GEO Task US-09-01a · 2010. 11. 1. · GEO Task US-09-01a: Critical Earth Observations Priorities...

GEO Task US-09-01a: Critical Earth Observations Priorities

Agriculture Societal Benefit Area

US-09-01a Task Lead: Lawrence Friedl, USA/NASA Agriculture SBA Analyst: Allan Sommer, Battelle Memorial Institute

2010

User Interface Committee

Earth Observation Priorities: Agriculture SBA SBA Report – Option Year 1 ● Page ii

Analyst & Advisory Group Members The following people provided input for the ad hoc Advisory Group for the Agriculture Societal Benefit Area (SBA) under GEO Task US-09-01a. The Advisory Group supported the Analyst by identifying source materials, reviewing analytic methodologies, assessing findings, and reviewing this report. Samira Omar ASEM, Kuwait Institute for Scientific Research, Kuwait Mark BRUSBERG, United States Department of Agriculture, USA Carmela CASCONE, Institute for Environmental Protection and Research Sustainable Use of

Natural Resources Service, Italy Jinlong FAN, GEO Secretariat, Switzerland Marie-Hélène FORGET, Bedford Institute of Oceanography, Canada Mike GRUNDY, Australian Commonwealth Scientific and Research Organization, Australia Chris JUSTICE, University of Maryland, USA Johnson OWARO, Disaster Preparedness and Refugees Transition and Recovery Programme for

North and Eastern Uganda, Uganda Jai S. PARIHAR, Space Applications Centre, India Basanta SHRESTHA, The International Centre for Integrated Mountain Development, Nepal Robert STEFANSKI, World Meteorological Organization, Switzerland Prasad THENKABAIL, United States Geological Survey, USA The following person served as the Analyst for the Agriculture SBA under GEO Task US-09-01a, providing overall coordination of the analysis and preparation of this report. Allan SOMMER, Battelle, USA The following staff supported the Analyst: Adam CARPENTER, Battelle, USA Acknowledgment The Analyst performed this task and prepared this report through the support of the National Aeronautics and Space Administration (NASA), 300 E Street SW, Washington, DC 20546, USA, contract no. GS-23F-8167H. GEO Task US-09-01a Lawrence FRIEDL (USA/NASA) is the Task Lead for GEO Task US-09-01a; he is a member of the GEO User Interface Committee. Amy Jo SWANSON (USA/Science Systems and Applications, Inc) is the US-09-01a Task Coordinator, providing logistics and coordination amongst the Analysts.

Earth Observation Priorities: Agriculture SBA SBA Report – Option Year 1 ● Page iii

Table of Contents 1. Introduction ........................................................................................................................................... 6

1.1. GEO and Societal Benefit Areas ................................................................................................... 6 1.2. Task US-09-01a ............................................................................................................................ 6 1.3. Purpose of Report ......................................................................................................................... 7 1.4 Scope of Report ............................................................................................................................. 7

2. Methodology and Process ..................................................................................................................... 8 2.1. Task Process .................................................................................................................................. 8 2.2. Analyst and Advisory Group ........................................................................................................ 9

2.2.1. Analyst .................................................................................................................................. 9 2.2.2. Advisory Group..................................................................................................................... 9

2.3. Methodology ............................................................................................................................... 10 2.3.1. Documents .......................................................................................................................... 10 2.3.2. Analytic Methods ................................................................................................................ 11 2.3.3. Prioritization Methods......................................................................................................... 12

3. Agriculture SBA ................................................................................................................................. 14 3.1. Description .................................................................................................................................. 14 3.2. Sub-areas ..................................................................................................................................... 14 3.3. Documents .................................................................................................................................. 15 3.4 User Types .................................................................................................................................. 16

4. Earth Observations for Agriculture SBA ............................................................................................ 17 4.1. Agriculture Specific .................................................................................................................... 17 4.1.1. Famine Early Warning ............................................................................................................ 17 4.1.2. Agricultural Production ........................................................................................................... 19 4.1.3. Seasonal / Annual Agricultural Forecasting and Risk Reduction ........................................... 24 4.2. Aquaculture Production .............................................................................................................. 29

5. Priority Observations .......................................................................................................................... 32 5.1. General description ..................................................................................................................... 32 5.2. Land-Based Agriculture Priority Observations and Characteristics ........................................... 32 5.3. Aquaculture Priority Observations and Characteristics .............................................................. 34 5.4. Agriculture SBA Final Priority Observations and Characteristics.............................................. 35

6. Additional Findings ............................................................................................................................ 36 7. Analyst Comments and Recommendations ........................................................................................ 37 Appendix A: Abbreviations and Acronyms ................................................................................................ 38 Appendix B: Bibliography .......................................................................................................................... 39 Appendix C: Input to the Cross-SBA Analysis .......................................................................................... 45

Earth Observation Priorities: Agriculture SBA SBA Report – Option Year 1 ● Page iv

List of Tables

Table 1. Advisory Group for Agriculture Societal Benefit Area ……………………………………….4 Table 2. Factors for Weighted Index Computation ...................................................................................... 9 Table 3. Agriculture Earth Observation User Types .................................................................................. 11 Table 4. Famine Early Warning Observation Categories (Aggregated and Unique Observations) ........... 12 Table 5. Agricultural Production Observation Categories (Aggregated and Unique Observations) ......... 15 Table 6. Seasonal/Annual Agricultural Forecasting and Risk Reduction Observation Categories (Aggregated and Unique Observations) ...................................................................................................... 19 Table 7. Aquaculture Production Observation Categories (Aggregated and Unique Observations) ......... 24 Table 8. Priority Observation Characteristics for 3 Agriculture Sub-Areas ............................................... 28 Table 9. Top Observation Category Characteristics for Aquaculture ......................................................... 29 Table 10. Priority Observation Characteristics for the Agriculture SBA ................................................... 30

List of Figures

Figure 1. Geographic Distribution of Documents for Famine Early Warning, Agriculture Production, Seasonal/Annual Agriculture Forecasting and Risk Reduction, and Aquaculture Production. .................. 10 Figure 2. Sub-Area Distribution of Documents for Famine Early Warning, Agriculture Production, Seasonal/Annual Agriculture Forecasting and Risk Reduction, and Aquaculture Production ................... 11 Figure 3. Top 10 Observation Categories for Famine Early Warning ....................................................... 14 Figure 4. Top 10 Observation Categories for Agricultural Production...................................................... 19 Figure 5. Top 10 Observation Categories for Seasonal/Annual Agriculture Forecasting and Risk Reduction .................................................................................................................................................... 23 Figure 6. Top 10 Observation Categories for Aquaculture Production ..................................................... 27 Figure 7. Top Observation Categories for 3 Agriculture Sub-Areas ......................................................... 28 Figure 8. Top Observation Priorities for the Agriculture SBA .................................................................. 30

Earth Observation Priorities: Agriculture SBA SBA Report – Option Year 1 ● Page v

Summary

This report is part of GEO Task US-09-01a, with the objective of identifying critical Earth observation priorities for nine Societal Benefit Areas (SBAs). This document discusses the Agriculture SBA, focusing on: (1) Famine early warning, (2) Agriculture production, (3) Seasonal/annual agriculture forecasting and risk reduction, and (4) Aquaculture production. In this effort, the Analyst enlisted the help of twelve Advisory Group members from a wide geographic distribution. The Analyst identified 102 documents potentially relevant to these sub-areas of the Agriculture SBA, and ultimately determined that 54 documents (out of 102) contained relevant Earth observation priorities. The Analyst located the documents through literature searches and through suggestions from Advisory Group members. The Analyst analyzed the documents by searching through them for references to desired agriculture-related Earth observations as well as information about the adequacy (or inadequacy) of currently available Earth observations for these sub-areas. The Analyst worked with the Advisory Group to determine and refine a prioritization method. The chosen prioritization method utilized three main factors: the number of times an observation category (groups of related observations) appeared in the documents, the cross-cutting applicability of the observation category, and the type of document. Table S-1 lists the resulting priority observations for Agriculture SBA end users, in approximate order of importance, from most to least important.

Table S-1. Priority Observation Characteristics for the Agriculture SBA (in approximate order, from most to least important)

Observation Category Vegetation Indices

Crop Area Disturbances Precipitation

Evapotranspiration Temperature

Solar radiation Wind Characteristics

Weather** Soil Chemistry Crop Residue

Hydrology Bathymetry Land Cover Crop Yield

Water Supply Crop Emergence

Land Surface Temperature ** Weather is listed generically to reflect the fact that many documents indicated a need for weather observations, rather than specific weather components. More specific components of weather are listed as such when the documents were more specific.

Earth Observation Priorities: Agriculture SBA SBA Report – Option Year 1 ● Page 6

1. Introduction This report articulates Earth observation priorities for the Agriculture SBA, based on an analysis of 54 publicly available documents, produced primarily by Group on Earth Observations’ Member Countries and Participating Organizations.

1.1. GEO and Societal Benefit Areas The Group on Earth Observations (GEO)1 is an intergovernmental organization working to improve the availability, access, and use of Earth observations to benefit society. GEO is coordinating efforts to build a Global Earth Observation System of Systems (GEOSS)2

GEO is focused on enhancing the development and use of Earth observations in nine SBAs:

. GEOSS builds on national, regional, and international observation systems to provide coordinated Earth observations from thousands of ground, in situ, airborne, and space-based instruments.

Agriculture Biodiversity Climate Disasters Ecosystems Energy Human Health Water Weather.

1.2. Task US-09-01a The objective of GEO Task US-09-01a is to establish and conduct a process to identify critical Earth observation priorities within each SBA and those common to the nine SBAs. Many countries and organizations have written reports, held workshops, sponsored projects, conducted surveys, and produced documents that specify Earth observation needs. Task US-09-01a focuses on compiling information on observation parameters from a representative sampling of these existing materials and analyzing across the materials to determine the priority observations.

This task considers ground, in situ, airborne, and space-based observations. The task includes both observed and derived parameters as well as model products. This task seeks to identify Earth observation needs across a full spectrum of user types and communities in each SBA, including observation needs from all geographic regions and significant representation from developing countries.

GEO will use the Earth observation priorities resulting from this task as an input to determine, prioritize, and communicate gaps in current and future Earth observations. GEO Member Countries and Participating Organizations can use the results in determining priority investment opportunities for Earth observations.

1 GEO Website: http://www.earthobservations.org 2 GEO 10-Year Implementation Plan: http://www.earthobservations.org/documents.shtml


1.3. Purpose of Report The primary purpose of this report is to articulate the critical Earth observation priorities for the Agriculture SBA. The intent of the report is to describe the overall process and specific methodologies used to identify documents, analyze them, and determine a set of Earth observation parameters and characteristics. The report describes the prioritization methodologies used to determine the priority Earth observations for this SBA. The report also provides information on key challenges faced, feedback on the process, and recommendations for process improvements. The primary audience for this report is the GEO User Interface Committee (UIC), which is managing Task US-09-01a for GEO. The GEO UIC will use the results of this report in combination with reports from the other eight SBAs. The GEO UIC will perform a meta-analysis across all nine SBA reports to identify critical Earth observation priorities common to many of the SBAs. Based on the nine SBA reports, the GEO UIC will produce an overall Task US-09-01a report, including the common observations and recommendations for GEO processes to determine Earth observation priorities in the future. The report’s authors anticipate that the GEO Secretariat, Committees, Member Countries, Participating Organizations, Observers, Communities of Practice, and the communities associated with the Agriculture SBA are additional audiences for this report.

1.4 Scope of Report This report addresses the Earth observation priorities for the Agriculture SBA. In particular, this report addressed the sub-areas of famine early warning, agriculture production, seasonal/annual agriculture forecasting and risk reduction, and aquaculture production. The report provides some background and contextual information about the Agriculture SBA. However, this report is not intended as a handbook or primer on the Agriculture SBA, and a complete description of the Agriculture SBA is beyond the scope of this report. Please consult the GEO website (referenced above) for more information about the Agriculture SBA. The report focuses on the Earth observations within the Agriculture SBA, independent of any specific technology or collection method. Thus, the report addresses the “demand” side of observation needs and priorities. The report does not address the specific source of the observations or the sensor technology involved with producing the observations, except contextually in instances where the source literature specified this information. Similarly, any discussions of visualization tools, decision support tools, or system processing characteristics (e.g., data format, data outlet) associated with the direct use of the observations are beyond the scope of this report, and the report mentions them only if necessary to put the underlying request in context. In this report, the term “Earth observation” refers to parameters and variables (e.g., physical, geophysical, chemical, biological) sensed or measured, derived parameters and products, and related parameters from model outputs. The term “Earth observation priorities” refers to the


parameters deemed of higher significance than others for the given SBA, as determined through the methodologies described within. The report uses the terms “user needs” and “user requirements” interchangeably to refer to Earth observations that are articulated and desired by the groups and users in the cited documents. The term “requirements” is used generally in the report to reflect users’ wants and needs; the use in this report does not imply technical, engineering specifications. Following this introduction, the report discusses the overall approach and methodologies used in this analysis (Section 2). Section 3 describes the Agriculture SBA and the specific sub-areas that were part of the analysis. Section 4 articulates the specific Earth observations for each Agriculture sub-area, and Section 5 presents the priority observations across the Agriculture SBA. Sections 6 and 7 present additional findings from the analysis of the documents and any recommendations. The appendices include the documents utilized in the analysis as well as a list of acronyms used throughout the document.

2. Methodology and Process

2.1. Task Process The basic methodology for identifying critical Earth observation priorities within an SBA relies on an Analyst working in coordination with an Advisory Group to select the scope of the analysis, identify and analyze relevant documents, and finally extract and prioritize relevant Earth observation parameters. The GEO UIC established a general process for each of the SBA Analysts to follow in order to ensure some consistency across the SBAs. This general process for each SBA involves 9 steps, as summarized in the following list:

Step 1: UIC Members identify Advisory Groups and Analysts for each SBA Step 2: Determine scope of topics for the current priority-setting activity Step 3: Identify existing documents regarding observation priorities for the SBA

Step 4: Develop analytic methods and priority-setting criteria Step 5: Review and analyze documents for priority Earth observations needs Step 6: Combine the information and develop a preliminary report on the priorities Step 7: Gather feedback on the preliminary report Step 8: Perform any additional analysis Step 9: Complete the report on Earth observations for the SBA.

A detailed description of the general US-09-01a process is available at the Task website http://sbageotask.larc.nasa.gov or the GEO website. For the Agriculture SBA analysis, many of these steps were conducted simultaneously. The Analyst identified existing documents (Step 3) concurrently with development of the analytic methods and priority-setting criteria (Step 4), in coordination with the Advisory Group. This allowed the methods to be tailored to the types of documents that were being identified. Also, as input was received from the Advisory Group, the Analyst continued to conduct Step 3 (document identification and analysis) iteratively throughout the process, as Advisory Group members continued to identify relevant documents.


2.2. Analyst and Advisory Group The Agriculture SBA had an “Analyst” and an “Advisory Group” to conduct the process of identifying documents, analyzing them, and prioritizing the Earth observations. The Analyst served as the main coordinator to manage the activities.

2.2.1. Analyst For the Agriculture SBA, the Analyst was Allan Sommer. Mr. Sommer is a Research Scientist at Battelle, and has over nine years of experience in environmental policy analysis and management. He has expertise in numerous environmental applications, including economic impact analysis and spatial analysis, and experience with various ecological and economic evaluation methodologies to analyze activities related to the U.S. Farm Bill, the U.S. Clean Water Act, and U.S. Clean Air Act.

Adam Carpenter assisted the Analyst. Mr. Carpenter is an Environmental Researcher at Battelle with a Masters degree in Environmental Science and Policy. The Agriculture SBA Analyst served through the Battelle Memorial Institute under contract to the National Aeronautics and Space Administration (NASA).

2.2.2. Advisory Group The Analyst assembled the Advisory Group, which consisted of twelve scientific and technical experts, recognized as credible and respected in the field of agriculture. The Advisory Group members are from both developed and developing countries, and encompass all regions of the world, representing GEO Countries and Participating Organizations, as listed in Table 1.

Table 1. Advisory Group for Agriculture Societal Benefit Area

Name Country or Organization Affiliation Geographic

Region Area of

Expertise/ Specialty

Samira Omar ASEM

Kuwait Kuwait Institute for Scientific Research Asia/Middle East

Famine Early Warning

Mark BRUSBERG

USA United States Department of Agriculture North America

Broad agriculture expertise

Carmela CASCONE

Italy Institute for Environmental Protection and Research Sustainable Use of Natural Resources Service

Europe


Jinlong FAN Switzerland GEO Secretariat Europe


Marie-Hélène FORGET

Canada Bedford Institute of Oceanography North America Aquaculture

Production


Table 1. Advisory Group for Agriculture Societal Benefit Area

Name Country or Organization Affiliation Geographic

Region Area of

Expertise/ Specialty

Mike GRUNDY Australia Australian Commonwealth Scientific and Research Organization

Oceania/Australia Broad agriculture expertise

Chris JUSTICE USA University of Maryland North America Agriculture Production

Johnson OWARO

Uganda Disaster Preparedness and Refugees Transition and Recovery Programme for North and Eastern Uganda

Africa

Global Agricultural Monitoring

Jai S. PARIHAR India Space Applications Centre Asia/Middle East


Basanta SHRESTHA

Nepal The International Centre for Integrated Mountain Development

Asia/Middle East Broad agriculture expertise

Robert STEFANSKSI

WMO World Meteorological Organization Europe

Global Agricultural Risk Reduction

Prasad THENKABAIL

USA United States Geological Survey North America


The Analyst developed the pool of candidates for the Advisory Group based on professional referrals and identifying leaders of major Earth observation related agriculture projects. The Analyst invited each potential candidate to participate and provided each with information on the expectations for Advisory Group members. The role of the Advisory Group was to help identify relevant documents, comment on the analytic methods and priority-setting criteria utilized, and review the Analyst’s findings, priorities, and reports. Communication was conducted by a combination of emails and group teleconferences.

2.3. Methodology

2.3.1. Documents The Analyst examined a wide variety of sources to locate documents with potentially relevant Earth observation requirements in them. The sources included:

- International, regional, and national documents focused on data sources, applications, or research priorities

- Project reports (e.g., findings from major regional/national projects)


- Workshop and conference summaries - Peer-reviewed journal articles.

All documents are publicly available and were obtained through organization websites or journal archives, were provided by Advisory Group members, or were accessed through similar methods. The Analyst made a concerted effort to ensure that all major geographic regions were represented and that there was a balance between developed and developing countries among the documents. First, the Analyst sought documents from prominent national and international organizations to locate international and national reports related to the chosen sub-areas. Such organizations included the United Nations Food and Agriculture Organization (FAO), the International Fund for Agricultural Development (IFAD), the World Food Program (WFP), and many others. Additionally, the Analyst conducted a comprehensive literature search of academic and trade journals and reviewed the websites of credible organizations. These were conducted using both specialized search tools and general search engines. The following are examples of the many search terms utilized:

- Agriculture Observations - Agriculture Priorities - Land use - Land Management - Famine Early Warning - Agriculture Production - Agriculture Risk Management - Agriculture Risk Reduction - Aquaculture - Aquaculture Management - Aquaculture Production.

Finally, the Analyst requested documents from the Advisory Group. Cumulatively, the Advisory Group contributed several dozen of the reports considered for the analysis. Many of the suggestions were articles and conference proceedings, some of which overlapped with the results of the Analyst’s literature search.

2.3.2. Analytic Methods The Analyst evaluated each document for its usefulness based on the inclusion of specific observation priorities related to famine early warning, agricultural production, seasonal/annual agricultural forecasting/risk reduction, and aquaculture. In order for a document to be included in the analysis, it had to explicitly identify required agriculture or aquaculture-related Earth observations. The characteristics recorded from these observations included the temporal resolution (frequency), spatial resolution, timeliness/latency (how quickly the observation is


available), accuracy/precision, and coverage or extent of the observation, when available. The analysis did not include information on the technical infrastructure required to collect and process critical Earth observations. The Analyst extracted detailed data from the documents that met the criteria for inclusion in the analysis. For each observation, the extracted information included the applicable sub-area (famine early warning, agricultural production, seasonal/annual agricultural forecasting/risk reduction, and aquaculture), the region of interest of the observation (Global, Africa, Europe, Oceania/Australia, Asia/Middle East, East Asia, North America, or Central/South America), the type of document (e.g., international working group report, peer-reviewed journal article, conference proceeding), and the desired characteristics of the observation, when available. The Analyst also grouped each specific observation parameter into a broader observation category. The aggregation of similar parameters in this manner provided a more robust analysis and avoided redundancy, since authors of the documents often used slightly different terms to describe the same parameter and frequently failed to identify detailed observations. For example, “NDVI,” and “LAI,” were combined into a single “Vegetation Indices” category. There were a total of 130 observation categories for the four sub-areas that the Analyst carried forward into the prioritization analysis. Famine early warning, agricultural production, and seasonal/annual agricultural forecasting/risk reduction are distinct but largely related. All three relate to land-based crop monitoring in some fashion, with different desired observational outcomes. Aquaculture, however, is very different from the others in that it is not land-based and represents one specific type of agriculture. Therefore, in order to make sure that all of the potentially relevant observations are included (as aquaculture’s priorities may be underrepresented because the other three sub-areas are related), priorities for aquaculture are also displayed separately in addition to being factored into the overall analysis, for clarity.

2.3.3. Prioritization Methods In order to determine which observation categories have the highest priority, the Analyst analyzed the sub-area observation categories using the following objective indexing method. This method generated an objective indexing scheme that weighted observation categories based on three factors:

1. The number of times that the observation category was mentioned in the documents as a priority

2. The cross-cutting applicability of the observation category 3. The type of document(s) that the priority is reported in.

The number of times that the observation category is mentioned in the documents as a priority is an integer value of at least 1. The cross-cutting applicability weight for each document is an integer value from 1 to 3 that is equal to the number of sub-areas (famine early warning, agriculture production, seasonal/annual agriculture forecasting and risk reduction) to which a single observation applies, as identified by the document. The aquaculture production sub-area


was analyzed separately, due to the unique nature of the Earth observations required, which had little overlap with the Earth observations for the other three sub-areas. Hence, the cross-cutting weight for Earth observations required for aquaculture production was set by the Analyst as 1. The weighting for the type of document is also an integer value from 1 to 3, based on the type of document, as outlined in Table 2. International working group or consensus documents carry the highest weight with a value of 3, since they typically represent the viewpoints of scientists from a broad range of geographic locations and technical specialties. National-level government or working group documents have a weight of 2. National-level documents have a slightly lower weight due to their narrower geographic focus. Journal articles, conference presentations, conference proceedings, and unpublished studies have a weight of 1, as they typically represent the viewpoint of one or a few scientists, have a narrow geographic focus, and are not always subject to the peer-review process. Table 2 summarizes the weighting factors and gives examples of each document category. To calculate the document-specific index value for each observation category,

odi , the Analyst took the product of the weighting factor for the number of disaster types applicable for the observation category in the document,

onw , and the weighting factor for the document type, dw , as seen in Equation 1.

dnd wwioo

= (Equation 1) The Analyst calculated the final aggregated weighted index for each observation category, Io

, for all documents by taking the sum of the document-specific index values for the observation category over all of the documents (Equation 2).

∑=d

do oiI (Equation 2)

By taking the sum of the index values over all of the documents, the Analyst calculated the aggregated index value, which takes into account how frequently the observation categories are identified as priorities. The final aggregated index values are the basis for the objective ranking of the observation priorities across all three hazard types and all documents. Observation categories that occurred most frequently in the documents have the highest aggregated index values.


Table 2. Factors for Weighted Index Computation Cross-Cutting Applicability

Weight Definition 1 1 Sub-Area 2 2 Sub-Areas 3 3 Sub-Areas

Document Type Weight Definition Example

1

Journal articles, conference presentations, conference proceedings and unpublished studies

Hutchinson, 1991

2 National-level government or working group documents

NOAA, 2008

3 International working group or consensus documents

FAO, 2007

3. Agriculture SBA

3.1. Description The GEOSS 10-Year Implementation Plan notes that the Agriculture SBA is focused on sustainable agriculture and combating desertification. Section 4.1.8 of the 10-Year Implementation Plan describes the Agriculture SBA as the following:

“Issues addressed by GEOSS will include: crop production; livestock, aquaculture and fishery statistics; food security and drought projections; nutrient balances; farming systems; land use and land cover change; and changes in the extent and severity of land degradation and desertification.”

The scope of the GEO Agriculture SBA includes crop production, forestry, management of rangelands, information related to famine, fisheries, aquaculture, and other areas.3

3.2. Sub-areas

Early in the process, the Analyst solicited the Advisory Group for suggestions of key sub-area of focus for this report. Taking this input into account, the Analyst tentatively decided the sub-areas, which were then vetted again through the Advisory Group.

3 For GEO Task US-09-01a, UIC Task Co-Lead Lawrence Friedl conferred with the SBA Analysts and decided that forests and forestry would be treated in a separate report, the results of which will be merged with the Agriculture and Ecosystems SBA reports for the final cross-SBA analysis.


3.3. Documents The Analyst sought documents from a wide variety of sources, with the goal of having a wide distribution geographically, many potential end-users, a representative distribution of the sub-areas, and other measures to make the analysis inclusive and reduce the chances for bias in the priorities. As described in Section 2.3.1, only those documents containing information about agriculture earth observation priorities relevant to one or more of the four chosen sub-areas were included in the final analysis. Geographically, a document was categorized as global if the focus was multi-regional. For example, if a document discussed minimum observational requirements for establishing forest biomass baselines throughout the world, it was a global document. If a document discussed needs specific to a particular region, such as observational requirements for Africa, it was categorized as a document with an African focus. Approximately half of the documents were global in nature. The next most common were East Asia and Europe, respectively. Although the distribution was not precisely even among the regions, there were documents representing all regions and a great deal of global documents.

Figure 1. Geographic Distribution of Relevant Documents for Famine Early

Warning, Agriculture Production, Seasonal/Annual Agriculture Forecasting and Risk Reduction, and Aquaculture Production

The Analyst also attempted to locate documents from all four sub-areas in roughly equal proportions. Because a document may cover many observations that apply to different sub-areas, a document may cover more than one sub-area. A tally of the documents found that famine early warning was represented the least, with 10% of the total representation, and agriculture production was represented the most with 36%. Although this is not a “perfect” distribution, all four sub-areas are well represented in the analysis.


Figure 2. Sub-Area Distribution of Relevant Documents for Famine Early Warning,

Agriculture Production, Seasonal/Annual Agriculture Forecasting and Risk Reduction, and Aquaculture Production

3.4 User Types The Analyst identified 12 general types of users of agricultural observations related to famine early warning, agriculture production, seasonal/annual forecasting and risk reduction, and aquaculture. These user types, meant to be a representative list (but not exhaustive) of users who could benefit from agricultural Earth observations, were grouped into 3 functional categories as shown in Table 3. The Analyst considered these user types and their functional categories in seeking out documents and validating those documents included in the analysis as being appropriate to cover all necessary user groups and not to bias towards one specific user type.

Table 3. Agriculture Earth Observation User Types

Application Area User Type

Forecasting and Monitoring

Conservationists

Land Use Planners

Land Developers

International and National Aid Analysts

Agriculture and Aquaculture Management

Farmers

Land Use Managers

Watershed Managers

National Agriculture Agency Analysts


Application Area User Type

Research

Microbiologists

Biologists

Ecologists

Agronomists

4. Earth Observations for Agriculture SBA

4.1. Agriculture Specific

4.1.1. Famine Early Warning The documents the Analyst reviewed for this analysis contained observations that were very similar but were not identical. For example, the documents contained four different references to crop area, including “crop area identification” and “cultivated area”. The observation categories for famine early warning are described in Table 4. Table 4. Famine Early Warning Observation Categories (Aggregated and Unique

Observations)

Aggregated Observation Observation* Albedo Albedo

Atmospheric Pressure Atmospheric Pressure Bulk Soil Electrical Conductivity Bulk soil Electrical Conductivity

Canopy Characteristics Canopy Temperature Variability

Canopy Characteristics Chemical Application Chemical Application

Conflicts Conflicts

Crop Area

Crop Area Identification Cultivated Area Cultivated Lands

Crop Types Crop Emergence Crop Emergence

Crop Prices Crop Prices

Crop Residue Tillage

Crop Residue Cover Crop Residue

Crop Variability Crop Variability Crop Water Stress Index Crop Water Stress Index

Crop Yield Crop Yield


Aggregated Observation Observation*

Disturbances

Earthquakes Tsunami

Flood Drought

Drought Stress Insect Infestation

Disturbances (Pests, Disease, Drought) Environmental Degradation Environmental Degradation

Evapotranspiration Actual Evapotranspiration

Relative Evapotransporation Fertilizer Use Fertilizer Application

Germination Rates Germination Rates Harvest Indices Harvest Indices

Insolation Insolation Irrigation Irrigation

Land Surface Temperature Land Surface Temperature Local Crop Calendars Local Crop Calendars Photography/Imagery Imagery

Plant Stress Levels Plant Stress Levels Precipitation Precipitation

Relative Humidity Relative Humidity Snow Characteristics Snow Distribution

Soil Chemistry Soil Nitrogen Soil Salinity

Soil Mineral Content Soil Moisture Surface Soil Moisture

Soil Type Soil Type Soil Variability Soil Variability Solar Radiation Solar Radiation

Stress Degree Day Stress Degree Day

Temperature Temperature Stress Day

Air Temperature Temperature

Thermal Kinetic Window Thermal Kinetic Window Vapor Pressure Vapor Pressure

Vegetation Indices NDVI LAI

Water Supply Reservoir Levels Weather Weather


Aggregated Observation Observation* Weed Prevalence Weed Prevalence

Wind Characteristics Wind Speed

Wind Yield Limiting Factors Yield Limiting Factors

*Note that some of the observations (and their associated observation categories) may not be directly observable or may refer to specific technologies. The Analyst attempted to follow the terminology used in the documents as closely as possible and therefore some such observations are present. The Analyst retained them to fully describe the contents of the documents, but none of those observations were in the top lists. Once the observations were organized into these categories, each instance was weighted and summed up according to the methodology in Section 2.3.2. Figure 3 contains the top ten observation categories for famine early warning.

Figure 3. Top 10 Observation Categories for Famine Early Warning

Disturbances had the highest score by a substantial margin, followed by crop area and vegetation indicies. Logically, given that famine early warning’s goal is to detect food shortages far enough in advance to take action, detection and characterization of disturbances fits the sub-area’s intent.

4.1.2. Agricultural Production Many of the observation categories for agricultural production were the same as those for famine early warning. However, since some documents related to one and not the other, the lists differ and the observation categories for agricultural production are displayed in Table 5:


Table 5. Agricultural Production Observation Categories (Aggregated and Unique Observations)


Access/Distance Proximity to Population Centers Agricultural Productivity Agricultural Productivity

Albedo Albedo Atmospheric Pressure Atmospheric Pressure

Bulk Soil Electrical Conductivity Bulk soil Electrical Conductivity


Canopy Characteristics

Chemical Application Chemical Application Climate Climate

Cloud Characteristics Cold Cloud Duration (CCD)

Cloud Height

Composite Surface Reflectance Composite Surface Reflectance

Crop Area

Arable Land Cropland Area Cultivated Area

Extent of Cultivation Cultivated Lands

Crop Types Cultivated Land

Crop Emergence Crop Emergence Period

Crop Emergence Crop Production Potential Crop Production Potential

Crop Residue

Residue Management Tillage

Crop Residue Cover Crop Residue

Crop Variability Crop Variability Crop Water Stress Index Crop Water Stress Index

Crop Yield Crop Yield

Cropping Intensity Cropping Intensity - Multiple Cropping Index (MCI)

Dew Point Dew Point

Disturbances Drought Stress

Insect Infestation



Disturbances (Pests, Disease, Drought)

Pests Disease

Hail Disturbances (Blizzards, Hurricanes,

Tropical Storms) Disturbances

Dry Matter Production Dry Matter Productivity Dry Matter Production

Erosion Soil Erosion Rates

Erosion


Relative Evapotranspiration Evapotranspiration

fAPAR fAPAR fPAR

Fertilizer Use Fertilizer Consumption Fertilizer Application

Germination Rates Germination Rates

GLCV GLCV (Fraction of Green Vegetation Covering the Soil)

Harvest Indices Harvest Indices

Hydrology

Water Hydrology

Water Balance Water Budget

Insolation Insolation Irrigation Irrigation

Land Cover Vegetation Land Cover

Land Cover – Corinne

Land Management Practices Land Management Practices

Land Quality Land Quality

Land Surface Temperature Land Surface Temperature

Soil Temperature Land Tenure Land Tenure

Land Use Change Land Use Change Local Crop Calendars Local Crop Calendars



Meteorological Data Meteorological Data NPP NPP

Pan Evaporation Pan Evaporation Photography/Imagery Imagery

Plant Stress Levels Plant Stress Levels Political Boundaries Political Boundaries

Population Population Density

Precipitation Precipitation

Rainfall Rainfall Intensity

RAOB’s RAOB’s

Relative Humidity Relative Humidity

Humidity SAR SAR

Sky Cover Sky Cover

Snow Characteristics Snow Distribution

Snow Depth

Soil Adjusted Vegetation Index (SAVI) Soil Adjusted Vegetation Index (SAVI)

Soil Bulk Density Soil Bulk Density

Soil Chemistry

Soil pH Soil Extractable Nitrogen

Soil Extractable Phosphorus Soil Extractable Potassium

Salinization (Soil) Soil Nitrogen Soil Salinity

Soil Mineral Content Soil Microbial Biomass Soil Microbial Biomass

Soil Moisture Surface Soil Moisture

Soil Moisture Soil Nutrient Depletion Soil Nutrient Depletion

Soil Organic Matter Soil Organic Matter Soil Quality Soil Quality Soil Texture Soil Texture

Soil Type Soil Type Soil Variability Soil Variability

Soil Water Holding Capacity Soil Water Holding Capacity Soils Soils

Solar Radiation Global Radiation



Sunshine Duration Solar Radiation

Solar Radiation Levels Southern Oscillation Index Southern Oscillation Index

Storm Characteristics Storm Tracks

Storm Strength Stress Degree Day Stress Degree Day

Temperature

Temperature Daily Temperature

Temperature Stress Day Air Temperature

Thermal Kinetic Window Thermal Kinetic Window

Topography Terrain

Topography Soil Terrain

Topsoil Depth topsoil Depth Vapor Pressure Vapor Pressure

Vegetation Indices NDVI

LAI – Leaf Area Index Water Supply Reservoir Levels

Weather Weather

Present Weather Weed Prevalence Weed Prevalence

Wind Characteristics Wind Speed

Wind Wind Direction

Yield Limiting Factors Yield Limiting Factors *Note that some of the observations (and their associated observation categories) may not be directly observable or may refer to specific technologies. The Analyst attempted to follow the terminology used in the documents as closely as possible and therefore some such observations are present. The Analyst retained them to fully describe the contents of the documents, but none of those observations were in the top lists.

Several of the same observation categories appeared in the top ten list for agricultural production as they did for famine early warning, but in a different order. Vegetation indices had the highest score by a wide margin, followed by crop area and evapotranspiration. The full top-ten list of priority observation categories is included in Figure 4:


Figure 4. Top 10 Observation Categories for Agricultural Production

4.1.3. Seasonal / Annual Agricultural Forecasting and Risk Reduction Many of the observation categories for seasonal/annual agriculture forecasting and risk reduction were the same or similar to the previous two sub-areas, with some exceptions. The observation categories for seasonal/annual agriculture forecasting and risk reduction are included in Table 6.

Table 6. Seasonal / Annual Agricultural Forecasting and Risk Reduction Observation Categories (Aggregated and Unique Observations)


Agricultural Productivity Agricultural Productivity Albedo Albedo

Atmospheric Pressure Atmospheric Pressure

Bulk Soil Electrical Conductivity Bulk Soil Electrical Conductivity


Canopy Characteristics Chemical Application Chemical Application

Climate Climate Data Cloud Characteristics Cold Cloud Duration (CCD)

Cloud Height

Composite Surface Reflectance Composite Surface Reflectance



Conflicts Conflicts

Crop Area

Arable Land Crop Area Identification

Cultivated Area

Extent of Cultivation

Cultivated lands Crop Types Crop Area

Crop Cycle Length Crop Cycle Length

Crop Emergence Crop Emergence Period

Crop Emergence

Crop Planting Date Crop Planting Date

Crop Prices Crop Prices

Crop Residue

Tillage

Crop Residue Cover

Crop Residue Crop Variability Crop Variability

Crop Water Stress Index Crop Water Stress Index

Crop Yield Crop yield

Dew Point Dew Point

Disturbances

Earthquakes Tsunamis

Pests Floods

Droughts Drought Stress

Insect Infestation



Disease Hail

Blizzards, Hurricanes, Tropical Storms

Disturbances

Dry Matter Production Dry Matter Productivity Dry Matter Production

Environmental Degradation Environmental Degradation


Evapotranspiration Relative evapotranspiration

fAPAR

fAPAR

fPAR

Fertilizer Use Fertilizer Application

Germination Rates Germination Rates

GLCV GLCV (Fraction of Green Vegetation Covering the Soil)

Harvest Indices Harvest Indices

Hydrology Water Budget

Insolation Insolation

Irrigation Irrigation Land Cover Land Cover

Land Surface Temperature Land Surface Temperature

Soil Temperature

Local Crop Calendars Local Crop Calendars NPP NPP

Pan Evaporation Pan Evaporation Photography/Imagery Imagery

Plant Stress Levels Plant Stress Levels

Precipitation Precipitation (Actual Rainfall)

Rainfall Rainfall Intensity



RAOB’s RAOB’s


Humidity SAR SAR

Sky Cover Sky Cover

Snow Characteristics Snow Distribution

Snow Depth

Soil Adjusted Vegetation Index (SAVI) Soil Adjusted Vegetation Index (SAVI)

Soil Chemistry Soil Nitrogen Soil Salinity

Soil Mineral Content

Soil Moisture

Surface Soil Moisture

Soil Moisture

Soil Type Soil Type

Soil Variability Soil Variability

Soil Water Holding Capacity Soil Water Holding Capacity

Solar Radiation

Global Radiation

Sunshine Duration

Solar Radiation

Southern Oscillation Index Southern Oscillation Index

Storm Characteristics Storm Tracks

Storm Strength Stress Degree Day Stress Degree Day

Temperature

Daily Temperature Temperature Stress Day

Air Temperature Temperature

Thermal Kinetic Window Thermal Kinetic Window Vapor Pressure Vapor Pressure



Vegetation Indices

NDVI

Vegetation Productivity Indicator

LAI – leaf area index Water Supply Reservoir Levels

Weather Weather

Present Weather Weed Prevalence Weed Prevalence

Wind Characteristics

Wind Speed Wind

Wind Direction

Yield Limiting Factors Yield Limiting Factors

*Note that some of the observations (and their associated observation categories) may not be directly observable or may refer to specific technologies. The Analyst attempted to follow the terminology used in the documents as closely as possible and therefore some such observations are present. The Analyst retained them to fully describe the contents of the documents, but none of those observations were in the top lists.

Vegetation indices again had the highest score (as it did in Agricultural Production), and several of the same observation categories appeared in the prioritization for this sub-area as the previously discussed ones, as seen in Figure 5:

Figure 5. Top 10 Observation Categories for Seasonal / Annual Agriculture

Forecasting and Risk Reduction


4.2. Aquaculture Production The nature of aquaculture is very different than that of the other sub-areas, because it takes place in the water instead of on land. Therefore, many of the observation categories are different, although there are some similarities with the other sub-areas.

Table 7. Aquaculture Production Observation Categories (Aggregated and Unique Observations)


Access/Distance

Access

Distance to Town

Distance to Piers

Distance to Land-Based Facility

Aerial Photography Aerial Photography

Aerosol Properties Aerosol Properties (Includes AOT)

Aquaculture Production Sites Production Sites

Archeological Sites Archeological Zones

Bathymetry Bathymetry Shoreline Position Reef Maps

Chlorophyll

Chlorophyll Concentration Chlorophyll Chlorophyll-a Phytoplankton Pigments (ex. chl-a) Chlorophyll Fluorescence

Colored Dissolved Organic Matter Colored Dissolved Organic Matter Currents Currents

Dissolved Oxygen Dissolved Oxygen

Evapotranspiration Evapotranspiration Habitat Maps Habitat Maps Harmful Algae Blooms Harmful Algae Blooms

Hydrology

Stream/River Discharge Surface Water Stream Flow River Discharge Water Bodies

Ice Cover Ice Cover



Land Cover Land Cover Land Cover/Land Use

Land Tenure Tenure Land Use Land Use

Land Use Change Land Use/Land Use Change Military Use Zones Military Use Zones Mineral Extraction Areas Mineral Extraction Areas Mooring Line Stress Mooring Line Stress

Nautical/Navigation Charts Nautical Charts

Navigation Areas

Night Time Lights Night Time Lights O2 O2 Ocean Color Ocean Color Oil Platforms Oil Platforms Optical Properties Optical properties (includes PAR) pCO2 pCO2

Photography/Imagery Photography Remote Images/Pictures

Population Population Port Facilities Port Facilities Precipitation Precipitation Protected Habitat Protected Habitat Radio Tags Radio Tags for Fish


Salinity Salinity

SAR Synthetic Aperture Radar

SAR

Sea Ice Sea Ice Sea Surface Height Sea Surface Height

Sea Surface Roughness Reflectance Slicks/Films (Sea Surface Roughness)

Sea Surface Temperature Sea Surface Temperature Sea Temperature SST

Ship Wrecks Ship Wrecks Soil Chemistry Soil Chemistry Storm Characteristics Storm Frequency


Aggregated Observation Observation* Sub-marine Cables Sub-Marine Cables Submerged Substrate Submerged Substrate

Suspended Sediments Suspended Sediments

Turbidity Total Suspended Matter

Temperature Atmospheric Temperature Temperature

Tidal Characteristics Tidal Excursion Tidal Amplitude

Topography Topography DEM

Tourist Areas Tourist Areas

Viewshed Viewshed

Water Chemistry Water pH

Water Chemistry Water Clarity Water Clarity

Water Color Water Color

Water Nutrients Nutrients

Water Supply

Water Use

Outfalls/Drains Freshwater Supply Brackish Water Supply Water Availability

Water Temperature Water Temperature

Wave Characteristics Wave Height

Wave Action Wave Direction

Wind Characteristics Surface Wind Winds Wind Direction and Speed

*Note that some of the observations (and their associated observation categories) may not be directly observable or may refer to specific technologies. The Analyst attempted to follow the terminology used in the documents as closely as possible and therefore some such observations are present. The Analyst retained them to fully describe the contents of the documents, but none of those observations were in the top lists.


The observation categories with the highest scores had few commonalities with other sub-areas. Bathymetry (water depth) had the highest score, followed by chlorophyll and then hydrology. These reflect the fact that aquaculture is largely different than the other three sub-areas and is focused on characteristics related to water where fish and other aquatic life can be grown.

Figure 6. Top 10 Observation Categories for Aquaculture Production

5. Priority Observations

5.1. General description Because both the nature of aquaculture and the resulting observation priorities are different than all three of the other sub-areas (which are themselves similar to each other), one way to display the prioritization for the Agriculture SBA is to provide one priority list for aquaculture and one priority list for the other three sub-areas. However, compiling one overall priority list for all four sub-areas is another valid approach. Therefore, the Analyst chose to display both here. Because the scope of this SBA report is to compile a single list, the priority list encompassing all four sub-areas is considered the final prioritization.

5.2. Land-Based Agriculture Priority Observations and Characteristics

As described throughout Section 4, the three land-based agriculture sub-areas (famine early warning, agricultural production, and seasonal/annual forecasting and risk reduction) were similar in many respects, including having similar priority parameters based upon the methodology throughout Section 2. The top observation categories and their characteristics are shown in Figure 7 and Table 8, respectively:


Figure 7. Top Observation Categories for 3 Agriculture Sub-Areas

Table 8. Priority Observation Characteristics for 3 Agriculture Sub-Areas

Priority Number

Observation Category Required Parameter Characteristics*

1 Vegetation Indices Frequency: Weekly – 10 Day

Spatial Resolution: 10-20m to 1km

2 Crop Area Frequency: Hourly (continental scale) to every 10 days (local scale)

Spatial Resolution: <1m for most confidence. 300m-1km for more general purposes

3 Disturbances Frequency: Hourly to Daily

4 Precipitation Frequency: 15 minute to Daily

Timeliness: Near Real-time

5 Evapotranspiration Frequency: Daily

6 Solar Radiation Frequency: Daily

7 Temperature Frequency: Hourly

8 Weather** Frequency: Hourly


Priority Number

Observation Category Required Parameter Characteristics*

9 Wind Characteristics

Not Indicated

10 Soil Chemistry Not Indicated

* Only the noted parameter characteristic requirements were specified in the documents.

** Weather is listed generically to reflect the fact that many documents asked generally for weather observations, rather than specific weather components. More specific components of weather were listed as such when the documents were more specific.

Many of the priority observation categories had only one or two of the four main characteristics (frequency, spatial resolution, accuracy/precision, and timeliness). Very few extracted observations, for these sub-areas and for the SBA as a whole, contained all of these characteristics.

5.3. Aquaculture Priority Observations and Characteristics The Analyst conducted a prioritization for the aquaculture sub-area separate from the other three sub-areas. The top observation categories from aquaculture and their characteristics are shown in Figure 6 in Section 4.2, and their characteristics are shown here in Table 9:

Table 9. Top Observation Category Characteristics for Aquaculture

Priority Number Observation Category

Required Parameter Characteristics*

1 Bathymetry Not Indicated 2 Chlorophyll Spatial Resolution:

1km 3 Hydrology Not Indicated 4 Wave

Characteristics Not Indicated

5 Topography Not Indicated 6 Currents Not Indicated 7 Wind


8 Suspended Sediments

Spatial Resolution: 1km

9 Water Supply Not Indicated 10 Sea Surface

Temperature Spatial Resolution: 1km



5.4. Agriculture SBA Final Priority Observations and Characteristics

Figure 8 shows the overall priority observations for the Agriculture SBA based upon all four chosen sub-areas combined. This list is the final priority list for the Agriculture SBA for the four chosen sub-areas. Table 10 contains the required characteristics for each observation, where available.

Figure 8. Top Observation Priorities for the Agriculture SBA

Table 10. Priority Observation Characteristics for the Agriculture SBA Priority Number

Observation Category

Required Parameter Characteristics*

1 Vegetation Indices Frequency: Weekly to 10 days Spatial Resolution: 10 to 20m to 1km

2 Crop Area Frequency: Hourly (continent scale) to Every 10 days (local scale) Spatial Resolution: <1m for most confidence. 300m-1km for more general purposes.

3 Disturbances Frequency: Hourly to Daily 4 Precipitation Frequency: 15 minute to Daily

Timeliness: Near real-time 5 Evapotranspiration Frequency: Daily 6 Temperature Frequency: Hourly 7 Solar radiation Frequency: Daily 8 Wind


9 Weather** Frequency: Hourly 10 Soil Chemistry Not Indicated 11 Crop Residue Not Indicated 12 Hydrology Not Indicated


13 Bathymetry Not Indicated 14 Land Cover Not Indicated 15 Crop Yield Not Indicated 16 Water Supply Not Indicated 17 Crop Emergence Not Indicated 18 Land Surface

Temperature Frequency: Daily Spatial Resolution: 1km


** Weather is listed generically to reflect the fact that many documents indicated a need for weather observations, rather than specific weather components. More specific components of weather are listed as such when the documents were more specific.

6. Additional Findings Several issues are worth highlighting based on the Analyst Team’s analysis of critical Earth observation priorities for end users associated with the Agriculture SBA. These issues include literature gaps and needs beyond Earth observation parameters such as modeling and user interfaces.

Looking across the sub-areas of focus, famine early warning was by far the least represented in terms of specification of end user needs with respect to Earth observation parameters. Only 10% of the critical Earth observation priorities identified in this report are applicable to famine early warning. The Analyst notes that many of the documents related to famine early warning systems focused on the underlying socioeconomic data, and also on the need for improvement in aspects such as the user interface, rather than on Earth observation needs.

There was also a gap in the literature identified through this task on end user needs with respect to agriculture in South and Central America, South Asia, the Middle East, and to some extent, Africa. This literature gap may be somewhat real, and is also likely an artifact of the search methods failing to capture non-English documents, and failing to capture documents that are not online and not well known to Advisory Group members. Additionally, many of the observations, when they exist in literature at all, were in grey literature that may or may not be from a reputable source. Finally, because developing country documents on end user needs, to the extent such documents exist, may fall into the category of not being online and being in other languages, there could be under-representation of developing country end user needs in this analysis.

The Analyst Team found that there is great demand in the literature for end-user products. Famine early warning was, in fact, well represented in literature that discussed needs for additional collaboration and better access to existing data. Hence, beyond critical Earth observation parameter needs, more systems and tools for sharing information on famine early warning is a critical need. More broadly, better interpretation of existing observation data was also commonly highlighted as a need in the documents reviewed.


7. Analyst Comments and Recommendations The nine-step process generally worked well for this analysis. Identification of the sub-areas was challenging. The Analyst sought feedback from the Advisory Group, and reviewed materials from the GEO Agriculture COP, but still had considerable difficulty in reaching agreement amongst the Advisory Group and UIC Task Co-Lead.

The Analyst also noted that many of the documents addressed needs for data in terms of need for better measurements from specific technologies. This merging of technology and Earth observation parameter needs was difficult to sort out for the Analyst Team. Targeted interviews with authors of some key documents may have been useful for separating out technology and Earth observation parameter needs. Additionally, an Advisory Group member suggested that workshops and interviews with end users would be a more effective way to determine earth observation priorities since there are significant gaps in the literature.

Maintaining engagement of the Advisory Group was an ongoing challenge. While some members were highly engaged and responsive, others provided little substantive feedback, and some provided too late in the process to be incorporated into the analysis. One advisory group member suggested having in-person meetings to have more interaction and provide more feedback. The Analyst agrees, however, the scope of this task did not allow for the time or budget to conduct such meetings.


Appendix A: Abbreviations and Acronyms AOT Aerosol Optical Thickness CCD Col Cloud Duration Chl-A Chlorophyll-A COP Community of Practice DEM Digital Elevation Model FAO Food and Agriculture Organization fAPAR Fraction of Absorbed Photosynthetically Active Radiation fPAR Fraction of Photosynthetically Active Radiation GEO Group on Earth Observations GEOSS Global Earth Observation System of Systems GLCV Fraction of Green Vegetation Covering the Soil IFAD International Fund for Agricultural Development LAI Leaf Area Index NASA National Aeronautics and Space Administration (USA) NDVI Normalized Difference Vegetation Index NPP Net Primary Productivity PAR Photosynthetically Active Radiation RAOB Rawinsonde Observations SAR Synthetic Aperture Radar SAVI Soil Adjusted Vegetation Index SBA Societal Benefit Area SST Sea Surface Temperature UIC User Interface Committee USDA United States Department of Agriculture WMO World Meteorological Organization


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Appendix C: Input to the Cross-SBA Analysis At the conclusion of the individual SBA priority-setting analysis, the Agriculture SBA Analyst provided input on the overall critical Earth observation parameters for the Agriculture SBA for inclusion in the Cross-SBA meta-analysis.4

As a subset of the Agriculture SBA, a separate Analyst conducted an analysis of the critical Earth observation parameters for forests. The results of the Agriculture SBA-Forests sub-analysis were merged with the results of the Agriculture SBA analysis for the purposes of the Cross-SBA meta-analysis.

Upon receiving input from the SBA Analysts, the Cross-SBA Analyst reviewed the priorities in order to harmonize the terminology employed across SBAs. The Cross-SBA Analyst aggregated observation parameters that are the same or very similar but have different names (e.g., precipitation intensity and precipitation duration). In some cases, the Cross-SBA Analyst also disaggregated observation parameters from observation categories that were identified as priorities by individual SBAs. As a result, the number of observation priorities identified by individual SBAs may vary from the number of observations that were included in the Cross-SBA analysis. To the extent possible, the Cross-SBA Analyst focused on retaining the observation parameter terminology employed by the majority of the SBAs. The Agriculture SBA Analyst determined the overall critical Earth observation priorities for the Agriculture SBA by using a weighted indexing method to rank the observations, as described in Section 2.3.3 and Chapter 5. Based on the results of the prioritization analysis, the 18 observations listed below have the highest rankings and, thus, are considered to be the observation priorities for the Agriculture SBA. The Agriculture SBA Analyst divided the 18 critical observations into three tiers representing “High,” “Medium,” and “Low” priorities. (Italicized observations were also included in Method 4, as explained below). High Vegetation Indices Crop Area Disturbances Precipitation Medium Evapotranspiration Temperature Solar Radiation Wind Characteristics Weather Soil Chemistry

4 For full description of methods and results, refer to: Group on Earth Observations. Task US-09-01a. Critical Earth Observation Priorities. Final Report. October 2010. Available on GEO Task US-09-01a website: http://sbageotask.larc.nasa.gov/.


Low Crop Residue Hydrology Bathymetry Land Cover Crop Yield Water Supply Crop Emergence Land Surface Temperature The Agriculture Analyst blended the 18 priority observations for the Agriculture SBA with the observation priorities for the Agriculture SBA-Forests sub-area and contributed a single integrated list of observation priorities for Methods 1-3 of the Cross-SBA analysis. Accounting for differences in observation terminology across the SBAs, the Agriculture SBA and Agriculture SBA-Forests Teams effectively contributed 61 observation parameters, provided below, for Methods 1-3. The “High,” “Medium,” and “Low” priority designations for the observation parameters on the single combined list were based on the higher of the two individual rankings by the Agriculture SBA and Agriculture SBA-Forests Analysts. Ambient Particulate Matter Concentration (fine) Bathymetry Biodiversity Biomass Burned Area/Fires Carbon (stores, uptake, flux) Carbon Dioxide Concentration Column Nitrogen Dioxide Concentration Column Particulate Matter Concentration (fine) Crop Emergence Crop Residue Crop Yield Cultivation Area Deforestation Desertification Direct Normal Irradiation (DNI) Ecosystem Function/Dynamics Evaporation Evapotranspiration EVI Floods Forest Cover Forest Litter Forest management practices Forest Structure

Fraction of Photosynthetically Active Radiation (fPAR) Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) Fuel Load/Characteristics Global Horizontal Irradiation (GHI) Gross Primary Productivity Hydrology Impervious Surface Extent Land Cover Land Surface Temperature Land Use Leaf Area Index NDVI Net Primary Productivity (NPP) Non-native species Photosynthetically Active Radiation (PAR) Population Precipitation SAVI Soil Carbon Soil Composition Soil Moisture Soil Thickness Soil Type Species Composition Stand Density/Height/Volume


Surface Air Temperature Surface Atmospheric Pressure Surface Humidity Surface Wind Direction Surface Wind Speed Upper Level Humidity

Upper Level Temperature Upper Level Winds Urbanization Vegetation Cover Vegetation Type

The Agriculture, Forests, and Cross-SBA Analysts collectively prepared a combined list of the “15 Most Critical” observations based on the commonality across the “Most Critical” observation lists for the Agriculture SBA analysis and Forests sub-analysis. Accounting for differences in observation terminology across the SBAs, the Agriculture and Forests Teams effectively contributed 35 observation parameters to Method 4 of the Cross-SBA analysis (similar to the number of observations contributed to Method 4 from other SBAs). The Agriculture SBA report contributed the italicized “High,” “Medium,” and “Low” priority observations (listed previously) to the overall Agriculture SBA “15 Most Critical” observations.

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