The global market for GIS software in agriculture is experiencing robust growth, driven by the increasing need for precision agriculture techniques and the rising adoption of smart farming practices. This sector leverages Geographic Information Systems (GIS) to optimize various agricultural operations, including land management, crop monitoring, yield prediction, and resource allocation. The market's value in 2025 is estimated at $2.5 billion, exhibiting a Compound Annual Growth Rate (CAGR) of 12% from 2025 to 2033. This growth trajectory is fueled by several key factors. Firstly, the escalating demand for higher crop yields and improved resource efficiency in the face of a growing global population is a primary driver. Secondly, advancements in sensor technologies, satellite imagery, and data analytics are providing increasingly precise and actionable agricultural insights. Finally, government initiatives promoting digital agriculture and precision farming technologies are further stimulating market expansion. Despite significant growth, challenges remain. High initial investment costs for GIS software and the required hardware can be a barrier to entry for smaller farms and developing economies. Furthermore, the complexity of implementing and effectively utilizing GIS solutions requires skilled personnel, creating a need for increased training and support. However, the long-term benefits of enhanced efficiency, reduced waste, and improved yields are overcoming these obstacles, creating a positive outlook for market expansion. Key players such as Autodesk, Esri, and Trimble are actively innovating and expanding their agricultural GIS offerings to cater to the evolving needs of the sector. The market is segmented by software type (desktop, web-based, mobile), deployment mode (cloud, on-premise), and application (precision farming, irrigation management, crop monitoring). The continued integration of AI and machine learning within GIS platforms promises further advancements in agricultural optimization, propelling market growth in the coming years.

The GreenReport®The Kansas Applied Remote Sensing Program (KARS) uses satellite data to produce a weekly map series called the GreenReport®, which illustrates current and relative vegetation conditions and trends for the conterminous U.S. Since 2002, KARS has also used these satellite data to forecast district, state, and national level crop yields for eight major crops in the U.S.The advantage of displaying satellite-based vegetation information in map form is that locally specific growing conditions can be ascertained. The GreenReport® combines current satellite data with historic data to present a more complete picture of vegetation condition and progress. The data archive underlying the GreenReport extends back to 1989. The raw data used for the GreenReport® are produced and distributed by the USGS EROS Data Center.GreenReport® maps (which are updated on a weekly basis throughout the growing season) present four different views of current vegetation condition:• current greenness (NDVI)• greenness change from the previous week• difference from the same week last year• difference from the long-term average greenness for the weekThe vegetation condition map illustrates vegetation health and levels of plant stress, and is based on current and historic vegetation greenness and surface temperature data collected by satellites.Since 2008, the GreenReport® maps have been featured in Planalytics’ Insight newsletters. Planalytics is a commercial partner of KARS through Lawrence-based TerraMetrics Agriculture, Inc. Planalytics also features KARS crop yield forecasts in their Life Sciences product line, in the form of biweekly, pre-harvest crop reports that integrate satellite and weather intelligence to assess the current outlook for U.S. winter wheat, corn, and soybean crops.For more information about the GreenReport®, contact John Lomas (johnl@ku.edu). For inquiries regarding the companion crop yield forecasts, contact Jude Kastens (jkastens@ku.edu). To learn more about the GreenReport® and yield forecasting products provided by Planalytics, contact Jed Lafferty (jlafferty@planalytics.com).

Net Primary Production (NPP) is a fundamental characteristic of an ecosystem, expressing the conversion of carbon dioxide into biomass driven by photosynthesis. The pixel value represents the mean daily NPP for that specific dekad.

Data publication: 2024-02-05

Supplemental Information:

No data value: -9999

Unit : gC/m²/day

Scale Factor : 0.001

Map code : L3-NPP-D.KWL

Scale factor: The pixel value in the downloaded data must be multiplied by

New dekadal data layers are released approximately 5 days after the end of a dekad. A higher quality version of the same data layer is uploaded after 6 dekads have passed. This final version of the dekadal dataset has a higher quality because gap filling and interpolation processes, where needed, have been based on more data observations.

Citation:

FAO WaPOR database, License: CC BY-NC-SA 4.0, [Date accessed: Day/Month/Year]

Contact points:

Resource Contact: WaPOR

Metadata Contact: WaPOR

Data lineage:

The calculation is based on the WaPOR-ETLook model described in the Wapor methodology document.

The core of the methodology for deriving NPP is detailed in Veroustraete et al. (2002), whilst the practical implementation, as developed for the MARS Crop Yield Forecasting System, is described in Eerens et al. (2004). These methodologies were improved within the framework of the Copernicus Global Land Component, the most important change being the incorporation of biome-specific light-use efficiencies (LUEs). The FRAME project applies this updated methodology, adding improvements which include the addition of a reduction factor to account for reduced water availability (i.e. soil moisture stress). The following data is used to calculate NPP:

Daily: Incoming solar radiation and temperature data (Tmin/Tmax);

Monthly: fAPAR and soil moisture stress;

Seasonal: Land Cover.

Data component are developed through collaboration with eLEAF. More information can be found on the WaPOR Website.

Resource constraints:

Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)

Online resources:

Download the data from Google Cloud Storage

Download the data from File-Browser

The best fit equations used for the prediction of potato yield in the three fields.

Geographic Information System (GIS) Software Market Outlook

According to our latest research, the global Geographic Information System (GIS) Software market size reached USD 11.6 billion in 2024, reflecting a robust demand for spatial data analytics and location-based services across various industries. The market is experiencing a significant growth trajectory, driven by a CAGR of 12.4% from 2025 to 2033. By the end of 2033, the GIS Software market is forecasted to attain a value of USD 33.5 billion. This remarkable expansion is primarily attributed to the integration of advanced technologies such as artificial intelligence, IoT, and cloud computing, which are enhancing the capabilities and accessibility of GIS platforms.

One of the major growth factors propelling the GIS Software market is the increasing adoption of location-based services across urban planning, transportation, and utilities management. Governments and private organizations are leveraging GIS solutions to optimize infrastructure development, streamline resource allocation, and improve emergency response times. The proliferation of smart city initiatives worldwide has further fueled the demand for GIS tools, as urban planners and municipal authorities require accurate spatial data for effective decision-making. Additionally, the evolution of 3D GIS and real-time mapping technologies is enabling more sophisticated modeling and simulation, expanding the scope of GIS applications beyond traditional mapping to include predictive analytics and scenario planning.

Another significant driver for the GIS Software market is the rapid digitization of industries such as agriculture, mining, and oil & gas. Precision agriculture, for example, relies heavily on GIS platforms to monitor crop health, manage irrigation, and enhance yield forecasting. Similarly, the mining sector uses GIS for exploration, environmental impact assessment, and asset management. The integration of remote sensing data with GIS software is providing stakeholders with actionable insights, leading to higher efficiency and reduced operational risks. Furthermore, the growing emphasis on environmental sustainability and regulatory compliance is prompting organizations to invest in advanced GIS solutions for monitoring land use, tracking deforestation, and managing natural resources.

The expanding use of cloud-based GIS solutions is also a key factor driving market growth. Cloud deployment offers scalability, cost-effectiveness, and remote accessibility, making GIS tools more accessible to small and medium enterprises as well as large organizations. The cloud model supports real-time data sharing and collaboration, which is particularly valuable for disaster management and emergency response teams. As organizations increasingly prioritize digital transformation, the demand for cloud-native GIS platforms is expected to rise, supported by advancements in data security, interoperability, and integration with other enterprise systems.

Regionally, North America remains the largest market for GIS Software, accounting for a significant share of global revenues. This leadership is underpinned by substantial investments in smart infrastructure, advanced transportation systems, and environmental monitoring programs. The Asia Pacific region, however, is witnessing the fastest growth, driven by rapid urbanization, government-led digital initiatives, and the expansion of the utility and agriculture sectors. Europe continues to demonstrate steady adoption, particularly in environmental management and urban planning, while Latin America and the Middle East & Africa are emerging as promising markets due to increasing investments in infrastructure and resource management.

Component Analysis

The GIS Software market is segmented by component into Software and Services, each playing a pivotal role in the overall value chain. The software segment includes comprehensive GIS platforms, spatial analytics tools, and specialized applications

The global satellite imaging market for agriculture is experiencing robust growth, driven by the increasing need for precise and timely agricultural data to optimize yields and resource management. The market, estimated at $4 billion in 2025, is projected to expand significantly over the forecast period (2025-2033), fueled by a Compound Annual Growth Rate (CAGR) of approximately 15%. This growth is primarily attributed to several key factors: the rising adoption of precision agriculture techniques, advancements in satellite technology offering higher resolution and more frequent imagery, and the increasing affordability of satellite data analysis. Furthermore, the growing awareness among farmers regarding the benefits of data-driven decision-making, coupled with supportive government initiatives promoting technological adoption in agriculture, is further accelerating market expansion. Key players like Planet Labs PBC, Airbus, and Maxar Technologies are actively shaping the market landscape through technological innovations and strategic partnerships. The market segmentation reveals a strong demand across various agricultural applications, including crop monitoring, precision irrigation, livestock management, and yield prediction. Regional variations exist, with North America and Europe currently dominating the market due to higher technological adoption and established agricultural practices. However, significant growth opportunities are expected in developing economies as farmers in these regions increasingly leverage satellite data for improved agricultural outcomes. Despite the growth potential, certain challenges remain, such as the need for reliable internet connectivity in remote areas, data security concerns, and the high initial investment costs associated with satellite data acquisition and analysis. Overcoming these barriers will be crucial for ensuring widespread adoption and unlocking the full potential of satellite imaging in revolutionizing the agricultural sector.

The global satellite remote sensing software market is experiencing robust growth, driven by increasing demand across diverse sectors. While precise figures for market size and CAGR aren't provided, a reasonable estimate based on industry reports and the stated study period (2019-2033) suggests a current market valuation (2025) in the range of $3-5 billion USD. This significant market size is fueled by several key factors. The agricultural sector relies heavily on remote sensing for precision farming, crop monitoring, and yield prediction, significantly contributing to market expansion. Similarly, the water conservancy and forest management sectors utilize satellite imagery and software for resource monitoring, disaster management, and sustainable practices. Government agencies and the public sector increasingly adopt these technologies for urban planning, environmental monitoring, and national security applications. The market's growth is further enhanced by advancements in open-source software, offering cost-effective alternatives and promoting wider adoption. Trends such as cloud-based solutions, improved data processing capabilities, and the integration of artificial intelligence are further accelerating market growth. However, the market faces certain constraints. High initial investment costs for software licenses and specialized hardware can act as a barrier for entry, particularly for smaller businesses and organizations in developing regions. Data security concerns and the need for skilled professionals to interpret the complex data generated also pose challenges. Despite these obstacles, the ongoing development of user-friendly interfaces, coupled with decreasing hardware costs and increasing availability of cloud-based services, is predicted to mitigate these restraints and sustain a healthy compound annual growth rate (CAGR) in the range of 8-12% throughout the forecast period (2025-2033). Segmentation by application (Agriculture, Water Conservancy, Forest Management, Public Sector, Others) and software type (Open Source, Non-Open Source) reveals distinct market dynamics, with the non-open source segment currently holding a larger share due to its advanced capabilities. This trend is expected to continue, though the open-source segment will show considerable growth driven by its affordability and accessibility.

Geographic Information Systems Market in Agriculture by Offering, Application (Soil & Agricultural Mapping, Crop Monitoring, Yield Prediction, Livestock Monitoring), Sub-sector (Crop Farming, Forestry, Livestock) - Global Forecast to 2032

The global satellite remote sensing software market is experiencing robust growth, driven by increasing demand across diverse sectors. While precise market size figures for 2025 aren't provided, considering a plausible CAGR of 10% (a conservative estimate given the technological advancements and expanding applications) and an assumed 2024 market size of $2 billion, we can project a 2025 market valuation of approximately $2.2 billion. This expansion is fueled by several key factors. Firstly, the agricultural sector is leveraging satellite imagery for precision farming, crop monitoring, and yield prediction, significantly enhancing efficiency and productivity. Secondly, advancements in water resource management are heavily reliant on remote sensing data for efficient irrigation and flood control. Furthermore, forest management and conservation efforts utilize this technology for deforestation monitoring and biodiversity assessment. The public sector, including government agencies and research institutions, is also a major consumer, relying on these tools for environmental monitoring, disaster response, and urban planning. The market is segmented by software type (open-source and non-open-source) and application, with non-open-source solutions currently commanding a larger share due to their advanced features and robust support. Growth is further propelled by continuous technological innovation leading to more sophisticated analytics capabilities and easier data accessibility. However, certain restraints hinder market expansion. High initial investment costs for software licenses and hardware can pose a significant barrier, particularly for smaller organizations. Furthermore, the need for specialized expertise to interpret and analyze the complex satellite data can limit widespread adoption. Data security and privacy concerns related to sensitive geographic information are also emerging challenges. Despite these limitations, the long-term outlook for the satellite remote sensing software market remains positive, fueled by ongoing technological advancements, increased government investments in space-based technologies, and the growing recognition of its importance in various sectors. The market is expected to continue its growth trajectory, creating opportunities for established players and new entrants alike. The diverse range of applications and continued integration with other technologies like AI and machine learning will significantly shape the future landscape of this market.

This product is generated for SERVIR-Mekong's Regional Drought and Crop Yield Information System (RDCYIS).

In the context of decision-making, RDCYIS helps to addresses the much-needed drought preparedness, monitoring and forecasting as well as crop yield information for selected crops while assessing economic, social and environmental impacts in the Lower Mekong Region (LMR) countries.

The RDCYIS deploys the Regional Hydrologic Extreme Assessment System (RHEAS) that is an integration of hydrological and crop simulation models developed by NASA-Jet Propulsion Laboratory. The core of the RHEAS framework is the Variable Infiltration Capacity (VIC) model and the Decision Support System for Agro-Technology Transfer (DSSAT) model that automates the deployment of nowcasting and forecasting hydrologic simulations and ingests satellite observations through data assimilation. It also allows coupling of other environmental models and facilitates the delivery of data products to users via a GIS enabled database. The system’s ability to carry our nowcast and forecast within the framework at the same time gives an upper edge to the present existing resources or systems available for drought and crop yield monitoring.Raw data can be accessed at ftp:IP: 203.170.246.170 port 21User name: ftpuserPassword: ftpuser

The spatially explicit water requirement satisfaction index (WRSI*) is an indicator of crop performance based on the availability of water to the crop during a growing season. FAO studies have shown that WRSI can be related to crop productivity using a linear yield-reduction function specific to a crop (FAO, 1977; FAO, 1979; FAO, 1999). Later, Verdin and Klaver (2002) and Senay and Verdin (2003) demonstrated a regional implementation of WRSI in a grid cell based modeling environment. WRSI for a season is based on the water supply and demand a crop experiences during a growing season. It is calculated as the ratio of seasonal actual evapotranspiration (AET) to the seasonal crop water requirement (WR).
AET WRSI = --------------- * 100 WR

Read more here http://earlywarning.usgs.gov/fews/product/126 * Originally developed by FAO, the WRSI has been adapted and extended by USGS in a geospatial application to support FEWS NET monitoring requirements. References FAO, 1977. Crop water requirements. FAO Irrigation and Drainage Paper No. 24, by Doorenbos J and W.O. Pruitt. FAO, Rome, Italy. FAO, 1979. Agrometeorological crop monitoring and forecasting. FAO Plant Production and Protection paper No. 17, by M. Frère and G.F. Popov. FAO, Rome, Italy. FAO, 1999. Early Agrometeorological crop yield forecasting. FAO Plant Production and Protection paper No. 73, by M. Frère and G.F. Popov. FAO, Rome, Italy. Senay, G.B. and J. Verdin, 2003. Characterization of Yield Reduction in Ethiopia Using a GIS-Based Crop Water Balance Model. Canadian Journal of Remote Sensing, vol. 29, no. 6, pp. 687-692. Verdin, J. and R. Klaver, 2002. Grid cell based crop water accounting for the famine early warning system. Hydrological Processes, 16:1617-1630.

The Geographic Information System (GIS) Services market is experiencing robust growth, driven by increasing adoption across diverse sectors. The market, estimated at $15 billion in 2025, is projected to expand significantly over the forecast period (2025-2033), fueled by a compound annual growth rate (CAGR) of 8%. This growth is primarily attributed to the escalating demand for spatial data analysis and visualization in various applications. Environmental agencies leverage GIS for resource management and pollution monitoring, while utility companies utilize it for network optimization and infrastructure planning. Infrastructure companies benefit from improved project management and risk assessment, and the telecommunications sector utilizes GIS for network planning and maintenance. The retail industry uses GIS for location analysis and market research, and government agencies leverage it for urban planning and public safety initiatives. Furthermore, the agricultural sector is increasingly adopting GIS for precision farming and yield optimization. The market is segmented by application (Environmental Agencies, Utility Companies, Infrastructure Companies, Telecommunications, Retail, Government, Agriculture, Others) and service type (Analyze, Visualize, Manage, Others). North America and Europe currently hold the largest market shares, driven by high technology adoption and advanced infrastructure. However, Asia Pacific is expected to witness significant growth in the coming years, propelled by rapid urbanization and economic development. Key players in the market include Intellias, EnviroScience, R&K Solutions, and others, constantly innovating to meet the evolving needs of their clients. The competitive landscape is characterized by a mix of large multinational corporations and specialized service providers. Larger companies often offer comprehensive end-to-end solutions encompassing data acquisition, analysis, and visualization, catering to large-scale projects. Smaller, specialized firms typically focus on niche applications or geographic regions. Ongoing technological advancements, such as cloud-based GIS solutions and the integration of artificial intelligence (AI) and machine learning (ML) capabilities, are further stimulating market growth. However, factors such as high initial investment costs and the need for skilled professionals could potentially restrain market expansion. Nevertheless, the overall market outlook remains positive, indicating substantial growth opportunities for businesses operating in this dynamic sector. The increasing availability of affordable and accessible GIS software and the rising adoption of mobile GIS technology are anticipated to further drive the market in the foreseeable future.

Net Primary Production (NPP) is a fundamental characteristic of an ecosystem, expressing the conversion of carbon dioxide into biomass driven by photosynthesis. The pixel value represents the mean daily NPP for that specific dekad.

Data publication: 2024-01-31

Supplemental Information:

No data value: -9999

Unit : gC/m²/day

Scale Factor : 0.001

Map code : L3-NPP-D.YAN

Scale factor: The pixel value in the downloaded data must be multiplied by

New dekadal data layers are released approximately 5 days after the end of a dekad. A higher quality version of the same data layer is uploaded after 6 dekads have passed. This final version of the dekadal dataset has a higher quality because gap filling and interpolation processes, where needed, have been based on more data observations.

Citation:

FAO WaPOR database, License: CC BY-NC-SA 4.0, [Date accessed: Day/Month/Year]

Contact points:

Resource Contact: WaPOR

Metadata Contact: WaPOR

Data lineage:

The calculation is based on the WaPOR-ETLook model described in the Wapor methodology document.

The core of the methodology for deriving NPP is detailed in Veroustraete et al. (2002), whilst the practical implementation, as developed for the MARS Crop Yield Forecasting System, is described in Eerens et al. (2004). These methodologies were improved within the framework of the Copernicus Global Land Component, the most important change being the incorporation of biome-specific light-use efficiencies (LUEs). The FRAME project applies this updated methodology, adding improvements which include the addition of a reduction factor to account for reduced water availability (i.e. soil moisture stress). The following data is used to calculate NPP:

Daily: Incoming solar radiation and temperature data (Tmin/Tmax);

Monthly: fAPAR and soil moisture stress;

Seasonal: Land Cover.

Data component are developed through collaboration with eLEAF. More information can be found on the WaPOR Website.

Resource constraints:

Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)

Online resources:

Download the data from the bucket

Download the data from File-Browser

Geospatial Imagery Analytics Market size was valued at USD 69.66 Billion in 2023 and is projected to reach USD 226.79 Billion by 2031, growing at a CAGR of 15.90% from 2024 to 2031.Key Market Drivers:Increasing Adoption of Geospatial Technology in Agriculture: The precision agriculture business is expanding rapidly as a result of increased geospatial technology usage, with GPS guidance systems in farm tractors increasing from 5% in 2001 to more than 65% in 2016, according to USDA data. This spike in use demonstrates an increasing dependence on geospatial imaging analytics to improve crop monitoring, yield prediction, and resource management. The main causes behind this development include the need for more effective farming practices, higher crop yields, and better resource management, all of which are made possible by geospatial technology's accurate and actionable insights.

The Earth Observation (EO) satellite, data, and service market is experiencing robust growth, driven by increasing demand across diverse sectors. The market, estimated at $15 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 7-9% from 2025 to 2033, reaching an estimated $28-32 billion by 2033. This expansion is fueled by several key factors. Precision agriculture, leveraging EO data for optimized crop management and yield enhancement, is a significant driver. Similarly, the burgeoning need for advanced weather forecasting, environmental monitoring (including climate change mitigation and disaster response), and resource management in sectors like forestry, fisheries, and mining contributes significantly to market growth. The adoption of EO data by governments and defense agencies for strategic applications further enhances market potential. Technological advancements, including the development of higher-resolution sensors and improved data analytics capabilities, are also contributing to this expansion. The market is segmented by application (e.g., precision agriculture, environmental monitoring, financial services), satellite type (based on orbital altitude), and region, with North America and Europe currently holding substantial market shares. Despite its impressive growth trajectory, the EO market faces certain restraints. High initial investment costs for satellite development and deployment can present a barrier to entry for smaller players. Data processing and analysis, requiring specialized expertise and significant computational resources, also pose a challenge. Furthermore, regulatory hurdles and data security concerns can hinder market expansion in certain regions. However, ongoing advancements in miniaturization, the emergence of new space technologies, and increasing collaboration between private companies and government agencies are likely to mitigate these challenges in the long term, paving the way for sustained market growth and wider accessibility of EO data and services. The competitive landscape is characterized by a mix of established aerospace companies and emerging technology firms, highlighting the dynamic and rapidly evolving nature of this sector.

Net Primary Production (NPP) is a fundamental characteristic of an ecosystem, expressing the conversion of carbon dioxide into biomass driven by photosynthesis. The pixel value represents the mean daily NPP for that specific dekad.

Data publication: 2024-01-31

Supplemental Information:

No data value: -9999

Unit : gC/m²/day

Scale Factor : 0.001

Map code : L3-NPP-D.MAL

Scale factor: The pixel value in the downloaded data must be multiplied by

New dekadal data layers are released approximately 5 days after the end of a dekad. A higher quality version of the same data layer is uploaded after 6 dekads have passed. This final version of the dekadal dataset has a higher quality because gap filling and interpolation processes, where needed, have been based on more data observations.

Citation:

FAO WaPOR database, License: CC BY-NC-SA 4.0, [Date accessed: Day/Month/Year]

Contact points:

Resource Contact: WaPOR

Metadata Contact: WaPOR

Data lineage:

The calculation is based on the WaPOR-ETLook model described in the Wapor methodology document.

The core of the methodology for deriving NPP is detailed in Veroustraete et al. (2002), whilst the practical implementation, as developed for the MARS Crop Yield Forecasting System, is described in Eerens et al. (2004). These methodologies were improved within the framework of the Copernicus Global Land Component, the most important change being the incorporation of biome-specific light-use efficiencies (LUEs). The FRAME project applies this updated methodology, adding improvements which include the addition of a reduction factor to account for reduced water availability (i.e. soil moisture stress). The following data is used to calculate NPP:

Daily: Incoming solar radiation and temperature data (Tmin/Tmax);

Monthly: fAPAR and soil moisture stress;

Seasonal: Land Cover.

Data component are developed through collaboration with eLEAF. More information can be found on the WaPOR Website.

Resource constraints:

Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)

Online resources:

Download the data from Google Cloud Storage

Download the data from File-Browser

The spatially explicit water requirement satisfaction index (WRSI*) is an indicator of crop performance based on the availability of water to the crop during a growing season. FAO studies have shown that WRSI can be related to crop productivity using a linear yield-reduction function specific to a crop (FAO, 1977; FAO, 1979; FAO, 1993). Later, Verdin and Klaver (2002) and Senay and Verdin (2003) demonstrated a regional implementation of WRSI in a grid cell based modeling environment.

WRSI for a season is based on the water supply and demand a crop experiences during a growing season. It is calculated as the ratio of seasonal actual evapotranspiration (AET) to the seasonal crop water requirement (WR).
AET WRSI = --------------- * 100 WR

Read more here http://earlywarning.usgs.gov/fews/product/126 * Originally developed by FAO, the WRSI has been adapted and extended by USGS in a geospatial application to support FEWS NET monitoring requirements.

References FAO, 1977. Crop water requirements. FAO Irrigation and Drainage Paper No. 24, by Doorenbos J and W.O. Pruitt. FAO, Rome, Italy.

FAO, 1979. Agrometeorological crop monitoring and forecasting. FAO Plant Production and Protection paper No. 17, by M. Frère and G.F. Popov. FAO, Rome, Italy.

FAO, 1993. Early Agrometeorological crop yield forecasting. FAO Plant Production and Protection paper No. 73, by M. Frère and G.F. Popov. FAO, Rome, Italy.

Senay, G.B. and J. Verdin, 2003. Characterization of Yield Reduction in Ethiopia Using a GIS-Based Crop Water Balance Model. Canadian Journal of Remote Sensing, vol. 29, no. 6, pp. 687-692.

Verdin, J. and R. Klaver, 2002. Grid cell based crop water accounting for the famine early warning system. Hydrological Processes, 16:1617-1630.

Link to the ScienceBase Item Summary page for the item described by this metadata record. Service Protocol: Link to the ScienceBase Item Summary page for the item described by this metadata record. Application Profile: Web Browser. Link Function: information

Link to the ScienceBase Item Summary page for the item described by this metadata record. Service Protocol: Link to the ScienceBase Item Summary page for the item described by this metadata record. Application Profile: Web Browser. Link Function: information