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Land
Special Issues
Land Use/Cover Change and Its Impacts on Regional Sustainable Development
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Land Use/Cover Change and Its Impacts on Regional Sustainable Development

A special issue of Land (ISSN 2073-445X). This special issue belongs to the section "Urban Contexts and Urban-Rural Interactions".

Deadline for manuscript submissions: 30 June 2025 | Viewed by 10024

Special Issue Editors

Dr. Shengbiao Wu

E-Mail Website
Guest Editor
Division of Landscape Architecture, Faculty of Architecture, The University of Hong Kong, Pokfulam, Hong Kong 999077, China
Interests: 3D morphology remote sensing; urban built environment; human-environment spatiotemporal interactions; Sustainable Development Goals (SDGs)
Special Issues, Collections and Topics in MDPI journals
Dr. Xiao Zhang

E-Mail Website
Guest Editor
1. Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
2. International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China
Interests: global land cover mapping and dynamic monitoring; impervious surface mapping
Special Issues, Collections and Topics in MDPI journals
Dr. Xidong Chen

E-Mail Website
Guest Editor
Division of Landscape Architecture, Faculty of Architecture, The University of Hong Kong, Hong Kong 999077, China
Interests: global land cover mapping; land cover change detection water dynamic mapping
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the global urbanization and economic development, monitoring and analyzing land use and land cover (LULC) change have become a focal point in many interdisciplinary research fields. Rapid urbanization, agricultural expansion and industrialization have contributed to significant alterations in land use and land cover patterns, often resulting in habitat fragmentation, deforestation and loss of biodiversity. These changes not only directly affect the ecological processes and functions of the affected areas, but also have far-reaching consequences on regional climate, water cycles and carbon sequestration. Moreover, the implications of LULC change on food security, human health and socio-economic well-being of communities cannot be underestimated. Therefore, accurately monitoring changes in LULC and comprehensively understanding their impacts on regional sustainable development is of paramount importance in formulating effective strategies for integrated land management. In this context, the integration of remote sensing, geographic information systems (GIS) and spatial modeling techniques has emerged as a powerful tool for monitoring, assessing and predicting LULC dynamics and their impacts on regional sustainable development. The application of these advanced technologies facilitates the generation of spatially explicit and temporally dynamic information on LULC patterns, allowing for a comprehensive analysis of the drivers, processes and consequences of LULC change.  

The goal of this Special Issue is to collect papers (original research articles and review papers) on the monitoring of changes in LULC and the assessment of their impacts related to sustainable development in the region, closely aligned with the scope of Land. The Special Issue specifically emphasizes the assessment of such impacts in relation to sustainable development in the region. Therefore, the papers published in this Special Issue are expected to contribute to the broader scope of research published in Land.

This Special Issue welcomes high-quality studies focusing on monitoring LULC changes and analyzing the impacts of their changes on regional sustainable development. Relevant themes include, but are not limited to:

  • Land cover and land use change monitoring;
  • Spatio-temporal data mining, data fusion, modeling and analysis of land cover change;
  • The relationship between land use/cover change and regional sustainable development;
  • The driving forces and mechanisms of land use/cover change;
  • Land cover changes and associated impacts on the environment.

We look forward to receiving your original research articles and reviews.

Dr. Shengbiao Wu
Dr. Xiao Zhang
Dr. Xidong Chen
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Land is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • land use/ land cover
  • change detection
  • remote sensing
  • sustainable development
  • geographic information systems
  • driving forces
  • environment

Benefits of Publishing in a Special Issue

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  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue polices can be found here.

Published Papers (6 papers)

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Research

22 pages, 11614 KiB  
Article
Analysis of the Spatial–Temporal Characteristics of Vegetation Cover Changes in the Loess Plateau from 1995 to 2020
by Zhihong Yao, Yichao Huang, Yiwen Zhang, Qinke Yang, Peng Jiao and Menghao Yang
Land 2025, 14(2), 303; https://doi.org/10.3390/land14020303 - 1 Feb 2025
Abstract
The Loess Plateau is one of the most severely affected regions by soil erosion in the world, with a fragile ecological environment. Vegetation plays a key role in the region’s ecological restoration and protection. This study employs the Geographical Detector (Geodetector) model to [...] Read more.
The Loess Plateau is one of the most severely affected regions by soil erosion in the world, with a fragile ecological environment. Vegetation plays a key role in the region’s ecological restoration and protection. This study employs the Geographical Detector (Geodetector) model to quantitatively assess the impact of natural and human factors, such as temperature, precipitation, soil type, and land use, on vegetation growth. It aims to reveal the characteristics and driving mechanisms of vegetation cover changes on the Loess Plateau over the past 26 years. The results indicate that from 1995 to 2020, the vegetation coverage on the Loess Plateau shows an increasing trend, with a fitted slope of 0.01021 and an R2 of 0.96466. The Geodetector indicates that the factors with the greatest impact on vegetation cover in the Loess Plateau are temperature, precipitation, soil type, and land use. The highest average vegetation coverage is achieved when the temperature is between −4.8 and 2 °C or 12 and 16 °C, precipitation is between 630.64 and 935.51 mm, the soil type is leaching soil, and the land use type is forest. And the interaction between all factors has a greater effect on the vegetation cover than any single factor alone. This study reveals the factors influencing vegetation growth on the Loess Plateau, as well as their types and ranges, providing a scientific basis and guidance for improving vegetation coverage in this region. Full article
(This article belongs to the Special Issue Land Use/Cover Change and Its Impacts on Regional Sustainable Development)
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Figure 1

Figure 1
<p>Map of the Loess Plateau geographic location.</p> Full article ">Figure 2
<p>The long−term average precipitation and temperature values of the Loess Plateau: (<b>a</b>) temperature; (<b>b</b>) precipitation.</p> Full article ">Figure 3
<p>Monthly average NDVI from 2001 to 2015.</p> Full article ">Figure 4
<p>Spatial distributions of natural and human factors in 2020: (<b>a</b>) slope; (<b>b</b>) aspect; (<b>c</b>) temperature; (<b>d</b>) precipitation; (<b>e</b>) soil type; (<b>f</b>) land use type; (<b>g</b>) population density; and (<b>h</b>) GDP.</p> Full article ">Figure 5
<p>The principle of geographical detector.</p> Full article ">Figure 6
<p>Annual mean FVC changes in the Loess Plateau from 1995 to 2020.</p> Full article ">Figure 7
<p>Trend of vegetation coverage change from 1995 to 2020, using the Mann–Kendall test.</p> Full article ">Figure 8
<p>Average FVC value for each precipitation zone in 1995, 2000, 2010, and 2020.</p> Full article ">Figure 9
<p>Average FVC value for each temperature zone in 1995, 2000, 2010, and 2020.</p> Full article ">Figure 10
<p>Average FVC under different soil types in 1995, 2000, 2010, and 2020.</p> Full article ">Figure 11
<p>Average FVC under different land use types in 1995, 2000, 2010, and 2020.</p> Full article ">Figure 12
<p><span class="html-italic">q</span> value for detection of interaction effects of various factors in 1995, 2000, 2010, and 2020: (<b>a</b>) 1995; (<b>b</b>) 2000; (<b>c</b>) 2010; (<b>d</b>) 2020.</p> Full article ">Figure 12 Cont.
<p><span class="html-italic">q</span> value for detection of interaction effects of various factors in 1995, 2000, 2010, and 2020: (<b>a</b>) 1995; (<b>b</b>) 2000; (<b>c</b>) 2010; (<b>d</b>) 2020.</p> Full article ">
17 pages, 7466 KiB  
Article
Long-Term Assessment of Soil Salinization Patterns in the Yellow River Delta Using Landsat Imagery from 2003 to 2021
by Yu Fu, Pengyu Wang, Wengeng Cao, Shiqian Fu, Juanjuan Zhang, Xiangzhi Li, Jiju Guo, Zhiquan Huang and Xidong Chen
Land 2025, 14(1), 24; https://doi.org/10.3390/land14010024 - 26 Dec 2024
Viewed by 417
Abstract
The Yellow River Delta (YRD), as a key area for the economic development of the Bohai Rim region, significantly impacts soil fertility and plant growth through soil salinization content. Accurately determining the spatial distribution of soil salinization in the YRD is vital for [...] Read more.
The Yellow River Delta (YRD), as a key area for the economic development of the Bohai Rim region, significantly impacts soil fertility and plant growth through soil salinization content. Accurately determining the spatial distribution of soil salinization in the YRD is vital for regional salinity management and agricultural development. In this study, we constructed and evaluated three soil salinization indices—NDSI, SI, and S5—using measured soil conductivity data and three machine learning methods: Random Forest, Support Vector Machine, and XGBoost. The results indicate that the Support Vector Machine achieved the best inversion effect on regional salinization levels, with an Area Under Curve (AUC) value of 0.88. The salinization level in the YRD has shown an increasing trend over the years, decreasing spatially from north to south, from east to west, and from the coast inland. From 2003 to 2009, salinization was primarily concentrated in northern and eastern coastal areas, while from 2009 to 2021, it gradually expanded inland. The salinized area increased from 538.4 km2 in 2003 to 761.5 km2 in 2021, particularly between 2009 and 2015, with a 47.95% increase. The main factors influencing salinization in the YRD were distance from the Bohai Sea, seasonal average potential evapotranspiration, and seasonal average normalized vegetation index, with interaction-driven effects being stronger than single-factor effects. This study provides crucial scientific support for sustainable salinization management and ecological restoration in the Bohai Sea region. Full article
(This article belongs to the Special Issue Land Use/Cover Change and Its Impacts on Regional Sustainable Development)
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Figure 1

Figure 1
<p>Geographic location of the study area and distribution of sampling points.</p> Full article ">Figure 2
<p>Quantitative classification map of salinity at sampling sites.</p> Full article ">Figure 3
<p>Salinity inversion distribution.</p> Full article ">Figure 4
<p>Change in area by category in YRD between 2003–2021. (The red dotted line is the building area).</p> Full article ">Figure 5
<p>Changes in area of the Region 1 and Region 2 land categories.</p> Full article ">Figure 6
<p>Geodetic survey results by year.</p> Full article ">
21 pages, 4247 KiB  
Article
Integrative Framework for Decoding Spatial and Temporal Drivers of Land Use Change in Malaysia: Strategic Insights for Sustainable Land Management
by Guanqiong Ye, Kehao Chen, Yiqun Yang, Shanshan Liang, Wenjia Hu and Liuyue He
Land 2024, 13(12), 2248; https://doi.org/10.3390/land13122248 - 21 Dec 2024
Viewed by 744
Abstract
Identifying the drivers of land use and cover change (LUCC) is crucial for sustainable land management. However, understanding spatial differentiation and conducting inter-regional comparisons of these drivers remains limited, particularly in regions like Malaysia, where complex interactions between human activities and natural conditions [...] Read more.
Identifying the drivers of land use and cover change (LUCC) is crucial for sustainable land management. However, understanding spatial differentiation and conducting inter-regional comparisons of these drivers remains limited, particularly in regions like Malaysia, where complex interactions between human activities and natural conditions pose significant challenges. This study presents a novel analytical framework to examine the spatial variations and complexities of LUCC, specifically addressing the spatiotemporal patterns, driving factors, and pathways of LUCC in Malaysia from 2010 to 2020. Integrating the land use transfer matrix, GeoDetector model, and Structural Equation Modeling (SEM), we reveal a significant expansion of farmland and urban areas alongside a decline in forest cover, with notable regional variations in Malaysia. Human-driven factors, such as population growth and economic development, are identified as the primary forces behind these changes, outweighing the influence of natural conditions. Critically, the interactions among these drivers exert a stronger influence on LUCC dynamics in Malaysia than any single factor alone, suggesting increasingly complex LUCC predictions in the future. This complexity emphasizes the urgency of proactive, multifaceted, and region-specific land management policies to prevent irreversible environmental degradation. By proposing tailored land management strategies for Malaysia’s five subnational regions, this study addresses spatial variations in drivers and climate resilience, offering a strategic blueprint for timely action that can benefit Malaysia and other regions facing similar challenges in sustainable land management. Full article
(This article belongs to the Special Issue Land Use/Cover Change and Its Impacts on Regional Sustainable Development)
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Figure 1

Figure 1
<p>Elevation and subregion distribution of Malaysia.</p> Full article ">Figure 2
<p>Framework for investigating the driving mechanisms of LUCC.</p> Full article ">Figure 3
<p>Spatial distribution (<b>a</b>), composition structure (<b>b</b>), and Single Land Use Dynamic Degree (SLUDD) (<b>c</b>) of land use types in Malaysia from 2010 to 2020.</p> Full article ">Figure 4
<p>Land use transfer matrix in Malaysia (<b>a</b>), Central Region (CR) (<b>b</b>), Eastern Region (ER) (<b>c</b>), Northern Region (NR) (<b>d</b>), Southern Region (SR) (<b>e</b>), and East Malaysia (EM) (<b>f</b>) from 2010 to 2020.</p> Full article ">Figure 5
<p>Spatial distribution of LUCC in Malaysia during 2010–2020 (<b>a</b>), and transition areas of the three main types of LUCC at the subnational level (<b>b</b>). Urban expansion consists of fives types of LUCC: Cropland → Urban, Forest → Urban, Grassland → Urban, Water → Urban, and Others → Urban.</p> Full article ">Figure 6
<p>The impact of six drivers on LUCC. (<b>a</b>) Results of factor detection; (<b>b</b>) results of interactive detection. Green shading indicates the enhanced, double-factor type, while orange shading indicates the enhanced, nonlinear type. White cells represent the q-statistic values of single factors.</p> Full article ">Figure 7
<p>The direct and indirect effects of human and natural factors on LUCC. All path coefficients and factor loadings are significant. Orange and red paths represent positive effects, while yellow paths indicate negative effects. Significant levels of each predictor are * <span class="html-italic">p</span> &lt; 0.05, *** <span class="html-italic">p</span> &lt; 0.001.</p> Full article ">Figure A1
<p>Spatial distribution of six driving factors in Malaysia.</p> Full article ">
21 pages, 16322 KiB  
Article
Response of Ecological Quality to Land Use/Cover Change During Rapid Urbanization of Xiong’an New Area
by Qi Sun, Ruitong Qiao, Quanjun Jiao, Huimin Xing, Can Wang, Xinyu Zhu, Wenjiang Huang and Bing Zhang
Land 2024, 13(12), 2167; https://doi.org/10.3390/land13122167 - 13 Dec 2024
Cited by 1 | Viewed by 591
Abstract
Rapid urbanization facilitates socioeconomic development but also exacerbates land use/cover change (LUCC), significantly impacting ecological environments. Timely, objective, and quantitative assessments of ecological quality changes resulting from LUCC are essential for safeguarding the natural environment and managing land resources. However, limited research has [...] Read more.
Rapid urbanization facilitates socioeconomic development but also exacerbates land use/cover change (LUCC), significantly impacting ecological environments. Timely, objective, and quantitative assessments of ecological quality changes resulting from LUCC are essential for safeguarding the natural environment and managing land resources. However, limited research has explored the potential interrelationships between the spatio-temporal heterogeneity of LUCC and ecological quality during urbanization. This study focuses on the Xiong’an New Area, a region experiencing rapid urbanization, utilizing the remote sensing-based ecological index (RSEI) to monitor ecological quality dynamics from 2017 to 2023. To address the computational challenges associated with large-scale regions, a streamlined RSEI construction method was developed using Landsat imagery and implemented via Google Earth Engine (GEE). A geographically weighted regression (GWR) analysis, integrated with Sentinel-2 land use data, was employed to examine the influence of LUCC on ecological quality. The findings reveal the following: (1) Ecological quality in the Xiong’an New Area has exhibited an overall positive trajectory, with improvements elevating the ecological status to above moderate levels. (2) Urban expansion resulted in a 17% reduction in farmland, primarily converted into construction land, which expanded by approximately 12%. (3) Ecological protection policies have facilitated the conversion of farmland into wetlands and urban green areas, which emerged as the principal contributors to ecological quality enhancement. (4) A positive correlation was observed between changes in ecological land and ecological quality, while a negative correlation was identified between shifts in the construction land and farmland and ecological quality. This research provides valuable scientific insights into ecological conservation and land use management, thereby establishing a foundation for the development of rational land resource planning and sustainable ecological development strategies in the Xiong’an New Area. Full article
(This article belongs to the Special Issue Land Use/Cover Change and Its Impacts on Regional Sustainable Development)
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Figure 1

Figure 1
<p>Location of study area.</p> Full article ">Figure 2
<p>The research framework in this study.</p> Full article ">Figure 3
<p>Detailed information regarding the PCA transformations for the period 2017–2023.</p> Full article ">Figure 4
<p>Average RSEI values (<b>a</b>) and RSEI level proportions (<b>b</b>) in the study area from 2017 to 2023.</p> Full article ">Figure 5
<p>Spatial distribution of RSEI in Xiong’an New Area during 2017–2023.</p> Full article ">Figure 6
<p>The land use and land cover classification maps of the Xiong’an New Area in 2017–2023.</p> Full article ">Figure 7
<p>Comparative analysis of LUCCs of Example 1 and Example 2 in <a href="#land-13-02167-f006" class="html-fig">Figure 6</a>.</p> Full article ">Figure 8
<p>Transfer flow of LULC in Xiong’an New Area from 2017 to 2023.</p> Full article ">Figure 9
<p>Ecological quality and area changes across various LULC types from 2017 to 2023: (<b>a</b>) farmland, (<b>b</b>) construction land, (<b>c</b>) flooded land, and (<b>d</b>) green areas.</p> Full article ">Figure 10
<p>Response of ecological quality to LUCCs during 2017–2023. (<b>a</b>) Spatial distribution of LUCCs; (<b>b</b>) Spatial distribution of GWR coefficients.</p> Full article ">
26 pages, 14395 KiB  
Article
Spatial–Temporal Pattern Analysis and Development Forecasting of Carbon Stock Based on Land Use Change Simulation: A Case Study of the Xiamen–Zhangzhou–Quanzhou Urban Agglomeration, China
by Suiping Zeng, Xinyao Liu, Jian Tian and Jian Zeng
Land 2024, 13(4), 476; https://doi.org/10.3390/land13040476 - 7 Apr 2024
Cited by 3 | Viewed by 1398
Abstract
The spatial–temporal distribution and evolution characteristics of carbon stock under the influence of land use changes are crucial to the scientific management of environmental resources and the optimization of land spatial layout. Taking the Xiamen–Zhangzhou–Quanzhou urban agglomeration in the southeastern coastal region of [...] Read more.
The spatial–temporal distribution and evolution characteristics of carbon stock under the influence of land use changes are crucial to the scientific management of environmental resources and the optimization of land spatial layout. Taking the Xiamen–Zhangzhou–Quanzhou urban agglomeration in the southeastern coastal region of China as an example, based on seven land use types from 1990 to 2020, including cultivated land, woodland, and construction land, we quantitatively investigate the spatial–temporal patterns of carbon stock development and the spatial correlation of carbon stock distribution. Additionally, two scenarios for the development of urban and ecological priorities in 2060 are established to investigate the effects of land use changes on carbon stock. The results indicate that (1) the research area has formed a land use spatial pattern centered around urban construction in the eastern bay area, with the western forest area and coastal forest belt serving as ecological barriers. Carbon stock is influenced by land use type, and the distribution of total carbon stock exhibits a spatial aggregation phenomenon characterized by “low in the southeast, high in the north, and medium in the center”. (2) Distance of trunk and secondary roads, elevation, slope, watershed borders, population size, and gross domestic product (GDP) factors are the main drivers of the growth of land use types. The primary causes of the reduction in carbon stock are the widespread conversion of cultivated land, woodland, and grassland into construction land, as well as water and unused land. (3) In 2060, there will be a decrease of 41,712,443.35 Mg in the urban priority development scenario compared to 2020, and a decrease of 29,577,580.48 Mg in the ecological priority development scenario. The estimated carbon stock under the two scenarios varies by 12,134,862.88 Mg. The average carbon storage of Zhangpu County, Quangang County, and Jimei County is expected to rise by one level under the ecological protection scenario, indicating that the vast forest area can become a potential area to maintain carbon stock. It is crucial to encourage the coordinated development of peri-urban agroforestry and ecological barriers, as well as to establish a harmonious spatial pattern of land use and carbon stock at the scale of urban agglomerations. Full article
(This article belongs to the Special Issue Land Use/Cover Change and Its Impacts on Regional Sustainable Development)
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Figure 1

Figure 1
<p>Geographical location and administrative division of the study area.</p> Full article ">Figure 2
<p>Soil carbon density distribution [<a href="#B63-land-13-00476" class="html-bibr">63</a>].</p> Full article ">Figure 3
<p>Quantitative structure of land use.</p> Full article ">Figure 4
<p>Land use changes in the Xiamen–Zhangzhou–Quanzhou urban agglomeration from 2000 to 2020. (<b>a</b>) Areas of land use conversion. (<b>b</b>) Multi-period land use changes. (<b>c</b>) Types of land use conversion.</p> Full article ">Figure 5
<p>Spatial and temporal distribution of carbon stocks in the Xiamen–Zhangzhou–Quanzhou urban agglomeration; from (<b>a</b>–<b>d</b>) in order: 2000, 2010, 2015, 2020.</p> Full article ">Figure 6
<p>Spatial autocorrelation analysis of Moran’s index (<b>a</b>) and LISA agglomeration map of carbon stocks in the Xiamen–Zhangzhou–Quanzhou urban agglomeration (<b>b</b>).</p> Full article ">Figure 7
<p>Hot spot analysis of carbon storage in the Xiamen–Zhangzhou–Quanzhou urban agglomeration; from (<b>a</b>–<b>d</b>) in order: 2000, 2010, 2015, 2020.</p> Full article ">Figure 8
<p>The contribution of the influencing factors, based on the random forest regression model. The contribution has been normalized.</p> Full article ">Figure 9
<p>Applicability of the PLUS model: (<b>a</b>) actual land use in 2020; (<b>b</b>) simulation results in 2020.</p> Full article ">Figure 10
<p>Land use projections for 2060: (<b>a</b>) urban priority development scenario; (<b>b</b>) ecological priority development scenario.</p> Full article ">Figure 11
<p>Projected distribution of carbon stocks in 2060 in the Xiamen–Zhangzhou–Quanzhou urban agglomeration: (<b>a</b>) urban development priority scenario; (<b>b</b>) ecological development priority scenario.</p> Full article ">Figure 12
<p>Gradation of average carbon stocks by administrative district; from (<b>a</b>–<b>f</b>) in order: 2000, 2010, 2015, 2020, 2060 (urban development priority scenario), and 2060 (ecological development priority scenario).</p> Full article ">Figure 13
<p>The ways to achieve China’s dual carbon goals.</p> Full article ">
13 pages, 2920 KiB  
Article
Development of Soil Fertility Index Using Machine Learning and Visible-Near-Infrared Spectroscopy
by Xiaolin Jia, Yi Fang, Bifeng Hu, Baobao Yu and Yin Zhou
Land 2023, 12(12), 2155; https://doi.org/10.3390/land12122155 - 12 Dec 2023
Cited by 3 | Viewed by 4208
Abstract
An accurate assessment of soil fertility is crucial for monitoring environmental dynamics, improving agricultural productivity, and achieving sustainable land management and utilization. The inherent complexity and spatiotemporal heterogeneity of soils result in significant challenges in soil fertility assessment. Therefore, this study focused on [...] Read more.
An accurate assessment of soil fertility is crucial for monitoring environmental dynamics, improving agricultural productivity, and achieving sustainable land management and utilization. The inherent complexity and spatiotemporal heterogeneity of soils result in significant challenges in soil fertility assessment. Therefore, this study focused on developing a rapid, economical, and precise approach to evaluate soil fertility through the application of visible-near-infrared spectroscopy (VNIR). To achieve this, we utilized the Land Use and Cover Area Frame Survey (LUCAS) dataset and employed a variety of prediction models, including partial least squares regression, support vector machines (SVMs), random forest, and convolutional neural networks, to estimate various soil properties and overall soil fertility. The results showed that the SVM model had the highest prediction accuracy, particularly for clay content (coefficient of determination (R2) = 0.79, ratio of performance to interquartile range (RPIQ) = 3.04), pH (R2 = 0.84, RPIQ = 4.54), total nitrogen (N) (R2 = 0.80, RPIQ = 2.40), and cation exchange capacity (CEC) (R2 = 0.83, RPIQ = 3.16). A soil fertility index (SFI) was developed based on factor analysis, integrating nine essential soil properties: clay content, silt content, sand content, pH, carbonate content, N, soluble phosphorus, soluble potassium, and CEC. We compared direct and indirect prediction models for estimating SFI and found that both models showed high accuracy (mean value of R2 = 0.80, mean value of RPIQ = 2.21). Additionally, SFI was classified into five classes to provide insights for precision agriculture. The kappa coefficient was 0.63, which indicated that the SFI evaluation results between VNIR and chemical analysis were relatively consistent. This study provides a theoretical foundation of real-time soil fertility monitoring for the optimization of agricultural practices. Full article
(This article belongs to the Special Issue Land Use/Cover Change and Its Impacts on Regional Sustainable Development)
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Figure 1

Figure 1
<p>Location of the soil sampling sites.</p> Full article ">Figure 2
<p>Workflow of the SFI estimation. (VNIR: visible-near-infrared spectra; SVM: support vector machine; RF: random forest; CNN: convolutional neural network).</p> Full article ">Figure 3
<p>Correlation matrix of the soil properties.</p> Full article ">Figure 4
<p>Biplot of factor loadings (PC: principal component).</p> Full article ">Figure 5
<p>Plots of predicted versus measured values of the different soil properties in the validation dataset using the optimal models. (SVM: support vector machine; CNN: convolutional neural network).</p> Full article ">Figure 6
<p>Selectivity ratio of each soil property based on PLSR models.</p> Full article ">
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