The China Maps Bibliographic Database is an historical collection of bibliographic information for more than 400 maps of China. The information resides in a searchable database and includes title, author/editor, publisher, location, projection, year, elevation, land cover type (forest, desert, marsh/swamp, grassland), vegetation, transportation (roads, railroads), rivers and lakes, spatial coverage (provincial, county, township), and language for maps published from 1765 to 1994. The information is available in both English and Chinese (GB Code for Chinese Characters). This data set is produced in collaboration with the University of Washington as part of the China in Time and Space (CITAS) project and the Center for International Earth Science Information Network (CIESIN).

The Fundamental GIS: Digital Chart of China, 1:1M, Version 1 consists of vector maps of China and surrounding areas. The maps include roads, railroads, drainage systems, contours, populated places, and urbanized areas for China proper, as well as for China and neighboring countries. The maps are at a scale of one to one million (1:1M). This data set is produced in collaboration with the University of Washington as part of the China in Time and Space (CITAS) project and the Columbia University Center for International Earth Science Information Network (CIESIN).

20 Global import shipment records of Map with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

The China County-Level Data on Provincial Economic Yearbooks, Keyed To 1:1M GIS Map consists of socioeconomic and boundary data for the administrative regions of China for 1990 and 1991. The socioeconomic data includes natural resources, population, employment, investment, wage, public finance, price, people's livelihood, agriculture, industry, energy, production, transportation, telecommunication, construction, trade, tourism, environmental protection, education, science, patents, culture, sports, health care, and social welfare. The boundary data are at a scale of one to one million (1:1M) at the county level. This data set is produced in collaboration with the University of Washington as part of the China in Time and Space (CITAS) project, University of Michigan Center of China Studies (CCS), and the Center for International Earth Science Information Network (CIESIN).

Hcropland30:A 30-m global cropland map by leveraging global land cover products and Landsat data based on a deep learning model

***Please note this dataset is undergoing peer review***

Version: 1.0

Authors: Qiong Hu ^{a, 1}, Zhiwen Cai ^{b, 1}, Liangzhi You ^{c, d}, Steffen Fritz ^e, Xinyu Zhang ^c, He Yin ^f, Haodong Wei^c, Jingya Yang ^g, Zexuan Li ^a, Qiangyi Yu ^g, Hao Wu ^a, Baodong Xu ^{b *}, Wenbin Wu ^{g, *}

^a Key Laboratory for Geographical Process Analysis & Simulation of Hubei Province/College of Urban and Environmental Sciences, Central China Normal University, Wuhan 430079, China

^b College of Resources and Environment, Huazhong Agricultural University, Wuhan 430070, China

^c Macro Agriculture Research Institute, College of Plant Science and Technology, Huazhong Agricultural University, Wuhan 430070, China

^d International Food Policy Research Institute, 1201 I Street, NW, Washington, DC 20005, USA

^e Novel Data Ecosystems for sustainability Research Group, International Institute for Applied Systems Analysis (IIASA), Schlossplatz 1, Laxenburg A-2361, Austria

^f Department of Geography, Kent State University, 325 S. Lincoln Street, Kent, OH 44242, USA

^g State Key Laboratory of Efficient Utilization of Arid and Semi-arid Arable Land in Northern China, the Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China

Introduction

We are pleased to introduce a comprehensive global cropland mapping dataset (named Hcropland30) in 2020, meticulously curated to support a wide range of research and analysis applications related to agricultural land and environmental assessment. This dataset encompasses the entire globe, divided into 16,284 grids, each measuring an area of 1°×1°. Hcropland30 was produced by leveraging global land cover products and Landsat data based on a deep learning model. Initially, we established a hierarchal sampling strategy that used the simulated annealing method to identify the representative 1°×1° grids globally and the sparse point-level samples within these selected 1°×1°grids. Subsequently, we employed an ensemble learning technique to expand these sparse point-level samples into the densely pixel-wise labels, creating the area-level 1°×1° cropland labels. These area-level labels were then used to train a U-Net model for predicting global cropland distribution, followed by a comprehensive evaluation of the mapping accuracy.

Dataset

1. Hcropland30: A hybrid 30-m global cropland map in 2020

****Data format: GeoTiff

****Spatial resolution: 30 m

****Projection: EPSG: 4326 (WGS84)

****Values: 1 denotes cropland and 0 denotes non-cropland

The dataset has been uploaded in 16,284 tiles. The extent of each tile can be found in the file of “Grids.shp”. Each file is named according to the grid’s Id number. For example, “000015.tif” corresponds to the cropland mapping result for the 15-th 1°×1° grid. This systematic naming convention ensures easy identification and retrieval of the specific grid data.

2. 1°×1° Grids: This file contains all 16,284 1°×1° grids used in the dataset. The vector file includes 18 attribute fields, providing comprehensive metadata for each grid. These attributes are essential for users who need detailed information about each grid’s characteristics.

****Data format: ESRI shapefile

****Projection: EPSG: 4326 (WGS84)

****Attribute Fields:

Id: The grid’s ID number.

area: The area of the grid.

mode: Indicates the representative sample grid.

climate: The climate type the grid belongs to.

dem: Average DEM value of the grid.

ndvi_s1 to ndvi_s4: Average NDVI values for four seasons within the grid.

esa, esri, fcs30, fromglc, glad, globeland30: Proportion of cropland pixels of different publicly available cropland products.

inconsistent: Proportion of inconsistent pixels within the grid according to different public cropland products.

hcropland30: Proportion of cropland pixels of our Hcropland30 dataset.

3. Samples: The selected representative pixel-level samples, including 32,343 cropland and 67657 non-cropland samples. The category information of each sample was determined based on visual interpretation on Google Earth image and three-year NDVI time series curves from 2019-2021.

****Data format: ESRI shapefile

****Projection: EPSG: 4326 (WGS84)

****Attribute Fields:

type: 1 denotes cropland sample and 0 denotes non-cropland sample.

Citation

If you use this dataset, please cite the following paper:

Hu, Q., Cai, Z., You, L., Fritz, S., Zhang, X., Yin, H., Wei, H., Yang, J., Li, Z., Yu, Q., Wu, H., Xu, B., Wu, W. (2024). Hcropland30: A 30-m global cropland map by leveraging global land cover products and Landsat data based on a deep learning model, Remote Sensing of Environment, submitted.

License

The data is licensed under Creative Commons Attribution 4.0 International (CC BY 4.0).

Disclaimer

This dataset is provided as-is, without any warranty, express or implied. The dataset author is not

responsible for any errors or omissions in the data, or for any consequences arising from the use

of the data.

Contact

If you have any questions or feedback regarding the dataset, please contact the dataset author

Qiong Hu (huqiong@ccnu.edu.cn)

Snapshot img

Digital Map Market Size 2025-2029

The digital map market size is forecast to increase by USD 31.95 billion at a CAGR of 31.3% between 2024 and 2029.

The market is driven by the increasing adoption of intelligent Personal Digital Assistants (PDAs) and the availability of location-based services. PDAs, such as smartphones and smartwatches, are becoming increasingly integrated with digital map technologies, enabling users to navigate and access real-time information on-the-go. The integration of Internet of Things (IoT) enables remote monitoring of cars and theft recovery. Location-based services, including mapping and navigation apps, are a crucial component of this trend, offering users personalized and convenient solutions for travel and exploration. However, the market also faces significant challenges.
Ensuring the protection of sensitive user information is essential for companies operating in this market, as trust and data security are key factors in driving user adoption and retention. Additionally, the competition in the market is intense, with numerous players vying for market share. Companies must differentiate themselves through innovative features, user experience, and strong branding to stand out in this competitive landscape. Security and privacy concerns continue to be a major obstacle, as the collection and use of location data raises valid concerns among consumers.

What will be the Size of the Digital Map Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
Request Free Sample

In the market, cartographic generalization and thematic mapping techniques are utilized to convey complex spatial information, transforming raw data into insightful visualizations. Choropleth maps and dot density maps illustrate distribution patterns of environmental data, economic data, and demographic data, while spatial interpolation and predictive modeling enable the estimation of hydrographic data and terrain data in areas with limited information. Urban planning and land use planning benefit from these tools, facilitating network modeling and location intelligence for public safety and emergency management.

Spatial regression and spatial autocorrelation analyses provide valuable insights into urban development trends and patterns. Network analysis and shortest path algorithms optimize transportation planning and logistics management, enhancing marketing analytics and sales territory optimization. Decision support systems and fleet management incorporate 3D building models and real-time data from street view imagery, enabling effective resource management and disaster response. The market in the US is experiencing robust growth, driven by the integration of Geographic Information Systems (GIS), Global Positioning Systems (GPS), and advanced computer technology into various industries.

How is this Digital Map Industry segmented?

The digital map industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Application

  Navigation
  Geocoders
  Others


Type

  Outdoor
  Indoor


Solution

  Software
  Services


Deployment

  On-premises
  Cloud


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    UK


  APAC

    China
    India
    Indonesia
    Japan
    South Korea


  Rest of World (ROW)

By Application Insights

The navigation segment is estimated to witness significant growth during the forecast period. Digital maps play a pivotal role in various industries, particularly in automotive applications for driver assistance systems. These maps encompass raster data, aerial photography, government data, and commercial data, among others. Open-source data and proprietary data are integrated to ensure map accuracy and up-to-date information. Map production involves the use of GPS technology, map projections, and GIS software, while map maintenance and quality control ensure map accuracy. Location-based services (LBS) and route optimization are integral parts of digital maps, enabling real-time navigation and traffic data.

Data validation and map tiles ensure data security. Cloud computing facilitates map distribution and map customization, allowing users to access maps on various devices, including mobile mapping and indoor mapping. Map design, map printing, and reverse geocoding further enhance the user experience. Spatial analysis and data modeling are essential for data warehousing and real-time navigation. The automotive industry's increasing adoption of connected cars and long-term evolution (LTE) technologies have fueled the demand for digital maps. These maps enable driver assistance applications,

China EQI: MoM: HS4: Maps and Hydrographic or Similar Charts of All Kinds, Including Atlases, Wall Maps, Topographical Plans and Globes, Printed. data was reported at 97.300 Average 12 Mths PY=100 in Mar 2025. This records an increase from the previous number of 52.300 Average 12 Mths PY=100 for Feb 2025. China EQI: MoM: HS4: Maps and Hydrographic or Similar Charts of All Kinds, Including Atlases, Wall Maps, Topographical Plans and Globes, Printed. data is updated monthly, averaging 82.500 Average 12 Mths PY=100 from Feb 2018 (Median) to Mar 2025, with 73 observations. The data reached an all-time high of 372.400 Average 12 Mths PY=100 in Mar 2020 and a record low of 35.451 Average 12 Mths PY=100 in Feb 2019. China EQI: MoM: HS4: Maps and Hydrographic or Similar Charts of All Kinds, Including Atlases, Wall Maps, Topographical Plans and Globes, Printed. data remains active status in CEIC and is reported by General Administration of Customs. The data is categorized under China Premium Database’s International Trade – Table CN.JE: Quantum Index: MoM: HS4 Classification.

5485 Active Global Map suppliers, manufacturers list and Global Map exporters directory compiled from actual Global export shipments of Map.

The China County-Level Data on Population (Census) and Agriculture, Keyed To 1:1M GIS Map consists of census, agricultural economic, and boundary data for the administrative regions of China for 1990. The census data includes urban and rural residency, age and sex distribution, educational attainment, illiteracy, marital status, childbirth, mortality, immigration (since 1985), industrial/economic activity, occupation, and ethnicity. The agricultural economic data encompasses rural population, labor force, forestry, livestock and fishery, commodities, equipment, utilities, irrigation, and output value. The boundary data are at a scale of one to one million (1:1M) at the county level. This data set is produced in collaboration with the University of Washington as part of the China in Time and Space (CITAS) project, University of California-Davis China in Time and Space (CITAS) project, and the Center for International Earth Science Information Network (CIESIN).

China IQI: YoY: HS4: Maps and Hydrographic or Similar Charts of All Kinds, Including Atlases, Wall Maps, Topographical Plans and Globes, Printed. data was reported at 376.800 Prev Year=100 in Feb 2025. This records an increase from the previous number of 95.000 Prev Year=100 for Jan 2025. China IQI: YoY: HS4: Maps and Hydrographic or Similar Charts of All Kinds, Including Atlases, Wall Maps, Topographical Plans and Globes, Printed. data is updated monthly, averaging 76.074 Prev Year=100 from Jan 2018 (Median) to Feb 2025, with 66 observations. The data reached an all-time high of 1,532.300 Prev Year=100 in Oct 2021 and a record low of 17.500 Prev Year=100 in Jan 2021. China IQI: YoY: HS4: Maps and Hydrographic or Similar Charts of All Kinds, Including Atlases, Wall Maps, Topographical Plans and Globes, Printed. data remains active status in CEIC and is reported by General Administration of Customs. The data is categorized under China Premium Database’s International Trade – Table CN.JE: Quantum Index: YoY: HS4 Classification.

his atlas is another summary result of the publication of "Chinese Vegetation" and other monographs by the vegetation ecology workers in China for more than 40 years. It is a basic map of the country's natural resources and natural conditions. It reflects in detail the distribution, horizontal zonality, and vertical zonal distribution patterns of 11 vegetation types, 54 vegetation types of 796 and subgroups, and reflects more than 2,000 plant dominant species in China. This Atlas consists of four editions, 280 pages, including a 1:1 000 000 fractional map of China's vegetation type 60, a 1:10 000 000 map of China's topography, a map of China's vegetation, and a map of China's vegetation zoning. Compare the legend.

This Atlas is a basic map of the national natural resources and natural geographical features. It is an indispensable scientific data and an important basis for studying global environmental change, biodiversity, environmental protection and monitoring.

China EVI: YoY: HS4: Maps and Hydrographic or Similar Charts of All Kinds, Including Atlases, Wall Maps, Topographical Plans and Globes, Printed. data was reported at 138.900 Prev Year=100 in Mar 2025. This records an increase from the previous number of 48.000 Prev Year=100 for Feb 2025. China EVI: YoY: HS4: Maps and Hydrographic or Similar Charts of All Kinds, Including Atlases, Wall Maps, Topographical Plans and Globes, Printed. data is updated monthly, averaging 86.700 Prev Year=100 from Jan 2018 (Median) to Mar 2025, with 73 observations. The data reached an all-time high of 335.748 Prev Year=100 in Feb 2018 and a record low of 23.734 Prev Year=100 in Feb 2019. China EVI: YoY: HS4: Maps and Hydrographic or Similar Charts of All Kinds, Including Atlases, Wall Maps, Topographical Plans and Globes, Printed. data remains active status in CEIC and is reported by General Administration of Customs. The data is categorized under China Premium Database’s International Trade – Table CN.JE: Trade Value Index: YoY: HS4 Classification.

This data is digitized according to the 1:1,000,000 Vegetation Atlas of China. The 60 maps in the atlas are digitized one by one (polygon attribute), then projected, matched and spliced. Finally, vegetation attributes are assigned to each polygon. The vegetation attributes include: vege_id (vegetation group number), new number, vegetation group and sub group, vegetation Type number, vegetation type, vegetation type group number, vegetation type group, vegetation category, and corresponding attribute information in English. The 1:1,000,000 Vegetation Atlas of China was edited by academician Hou Xueyu, a famous vegetation ecologist, and jointly compiled by more than 250 experts from relevant research institutes of the Chinese Academy of Sciences, relevant ministries and commissions, relevant departments of various provinces and regions, colleges and universities and other 53 units. It was officially published by Science Press in 2001 and publicly distributed at home and abroad. This atlas is another summative achievement of vegetation ecology workers in China for more than 40 years after the publication of "Chinese vegetation" and other monographs. It is the basic map of national natural resources and natural conditions. It reflects in detail the distribution of vegetation units of 11 vegetation type groups, 796 formations and sub formations of 54 vegetation types, horizontal and vertical zonal distribution laws, and also reflects the actual distribution of more than 2000 dominant species of plants, major crops and cash crops in China, as well as the close relationship between dominant species and soil and ground geology. Because this atlas is a kind of realistic vegetation map, it reflects the quality of vegetation in China. This atlas is in quarto format, 280 pages, including 60 vegetation type maps of 1:1,000,000 in China, 1 topography of China at a scale of 1:10,000,000, 1 vegetation map of China and 1 vegetation zoning map of China, with Chinese and English legend. This atlas is the basic map of national natural resources and natural geographical characteristics, and it is the essential scientific data and important basis for the study of global environmental change, biodiversity, environmental protection and monitoring. Vegetation map is the specific expression of existing vegetation spatial distribution on the map. One millionth of China's vegetation map is the most detailed and accurate vegetation map in China so far. The data collection time is 2011-2012, it can serve students and researchers engaged in vegetation ecology research. This data is limited to the internal exchange of the Institute. Alberts projection is adopted for the map, and its parameters are as follows: · coordinate system: geodetic coordinate system · projection: Alberts positive axis equal area double standard weft conic projection · South standard weft: 25 ° n · North standard weft: 47 ° n · central longitude: 105 ° e · coordinate origin: intersection of 105 ° E and the equator · latitudinal migration: 0 · meridional migration: 0

Context

China regions map, covered by M.I.T. license. Original file downloaded from: https://github.com/deldersveld/topojson/

Content

China regions map.

Acknowledgements

This data is attributed to: https://github.com/deldersveld/topojson/. All credit for the data goes to the original authors.

Inspiration

Use this data to display for example the recent evolution of 2019 Coronavirus in Chinese provinces. Or use World economic data to show economic indicators distribution on Mainland China different regions.

China Trade Index: MoM: Unit Value: Import HS4: Maps and Hydrographic or Similar Charts of All Kinds, Including Atlases, Wall Maps, Topographical Plans and Globes, Printed. data was reported at 176.600 Average 12 Mths PY=100 in Feb 2025. This records an increase from the previous number of 89.700 Average 12 Mths PY=100 for Jan 2025. China Trade Index: MoM: Unit Value: Import HS4: Maps and Hydrographic or Similar Charts of All Kinds, Including Atlases, Wall Maps, Topographical Plans and Globes, Printed. data is updated monthly, averaging 95.500 Average 12 Mths PY=100 from Jan 2018 (Median) to Feb 2025, with 67 observations. The data reached an all-time high of 176.600 Average 12 Mths PY=100 in Feb 2025 and a record low of 51.700 Average 12 Mths PY=100 in May 2020. China Trade Index: MoM: Unit Value: Import HS4: Maps and Hydrographic or Similar Charts of All Kinds, Including Atlases, Wall Maps, Topographical Plans and Globes, Printed. data remains active status in CEIC and is reported by General Administration of Customs. The data is categorized under China Premium Database’s International Trade – Table CN.JE: Unit Value Index: MoM: HS4 Classification.

Geologic maps of the Moon provide comprehensive information about the geologic strata, structural features, lithologies, and chronology of the lunar crustal surface, which reflect the evolution of the lunar crust under igneous processes, catastrophic impacts, and volcanic activities. The map in this repository is the first 1:2,500,000-scale lunar global geologic map, which incorporates the most comprehensive knowledge about the Moon by taking advantage of the latest exploration results and scientific findings. An updated lunar time scale is employed in this map to better reflect the dynamic evolution of the Moon. The map provides a state-of-the-art illustration of impact basins and craters of different periods, the distributions of 17 types of rocks and 14 types of structures. The map is free to use for non-commercial forms including scientific research and science promotion under proper citation. This data is provided for understanding the associated research paper (https://doi.org/10.1016/j.scib.2022.05.021). The geologic map will be officially published in both Chinese and English copies by the Geological Publishing House after being proofed.

The China location-based services (LBS) market is experiencing robust growth, projected to reach a substantial market size. Driven by the rapid expansion of smartphone penetration, increasing adoption of IoT devices, and the burgeoning digital economy, the market is poised for significant expansion. Key application areas such as mapping and navigation, business intelligence and analytics, and location-based advertising are fueling this growth. Furthermore, the integration of LBS into various sectors, including transportation and logistics, IT and telecom, and healthcare, is creating new opportunities. The government's investment in digital infrastructure and supportive policies further strengthens the market's trajectory. Competition is fierce, with both domestic and international players vying for market share. Companies like Alibaba Cloud (Alibaba Amap), Tencent, Baidu, and international firms like TomTom and Here Technologies are actively engaged in developing innovative LBS solutions. While data privacy concerns and regulatory hurdles present potential restraints, the overall market outlook remains positive. The continued advancement of technologies like 5G, AI, and big data analytics is expected to significantly impact the market's future. These technologies are enabling the development of more accurate, efficient, and personalized LBS applications. The growing demand for real-time location data across various industries is another crucial factor driving growth. The increasing adoption of LBS in emerging sectors such as smart cities and autonomous vehicles is also anticipated to contribute significantly to the market's expansion over the forecast period. Despite some potential challenges, the positive market dynamics suggest a sustained period of growth for the China LBS market, outpacing global averages. The focus on developing robust and secure LBS technologies while adhering to stringent data privacy regulations will be crucial for sustained growth in the coming years. Recent developments include: August 2023: The Chinese government revealed the "2023 edition of the standard map of China," confirming its territorial claims over disputed regions. Following the release of the standard map for public use, the Ministry of Natural Resources was also expected to release digital maps, navigation, and positioning for use in various fields, such as location-based services, platform economy, precision agriculture, and intelligent connected vehicles., December 2022: According to the Beijing Institute of Space Science and Technology Information, China's BeiDou navigation satellite system (BDS) became one of the key guidance service providers for domestic Gaode Map. The company outpaced GPS in becoming a navigation service provider. Gaode Map used BeiDou satellites to make more than 210 billion positioning calls daily. The combination of BDS and map navigation provides better public services.. Key drivers for this market are: Technological Advancements and Supportive Government Initiatives, Increasing Importance of Location Analytics. Potential restraints include: Technological Advancements and Supportive Government Initiatives, Increasing Importance of Location Analytics. Notable trends are: Rising Adoption of Smartphones.

20 Global import shipment records of Maps with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

China Export: HS 8: Maps and Hydrographic or Similar Charts, Globes, Other data was reported at 3.037 RMB mn in Mar 2025. This records an increase from the previous number of 1.994 RMB mn for Feb 2025. China Export: HS 8: Maps and Hydrographic or Similar Charts, Globes, Other data is updated monthly, averaging 3.709 RMB mn from Jan 2022 (Median) to Mar 2025, with 39 observations. The data reached an all-time high of 6.868 RMB mn in Jan 2022 and a record low of 0.699 RMB mn in Feb 2023. China Export: HS 8: Maps and Hydrographic or Similar Charts, Globes, Other data remains active status in CEIC and is reported by General Administration of Customs. The data is categorized under China Premium Database’s International Trade – Table CN.JKF: RMB: HS49: Printed Books, Newspapers, Pictures and Other Products of the Printing Industry; Manuscripts, Typescripts and Plans.

As one of the largest producers of winter wheat worldwide, China contributes more than 19 % of the global production of winter wheat and playing a vital role in maintaining sustainable wheat production. Therefore, mapping the spatial distribution of winter wheat in China over long time scale precisely is of great importance for ensuring food security and investigating the spatiotemporal pattern of winter wheat at a national scale. Nevertheless, existing high resolution remote sensing datasets which can provide continuous observation for 20 years suffer from data gaps caused by cloud cover, making it difficult to map winter wheat area over long time scale. In this study, we used a phenology-based method to identify winter wheat area by integrated three key growing features of winter wheat into one variable for calculating the planting probability of winter wheat, which enlarged phenological differences between winter wheat and non-winter wheat fields. On this basis, we produced a long-term winter wheat mapping dataset at 30 m spatial resolution in China from 2001 to 2020 using the fusion dataset. Validations based on a total of 32,957 field survey samples displayed high accuracy, with the user’s, producer’s, and overall accuracies of 91.17%, 90.92%, and 91.6% in China, respectively. Furthermore, the identified winter wheat area aggregated to the municipal- and county-level exhibited good correlations with the agricultural statistical data. Based on this dataset, we found that the winter wheat planting condition in China exhibited a high frequency of continuous planting, with 54.45% of pixels having continuously planted winter wheat for over 10 years. During the period of 2001–2020, approximately 40% of the winter wheat pixels showed significantly increasing trends, which mainly concentrated in the major production area for winter wheat, such as Shandong, Henan, and Anhui province. Conversely, less than a quarter of winter wheat pixels showed significantly decreasing trends, which distributed in central and western provinces in China.