Agricultural Mapping Software Market Outlook

The global agricultural mapping software market size was valued at approximately USD 1.2 billion in 2023 and is projected to reach around USD 3.4 billion by 2032, growing at a Compound Annual Growth Rate (CAGR) of 12.5% during the forecast period. This promising growth is driven by increasing adoption of precision farming techniques and the need for efficient agricultural management practices. Advances in technology, coupled with rising demand for food production, are significant factors propelling the agricultural mapping software market.

One of the primary growth factors for the agricultural mapping software market is the increasing need for precision farming. Precision farming techniques rely on detailed data collection and analysis, which is facilitated by advanced agricultural mapping software. These tools help farmers make informed decisions about planting, watering, and harvesting, thereby maximizing crop yield and resource efficiency. The emphasis on data-driven farming is expected to drive significant adoption of mapping software across the globe.

Another crucial growth factor is the rising global population, which directly correlates with the increasing demand for food. As the world population continues to grow, the pressure on agricultural systems becomes more intense. Agricultural mapping software aids in optimizing land use, monitoring crop health, and predicting yields, thus playing a pivotal role in meeting the escalating food demands. The software's ability to enhance productivity and sustainability is highly appealing to stakeholders in the agricultural sector.

Technological advancements in GIS (Geographic Information Systems) and remote sensing are also propelling the market. The integration of satellite imagery, drones, and IoT (Internet of Things) devices with agricultural mapping software enables real-time data acquisition and analysis. These technologies provide farmers with detailed insights into their fields, enabling them to detect issues early and take corrective action promptly. The continuous innovation in these technologies is expected to further boost market growth.

From a regional perspective, North America is anticipated to hold the largest market share due to the high adoption rate of advanced farming technologies and substantial investments in agricultural research. Europe follows closely, driven by stringent agricultural policies and a strong focus on sustainable farming practices. The Asia Pacific region is expected to witness the fastest growth, attributed to increasing government initiatives to modernize agriculture and substantial investments in agritech startups. Latin America and the Middle East & Africa also present significant growth opportunities due to expanding agricultural activities and adoption of modern farming techniques.

Crop Monitoring Software plays a pivotal role in the agricultural mapping software market by providing farmers with the tools necessary to maintain and enhance crop health. This software allows for continuous observation and analysis of crops, ensuring that any potential issues such as diseases, pest infestations, or nutrient deficiencies are identified early. By leveraging real-time data, farmers can make informed decisions that lead to improved crop yields and quality. The integration of Crop Monitoring Software with other agricultural technologies further enhances its capabilities, making it an indispensable tool for modern farming practices. As the demand for efficient and sustainable agriculture grows, the adoption of such software is expected to rise, contributing significantly to the market's expansion.

Component Analysis

The agricultural mapping software market by component is divided into two primary segments: software and services. The software segment encompasses a range of solutions tailored to various agricultural needs, including GIS software, remote sensing software, and farm management software. These tools are designed to collect, analyze, and interpret data to support decision-making processes in farming operations. The sophistication and variety of available software solutions are continually expanding, driven by ongoing research and development efforts in agritech.

In contrast, the services segment includes consulting, training, maintenance, and support services that complement the software solutions. As more farmers and agricultural enterprises adopt mapp

The Agricultural Mapping Services Market is projected to exceed USD 7.7 billion by 2035, rising from an estimated USD 5.8 billion in 2025. A compound annual growth rate (CAGR) of 2.9% has been forecast for the 2025 to 2035 period.

Semi-annual update

Country-Wise Insights

The global agricultural mapping software market is experiencing robust growth, driven by increasing demand for precision agriculture techniques and the rising adoption of technology in farming practices. This market is projected to reach a substantial size, with a Compound Annual Growth Rate (CAGR) reflecting significant expansion. While the exact market size and CAGR figures are not provided, based on industry reports and observed trends in related sectors like agricultural technology and precision farming, a reasonable estimate would place the 2025 market value at approximately $2.5 billion, growing at a CAGR of 15% from 2025 to 2033. This growth is fueled by several factors, including the increasing need for efficient resource management (water, fertilizers, pesticides), improved crop yields, and enhanced farm profitability. Farmers are increasingly adopting cloud-based solutions for their ease of use and accessibility, leading to a significant segment of the market focused on cloud-based software. Furthermore, the integration of GPS, GIS, and remote sensing technologies into these platforms is boosting market expansion, allowing for precise field monitoring, data analysis, and informed decision-making. The market is segmented by deployment type (cloud-based and on-premise) and application (personal farms and animal husbandry companies). The cloud-based segment is expected to maintain a dominant share owing to its scalability and cost-effectiveness. The competitive landscape comprises established players like Trimble and CNH Industrial, alongside specialized agricultural technology companies such as Agrivi and Xfarm. These companies are constantly innovating and expanding their product offerings to cater to diverse farming needs and geographical locations. Regional market penetration varies, with North America and Europe currently holding significant shares due to advanced agricultural practices and higher technology adoption rates. However, rapidly developing economies in Asia-Pacific and other regions are showing promising growth potential, fuelled by increasing government initiatives promoting digital agriculture and the rising awareness of precision farming techniques. Challenges remain, such as the need for robust internet connectivity in remote areas and the digital literacy gap among some farmers, but overall market projections remain positive, indicating a strong future for agricultural mapping software.

The agricultural mapping software market is projected to reach a value of USD XX million by 2033, with a CAGR of XX% during the forecast period (2025-2033). The growth of the market is attributed to the increasing adoption of precision farming practices, rising demand for food security, and government initiatives to support sustainable agriculture. Key market drivers include technological advancements in mapping and data analysis, increased availability of remote sensing data, and the growing use of drones and other aerial imagery in agriculture. The market is segmented based on application (personal farm, animal husbandry company), type (cloud-based, on-premise), and region. North America is expected to dominate the market during the forecast period, followed by Europe and Asia Pacific. Key players in the market include Trimble, CNH Industrial, Geosys, Agrivi, Xfarm, Agremo, FarmFacts, CHC Navigation, Almaco, and Augmenta Agriculture Technologies. These companies are investing in research and development to enhance their product offerings and gain a competitive advantage in the market. The report also provides a detailed analysis of the competitive landscape, key market trends, and growth opportunities in the agricultural mapping software market.

[Metadata] Description: Agricultural Land Use Maps (ALUM) for islands of Kauai, Oahu, Maui, Molokai, Lanai and Hawaii as of 1978-1980. Sources: State Department of Agriculture; Hawaii Statewide GIS Program, Office of Planning. Note: August, 2018 - Corrected one incorrect record, removed coded value attribute domain.For more information on data sources and methodologies used, please refer to complete metadata at https://files.hawaii.gov/dbedt/op/gis/data/alum.pdf or contact Hawaii Statewide GIS Program, Office of Planning and Sustainable Development, State of Hawaii; PO Box 2359, Honolulu, HI 96804; (808) 587-2846; email: gis@hawaii.gov; Website: https://planning.hawaii.gov/gis.

The agricultural mapping software market is experiencing robust growth, driven by the increasing adoption of precision agriculture techniques and the rising need for efficient resource management. The market's expansion is fueled by several factors, including advancements in GPS technology, the proliferation of affordable drones and sensors, and the growing availability of cloud-based data analytics platforms. Farmers are increasingly recognizing the value of detailed field mapping for optimizing planting, fertilization, irrigation, and pest control, leading to improved crop yields and reduced input costs. The integration of AI and machine learning capabilities further enhances the sophistication of these software solutions, providing farmers with actionable insights to improve decision-making. This market is segmented based on software type (e.g., field mapping, yield monitoring, soil analysis), deployment mode (cloud-based, on-premise), and farm size. Key players like Trimble, CNH Industrial, and others are constantly innovating to provide comprehensive solutions that cater to the evolving needs of farmers. The market's growth is, however, subject to certain restraints, such as the initial investment costs associated with adopting new technologies and the need for reliable internet connectivity in rural areas. Despite these challenges, the long-term outlook for agricultural mapping software remains positive, with a projected steady Compound Annual Growth Rate (CAGR) leading to significant market expansion over the forecast period. The competitive landscape is characterized by a mix of established players and emerging technology providers. While large agricultural equipment manufacturers like Trimble and CNH Industrial offer integrated solutions, smaller companies focus on specialized software or niche applications. The market is witnessing increased mergers and acquisitions, strategic partnerships, and product innovation to maintain competitiveness. Geographical distribution varies, with North America and Europe currently dominating the market due to higher adoption rates and technological advancements. However, developing regions in Asia and Africa are showing promising growth potential driven by increasing agricultural production and government initiatives promoting precision farming techniques. The ongoing trend towards data-driven agriculture, coupled with improvements in sensor technology and affordability, will continue to propel the growth of this dynamic sector in the coming years. Future advancements are likely to include enhanced data integration capabilities, improved AI-powered analytics, and the incorporation of blockchain technology for ensuring data security and traceability.

Agricultural Mapping Services Market Outlook

The global Agricultural Mapping Services market size was valued at approximately USD 2.5 billion in 2023 and is anticipated to grow significantly to reach around USD 5.8 billion by 2032, reflecting a Compound Annual Growth Rate (CAGR) of approximately 9.8%. The primary growth driver for this market is the increasing demand for precision agriculture practices worldwide, which necessitate the use of detailed mapping services to maximize crop yield and optimize resource utilization. The convergence of technology with agriculture has catalyzed a significant transition in farming methodologies, empowering farmers to make data-driven decisions and thereby enhancing productivity and sustainability.

A major growth factor contributing to the expansion of the Agricultural Mapping Services market is the increasing awareness and adoption of precision farming techniques. Precision agriculture relies heavily on accurate and timely mapping services to monitor and manage field variability in crops. Factors such as climate change and unpredictable weather patterns have also intensified the need for sophisticated agricultural mapping to ensure food security and optimize crop production. Furthermore, government initiatives and subsidies promoting the adoption of advanced agricultural technologies are providing an additional impetus to this market, encouraging both small and large-scale farmers to invest in mapping services.

Another significant factor propelling market growth is the technological advancements in Geographic Information System (GIS), remote sensing, and drone technologies. These advanced tools facilitate the collection and analysis of critical agricultural data, enabling more precise crop monitoring and management. The integration of Artificial Intelligence (AI) and machine learning into mapping technologies further enhances the accuracy and efficiency of agricultural mapping services, providing actionable insights that help in predictive analysis and risk management. As a result, farmers and agronomists are increasingly turning to these technologies to gain a competitive edge and improve their agricultural outputs.

The rising global population and the consequent increase in food demand are also pivotal growth drivers for the Agricultural Mapping Services market. As the world population continues to grow, there is mounting pressure on the agricultural sector to enhance productivity to meet food supply needs. Agricultural mapping services play a crucial role in this context by optimizing land use and improving crop yields. Additionally, the trend towards sustainable agriculture and the need to manage resources more judiciously are fueling the demand for mapping services, which help minimize environmental impact while maximizing crop production.

The integration of GIS Software In Agriculture has revolutionized the way farmers approach precision agriculture. By utilizing GIS technology, farmers can create detailed maps that illustrate various aspects of their fields, such as soil types, crop health, and water availability. This spatial data is crucial for making informed decisions about planting, fertilization, and irrigation, ultimately leading to improved crop yields and resource efficiency. GIS software allows for the layering of different data sets, providing a comprehensive view of the agricultural landscape that helps in identifying patterns and trends. As a result, farmers can optimize their operations, reduce waste, and enhance sustainability, making GIS an indispensable tool in modern agriculture.

Regionally, North America is anticipated to dominate the Agricultural Mapping Services market, owing to the early adoption of advanced agricultural technologies and strong government support. Europe follows closely, with significant investments in agricultural innovation and a focus on sustainable farming practices. The Asia Pacific region, however, is projected to witness the fastest growth during the forecast period, driven by the increasing penetration of precision agriculture practices and the rapid development of the agricultural sector in countries like China and India. Latin America and the Middle East & Africa are also expected to experience substantial growth as these regions strive to enhance agricultural productivity and security.

Service Type Analysis

The Agricultural Mapping Services market is segmented by service type into Soil Mapping, Yield Mapping, Crop Health Monitoring, and Othe

The agricultural mapping software market is experiencing robust growth, driven by the increasing adoption of precision agriculture techniques and the need for efficient farm management. The market, estimated at $1.5 billion in 2025, is projected to achieve a Compound Annual Growth Rate (CAGR) of 12% from 2025 to 2033, reaching approximately $4.2 billion by 2033. This expansion is fueled by several key factors. Firstly, the rising demand for higher crop yields and improved resource utilization is compelling farmers to adopt technology-driven solutions. Agricultural mapping software provides crucial insights into field conditions, allowing for optimized planting, fertilization, and irrigation strategies, leading to significant cost savings and increased profitability. Secondly, advancements in sensor technology, GPS accuracy, and data analytics are enhancing the capabilities of agricultural mapping software, making it more accessible and user-friendly. Finally, government initiatives promoting precision agriculture and digital farming are further stimulating market growth. The market is segmented by software type (e.g., cloud-based, on-premise), application (e.g., yield mapping, soil analysis), and farm size. Key players like Trimble, CNH Industrial, and Geosys are actively shaping the market through continuous innovation and strategic partnerships. Despite the significant growth potential, certain challenges remain. High initial investment costs for software and hardware can act as a barrier to entry for small-scale farmers. Furthermore, the reliance on robust internet connectivity and technical expertise can hinder adoption in regions with limited infrastructure. However, ongoing technological advancements, coupled with the increasing availability of affordable solutions and training programs, are gradually addressing these limitations. The market will continue to witness consolidation through mergers and acquisitions, as larger players seek to expand their market share and offerings. Future growth will be particularly driven by the integration of artificial intelligence and machine learning into agricultural mapping software, enabling more predictive and insightful analytics for improved farm management.

The agricultural mapping services market is experiencing robust growth, driven by the increasing need for precision agriculture techniques to optimize yields and resource management. The market's expansion is fueled by several key factors: the rising adoption of advanced technologies like drones, satellite imagery, and GIS software; the increasing demand for data-driven decision-making in farming; and government initiatives promoting sustainable agriculture practices. The market is segmented based on various factors including technology (e.g., LiDAR, multispectral imaging), application (e.g., yield prediction, soil analysis, irrigation management), and end-user (e.g., large farms, smallholder farmers). While precise market sizing for 2025 requires more granular data, a reasonable estimation, considering industry reports and growth trajectories for similar technology sectors, could place the market value around $2 billion USD in 2025. The Compound Annual Growth Rate (CAGR) is expected to remain strong, further driving market expansion throughout the forecast period (2025-2033). Key players in the market are leveraging technological advancements and strategic partnerships to gain a competitive edge, fueling innovation and the development of integrated solutions. Despite the positive outlook, several challenges exist. High initial investment costs for technology and expertise can act as a barrier to entry for smallholder farmers. Data security and privacy concerns, along with the need for reliable internet connectivity in rural areas, present ongoing hurdles. Furthermore, variations in climate and soil conditions across different regions require adaptable and region-specific solutions, demanding continuous research and development. Overcoming these challenges through accessible technology, affordable services, and robust data management practices will be crucial for realizing the full potential of the agricultural mapping services market.

This map displays drought, climate and agriculture data for the United States. The map was created by the National Integrated Drought Information System (NIDIS) and is a component of the Agriculture Sector web mapping application, a tool for exploring the relationship between drought, climate and the agricultural sector in the United States.Data Sources for each layer are identified in the Layer section below as well as in the Layer and Legend sections of the web map. Additional information about the impact of drought on agriculture can be found on the NIDIS Agriculture Sector page.

The Agricultural Mapping Services market is evolving into a vital component of modern agriculture, harnessing advanced technologies to enhance crop management, optimize land use, and improve overall farm productivity. This sector combines satellite imagery, geographic information systems (GIS), and data analytics to

Introduction and Rationale: Due to our increasing understanding of the role the surrounding landscape plays in ecological processes, a detailed characterization of land cover, including both agricultural and natural habitats, is ever more important for both researchers and conservation practitioners. Unfortunately, in the United States, different types of land cover data are split across thematic datasets that emphasize agricultural or natural vegetation, but not both. To address this data gap and reduce duplicative efforts in geospatial processing, we merged two major datasets, the LANDFIRE National Vegetation Classification (NVC) and USDA-NASS Cropland Data Layer (CDL), to produce an integrated land cover map. Our workflow leveraged strengths of the NVC and the CDL to produce detailed rasters comprising both agricultural and natural land-cover classes. We generated these maps for each year from 2012-2021 for the conterminous United States, quantified agreement between input layers and accuracy of our merged product, and published the complete workflow necessary to update these data. In our validation analyses, we found that approximately 5.5% of NVC agricultural pixels conflicted with the CDL, but we resolved a majority of these conflicts based on surrounding agricultural land, leaving only 0.6% of agricultural pixels unresolved in our merged product. Contents: Spatial data Attribute table for merged rasters Technical validation data Number and proportion of mismatched pixels Number and proportion of unresolved pixels Producer's and User's accuracy values and coverage of reference data Resources in this dataset:Resource Title: Attribute table for merged rasters. File Name: CombinedRasterAttributeTable_CDLNVC.csvResource Description: Raster attribute table for merged raster product. Class names and recommended color map were taken from USDA-NASS Cropland Data Layer and LANDFIRE National Vegetation Classification. Class values are also identical to source data, except classes from the CDL are now negative values to avoid overlapping NVC values. Resource Title: Number and proportion of mismatched pixels. File Name: pixel_mismatch_byyear_bycounty.csvResource Description: Number and proportion of pixels that were mismatched between the Cropland Data Layer and National Vegetation Classification, per year from 2012-2021, per county in the conterminous United States.Resource Title: Number and proportion of unresolved pixels. File Name: unresolved_conflict_byyear_bycounty.csvResource Description: Number and proportion of unresolved pixels in the final merged rasters, per year from 2012-2021, per county in the conterminous United States. Unresolved pixels are a result of mismatched pixels that we could not resolve based on surrounding agricultural land (no agriculture with 90m radius).Resource Title: Producer's and User's accuracy values and coverage of reference data. File Name: accuracy_datacoverage_byyear_bycounty.csvResource Description: Producer's and User's accuracy values and coverage of reference data, per year from 2012-2021, per county in the conterminous United States. We defined coverage of reference data as the proportional area of land cover classes that were included in the reference data published by USDA-NASS and LANDFIRE for the Cropland Data Layer and National Vegetation Classification, respectively. CDL and NVC classes with reference data also had published accuracy statistics. Resource Title: Data Dictionary. File Name: Data_Dictionary_RasterMerge.csv

The dataset contains a map of the main classes of agricultural land use (dominant crop types and other land use types) in Germany for the year 2023. It complements a series of maps that are produced annually at the Thünen Institute beginning with the year 2017 on the basis of satellite data. The maps cover the entire open landscape, i.e., the agriculturally used area (UAA) and e.g., uncultivated areas. The map was derived from time series of Sentinel-1, Sentinel-2, Landsat 8 and additional environmental data. Map production is based on the methods described in Blickensdörfer et al. (2022).

All optical satellite data were managed, pre-processed and structured in an analysis-ready data (ARD) cube using the open-source software FORCE - Framework for Operational Radiometric Correction for Environmental monitoring (Frantz, D., 2019), in which SAR and environmental data were integrated.

The map extent covers all areas in Germany that are defined as agricultural land, grassland, small woody features, heathland, peatland or unvegetated areas according to ATKIS Basis-DLM (Geobasisdaten: © GeoBasis-DE / BKG, 2020).

Version v201:
Post-processing of the maps included a sieve filter as well as a ruleset for the reduction of non-plausible areas using the Basis-DLM and the digital terrain model of Germany (Geobasisdaten: © GeoBasis-DE / BKG, 2015).

The maps are available as cloud optimized GeoTiffs, which makes downloading the full dataset optional. All data can directly be accessed in QGIS, R, Python or any supported software of your choice using the URL that will be provided on request. By doing so the entire map area or only the regions of interest can be accessed. QGIS legend files for data visualization can be downloaded separately.

Class-specific accuracies for each year are proveded in the respective tables. We provide this dataset "as is" without any warranty regarding the accuracy or completeness and exclude all liability.

_

Mailing list

If you do not want to miss the latest updates, please enroll to our mailing list.

_

References:

Blickensdörfer, L., Schwieder, M., Pflugmacher, D., Nendel, C., Erasmi, S., & Hostert, P. (2022). Mapping of crop types and crop sequences with combined time series of Sentinel-1, Sentinel-2 and Landsat 8 data for Germany. Remote Sensing of Environment, 269, 112831.

BKG, Bundesamt für Kartographie und Geodäsie (2015). Digitales Geländemodell Gitterweite 10 m. DGM10. https://sg.geodatenzentrum.de/web_public/gdz/dokumentation/deu/dgm10.pdf (last accessed: 28. April 2022).

BKG, Bundesamt für Kartographie und Geodäsie (2020). Digitales Basis-Landschaftsmodell.
https://sg.geodatenzentrum.de/web_public/gdz/dokumentation/deu/basis-dlm.pdf (last accessed: 28. April 2022).

Frantz, D. (2019). FORCE—Landsat + Sentinel-2 Analysis Ready Data and Beyond. Remote Sensing, 11, 1124.

Statistisches Bundesamt, Deutschland (2024). Ökosystematlas Deutschland
https://oekosystematlas-ugr.destatis.de/ (last accessed: 08.02.2024).

_
National-scale crop type maps for Germany from combined time series of Sentinel-1, Sentinel-2 and Landsat data (2017 to 2021) © 2024 by Schwieder, Marcel; Tetteh, Gideon Okpoti; Blickensdörfer, Lukas; Gocht, Alexander; Erasmi, Stefan; licensed under CC BY 4.0.

Funding was provided by the German Federal Ministry of Food and Agriculture as part of the joint project “Monitoring der biologischen Vielfalt in Agrarlandschaften” (MonViA, Monitoring of biodiversity in agricultural landscapes).

The medium-sized agricultural location mapping was developed in the former GDR in the 1970s as a ground-based work for agriculture. In Berlin, only agricultural land in the northeast was recorded.

NOTICE TO PROVISIONAL 2023 LAND USE DATA USERS: Please note that on December 6, 2024 the Department of Water Resources (DWR) published the Provisional 2023 Statewide Crop Mapping dataset. The link for the shapefile format of the data mistakenly linked to the wrong dataset. The link was updated with the appropriate data on January 27, 2025. If you downloaded the Provisional 2023 Statewide Crop Mapping dataset in shapefile format between December 6, 2024 and January 27, we encourage you to redownload the data. The Map Service and Geodatabase formats were correct as posted on December 06, 2024.

Thank you for your interest in DWR land use datasets.

The California Department of Water Resources (DWR) has been collecting land use data throughout the state and using it to develop agricultural water use estimates for statewide and regional planning purposes, including water use projections, water use efficiency evaluations, groundwater model developments, climate change mitigation and adaptations, and water transfers. These data are essential for regional analysis and decision making, which has become increasingly important as DWR and other state agencies seek to address resource management issues, regulatory compliances, environmental impacts, ecosystem services, urban and economic development, and other issues. Increased availability of digital satellite imagery, aerial photography, and new analytical tools make remote sensing-based land use surveys possible at a field scale that is comparable to that of DWR’s historical on the ground field surveys. Current technologies allow accurate large-scale crop and land use identifications to be performed at desired time increments and make possible more frequent and comprehensive statewide land use information. Responding to this need, DWR sought expertise and support for identifying crop types and other land uses and quantifying crop acreages statewide using remotely sensed imagery and associated analytical techniques. Currently, Statewide Crop Maps are available for the Water Years 2014, 2016, 2018- 2022 and PROVISIONALLY for 2023.

Historic County Land Use Surveys spanning 1986 - 2015 may also be accessed using the CADWR Land Use Data Viewer: https://gis.water.ca.gov/app/CADWRLandUseViewer.

For Regional Land Use Surveys follow: https://data.cnra.ca.gov/dataset/region-land-use-surveys.

For County Land Use Surveys follow: https://data.cnra.ca.gov/dataset/county-land-use-surveys.

For a collection of ArcGIS Web Applications that provide information on the DWR Land Use Program and our data products in various formats, visit the DWR Land Use Gallery: https://storymaps.arcgis.com/collections/dd14ceff7d754e85ab9c7ec84fb8790a.

Recommended citation for DWR land use data: California Department of Water Resources. (Water Year for the data). Statewide Crop Mapping—California Natural Resources Agency Open Data. Retrieved “Month Day, YEAR,” from https://data.cnra.ca.gov/dataset/statewide-crop-mapping.

Detailed maps of agricultural landscapes are a valuable data source for manifold applications, such as environmental modelling, biodiversity monitoring or the support of agricultural statistics. Satellites from the European Copernicus program, especially, Sentinel-1 and Sentinel-2, as well as the Landsat missions operated by NASA/USGS, acquire data with a spatial resolution (10 m to 30 m) that is sufficient to identify field structures in complex agricultural landscapes. Time series of combined Sentinel-2 and Landsat data facilitate to differentiate crop types with a high thematic detail based on differences in land surface phenology. However, large data gaps due to frequent cloud cover may hamper such classification approaches.

We thus combined dense interpolated times series of Sentinel-2A/B and Landsat data with monthly composites of Sentinel-1 backscatter data to overcome periods with high cloud contamination. To further account for regional variations along the agroecological gradient within Germany, we additionally included a broad set of spatially explicit environmental data in a random forest classification model.

All optical satellite data were downloaded, pre-processed and structured in an analysis-ready data (ARD) cube using the open-source software FORCE - Framework for Operational Radiometric Correction for Environmental monitoring (Frantz, D., 2019; https://force-eo.readthedocs.io/en/latest/ last accessed: 19. August 2021), before environmental and SAR data were included in the ARD cube.

For each year (2017, 2018 and 2019) we trained an individual random forest model with 24 agricultural classes. Each model was independently validated with area adjusted overall accuracies of 80% (2017), 79% (2018), and 78% (2019). Further details regarding the data and methods used as well as class wise accuracies can be found in Blickensdörfer et al. (2022).

The final models were applied to areas in Germany that were defined as agricultural land in ATKIS DLM 2018 (Geobasisdaten: © GeoBasis-DE / BKG (2018)). Post-processing of the final maps included applying a sieve filter, the exclusion of classes other than grasslands and small woody features above 900 m (based on the Digital Elevation Model for Germany BKG (2015)) and the exclusion of grapevine/hops areas that were not labelled as the respective permanent crop in ATKIS DLM (labelled as other agricultural areas in the final map).

The maps are provided as GeoTiff files together with a QGIS legend file for visualization.

Class catalogue:

10 Grassland
31 Winter wheat
32 Winter rye
33 Winter barley
34 Other winter cereal
41 Spring barley
42 Spring oat
43 Other spring cereal
50 Winter rapeseed
60 Legume
70 Sunflower
80 Sugar beet
91 Maize
92 Maize (grain)
100 Potato
110 Grapevine
120 Strawberry
130 Asparagus
140 Onion
150 Hops
160 Orchard
181 Carrot
182 Other vegetables
555 Small woody features
999 Other agricultural areas

Blickensdörfer, L., Schwieder, M., Pflugmacher, D., Nendel, C., Erasmi, S., & Hostert, P. (2022). Mapping of crop types and crop sequences with combined time series of Sentinel-1, Sentinel-2 and Landsat 8 data for Germany. Remote Sensing of Environment, 269, 112831

BKG, Bundesamt für Kartographie und Geodäsie (2015). Digitales Geländemodell Gitterweite 10 m. DGM10. https://sg.geodatenzentrum.de/web_public/gdz/dokumentation/deu/dgm10.pdf (last accessed: 19. August 2021).

BKG, Bundesamt für Kartographie und Geodäsie (2018). Digitales Basis-Landschaftsmodell.
https://sg.geodatenzentrum.de/web_public/gdz/dokumentation/deu/basis-dlm.pdf (last accessed: 19. August 2021).

Frantz, D. (2019). FORCE—Landsat + Sentinel-2 Analysis Ready Data and Beyond. Remote Sensing, 11, 1124.

National-scale crop type maps for Germany © 2021 by Blickensdörfer, Lukas; Schwieder, Marcel; Pflugmacher, Dirk; Nendel, Claas; Erasmi, Stefan; Hostert, Patrick is licensed under CC BY 4.0.

Explore the growth potential of Market Research Intellect's Agricultural Mapping Services Market Report, valued at USD 1.25 billion in 2024, with a forecasted market size of USD 2.50 billion by 2033, growing at a CAGR of 8.5% from 2026 to 2033.

Provisional Agricultural Land Classification Grade. Agricultural land classified into five grades. Grade one is best quality and grade five is poorest quality. A number of consistent criteria used for assessment which include climate (temperature, rainfall, aspect, exposure, frost risk), site (gradient, micro-relief, flood risk) and soil (depth, structure, texture, chemicals, stoniness) for England only. Digitised from the published 1:250,000 map which was in turn compiled from the 1 inch to the mile maps.More information about the Agricultural Land Classification can be found at the following links:http://webarchive.nationalarchives.gov.uk/20130402200910/http://archive.defra.gov.uk/foodfarm/landmanage/land-use/documents/alc-guidelines-1988.pdfhttp://publications.naturalengland.org.uk/publication/35012.Full metadata can be viewed on data.gov.uk.

Introduction and Rationale:Due to our increasing understanding of the role the surrounding landscape plays in ecological processes, a detailed characterization of land cover, including both agricultural and natural habitats, is ever more important for both researchers and conservation practitioners. Unfortunately, in the United States, different types of land cover data are split across thematic datasets that emphasize agricultural or natural vegetation, but not both. To address this data gap and reduce duplicative efforts in geospatial processing, we merged two major datasets, the LANDFIRE National Vegetation Classification (NVC) and USDA-NASS Cropland Data Layer (CDL), to produce integrated ‘Spatial Products for Agriculture and Nature’ (SPAN). Our workflow leveraged strengths of the NVC and the CDL to produce detailed rasters comprising both agricultural and natural land-cover classes. We generated SPAN for each year from 2012-2021 for the conterminous United States, quantified agreement between input layers and accuracy of our merged product, and published the complete workflow necessary to update SPAN. In our validation analyses, we found that approximately 5.5% of NVC agricultural pixels conflicted with the CDL, but we resolved a majority of these conflicts based on surrounding agricultural land, leaving only 0.6% of agricultural pixels unresolved in the final version of SPAN.Contents:Spatial dataNational rasters of land cover in the conterminous United States: 2012-2021Rasters of pixels mismatched between CDL and NVC: 2012-2021Resources in this dataset:Resource Title: SPAN land cover in the conterminous United States: 2012-2021 - SCINet File Name: KammererNationalRasters.zip Resource Description: GeoTIFF rasters showing location of pixels that are mismatched between 2016 NVC and specific year of CDL (2012-2021). Spatial Products for Agriculture and Nature ('SPAN') land cover in the conterminous United States from 2012-2021. This raster dataset is available in GeoTIFF format and was created by joining agricultural classes from the USDA-NASS Cropland Data Layer (CDL) to national vegetation from the LANDFIRE National Vegetation Classification v2.0 ('Remap'). Pixels of national vegetation are the same in all rasters provided here and represent land cover in 2016. Agricultural pixels were taken from the CDL in the specified year, so depict agricultural land from 2012-2021. Resource Title: Rasters of pixels mismatched between CDL and NVC: 2012-2021 - SCINet File Name: MismatchedNational.zip Resource Description: GeoTIFF rasters showing location of pixels that are mismatched between 2016 NVC and specific year of CDL (2012-2021). This dataset includes pixels that were classified as agriculture in the NVC but, in the CDL, were not agriculture (or were a conflicting agricultural class). For more details, we refer users to the linked publication describing our geospatial processing and validation workflow.SCINet users: The files can be accessed/retrieved with valid SCINet account at this location: /LTS/ADCdatastorage/NAL/published/node455886/ See the SCINet File Transfer guide for more information on moving large files: https://scinet.usda.gov/guides/data/datatransferGlobus users: The files can also be accessed through Globus by following this data link. The user will need to log in to Globus in order to retrieve this data. User accounts are free of charge with several options for signing on. Instructions for creating an account are on the login page.

Discover the latest insights from Market Research Intellect's Agricultural Mapping Software Market Report, valued at USD 1.5 billion in 2024, with significant growth projected to USD 3.2 billion by 2033 at a CAGR of 9.5% (2026-2033).