Land Cover Research Articles

Dust over water: Analyzing the impact of lake desiccation on dust storms on the Iranian Plateau

The desiccation of lakes on the Iranian Plateau, driven by both natural processes and anthropogenic activities, has led to significant land cover changes, exposing lake beds and triggering adverse environmental consequences. These transformations pose serious threats to ecological conditions and the provision of ecosystem services. This study investigates the potential links between lake desiccation and the occurrence of dust storms across major lakes on the Iranian Plateau. By utilizing remote sensing data and time series statistical analysis, including the Mann-Kendall test, Ordinary Least Square (OLS) regression, and frequency analysis, we assessed changes in lake extent and their effects on Aerosol Optical Depth (AOD) and Particular Matter (PM2.5) from 1985 to 2020. An analysis of Landsat imagery reveals that lake desiccation commenced in the late 1990s, exhibiting a marked downward trend that has intensified in recent years. The results show a significant inverse correlation between aerosol parameters (AOD and PM2.5) and lake extent. Reduced lake area is associated with elevated AOD and PM2.5 concentrations, with the highest aerosol levels observed when lakes reach their smallest extent. Notably, despite an 81 % reduction in the lake area, dust storm occurrences have increased by 20%. These findings highlight the critical role of lake desiccation in exacerbating dust storms in the surrounding regions. Furthermore, the temporal alignment between fluctuations in lake extent and dust storm frequency is validated through regression analysis with a 95 % confidence level. The consistency between our results and existing literature underscores the reliability of the observed relationship, emphasizing the urgent need to address the environmental consequences of lake desiccation to mitigate its broader ecological impacts and potential human health impacts.Graphical Abstract

Atmospheric Correction Effects on the NDSI: Snow Detection Characteristics across Land Cover Types

Atmospheric Correction Effects on the NDSI: Snow Detection Characteristics across Land Cover Types

Impacts of Climate Change on Forest Biodiversity Changes in Northeast China

Vegetation plays a vital role in connecting ecosystems and climate features. The biodiversity of vegetation is one of the most important features for evaluating ecosystems and it is becoming increasingly important with the threat of global warming. To clarify the effects of climate change on forest biodiversity in Northeast China, time-series NDVI data, meteorological data and land cover data from 2010 to 2021 were acquired, and the forest biodiversity of Northeast China was evaluated. The effect of climate change on forest biodiversity was analyzed, and the results indicated that the forest biodiversity features increased from west to east in Northeast China. There was also an increasing trend from 2010 to 2021, but the rate at which forest biodiversity was changing varied with different forest types of Northeast China, as different climatic factors had a different impact on forest biodiversity in different forest types. Average annual temperature, annual accumulated precipitation, CO2 fertilization and solar radiation were the main factors affecting forest biodiversity changing trends. This research indicated the potential impact of climate change on forest ecosystems, as it emphasized with evidence that climate change has a catalytic effect on forest biodiversity in Northeast China.

Supervised land cover classification of Nueva Ecija using random forest in google earth

Land cover classification is essential for environmental monitoring, urban planning, and sustainable land use management. This study presents a supervised land cover classification in Nueva Ecija, Philippines, utilizing Google Earth Engine (GEE) and the Random Forest (RF) algorithm, applied to Landsat 8 imagery. A total of 1,523 samples were collected representing five land cover types: built-up areas, agricultural lands, water bodies, forests, and barren land. The classification achieved an overall accuracy of 87.69% with a Kappa coefficient of 0.841. Future work should explore the integration of seasonal imagery and topographic indices for improved performance. This methodology provides crucial insights for resource management and supports regional policy development in Nueva Ecija.

Geographic Information System (GIS) and Multi-purpose Household Survey (MPHS) Based Tax Assessment Approach for manifold revenue generation in Mandav, Dhar (M.P.), India: A pilot ward study

Abstract: This study has been done under project entitled “Prepare Property Tax Register based on GIS & Multi-Purpose Household Survey its integration E-Nagarpalika with Technical Handholding Support” by M.P. Government. In Mandav, as a pilot ward 05 (Rampura Ward) were selected and that aims to find out the comparison of revenue generation in terms of taxation for individual household properties in the same ward. Study ward had 98 existing house hold properties according to Nagar Parishad, Mandav records and after MPH survey it became 164. Using GIS techniques and existing tax assessment approach, in a totality 191% demand growth has been observed. 100% demand growth has been found in commercial properties followed by 39% growth in residential properties and 16% growth in mixed properties. Land Use and Land Cover map has also been made using drone imagery (resolution 0.35cm) and GIS techniques. In study ward agriculture land were found 35.44% of the total area followed by built-up area 27.16%, waste land 17.63%, water bodies 14.36%. Keywords: Manifold Revenue Generation, Demand Growth, Land Use and Land Cover, Multi-purpose Households Survey and GIS techniques.

Dam Siltation in the Mediterranean Region Under Climate Change: A Case Study of Ahmed El Hansali Dam, Morocco

Dams are vital for irrigation, power generation, and domestic water needs, but siltation poses a significant challenge, especially in areas prone to water erosion, potentially shortening a dam’s lifespan. The Ahmed El Hansali Dam in Morocco faces heightened siltation due to its upstream region being susceptible to erosion-prone rocks and high runoff. This study estimates the siltation at the dam from its construction up to 2014 using bathymetric data and the Brown model, which is a widely-used empirical model that calculates reservoir trap efficiency. Additionally, the study evaluates the impact of Land Use and Land Cover (LULC) changes and projected future rainfall until around 2076 based on siltation rates. The results indicate that changes in LULC, particularly temporal variations in precipitation, have a significant impact on the siltation of the Ahmed El Hansali dam. Notably, rainfall is strongly correlated with the siltation rate, with an R2 of 0.92. The efficiency of sediment trapping (TE) is 97.64%, meaning that 97.64% of the sediment in the catchment area is trapped or deposited at the bottom of the dam. The estimated annual specific sediment yield is about 32,345.79 tons/km2/yr, and the sediment accumulation rate is approximately 4.75 Mm3/yr. The dam’s half-life is estimated to be around 2076, but future precipitation projections may extend this timeframe due to the strong correlation between siltation and precipitation. Additionally, soil erosion driven by land management practices plays a crucial role in future siltation dynamics. Hence, this study offers a comprehensive assessment of the siltation dynamics at the Ahmed El Hansali dam, providing essential information on the long-term effects of erosion, land use changes, and climate projections. These findings may assist decision makers in managing dam reservoir sedimentation more effectively, ensuring the durability of the dam and extending the reservoir life.

Spatiotemporal Analysis of Urban Growth and PM2.5 Concentrations in Sylhet, Bangladesh

Environmental impacts of urbanization, such as increased air pollution, pose significant challenges for developing countries. This study examines land cover changes and their relationship with particulate matter 2.5 (PM2.5) concentrations in two upazilas of Bangladesh from 2001 to 2019 using GIS and remote sensing techniques. Results show significant urban expansion (i.e., increase in built-up area) in both upazilas, corresponding with increasing PM2.5 levels. Linear regression reveals correlations between land cover types and PM2.5 levels. Mixed forests and waterbodies tend to be negatively associated with PM2.5 concentrations; on the other hand, built-up and barren land show a positive correlation with PM2.5. The most significant increase in built-up land and PM2.5 was in Madhabpur, indicating urgent environmental and health issues. This study emphasizes the critical role of sustainable urban planning and environmental conservation in mitigating urbanization’s adverse effects on air quality, advocating for preserving natural landscapes to maintain ecological balance, protecting urban health, and providing policymakers with insights to develop strategies addressing urban expansion and air pollution.

A Hierarchical Coarse–Fine Adaptive Fusion Network for the Joint Classification of Hyperspectral and LiDAR Data

Hyperspectral image (HSI) and light detection and ranging (LiDAR) data have gained significant attention due to their excellent complementarity, which can be combined to improve the accuracy of land cover classification. However, there are still many challenges, such as unrelated physical characteristics, different data structures, and a lack of labeled samples. Many methods fail to leverage the full potential of multi-source data, particularly hierarchical complementary information. To address these problems, a hierarchical coarse–fine adaptive (HCFA) fusion network with dynamic convolution and a transformer is proposed for multi-source remote sensing land cover classification. Although fusing hierarchical information can improve the classification accuracy of the model, improper hierarchical feature selection and optimization may negatively affect the classification results. Therefore, a coarse–fine mutual learning strategy is proposed to dynamically fuse hierarchical information. Additionally, the disparity between multi-source data continues to prevent the realization of effective fusion. To tackle this challenge, cross-tokenization and cross-token attention are implemented to enhance information interaction. Furthermore, to improve the model representation with limited computational cost, we combine the advantages of dynamic convolution with a transformer. Validation on three standard datasets demonstrates that HCFA achieves high accuracy with just 1% of the training set while maintaining low computational costs.

Land Cover Dynamics of Al-Hammar Marsh: Restoration Plan Methodology Assessment

Land Cover Dynamics of Al-Hammar Marsh: Restoration Plan Methodology Assessment

Intelligent Sentinel satellite image processing technology for land cover mapping

Purpose. This article proposes to develop an intelligent Sentinel satellite image processing technology for land cover mapping using convolutional neural networks. The result will be an image with improved spatial resolution. Methodology. The paper presents a technology using a combination of biquadratic interpolation, histogram alignment, PCA transform, as well as a parallel residual architecture of convolutional neural networks. The technology increases the information content of Sentinel-2 optical images by combining 10 and 20-meter resolution data, resulting in primary 20-meter images with improved spatial resolution. Findings. The root mean square error (RMSE = 3.64) indicates a high accuracy in reproducing the spectral properties of the images. The correlation coefficient (CC = 0.997) confirms a high linear relationship between the estimated and observed images. The low value of Spectral Angle Mapper (SAM = 0.52) with the high Universal Image Quality Index (UIQI = 0.999) indicates high quality and structural similarity between the synthesized and reference images. These results confirm the proposed technology’s effectiveness in enhancing the spatial resolution of Sentinel satellite images. Originality. Traditional pansharpening methods of multispectral images developed for satellite images with panchromatic channels cannot be directly applied to Sentinel multispectral data, because these images do not contain a panchromatic channel. In addition, atmospheric conditions and the presence of clouds affect the quality of optical images, complicating their further thematic processing. The proposed technology, using biquadratic interpolation, histogram alignment, convolutional neural networks, and PCA transformation, removes clouds and enhances the spatial resolution of the primary 20-meter optical satellite image channels of Sentinel-2. This technology reduces color distortion and increases the detail of digital optical images, which allows for more accurate analysis of the state of the earth’s surface. Practical value. The results obtained can be used to improve the methods for processing Sentinel satellite images, which provide high spatial resolution and accurate preservation of spectral characteristics. It provides the foundation for the development of new geographic information systems for land cover monitoring.

AFA–Mamba: Adaptive Feature Alignment with Global–Local Mamba for Hyperspectral and LiDAR Data Classification

The joint classification of hyperspectral imagery (HSI) and LiDAR data is an important task in the field of remote sensing image interpretation. Traditional classification methods, such as support vector machine (SVM) and random forest (RF), have difficulty capturing the complex spectral–spatial–elevation correlation information. Recently, important progress has been made in HSI-LiDAR classification using Convolutional Neural Networks (CNNs) and Transformers. However, due to the large spatial extent of remote sensing images, the vanilla Transformer and CNNs struggle to effectively capture global context. Moreover, the weak misalignment between multi-source data poses challenges for their effective fusion. In this paper, we introduce AFA–Mamba, an Adaptive Feature Alignment Network with a Global–Local Mamba design that achieves accurate land cover classification. It contains two main core designs: (1) We first propose a Global–Local Mamba encoder, which effectively models context through a 2D selective scanning mechanism while introducing local bias to enhance the spatial features of local objects. (2) We also propose an SSE Adaptive Alignment and Fusion (A2F) module to adaptively adjust the relative positions between multi-source features. This module establishes a guided subspace to accurately estimate feature-level offsets, enabling optimal fusion. As a result, our AFA–Mamba consistently outperforms state-of-the-art multi-source fusion classification approaches across multiple datasets.

Predictive Modelling of Land Cover Changes in the Greater Amanzule Peatlands Using Multi-Source Remote Sensing and Machine Learning Techniques

The Greater Amanzule Peatlands (GAP) in Ghana is an important biodiversity hotspot facing increasing pressure from anthropogenic land-use activities driven by rapid agricultural plantation expansion, urbanisation, and the burgeoning oil and gas industry. Accurate measurement of how these pressures alter land cover over time, along with the projection of future changes, is crucial for sustainable management. This study aims to analyse these changes from 2010 to 2020 and predict future scenarios up to 2040 using multi-source remote sensing and machine learning techniques. Optical, radar, and topographical remote sensing data from Landsat-7, Landsat-8, ALOS/PALSAR, and Shuttle Radar Topography Mission derived digital elevation models (DEMs) were integrated to perform land cover change analysis using Random Forest (RF), while Cellular Automata Artificial Neural Networks (CA-ANNs) were employed for predictive modelling. The classification model achieved overall accuracies of 93% in 2010 and 94% in both 2015 and 2020, with weighted F1 scores of 80.0%, 75.8%, and 75.7%, respectively. Validation of the predictive model yielded a Kappa value of 0.70, with an overall accuracy rate of 80%, ensuring reliable spatial predictions of future land cover dynamics. Findings reveal a 12% expansion in peatland cover, equivalent to approximately 6570 ± 308.59 hectares, despite declines in specific peatland types. Concurrently, anthropogenic land uses have increased, evidenced by an 85% rise in rubber plantations (from 30,530 ± 110.96 hectares to 56,617 ± 220.90 hectares) and a 6% reduction in natural forest cover (5965 ± 353.72 hectares). Sparse vegetation, including smallholder farms, decreased by 35% from 45,064 ± 163.79 hectares to 29,424 ± 114.81 hectares. Projections for 2030 and 2040 indicate minimal changes based on current trends; however, they do not consider potential impacts from climate change, large-scale development projects, and demographic shifts, necessitating cautious interpretation. The results highlight areas of stability and vulnerability within the understudied GAP region, offering critical insights for developing targeted conservation strategies. Additionally, the methodological framework, which combines optical, radar, and topographical data with machine learning, provides a robust approach for accurate and detailed landscape-scale monitoring of tropical peatlands that is applicable to other regions facing similar environmental challenges.

Dynamics of Land Use and Coverage and its Impacts on the Upper Moquegua River Hydrographic Basin

Objective: Evaluate; identify and qualify the impacts of the dynamics of land use and coverage in the years 1973 and 2016 in the Hydrographic Basin and in the marginal strip of the Upper Moquegua River in Moquegua Peru. Theoretical framework: In the area studied, human activities and global climate change have disturbed the balance of natural systems, causing major impacts and complex consequences on land use and cover. Method: Digital image processing techniques and supervised classification using the maximum likelihood method were used, followed by matrix editing with refinement of the base and thematic cartography, reaching 94.4% accuracy in interpretation for the year 2016. Result and discussion: The main changes identified regarding the dynamics of land use and coverage were similar in the basin area and in the marginal strip. An increase was noted in the urban area of the entire basin of 568.53ha (1,097.96%) and 134.86ha (1,263.33%) in the marginal strip, which may have been largely influenced by mining activity, which was not observed in 1973, but present in 2016, occupying an area of 1,092.77ha throughout the basin, with 171.99ha in the marginal strip. The areas that had the greatest reduction were those of the glacier, with 2,275.39ha (-93.33%) in the entire basin and 83.77ha (-100.00%) in the marginal strip. Conclusion: With the results of this research, we hope to contribute to the literature related to the topic and to actions and public policies that encourage the appropriate use of land in the Upper Moquegua River Basin, especially in environmental protection areas.

Landscape-level analysis of disturbance regimes in Chinnar Wildlife Sanctuary, a protected area of Western Ghats, India

ABSTRACT The degradation, fragmentation, and depletion of forests have occurred all over the world, simultaneously, with the exponential growth of the human population. The present study assesses the forest disturbance regime of Chinnar Wildlife Sanctuary (CWS), an ecologically significant biodiversity hotspot of Western Ghats (WG) through satellite image-based forest and land cover mapping and landscape metric analysis. The on-screen analysis based on field knowledge, facilitated, mapping of seven types of vegetation cover. The majority of forest area was covered by dry deciduous forest (39.2 km2 or 43.15%), whereas the minimum area was covered by disturbed forest (2.05 km2 or 2.26%). The disturbance index (DI) was calculated by linearly combining the land use land cover (LULC), road and settlement closeness, fire risk, porosity, patchiness interspersion, normalized difference vegetation index (NDVI), and fragmentation through a probabilistic weight-based linear additive model. The values of the DI (120–310) reclassified into four classes viz. low, moderate, high, and very high. The results revealed that 74.62 km2 (82.52%) of the forest area has low to moderate disturbance, while the rest of the forest area that is 15.8 km2 (17.48%) has high to very high disturbance. These analyses are based on remote sensing and GIS crucial for forest conservation and management strategies formulation.

Spatiotemporal Variability in Snow and Land Cover in Sefid-Rud Basin, Iran

Snow cover has a key role in balancing the Earth’s surface temperature and can help in filling rivers and reservoirs. In this study, 8-day MOD10A2 images are employed to monitor the spatiotemporal changes in snow cover in the Sefid-Rud basin and its eleven sub-basins during 2000–2019. The non-parametric Mann–Kendall (MK) test and its associated Sen’s slope estimator are utilized to estimate the trends in annual, seasonal, and monthly snow cover changes. The Sen’s slope results show a decrease in the snow cover for the basin, statistically significant toward the central and southern parts of the basin. In the winter season, a decreasing trend is observed, where its decreasing rate is higher than the annual rate. The trends in the calendar months are like the seasons, i.e., December, January, and February exhibit a decreasing trend, like the winter season. The Goltapeh-Zarinabad and Ghorveh-Dehgolan sub-basins show decreasing snow cover rates of −0.51 and −0.68 (%/year) during 2000–2019, respectively, the only two sub-basins whose gradients are statistically significant at the 95% confidence level. The Pearson correlation analysis between elevation and snow cover for each year shows that the highest and lowest correlations are 0.81 for 2007 and 0.59 for 2017. Finally, analysis of the MCD12Q1 land cover data shows that a significant portion of non-vegetated lands have turned into grasslands, mainly in the central part of the basin, where the significant gradual snow cover decline is observed. The results can guide stakeholders and policymakers in the development of a sustainable environment in the face of climate change.

Multi-criteria analysis and mapping of the vulnerable flood risk areas of Jigawa State, Nigeria

This study evaluates flood risk factors and maps flood-prone areas in Jigawa State, Nigeria, using geospatial techniques. By integrating various flood-causing factors—such as rainfall distribution, elevation, slope, drainage network and density, land cover and use, flow accumulation, and soil type— through a multi-parametric approach, it employs the Analytical Hierarchy Process (AHP) to perform pairwise comparisons and assign priority weights to each factor. Spatial multi-criteria analysis (MCA) then ranks and identifies potential flood locations. Using ArcGIS's weighted overlay tool, these layers are combined to produce a final flood risk map, classifying areas into high, very high, moderate, low, and very low-risk categories. Elevation and slope were found to have the greatest impact on flooding, with normalized criterion weights of 30 and 22, respectively. The Consistency Ratio (CR) of 0.06 validated the strength of the judgment, and these AHP-derived factor weights were used to create the flood risk map showing areas with varying risk levels, highlighting eight local governments in the study area. —Auyo, Hadejia, Kirika-samma, Kafin-Hausa, Ringim, Miga, Jahun, and Dutse—compose areas of very high risk, while Guri, some portions of northern Kiri-kasamma, southern Jahun, northern Auyo, Birnin-Kudu, and northwestern Ringim local governments comprise high-risk areas. Areas of moderate risk comprise a small portion of northern Kirika-samma, larger portions of Gwaram, small portions of Jahun, Kafin-Hausa, Dutse and Birnin-kudu local governments respectively and areas of low to very low risks consist of many parts of Gwaram and some tiny portions of Birnin-kudu.

Urban Heat Island and Environmental Degradation Analysis Utilizing a Remote Sensing Technique in Rapidly Urbanizing South Asian Cities

Rapid urbanization in South Asian cities has triggered significant changes in land use and land cover (LULC), degrading natural biophysical components and intensifying urban heat islands (UHIs). This study investigated the impact of LULC changes on land surface temperature (LST) and the role of biophysical indicators in enhancing urban resilience to thermal extremes. We used Landsat satellite imageries from 1993 to 2023, conducted a comprehensive analysis of LULC changes, and estimated LST variations at 6-year intervals in the Dhaka, Gazipur, and Narayanganj districts in Bangladesh. Afterward, we performed statistical analysis upon employing correlation, regression, and principal component analysis (PCA) techniques to summarize information. The results reveal that 339.13 km2 worth of urban expansion has occurred in last 30 years, with an average annual growth rate of 3.5%, accompanied by a substantial reduction in water bodies (−139.17 km2) and vegetation cover. Consequently, summer temperatures exceeded approximately 36.52 °C in dense urban areas. Also, the results highlighted the strong influence of built-up areas (BSI and SAVI) on LST, while vegetation (NDVI) and water indices (NDWI) exhibited a negative association. The findings emphasize the urgency of integrating green infrastructure and deploying sustainable urban planning policies to mitigate the potential adverse impacts of scattered urbanization in the face of climate change.

Engineering Study of Erosion to Protect the Gometseri Alazani and Pirikiti Alazani Catchment Area (Kakheti region, Akhmeta municipality, Tusheti)

Soil erosion is a global environmental issue that reduces soil productivity, affects water quality, leads to sediment deposition, and increases the likelihood of agricultural land degradation. Combating erosion requires both quantitative and qualitative assessment of potential soil erosion on specific sites, along with knowledge of local terrain, soil types, land use systems, and management practices. From a theoretical and practical point of view, erosion processes are widespread and dangerous in Georgia, especially in mountainous areas. Slopes that have not been eroded or have not formed ravines are rare. Intensive landslides and mudslides are also observed here. Changes in factors associated with erosion are of interest, including the impact of climate change and human activity on components of erosional geosystems, especially on soil cover. Modern geoinformation systems (GIS) provide qualitatively new opportunities for research, modelling and optimization of the use of erosion-prone lands. Despite its wide application in many spheres of human activity, its potential in erosion research has not yet been fully realized. This article discusses about the Gometseri Alazani and Pirikiti Alazani (Akhmeta Municipality, Tusheti) catchment areas. The erosion-landslide processes developed in the research area are studied using the Revised Universal Soil Loss Equation (RUSLE). Erosion-vulnerable areas have been identified, where it is necessary to carry out additional engineering protection measures.

Land use and land cover mapping of the Saco River’s watershed, State of Maranhão, Brazil

Land use and land cover mapping benefits landscape understanding and its changes, especially those resulting from anthropogenic actions in the physical environment. The present study aimed at mapping land use and coverage of the Saco River’s watershed (located in Codó, state of Maranhão). The area comprises the coconut forest (named Mata dos Cocais), a region that encompasses the characteristics of different biomes on the same temporal and spatial scale. The methodology applied was based on remote sensing techniques developed in a Geographic Information System (GIS) environment, processing data from Landsat 8 and supervised classification. The results showed a predominance of dense vegetation in the basin under study, in addition to a reduced occupation by classes of urban area and water bodies. The overall accuracy was 79% and dense vegetation presented a higher user and producer accuracy than the general, with 91 and 87%, respectively. On the other hand, the highest commission and omission errors were those in urban areas and water bodies, which coincided with the lowest occupation classes in the Saco River basin. These results are pioneering for the coconut forest and provide data for strategic planning of environmental actions.

A 28-time-point cropland area change dataset in Northeast China from 1000 to 2020

Abstract. Based on historical documents, population data, published results, remote sensing data products, statistical data, and survey data, this study reconstructed the cropland area and the spatial pattern changes at 28 time points from 1000 to 2020 in Northeast China. The period from 1000 to 1600 corresponds to historical provincial-level administrative districts, while the period from 1700 to 2020 corresponds to modern county-level administrative districts. The main findings are as follows: (1) the cropland in Northeast China exhibited phase changes of expansion–reduction–expansion over the past millennium. (2) The cropland area in Northeast China increased from 0.55×104 km2 in 1000 to 37.90×104 km2 in 2020, and the average cropland fraction increased from 0.37 % to 26.27 %; (3) from 1000 to 1200, the cropland area exhibited an increasing trend, which peaked in 1200. The scope of land reclamation was comparable to modern times, but the overall cropland fraction remained low. The cropland area significantly decreased between 1300 and 1600, with the main land reclamation area being reduced southward into Liaoning province. From 1700 to 1850, the cropland area increased slowly and the agricultural reclamation gradually expanded northward. After 1850, there was almost exponential growth, with the cropland area continuously expanding to the whole study area, and this growth trend persists until 2020; (4) the dataset of changes in the cropland of administrative districts in Northeast China, reconstructed based on multiple data sources and improved historical cropland reconstruction methods, significantly enhances time resolution and reliability. Additionally, the dataset shows relatively better credibility assessment results, which can provide a refined database for historical land use and land cover change (LUCC) dataset reconstruction, carbon emission estimation, climate data construction, etc. The dataset can be downloaded from https://doi.org/10.6084/m9.figshare.25450468.v2 (Jia et al., 2024).