielectric spectrum and frequency range closely related to soil salinity. In this study, 129 surface soil samples were collected,…
Ameliorating saline‐sodic soils
As the demand for food continues to rise, soil salinization and sodification pose an increasingly pressing challenge. Currently, there is a knowledge gap regarding how to effectively improve saline‐sodic soils to support sustainable agricultural production, especially the lack of systematic analysis on the effects of different amendments at a global scale. To address this gap, this study aims to explore the feasibility of using exogenous amendmen…
Future changes in soil salinization across Central Asia under CMIP6 forcing scenarios
Soil salinization is a critical environmental and socio‐economic concern with global implications, and its severity is expected to amplify under changing climate. The impact of climate change on salinization in Central Asia is still not fully understood. This study addresses this gap by employing a digital soil mapping (DSM) framework. Cubist, random forest (RF), and quantile regression forests (QRF) are utilized to project variations in soil sur…
Advanced Dynamic Monitoring and Precision Analysis of Soil Salinity in Cotton Fields Using CNN ‐Attention and UAV Multispectral Imaging Integration
Accurate and timely estimates of crop exposure to salt stress are essential for monitoring crop growth and implementing effective management practices. However, most contemporary research has focused on single‐period soil salinity estimations and relied on traditional machine learning methods, which struggle to account for the temporal dynamics of soil salinity. This study proposed a modeling framework that combined multi‐temporal UAV multispectr…
Assessing Surface Water Hydrological Connectivity and Spatiotemporal Evolution in Xinjiang (2000–2020)
Assessing hydrological connectivity is crucial for maintaining the health and integrity of wetland and river‐lake ecosystems in arid regions as it plays a key role in watershed ecological balance and sustainable development. We utilized the Joint Research Center's global surface water dataset. We combined these data with connectivity indices and circuit theory to analyze the hydrological connectivity and spatiotemporal evolution of surface water …
Soil Organic Carbon Sequestration Potential, Storage, and Influencing Mechanisms in China
The soil organic carbon sequestration potential (SOC sp ) has important implications for the global carbon cycle and responses to climate change. However, there is a dearth of spatial information specifically for China within this field, and our knowledge regarding the factors influencing SOC sp remains somewhat limited. To solve this problem, this study utilized legacy soil data collected in the 1980s (1979–1984s), combined with climatic landsca…
Assessing Natural Resource Carrying Capacity in Xinjiang Using Remote Sensing
Xinjiang has undergone rapid oasis expansion, leading to significant changes in land use and, consequently, to unsustainable issues regarding natural resources. Most studies on natural resource carrying capacity (NRCC) focus on single resource elements and rarely examine patterns at the pixel scale. Although remote sensing technologies and cloud platforms offer significant advantages in NRCC assessment, this potential remains underutilized. Meanw…
Application of the Process‐Oriented and Machine Learning Combined Model in Mapping of Soil Organic Carbon of Non‐Waterlogged Mineral Soils at National Scale
Integrating process‐oriented (PO) and machine learning (ML) models is effective for obtaining dynamic spatial information on soil organic carbon (SOC) stocks. However, PO‐ML integration, particularly at large scales, has received insufficient attention. This gap limits our understanding of and predictive capabilities regarding SOC dynamics. To explore the adaptability and effectiveness of PO‐ML integration on a large scale, we constructed a natio…
Fractional Order Differentiation Preprocessing Based Fusion of Vis– NIR and pXRF
Soil contamination by heavy metals has become a significant issue threatening the ecological security of global agriculture, particularly in arid regions, where accurate monitoring of low‐concentration heavy metals remains a technical challenge. This study proposes a proximal sensing method based on the fusion of visible–near infrared (Vis–NIR) spectroscopy and portable X‐ray fluorescence (pXRF) sensors, aiming to address the limitations of tradi…
Unveiling the Advantages of UV −Vis/ NIR − pXRF Data Fusion for Precise Estimation of Soil Heavy Metals in Farmland
Heavy metal contamination in agricultural soils threatens ecosystem stability and food safety. Rapid and accurate estimation of arsenic (As), cadmium (Cd), and lead (Pb) is therefore essential for environmental protection and soil remediation. Near‐field sensing technologies provide a fast and cost‐efficient alternative to laboratory analysis, yet single‐spectrum approaches often suffer from limited information coverage and reduced prediction acc…
Hybrid Modeling of Physics and Machine Learning for Soil Moisture Inversion in Arid Regions on Google Earth Engine
Soil moisture content (SMC) is vital for agriculture and water management, but accurate monitoring remains challenging. Current methods rely on standalone physical models (e.g., Water Cloud Model) or machine learning (ML), but physical models lack accuracy in complex environments, while ML lacks physical constraints. We hypothesize that hybrid models can improve SMC estimation. Using 90 soil samples from arid oasis ecosystems, we compared four ap…
Environmental Science (12 works) · Soil water (11 works) · Geology (10 works) · Soil Geostatistics and Mapping (10 works) · Soil Science (9 works) · Arid (6 works) · Salinity (6 works) · Soil and Unsaturated Flow (6 works) · Ecology (5 works) · Geography (5 works)