Production Of Maps Research Articles

Geohazard features of the Aeolian Island slopes and the North-Eastern Sicily offshore

ABSTRACT The MaGIC project (Marine Geohazard along the Italian Coasts) had the aim of mapping the geohazard in the Italian seas and resulted in the production of numerous maps covering parts of the Italian Seas. In this paper, we present the maps: ‘The submerged portions of the Aeolian volcanic islands and the north-eastern Sicilian margin’, located in the south-eastern Tyrrhenian Sea. Both areas are affected by active geological processes, which represent important geohazards elements. Inthe submarine parts of the Stromboli volcanoremobilization of volcaniclastic deposits occur along the Sciara del Fuoco, where small-scale instabilities may represent a source of geohazard. Hydrothermal activity occurs on Enarete and Enaretino conical seamounts. The north-eastern Sicilian margin has a narrow continental shelf. Numerous canyon heads indent the shelf and, sometimes, reach close to the coast. Canyons have often a retrogradational trend and further eventual landward shift through sliding can iendangeri coastal or offshore infrastructures. Many of the canyons connect with leveed channels with widespread sediment instability. In the Gioia Basin, some of the channels connect to form the Stromboli slope Valley. Volcanic unrest or local and regional earthquakes are proven to have caused submarine landslides and tsunamis.

LOLA DEM Assisted Photogrammetric Processing of Lunar Reconnaissance Orbiter NAC Images for the Lunar South Pole

Abstract. Because of its special significance, exploration missions and research at the Lunar South Pole (LSP) are of considerable importance. The mapping products of the LSP such as Digital Ortho Maps (DOMs) and Digital Elevation Models (DEMs) are essential to support exploration missions. However, in the LSP region, the available data are relatively limited. The large amounts of shadows and the special environment of the LSP has an important impact on both image matching and generation of mapping products. In this paper, we propose a photogrammetric processing method to improve the accuracy of the LRO NAC images by using LOLA DEM as auxiliary control data. The LOLA DEM was first rendered using the same illumination conditions as the corresponding LRO NAC raw images, and then matched to the LRO NAC DOMs. Next, the corresponding elevations of the tie points between LOLA DEMs and LRO NAC images were extracted and automatically added to the control network for bundle adjustment. In order to verify the feasibility of the method, we selected 17 LRO NAC images near the LSP at 85° south latitude. The results show that the proposed method can efficiently acquire uniformly distributed elevation control points for bundle adjustment and the generated DEM has a good consistency with the LOLA DEM.

Different data-driven prediction of global ionospheric TEC using deep learning methods

Ionospheric Total Electron Content (TEC) is a crucial parameter for monitoring the ionosphere and space weather disasters. Its accurate prediction is vital for precise applications of Interferometric Synthetic Aperture Radar (InSAR) and Global Navigation Satellite System (GNSS). This study proposes a novel method for ionospheric TEC prediction that considers multiple TEC-related factors. We present the random forest method and the Autoformer deep learning with multilayer perceptron (Autoformer-MLP) to predict the global TEC by incorporating the geomagnetic and solar activity parameters. Two schemes with different ionospheric data, i.e., spherical harmonic coefficients (SHC) and vertical TEC (VTEC), are performed by Autoformer-MLP. Ionospheric products from 2009 to 2019 (Cycle 24), obtained from the Center for Orbit Determination in Europe (CODE), are collected to test and evaluate the proposed method. Experimental results demonstrate that the root mean square errors (RMSEs) of predicted global ionospheric maps (GIMs) using the SHC scheme are 3.79 and 1.39 TECU in 2015 and 2019, respectively, and those are 3.55 and 1.28 TECU for the VTEC scheme. Moreover, the RMSEs of the prediction results are 0.5 and 0.2 TECU lower than that of CODE's 1-day predicted global ionospheric map (GIM) product (C1PG) during high and low solar activity years, respectively. These analyses indicate that the proposed random forest and Autoformer-MLP deep learning methods exhibit high accuracy and stability for data-driven prediction of global ionospheric TEC in various scenarios.

Geographical Information Systems (GIS) in Municipal Planning and Management: A Pathway to Improve South African Service Delivery

In the years after 1994, the democratic government of South Africa worked to transfer and extend basic service provision to practically all sections of the country. The delivery of public services continues to be an embarrassment to humanity and the developmental state. Without proper basic services such as water, sanitation, power, infrastructure, health facilities, and housing, a large section of South Africa’s people would continue to live and die. As a result, society remains disenfranchized and exposed to socioeconomic shocks, contributing to the economy's decline and destruction. To support successful service delivery, the integration of Geographical Information Systems (GIS) as a shift from human-led operations to computerized operations is now recognized and required in municipal planning and management. The central purpose of this study is to evaluate the feasibility of applying GIS as a crucial planning tool infused within integrated development planning in municipal planning and management. The current state of service delivery in existing municipal planning and management procedures, as well as the factors that influence it and the effect it has on society, will be examined. This conceptual study employs secondary data and follows a certain methodology. Using the Critical Discourse Analysis (CDA) methodology, secondary data were examined. The results demonstrate that political influence and manipulation, a lack of openness and accountability, insufficient citizen participation, a lack of personnel capability, and poor planning, monitoring, and evaluation contribute to poor public service delivery in South Africa. Furthermore, the findings indicate that GIS functions as a tool for visualization and collation of spatial information, map production, and data capturing and storage in local municipalities with the aim of enhancing public service delivery.

Pemetaan Penggunaan Lahan Dusun Sukamaju Desa Natar Kabupaten Lampung Selatan

Natar Village is located in Natar District, South Lampung Regency which is divided into several hamlets, one of which is Sukamaju Hamlet which is a hamlet that is experiencing rapid development because it is a strategic area where there are several facilities. In recent years, there have been changes in land use in this hamlet, such as the expansion of agricultural land, housing developments, and other activities. To understand and manage land use effectively and sustainably, it is necessary to carry out comprehensive land use mapping. Therefore, the technical team plans to carry out land use mapping activities using Satellite Imagery. This activity aims to carry out land use mapping and understand land use trend patterns in the area. The mapping method used is an on-screen digitized guided classification based on 9 key image interpretations. The results of the service in Sukamaju Hamlet are a 1:3000 scale land use map product consisting of a residential area of 17.76 ha, plantations of 5.35 ha, rice fields of 0.85 ha, and a road length of 10,452.16 m and waters of 2,686.40 m. This of course influences the trend pattern in areas dominated by residential land. This land use map can provide the latest information regarding the development of sustainability areas in Sukamaju Hamlet for village officials and residents.

PROTOCOL: Methods used in the development, production and updating of evidence and gap maps: A scoping review.

Evidence and gap maps (EGMs) are an increasinly popular approach used in evidence synthesis. As an approach they address broad research questions, describing the existing evidence base, highlighting evidence gaps and providing an interactive visual tool for knowledge users. The purpose of this methodological study is to explore the the processes used in the development of EGM's and how they are reported. The aim is to better understand current practice and identify where clearer guidance is needed to support their production.

Apportionment of NOx and NH3 emission sources in an urban coastal airshed: Insights from stable isotopes and a novel approach to intermittent sources

Inorganic nitrogen (IN) wet deposition flux and emission sources have been intensively investigated over the past two decades with a primary focus in rural areas and large metropolitan cities. Smaller coastal cities are often overlooked in these studies despite a substantial portion of the earth's population inhabiting these regions. Ammonia and NOx emission source apportionment estimations in these studies can be misleading without proper inspection of intermittent source (e.g., biomass burning and lightning) impacts on the airmass of rain events. This study measured the chemical composition (NH4+, NO3−, Cl−, SO42− and pH) and isotopic composition of ammonium and nitrate (δ15N–NH4+, δ15N–NO3-, and δ18O–NO3-) in rainwaters collected in a small coastal city (Corpus Christi, TX, USA) to aid in determining the emission source apportionments of NH3 and NOx. A novel approach coupling lightning and fire/smoke mapping products along with airmass back trajectories was developed to help determine the influence of biomass burning and lightning on individual rain events before applying to isotope source apportionment models. The annual IN wet deposition was 3.9 kg N·ha−1·yr−1, in which NH4+ and NO3− constituted 65% and 35%, respectively. Isotope mixing model results suggest vehicle emission contribution to NH3 can rival agriculture sources (i.e., fertilizer and livestock waste) in urban areas. Vehicles, biomass burning and lightning were significant NOx sources, while soil “biogenic” emissions increased substantially in certain rain events occurring in warmer months. This work qualified IN wet deposition flux and estimated IN emission source apportionments in a coastal small-sized city, which will inform regional N emission regulations and watershed restoration practices.

A novel space–spectrum array tile probability random-forest model enhances LULC mapping accuracy on Google Earth Engine: An experiment in Ordos, China

The rapid renewal of land use and land cover (LULC) maps using remote-sensing technologies constitutes a sine qua non for judicious land resource management at both regional and national scales. Existing research conducted on the Google Earth Engine (GEE) platform has overwhelmingly focused on pixel-based LULC classification techniques, often neglecting the role of spatial context via neighbouring valuable pixel information. Remarkably, little attention has been paid to the amalgamation of 3 × 3 neighbouring pixels into a three-dimensional space–spectrum array that can emulate the functionalities of object-based image analysis. In this study, we developed a novel integrated model consisting of a space–spectrum array (SSA) model based on 3 × 3 neighbouring pixels, a tile model based on random forest, and a multiple probabilistic classification model (SSA-TPRF) on the GEE platform to generate a LULC map with high overall accuracy (OA) for Ordos in 2020. Three bimonthly median value images were synthesised and feature collections, including spectral bands and vegetation indices, were constructed. Five experimental groups (EXP1–EXP5) were used to assess the different model combinations. Subsequent validation procedures employed abundant reference samples and compared the results with those of the three extant LULC mapping products. The results showed that EXP2, which was grounded in the tile-based model, yielded an OA of 87.53%, surpassing that of EXP1 (84.99%), which employed a traditional overall model. Furthermore, EXP3, which integrated the multiple probabilistic classification model with the traditional overall model, exhibited an OA of 85.19%, exceeding that of EXP1. A comparison of the five experimental groups using the four regional spatial subtlety features revealed that the EXP5, employing the SSA-TPRF model, successfully decreased the salt and pepper noise. The OA of six tile sizes ranging from 10 km to 100 km were compared, and the highest OA (88.35%) was achieved at a tile size of 25 km. The resultant LULC map in Ordos, derived from the SSA-TPRF model, showed superior OA compared with the extant LULC products. This study thus contributes to a versatile and scalable model within the GEE framework, offering avenues for facile adaptation and recurrent application across disparate geographical locations and temporal settings. The adaptability of this model is particularly advantageous for developing nations and regions typified by diverse landscapes, thereby catalysing the iterative updating of LULC maps through advanced remote-sensing paradigms.

Dialogues across time and space in a video-based collaborative learning environment

In this study, we investigate how pre-service teachers’ group dialogues emerged and intersected across time and space as students collaboratively constructed a video-based mind map to prepare for oral exams in a pedagogy course. The study was conducted as part of a design-based research project investigating the ways that video-based mind maps can support learning as both a collaborative activity and a classroom resource. We applied interaction analysis methods to recordings taken during the production of the mind map as well as the videos made by students within the mind map itself to analyze synchronous and asynchronous dialogues among group members as they viewed, recorded, and uploaded videos. The findings offer an in-depth understanding of how collaboration occurs in different space-time configurations within and across groups as mediated by video resources. We discuss how these findings contribute to computer-supported collaborative learning (CSCL) research on the ways collaboration can emerge across different levels of activity as well as the pedagogical implications for introducing video-based dialogues into the classroom.

Correction to: Potential of Multi-resolution Satellite Imagery Products for Scale Variant Topographic Mapping

Correction to: Potential of Multi-resolution Satellite Imagery Products for Scale Variant Topographic Mapping

Enhanced Interactive Rendering for Rovers of Lunar Polar Region and Martian Surface

Appropriate environmental sensing methods and visualization representations are crucial foundations for the in situ exploration of planets. In this paper, we developed specialized visualization methods to facilitate the rover’s interaction and decision-making processes, as well as to address the path-planning and obstacle-avoidance requirements for lunar polar region exploration and Mars exploration. To achieve this goal, we utilize simulated lunar polar regions and Martian environments. Among them, the lunar rover operating in the permanently shadowed region (PSR) of the simulated crater primarily utilizes light detection and ranging (LiDAR) for environmental sensing; then, we reconstruct a mesh using the Poisson surface reconstruction method. After that, the lunar rover’s traveling environment is represented as a red-green-blue (RGB) image, a slope coloration image, and a theoretical water content coloration image, based on different interaction needs and scientific objectives. For the rocky environment where the Mars rover is traveling, this paper enhances the display of the rocks on the Martian surface. It does so by utilizing depth information of the rock instances to highlight their significance for the rover’s path-planning and obstacle-avoidance decisions. Such an environmental sensing and enhanced visualization approach facilitates rover path-planning and remote–interactive operations, thereby enabling further exploration activities in the lunar PSR and Mars, in addition to facilitating the study and communication of specific planetary science objectives, and the production and display of basemaps and thematic maps.

Production of a high-resolution genetic map in carrot from multi-parental populations

Production of a high-resolution genetic map in carrot from multi-parental populations

Rapid Production Method of Massive Thematic Maps Based on Geospatial Knowledge Extraction

<p>Geospatial knowledge in massive academic papers can provide knowledge services such as location-based research hotspot analysis, spatio-temporal data aggregation, research results recommendation, etc. However, geospatial knowledge often exists implicitly in literature resources in unstructured form, which is difficult to be directly accessed and mined and utilized for rapid production of massive thematic maps. In this paper, we take the geospatial knowledge of the area studied as an example and introduce its extraction method in detail. An integrated feature template matching and random forest classification algorithm is proposed for accurately identifying research areas from the abstract texts of academic papers and producing thematic maps. Firstly, the precise recognition of geographical names is achieved step by step based on BiLSTM-CRF algorithm and improved heuristic disambiguation method; then, the area studied is extracted by the designed integrated feature recognition template of area studied using random forest classification algorithm, and a fast thematic map is designed for the knowledge of area studied, topic and literature. The experimental results show that the area studied recognition accuracy can reach 97%, the F-value is 96%, and the recall rate reaches 96%, achieving high accuracy and high efficiency of area studied extraction in text. Based on the geospatial knowledge, the thematic map can achieve the effect of fast map formation and accurate expression.</p> <p> </p>

Focus Group Discussion Pembuatan Peta Batas Wilayah Padukuhan di Kalurahan Kebonharjo, Kapanewon Samigaluh, Kabupaten Kulon Progo

Geospatial information has expanded widely, including development and regional aspects. Kebonharjo sub-district, which is in the Kapanewon Girimulyo administrative area, Kulon Progo Regency, is committed to participating in regional development by trying to optimize its various potentials. However, the database and profile of potentials (and problems) are still limited, need better documented, and are still limited to tabular data. The database and profile will have added value if presented with a spatial approach like a map. Community service programs were carried out to make a map as a basis for developing spatial data and profiles. In the preparation step, the agendas include discussion with the village official to scope the activities and products, downloading satellite image data as a basis for mapping, and collecting maps for initial reference for area boundaries. Next, the technical activities for making the map were started by carrying out the Focus Group Discussion with village officials and community representatives from all of the hamlets to obtain agreement on the boundaries of the hamlet and Rukun Tetangga (RT) areas. The results of the Focus Group Discussion were digitized and processed in the laboratory to obtain map products and an overview of public understanding regarding mapping. The output achieved from the community service was the Padukuhan and Rukun Tetangga Area Boundary Map products, both in printed and soft file versions.

Enhancing environmental policy through evidence synthesis: a review of the Environmental Evidence for the Future (EEF) Initiative

The Environmental Evidence for the Future (EEF) Initiative emerged in response to the challenges and opportunities presented by the UK’s decision to leave the European Union and its associated Environmental Frameworks. The Natural Environment Research Council (NERC), working closely with the Collaboration for Environmental Evidence (CEE) and UK stakeholders, developed the initiative to identify and address crucial evidence gaps, offering a long-term vision for environmental policy and sustainability. The EEF Initiative progressed through three stages: strategic priority identification, NERC panel award selection, and the production of Systematic Maps of existing evidence. The first stage involved collaborative workshops across the UK to identify key knowledge gaps in environmental science. The subsequent prioritisation resulted in 65 challenges across 10 thematic areas. The second stage saw NERC initiating, with CEE support, an open call for research proposals emphasising the use of evidence synthesis methodology. The selection process, balancing topic importance and applicant expertise, led to funding for five projects. The final stage involved the production of Systematic Maps of existing evidence based on the CEE Guidelines and Standards, providing a structured overview of existing literature on specific topics. The EEF Initiative demonstrated effective collaboration between UKRI (NERC), an independent non-profit (CEE), academia, and government agencies, addressing critical environmental challenges through rigorous evidence synthesis methodologies. The programme enhanced understanding and utilisation of these methodologies within the research community. Key lessons include the importance of inclusive priority-setting, differentiation between broad policy questions and specific Systematic Map questions, recognition of the value of Systematic Maps, and the role of experience in evidence synthesis teams. As policymakers and researchers navigate environmental policies in a resource-constrained environment, the EEF Initiative highlights the cost-effectiveness and efficiency of systematic mapping and review processes for evidence-based decision-making. The success of funding through NERC sets a precedent for future thematic evidence focused programmes, emphasising the need for continued support in developing synthesis skills among researchers and encouraging direct government commissions for targeted and responsive evidence. The EEF Initiative serves as a model for effective collaboration, providing valuable insights into addressing evidence gaps and shaping evidence-based policymaking in an ever-evolving environmental landscape.

Cost-effectiveness of Microsoft Academic Graph with machine learning for automated study identification in a living map of coronavirus disease 2019 (COVID-19) research.

Identifying new, eligible studies for integration into living systematic reviews and maps usually relies on conventional Boolean updating searches of multiple databases and manual processing of the updated results. Automated searches of one, comprehensive, continuously updated source, with adjunctive machine learning, could enable more efficient searching, selection and prioritisation workflows for updating (living) reviews and maps, though research is needed to establish this. Microsoft Academic Graph (MAG) is a potentially comprehensive single source which also contains metadata that can be used in machine learning to help efficiently identify eligible studies. This study sought to establish whether: (a) MAG was a sufficiently sensitive single source to maintain our living map of COVID-19 research; and (b) eligible records could be identified with an acceptably high level of specificity. We conducted an eight-arm cost-effectiveness analysis to assess the costs, recall and precision of semi-automated workflows, incorporating MAG with adjunctive machine learning, for continually updating our living map. Resource use data (time use) were collected from information specialists and other researchers involved in map production. Our systematic review software, EPPI-Reviewer, was adapted to incorporate MAG and associated machine learning workflows, and also used to collect data on recall, precision, and manual screening workload. The semi-automated MAG-enabled workflow dominated conventional workflows in both the base case and sensitivity analyses. At one month our MAG-enabled workflow with machine learning, active learning and fixed screening targets identified 469 additional, eligible articles for inclusion in our living map, and cost £3,179 GBP per week less, compared with conventional methods relying on Boolean searches of Medline and Embase. We were able to increase recall and coverage of a large living map, whilst reducing its production costs. This finding is likely to be transferrable to OpenAlex, MAG's successor database platform.

Application of the domain adaptation method using a phenological classification framework for the land-cover classification of North Korea

Efforts to achieve carbon neutrality are global, encompassing a wide range of factors. For the estimation of greenhouse gas emissions from the agriculture, forestry, and other land use (AFOLU) sector, the Intergovernmental Panel on Climate Change has proposed an advanced method that requires Approach 3, the highest level of suggested method, of activity data. Accordingly, we propose a phenological classification framework (PCF) that can perform land-cover classification by training the climatic repeatability of the annual cycle using a U-Net deep learning model. Additionally, the domain adaptation (DA) method can be applied to classify areas with insufficient data. We applied these methods to classify North Korea (i.e., using South Korean data), with an accuracy of 81.31%; overall this effort culminated in the simultaneous classification of the Korean Peninsula. Domain distribution comparison showed that the results for the two regions were similar. The PCF and DA methods proposed in this study allow for annual production of a land-cover map and change matrix, regardless of the presence or absence of data. The application of these methods is expected to provide a scientific basis for policy decisions that can facilitate the global attainment of carbon neutrality.

A two-step species distribution modeling to disentangle the effect of habitat and bioclimatic covariates on Psacothea hilaris, a potentially invasive species

Integrating host plants in distribution modeling of phytophagous species and disentangling the effect of habitat and bioclimatic variables are key aspects to produce reliable predictions when the aim is to identify suitable areas outside species’ native range. To this aim, we implemented a framework of Species Distribution Model aimed at predicting potential suitable areas of establishment for the beetle Psacothea hilaris across the world. Since habitat (including host plants) and bioclimatic variables affect species distribution according to processes acting at different geographical scales, we modeled these variables separately. For the species native range, we fitted a habitat (HSM) and a bioclimatic (BSM) suitability model calibrated on a local and a large scale, respectively; the overall suitability map was obtained as the spatial product of HSM and BSM projection maps. ROC, TSS and Cohen’s Kappa obtained in validation confirmed a good predictive performance of modeling framework. Within HSM, host plants played a substantial effect on species presence probability, while among bioclimatic variables, precipitation of the warmer quarter and isothermality were the most important. Native HSM and BSM models were used to realize an overall suitability map at world scale. At global scale, many areas resulted suitable for habitat, some for bioclimate, and few for both conditions; indeed, if the species would not be able to modify its bioclimatic niche, it might not be considered a major invasive species. However, the high rate of range expansion documented for P. hilaris in Northern Italy, a poorly suitable bioclimatic area, suggests a plasticity of the species that requires increasing the level of attention to its invasive potential.

Importance of Habitat Context in Modelling Risk Maps for Two Established Invasive Alien Plant Species: The Case of Ailanthus altissima and Phytolacca americana in Slovenia (Europe)

Forests are important ecosystems that face threats from climate change and global environmental shifts, with invasive alien plant species being a significant concern. Some of these invasive species have already become established, while others are in the process of naturalisation. Although forests are a relatively stable ecosystem, extreme weather events increase their vulnerability to change, and clearings left after natural disturbances are particularly susceptible to invasion by alien plant species (IAPS). We created risk maps of two species that have spread rapidly in the last decade: American pokeweed (Phytolacca americana) and the tree of heaven (Ailanthus altissima). We prepared a generalised linear model based on the occurrence data collected within the LIFE ARTEMIS project. Eleven environmental variables were used to determine habitat characteristics. We constructed two models for each species: one covering the entirety of Slovenia and the other specifically for the forested areas in Slovenia, with the latter incorporating forest-specific variables (such as forest sanitation felling and monocultures). We observed the presence of both species at lower altitudes and in close proximity to water sources. American pokeweed tends to occur nearer to railways, while the presence of the tree of heaven is associated with areas lacking carbonate parent material and influenced by land use patterns. In forested areas, the occurrence of American pokeweed is influenced by forest habitat characteristics, such as disturbances caused by extreme weather events or the prevalence of Norway spruce monocultures. In contrast, the occurrence of the tree of heaven is influenced by more general environmental variables, such as altitude and proximity to railways. Consequently, we have generated risk maps for the entirety of Slovenia and separately for forested areas, both of which indicate similar levels of risk, particularly for the tree of heaven. The risk map for American pokeweed highlights numerous vulnerable areas, especially forest edges, which are highly susceptible to invasion. Furthermore, there is a higher likelihood of this species occurring in areas that have undergone sanitation felling. This study suggests that the production of risk maps of IAPS could be improved by focussing on habitat types and taking into account habitat-specific variables. This approach could enhance the early detection and management of these invasive species.

Spatial domain transfer: Cross-regional paddy rice mapping with a few samples based on Sentinel-1 and Sentinel-2 data on GEE

The global population is on the rise, leading to an increased demand for food resources. Accurate mapping and monitoring of paddy rice fields have become crucial for effective food management and yield estimation. Several large-scale paddy rice mapping products with medium-to-high resolution have been made by using threshold-based phenological methods. The main shortcoming is that paddy rice varies in different regions due to differences in climate and planting habits, which results in the limitations of fixed phenological indicator thresholds for large-scale paddy rice mapping. Therefore, paddy rice samples from different regions are needed to account for regional differences between paddy rice and improve paddy rice mapping accuracy. To address two key shortcomings in existing sample generation methods (the non-robustness of DTW distance and the limitation of fixed phenological indicator thresholds for generating samples) and the three difficulties of cross-regional sample generation (different planting times, growth cycles, and planting intensities), this study proposed a novel approach for cross-regional paddy rice sample generation based on spatial domain transfer, achieving cross-regional paddy rice mapping. The accuracy results (OA of 95.16–97.54%) in three study regions with different climatic and topographic conditions in China and improved accuracy (OA improvement of 5.89–19.06%) compared with the method of NESEA-Rice10, proved the effectiveness of the proposed method and the potential of the proposed method in large-scale paddy rice mapping. The proposed method can obtain high-accuracy paddy rice mapping results in regions without paddy rice samples and be applied to the mapping of other crops and land cover types.