Mexico, 14-15 December 2013
Remote Sensing – Beyond Images Sevilla Palace Hotel Mexico City, Mexico 14-15 December 2013
“There are no miracles in agricultural production.” Norman Borlaug, Nobel prize winner and founding father of CIMMYT. Poor people are the first to be hit by food shortages; it is important to make use of all possible technologies and improve policies to ensure a steady increase in food production to match the rising demand. Insights and recommendations based on remote sensing have the large potential to help improve the livelihoods of resource poor farmers. These farmers can benefit directly through specific crop management information or indirectly through better seed or informed policy decisions by governments. Farmer’s organizations, NGOs, governments, policy makers and others can make use of geo-spatial information to create more equitable, fairer and more transparent conditions for these farmers. During this workshop, we will seek to define collaborative research projects that will make best use of our (research) resources. This book of abstracts is published in support of the workshop “Remote sensing: Beyond images” held on 14-15 December 2013 in the Sevilla Palace Hotel, Mexico City, Mexico. The workshop is funded by the Bill & Melinda Gates Foundation, the Mexican Secretariat of Agriculture, Livestock, Rural Development, Fisheries and Food (SAGARPA), the International Maize and Wheat Improvement Center, CGIAR Research Program on Maize, the Cereal System Initiative South Asia (CSISA) and the Sustainable Modernization of the Traditional Agriculture (MasAgro). The International Maize and Wheat Improvement Center, known by its Spanish acronym, CIMMYT® (www.cimmyt.org), is a not-for-profit research and training organization with partners in over 100 countries. The center works to sustainably increase the productivity of maize and wheat systems and thus ensure global food security and reduce poverty. The center’s outputs and services include improved maize and wheat varieties and cropping systems, the conservation of maize and wheat genetic resources, and capacity building. CIMMYT belongs to and is funded by the Consultative Group on International Agricultural Research (CGIAR) (www.cgiar.org) and also receives support from national governments, foundations, development banks, and other public and private agencies. CIMMYT is particularly grateful for the generous, unrestricted funding that has kept the center strong and effective over many years. © International Maize and Wheat Improvement Center (CIMMYT) 2013. All rights reserved. The designations employed in the presentation of materials in this publication do not imply the expression of any opinion whatsoever on the part of CIMMYT or its contributory organizations concerning the legal status of any country, territory, city, or area, or of its authorities, or concerning the delimitation of its frontiers or boundaries. CIMMYT encourages fair use of this material. Proper citation is requested. Workshop Organizing Committee Jill Cairns Perla Chavez Irene Christiansen Liliana Diaz Bruno Gerard Jose Luis Maldonado Ivan Ortiz-Monasterio Genevieve Renard Francelino Rodriguez Kate Schneider Urs Schulthess Chair Sam Storr Maria Tattaris Co-chair Stanley Wood Layout and design Miguel Mellado Eliot Sanchez
Introduction from Thomas Lumpkin The food crisis in 2008 was anticipated by specialists, but for many political leaders and donor agencies it appeared to explode overnight. The world became increasingly concerned about the sharp increases in the prices of maize and wheat. How would higher food prices affect those with low income – particularly in the developing world? What impact would these food price increases have on global efforts to reduce poverty? Coupled with these difficulties is the ever-increasing demand for maize and wheat from a growing worldwide population, changing diets and the demand for biofuels. The world’s climate is changing; temperatures are rising and extreme weather events are becoming more common; natural resources are being depleted; new crop diseases are emerging; and yields are stagnating. Remote sensing has the potential to tackle these problems and improve the lives of resource-poor farmers and consumers. Agronomy driven by remote sensing can address these challenges by monitoring weather, crop growth and yield, making better use of existing soil resources, improving nutrient and irrigation management and supporting genetic enhancement through phenotyping. CIMMYT’s system projects like Cereal Systems Initiative for South Asia (CSISA), Sustainable Intensification of Maize-Legume cropping systems for food security in Eastern and Southern Africa (SIMLESA) and Sustainable Modernization of the Traditional Agriculture (MasAgro) are already providing farmers with a wealth of information to improve crop management practices through Information and communications technology (ICT) tools. With the rapid advancement and availability of technologies and data processing, remote sensing is increasingly becoming an important tool for the application of agricultural monitoring and decisionmaking. Both governments and the private sector alike operate remote sensing satellites, drones, etc. The quantity and quality of data is increasing, while prices are decreasing. In fact, in many instances data is freely available to the public. Please map a plan to dramatically increase the use of remote sensing to improve rural incomes and livelihoods and reduce the impact of agriculture on the environment.
Welcome,
Tom Lumpkin Director General, CIMMYT
Workshop Program, 14-15 December 2013 Saturday Maria Tattaris and Urs Schulthess
9:00
9:05
Welcome
9:05
9:20
Opening address
Tom Lumpkin
User's needs and expectations
Petr Kosina Stanley Wood
9:20
9:30
Donors and policy makers
9:30
9:40
Approaches and needs of remote sensing in phenotyping for plant breeding
9:40
9:50
Irrigation scheme managers
Enrique Mejia
9:50
10:00
Remote Sensing of Wheat Rusts - A dream or reality?
Dave Hodson
10:00
10:10
Precision agriculture for smallholder farmers: An option?
Bruno Gerard
10:10
10:30
10:30
Mariano Cossani
Coffee
Use Cases
Petr Kosina
10:45 Introduction
Stanley Wood
Sensors and products
Petr Kosina
10:45
Stress detection using fluorescence, narrowband spectral indices and thermal imagery 11:00 acquired from manned and unmanned aerial vehicles
11:00
11:15
11:15
11:30 Challenges of UAV platforms
Lucio Andre De Castro Jorge
11:30
11:45 MODIS vegetation indices
Ramon SolanoBarajas
11:45
12:00
12:00
13:15
Radar’s Potential to Estimate Crop Bio-Physical Parameters and Beyond
Optical and microwave remote sensing for crop monitoring in Mexico
Lunch
Pablo Zarco-Tejada
Jialing Zhang
Jesús Soria Ruiz
Session A
Nutrient and Water management & Disease monitoring
Urs Schulthess Stephen Maas
13:15
13:30
Water management from the water shed to the field level
13:30
13:45
Daily evapotranspiration by combining remote sensing with ground observations: Study from Maricopa, Arizona USA
13:45
14:00
Agronomy in the context of conservation agriculture: Nutrient management
14:00
14:15
From GreenSeeker to GreenSat
14:30
14:45
Delivering information products to small-scale farmers: IRRI's experience with the Crop Manager
Roland Buresh
14:45
15:00
Data and information delivery: Digilab 2.0 and Apps by BASF
Luis Miguel Ramos
15:00
15:15
Disease monitoring
Session B
15:15
Andy French
Eric Miller Ivan OrtizMonasterio
Perla Chavez and Pawan Singh
Crop Production Estimation
Jill Cairns
13:15
13:30
Spatiotemporal data mining framework for biomass monitoring at scale
Raju Vatsavai
13:30
13:45
Empirical geo-based approach to wheat yield forecasting within major export countries and exploring its adaptation to smallholder systems
Imbal BeckerReshef
13:45
14:00
Forecasting wheat yield and production for Punjab Province, Pakistan from satellite image time series
Jan Dempewolf
14:00
14:15
Monitoring current rice production areas and assessing the potential for development.
Sander Zwart
14:15
14:30
Agricultural statistics of roots and tubers
Percy Zorogastúa
14:30
14:45
Crops yield estimation through remote sensing
Víctor Rodríguez Moreno
14:45
15:00
Global yield assessment with a dynamic vegetation and water balance model
Katharina Waha
15:00
15:15
Scalable yield gap analysis
15:35
Coffee
David Lobell
15:35
Breakout sessions A & B
16:50
Image providers
Perla Chavez
17:00
17:15
DigitalGlobe
Kumar Navulur
17:15
17:25
Skybox Imaging
John Clark
17:25
17:35
BlackBridge solutions for agriculture
Leopoldo Zamudio
17:35
17:45
Hyperspectral Imaging in remote sensing applications: Using the SOC710-GX and SOC716 to collect hyperspectral data from an airborne platform
Mike Zemlan
17:45
17:55
ERMEX
Javier Aguilar Lara
17:55
18:10
The global earth observation system of systems - remote sensing and in situ resources
Sergio Camacho
18:15
19:30
20:00
Poster session/Cocktail Workshop Dinner
Sunday Session A
Phenotyping
Francelino Rodrigues
8:00
8:15
Affordable field high-throughput phenotyping some tips
Jose Luis Araus
8:15
8:30
Measuring and mapping canopy traits from the lab to the field – the potential of optical remote sensing for crop phenotyping
Uwe Rascher
8:30
8:45
Remote sensing for field phenotyping at CIMMYT
Maria Tattaris, Mainassara ZamanAllah and Jill Cairns
8:45
9:00
Field-based phenomics in Rice: Remote sensing methods for analysing morpho-physiological traits under different Nitrogen regimes
Michael Gomez Selvaraj
9:00
9:15
New remote and proximal sensing methodologies in high throughput field phenotyping
Jose A. JimenezBerni
9:15
9:30
PlantScreenTM new plant phenotyping platform for the lab and field
Steve Hunt
9:30
9:45
Ground penetrating radar
Sean Thompson, Alfredo Delgado
9:45
10:00
Investigation of water flow dynamics through the xylem using synchrotron X-ray imaging
Hae Koo Kim
10:00
10:15
Remote sensing based drought tolerant maize targeting in SSA
Kai Sonder
10:15
10:30
Coffee
10:15
12:00
Breakout session
Session B
12:00
Session A
MODIS
Urs Schulthess
8:00
8:20
Monitoring Biomass Dynamics at Scale: Emerging Trends and Recent Successes
Bhaduri Budhendra
8:20
8:55
Increasing access to terrestrial ecology and remote sensing (MODIS) data through Web services and visualization tools
S.K. Santhana Vannan
Land use planning and monitoring
Ivan Ortiz Monasterio
9:00
9:15
Land health surveillance
Keith Shepherd
9:15
9:30
Case study from Bangladesh
Urs Schulthess
9:30
9:45
The REDD+ satellite based land cover monitoring system for Mexico
Steffen Gebhardt
9:45
10:00
Estimating crop biomass in smallholder fields with very high resolution imagery
Sibiry Traore
10:00
10:20
10:20
10:35
Remote sensing based soil moisture detection
Sanaz Shafian
10:35
10:50
Remote sensing for assessing crop residue cover and soil tillage intensity
Craig Daughtry
10:50
12:00
Coffee
Breakout session Lunch
13:15
Spatial data analysis and delivery
Maria Tattaris
13:15
13:30
Merging data sources – proximal to remote sensing
Francelino Rodrigues
13:30
13:45
Various methods of field data collection and spatial analysis
Alex Melnitchouck
13:45
14:00
Meteorological stations and weather and climate forecasts
Arturo Corrales Suastegui and Miguel Gonzáles González
14:15
15:15
Breakout Session
Session B
15:15
Crop Insurance
B. Gérard
13:15
13:30
Agricultural index insurance through remote sensing: experiences from East Africa
Laura Johnson
13:30
13:45
Remote sensing products in support of crop subsidies in Mexico
Carlos Dobler
13:45
14:00
Remote sensing technology for crop insurance – applications and limitations
Joachim Herbold
14:00
15:15
Breakout Session Poster session with Coffee
16:15
Panel Discussion
Philippa Zamora
16:15
16:45
Challenges of delivering information and services to end users
Panelists
16:45
17:00
Feedback from the users
Petr Kosina
17:00
17:15
Closing Note
Bruno Gerard and Stanley Wood
Table of contents Introduction from Thomas Lumpkin, CIMMYT Director General . ................................................................................... iii Workshop program.......................................................................................................................................................... iv Abstracts . ......................................................................................................................................................................... 1
User’s needs and expectations................................................................................................................................... 1
Approaches and the needs of remote sensing in phenotyping for plant breeding............................................ 1
Remote sensing of wheat rusts: a dream or reality?.......................................................................................... 2
Precision agriculture for smallholder farmers: an option? ................................................................................ 3
Sensors and products................................................................................................................................................. 4
Stress detection using fluorescence, narrow-band spectral indices and thermal imagery acquired from manned and unmanned aerial vehicles............................................................................................................. 4
Radar’s potential to estimate crop bio-physical parameters and beyond.......................................................... 6
Challenges of UAV platforms.............................................................................................................................. 7
MODIS vegetation indices.................................................................................................................................. 9
Optical and microwave remote sensing for crops monitoring in Mexico......................................................... 10
Nutrient and water management & disease monitoring......................................................................................... 11
Remote sensing based water management from the watershed to the field level......................................... 11
Daily evapotranspiration by combining remote sensing with ground observations: Study from Maricopa, Arizona USA...................................................................................................................................................... 12
Agronomy in the context of conservation agriculture: Nutrient management................................................ 14
From GreenSeeker to GreenSat in irrigated wheat in Mexico.......................................................................... 15
Data and information delivery: Digilab 2.0 and Apps by BASF......................................................................... 16
Diagnosis monitoring in wheat through hyperspectral remotely sensed data................................................. 17
Crop production estimation..................................................................................................................................... 18
Spatiotemporal data mining framework for biomass monitoring at scale....................................................... 18
Empirical EO-based approach to wheat yield forecasting within major export countries and potential adaptation to smallholder systems.................................................................................................................. 19
Forecasting wheat yield and production for Punjab Province, Pakistan from satellite image time series....... 21
Monitoring current rice production areas and assessing the potential for development................................ 22
Agricultural statistics of roots and tubers......................................................................................................... 23
Crops yield estimation through remote sensing.............................................................................................. 24
Global yield assessment with a dynamic vegetation and water balance model.............................................. 25
Scalable yield gap analysis................................................................................................................................ 26
Image providers........................................................................................................................................................ 27
Trends in commercial remote sensing.............................................................................................................. 27
Introducing Skybox: high fidelity imagery & infinite analytics......................................................................... 28
BlackBridge solutions to support agriculture................................................................................................... 29
Hyperspectral imaging in remote sensing applications: Using the SOC710-GX and SOC716 to collect hyperspectral data from an airborne platform................................................................................................. 30
The global earth observation system of systems - remote sensing and in situ data and information resources...................................................................................................................................... 31
Phenotyping............................................................................................................................................................. 32
Affordable field high-throughput phenotyping - some tips............................................................................. 32
Measuring and mapping canopy traits from the lab to the field – the potential of optical remote sensing for crop phenotyping........................................................................................................................... 33
Remote sensing for field phenotyping at CIMMYT........................................................................................... 35
Field-based phenomics in rice: Remote sensing methods for analysing morpho-physiological traits under different Nitrogen regimes.................................................................................................................... 36
New remote and proximal sensing methodologies in high throughput field phenotyping.............................. 38
PlantscreenTM new plant phenotyping platform for the lab and field.......................................................... 39
Estimating wheat root biomass using ground penetrating radar..................................................................... 40
Investigation of water flow dynamics through the xylem using synchrotron X-ray imaging............................ 41
Remote sensing based drought tolerant maize targeting in SSA...................................................................... 42
MODIS...................................................................................................................................................................... 43
Monitoring biomass dynamics at scale: emerging trends and recent successes............................................. 43
Increasing access to terrestrial ecology and remote sensing (MODIS) data through Web services and visualization tools............................................................................................................................................. 45
Land use planning and monitoring........................................................................................................................... 46
Land health Surveillance.................................................................................................................................. 46
Land use planning: Case study from Bangladesh............................................................................................. 47
The REDD+ satellite based land cover monitoring system for Mexico............................................................. 48
Remote sensing based soil moisture detection................................................................................................ 49
Remote sensing for assessing crop residue cover and soil tillage intensity..................................................... 50
Spatial data analysis and delivery............................................................................................................................ 52
Merging data sources: Proximal to remote sensing......................................................................................... 52
Various methods of field data collection and spatial analysis.......................................................................... 53
A national network of automated agrometeorological stations in Mexico - weather and climate forecasts... 54
Crop Insurance......................................................................................................................................................... 56
Agricultural index insurance through remote sensing: experiences from East Africa...................................... 56
Remote sensing products in support of crop subsidy in Mexico...................................................................... 57
RS technology for crop insurance..................................................................................................................... 58
Acronyms................................................................................................................................................................. 61 List of participants ................................................................................................................................................... 62
Abstracts User’s needs and expectations Approaches and the needs of remote sensing in phenotyping for plant breeding C.M. Cossani and M.P. Reynolds Presenting author’s e-mail:
[email protected] Affiliation:International Maize and Wheat Improvement Center (CIMMYT) – Global Wheat Program Remote sensing (RS) is a key technology to develop high throughput protocols to accelerate and improve precision in phenotyping. Crop phenotyping by RS is based heavily on the measurement of spectral indices related to specific traits of interest. To interpret results properly a good understanding of the link between canopy reflectance properties and desirable selection characteristics is needed. It should also be borne in mind that a pre-requisite for meaningful phenotyping involves selection of appropriate genetic material. The a-priori development of closephenology populations will improve QTL analysis for example, by avoiding confounding effects of major genes and/or environmental effects. Additionally, accurate design and/or definition of the experimental environment are crucial for meaningful interpretation and extrapolation of the data. RS offers the opportunity to screen big populations at lower cost and faster than conventional phenotyping, allowing an increased number of accessions and/or traits to be evaluated in reduced time. In that sense, RS provides breeding programs an enormous opportunity to assess genetic diversity and increase yield potential and stress tolerance. For instance, RS has been used to screen more than 70,000 wheat genetic resources from the World Wheat Collection at CIMMYT for heatand drought-adaptive traits in the field. One of the most valuable applications is the use of RS to estimate temperature of the crop canopy using infrared sensors and imagery that has achieved significant impacts already in crop breeding, and QTL identification. Under drought, canopy temperature is related to water uptake by the roots, and under well watered conditions it may be a surrogate for photosynthetic rate. Another application of RS is ground penetrating radar (GPR). Ongoing projects at CIMMYT are analyzing the relationship between GPR data and destructive sampling of roots in order to develop high throughput methods for root measurement in the field. RS can also be used to estimate photosynthetic and photo-protective pigment composition. Another, successful example is the use of vegetation indices (e.g. NDVI) to estimate senescence rates using portable dedicated sensors like the GreenSeeker. The next stage of RS application in breeding is the application of light and relatively inexpensive sensors for aerial imaging to increase temporal and spatial resolution.
1
Remote Sensing of Wheat Rusts: A dream or reality? D. Hodson Presenting author’s e-mail:
[email protected] Affiliation: International Maize and Wheat Improvement Center (CIMMYT) – Addis Ababa, Ethiopia Objectives To challenge the remote sensing community to consider the role that remote sensing may play in the operational detection and monitoring of wheat rust diseases. Background and Challenges • Wheat rusts (stem rust, stripe rust, leaf rust) are the major diseases affecting wheat worldwide. All are capable of devastating epidemics. Stripe rust is currently the major problem globally, but new virulent races of stem are a major concern. Stem and stripe rust cause the largest production losses and would be the main target for any remote sensing application. • A global cereal rust monitoring system has been implemented by CIMMYT and partners. This is probably the most comprehensive, functional disease monitoring system for any major crop. The current system relies on a global network of partners collecting standardized, geo-referenced survey data (Park et al., 2011). • To increase the efficiency and efficacy of the current global monitoring system the question is raised as to whether remote sensing can play a role in the early detection of rust outbreaks, areas of climatic suitability, host suitability and damage assessments. • The questions are not new and the promise of remote sensing for disease monitoring has been discussed for 30 years (Sharp et al., 1985). Several groups are actively undertaking research on this topic and several case studies indicate promising results (Wenjiang Huang et al., 2012). However, no functional operational platform on a large-scale appears to exist at the current time.
Further reading Park, R., Fetch, T., Hodson, D., Jin, Y., Nazari, K., Mohinder Prashar, M., Pretorius, Z. 2011. International surveillance of wheat rust pathogens - progress and challenges. Euphytica. DOI 10.1007/s10681-011-0375-4. Sharp, E.L., Perry, C.L., Scharen, A.L., Boatwright, G.O., Sands, D.C., Lautenschlager, L.F., Yahyaoui, C.M., and Ravet, F.W. 1985. Monitoring Cereal Rust Development with a Spectral Radiometer. Phytopathology 75(8). 936-939. Wenjiang Huang, Juhua Luo, Jingcheng Zhang, Jinling Zhao, Chunjiang Zhao, Jihua Wang, Guijun Yang, Muyi Huang, Linsheng Huang and Shizhou Du (2012). Crop Disease and Pest Monitoring by Remote Sensing, Remote Sensing - Applications, Dr. Boris Escalante (Ed.), ISBN: 978-953-51-0651-7, InTech, Available from: http://www.intechopen.com/books/remote-sensing-applications/crop-disease-and-pest-monitoring-byremotesensing
2
Precision agriculture for smallholder farmers: An option? B.Gerard, J. Hellin, B. Govaerts, A. McDonald and T. Krupnik Presenting author’s e-mail:
[email protected] Affiliation: International Maize and Wheat Improvement Center (CIMMYT) Rationale: Thinking about precision agriculture (PA) approaches to help small-scale farmers improving their livelihood may appear at first as a ‘blue sky’ idea given that PA concepts have had so far relatively low adoption in most of the developing world agriculture and required investments beyond reach for poor small-scale enterprises without intervention of key stakeholders. However as stated by Cook et al., (2003): “From the description of site-specific activities it is obvious that although PA, as seen in Europe and North America, is largely irrelevant in developing countries. The need for spatial information is actually greater, principally because of stronger imperative for change and lack of conventional support.” CIMMYT covers diverse agro-ecosystems on three continents and ‘precision agriculture concepts’ will have to adapt to various realities and institutions on the ground to be adoptable and adopted. The concept should also be broader than what is thought at precision agriculture in developed countries: our efforts should help farmers to practice a ‘more precise agriculture,’ which implies better allocation of resources in space and time, in some place producing more with less and others producing more with more! Components of precision agriculture for smallholders • Diagnosis: Remote sensing and other monitoring tools (weather, soil monitoring) • Decision support tools: Nutrient, water and disease management, crop modeling • Information and Communication Technologies: how do you get diagnosis and provide recommendations • Small mechanization (how do you apply recommendations)
Source: IRRI
3
Sensors and Products Stress detection using fluorescence, narrow-band spectral indices and thermal imagery acquired from manned and unmanned aerial vehicles P.J. Zarco-Tejada Presenting author’s e-mail:
[email protected] Affiliation: Laboratory Director, QuantaLab Remote Sensing Laboratory, Instituto de Agricultura Sostenible (IAS), Consejo Superior de Investigaciones Científicas (CSIC), Cordoba, Spain Objectives Micro-hyperspectral imagers and thermal cameras on board manned and unmanned vehicles (UAVs) are proposed for mapping stress in commercial fields and on wheat and maize phenotyping experiments. Remote sensing narrow-band indices, fluorescence and temperature were related to physiological indicators of stress. Highlights • Micro-hyperspectral and thermal cameras on board UAVs (Zarco-Tejada et al., 2012; González-Dugo et al., 2013) and manned aircrafts enable the generation of narrow-band spectral indices, fluorescence and temperature maps at high resolution in commercial and phenotyping experiments. • Fluorescence, temperature and narrow-band indices demonstrate good relationships with physiological indicators such as stomatal conductance, water potential and leaf photosynthesis. • These recent studies demonstrate that fluorescence quantification using the FLD principle from micro hyperspectral cameras is feasible from regular aircraft and from small unmanned vehicles.
Canopy Temperature
Chlorophyll Fluoresence
Maps of canopy chlorophyll fluorescence (left) and canopy temperature (right) obtained from hyperspectral and thermal imagery acquired from an aircraft following a flight plan over wheat and maize experiments.
4
Further reading Zarco-Tejada, P.J., González-Dugo, V., Berni, J.A.J. (2012). Fluorescence, temperature and narrow-band indices acquired from a UAV platform for water stress detection using a micro-hyperspectral imager and a thermal camera, Remote Sensing of Environment, 117, 322-337. González-Dugo, V., Zarco-Tejada, P.J., Nicolás, E., Nortes, P.A., Alarcón, J.J., Intrigliolo, D.S., Fereres, E. (2013). Using high resolution UAV thermal imagery to assess the variability in the water status of five fruit tree species within a commercial orchard, Precision Agriculture, DOI 10.1007/s11119-013-9322-9.
5
Radar’s potential to estimate crop bio-physical parameters and beyond J.Shang, H.McNairn, C. Champagne and X. Jiao Presenting author’s e-mail:
[email protected] Affiliations: Agriculture and Agri-Food Canada; Natural Resources Canada Background To meet national, regional and global challenges in food supply and demand, timely and cost-effective geospatial information is needed to make sound policies and risk management strategies. Earth observation satellites offer an efficient means to acquire accurate information on the location, extent and condition of crops. Optical sensors are very valuable information sources for agricultural production monitoring, However due to unfavorable weather conditions occur in most of the agricultural regions around the globe, the availability of optical data is often limited hence hinder the application of optical remote sensing operationally. Microwave remote sensing, on the other hand, is not affected by most weather conditions such as cloud, haze and light rain. This all-weather capability makes radar a powerful and reliable data source for crop inventory and growth monitoring. Objectives Research activities are carried out to develop suitable methodology for information extraction from multifrequency and multi-sensor radar data. Highlights • Satisfactory crop classification (over 85% accuracy) can be produced using radar data alone. • Many radar parameters (HV backscatter, volume scatter, pedestal height) are sensitive to crop biophysical parameters such crop height, leaf area index and biomass. • AAFC has developed models to estimate field-level soil moisture using Canada’s RADARSAT-2. • Passive microwave remote sensing at longer wavelengths is sensitive to moisture conditions in the surface soil layers. Using the Land Parameter Retrieval Model (LPRM) with the AMSR-E soil moisture record was the optimal dataset for monitoring soil moisture conditions in Canada.
Figure 1. Crop Map Generated Using X-, C- and L- Band SAR: Carman, Manitoba, Canada (overall accuracy 91.4%) 6
Challenges of UAV platforms L.A.C. Jorge Presenting author’s e-mail:
[email protected] Affiliation: Agricultural Instrumentation Center of Brazilian Agricultural Research Corporation (EMBRAPA) – São Carlos, SP – Brazil Objectives The Interest in Unmanned Aerial Vehicles (UAVs) has grown around the world and several efforts are underway to integrate UAV operations routinely and safely into remote sensing applications, specially applied in precision agriculture. Reviewing the use of UAV in agriculture, it shows limitations and opportunities. So the challenges of UAV platforms for remote sensing and precision agriculture and recommended actions for moving forward were identified. Highlights Recent advances in computer technology, software development, sophisticated lighter materials, global navigation systems, advanced data links, sensors and miniaturization are the reasons for the increasing developments of UAVs. Today, more than 40 countries have UAV development work for different markets. The United States appear as one of the leaders in terms of sizes, types and sophistication of systems, mostly for the military market. Other countries include Japan, South Korea, Australia, France, England, Italy, Germany and Israel. Japan and South Africa stand out with more than 2,000 drones and applied in spray other applications in agriculture. Despite advances in the past century, drones are still considered by many as being in their embryonic stage. The technologies that are being employed in UAVs today are evolving very fast and present with great promise. Systems autonomous are becoming more sophisticated and reliable. UAVs, by their ability to take on high-risk missions and their potential for operations at low cost compared to manned aircraft, has become an ideal proposal for the development of new technologies. Research in such areas as new materials, cell fuel adaptive software; memories; film and spraying on the antennas, communications and laser can reshape the market for new applications. Here we are summarizing those advances and the applications on remote sensing and agriculture.
7
UAVs in Brazil Sugar-cane crops and images from citrus areas and processing layers of information.
UAVs developed in Brazil in 1998 to precision agriculture, and image processing applied to analysis of weeds infestation, crop growing, shape and size of trees and NIR images to identify crop diseases.
8
MODIS Vegetation Indices R. Solano-Barajas Presenting author’s e-mail:
[email protected] Affiliation: Universidad de Colima (U de C), Colima, Mexico Abstract Vegetation Indices (VIs) have been widely used due to their high correlation with vegetation health, but also with some biophysical variables such as Leaf Area Index (LAI) and Gross Primary Production (GPP). There are some longterm (20+ years) VI datasets such as the AVHRR-based Pathfinder and the GIMMS that have allowed us to significantly advance our understanding of global and regional ecosystem functioning. In addition, VI time series are also available from a number of other sensors, such as the MODIS radiometer, onboard the Terra and Aqua satellites (1999 - current date). MODIS products represent a breakthrough in data quality and set new standards for VI data production and availability. Daily VI data are readily available, but due to visibility, technical and other difficulties, VI data are usually aggregated as multi-day composites (e.g. 10- or 16-day composites) for systematic usage. In this talk, we will address some of the key aspects of the MODIS VIs that play an important role for a better understanding and usage of these products, such as: • MOD13 product family • Product updates (MODIS Collections) • Additional radiometric data bundled (SDS) • Quality assurance (QA) and environmental data included • Compositing algorithms and effects • Product perspectives Further reading Solano, R., Didan, K., Jacobson, A. and Huete, A, MODIS Vegetation Indices (MOD13) C5 Users’s Guide, http://www.ctahr.hawaii.edu/grem/modis-ug.pdf, The University of Arizona, 2010
Example of an advanced usage of the MODIS VI time series. Left: NDVI anomaly for a given three-month period, computed from the aggregate1-km 16-day NDVI product (9 years); Right: corresponding aerosol climatology extracted from the companion QA metadata (legend: 1=climatology, 2=low, 3=high aerosols respectively) 9
Optical and microwave remote sensing for crops monitoring in Mexico J. Soria-Ruiz1 and Y. Fernández-Ordoñez2 Presenting author’s e-mail:
[email protected] Affiliations: 1National Institute of Research for Forestry Agricultural and Livestock (INIFAP). Geomatics Lab. 2 Zinacantepec 52107, México. Postgraduate College in Agricultural Sciences (COLPOS). Montecillo 5240, México. Objectives In the agricultural sector, we are using optical and radar remote sensing for crop monitoring in Mexico, with emphasis on crop area, crop yield prediction, detection of pests and diseases, land/use and land/cover assessment and damage to agriculture caused by extreme weather events, etc. Highlights • To determine crop area and crop yield prediction of economic importance in Mexico (corn, wheat, beans, etc.), have been used Landsat 7 and SPOT 5 panchromatic and multispectral. • For detection of pests and diseases in annual crops and avocado plantations, have been used high spatial resolution images, such as Ikonos and QuickBird. • In addition, we used MODIS, SPOT 5 and Radarsat 1 to evaluate flood damage, and RADARSAT 2 to determine biomass in corn crops, with emphasis on regions with continuous clouds. (a)
(b)
Crops areas using SPOT 5 images and crops yield map of corn in State of Mexico (a), and flooded areas in Tabasco, Mexico with RADARSAT 1 (b).
Further reading Soria-Ruiz, J., Fernandez-Ordoñez, Y. and Woodhouse, I.H. (2010). Land-cover classification using radar and optical images: a case study in Central Mexico. International Journal of Remote Sensing, 31:12, 3291-3305.
10
Nutrient and Water Management & Disease Monitoring Remote sensing based water management from the watershed to the field level S. Maas S1 and N. Rajan2 Presenting author’s e-mail:
[email protected] 1 Affiliations: Texas Tech University and Texas A&M AgriLife Research, Lubbock, Texas, USA 2 Texas A&M AgriLife Research, Vernon, Texas, USA Summary Effective water management for agro-ecosystems at spatial scales from the field to the watershed is based on information quantifying the water used by the vegetation in the area of interest. We have pioneered the use of the “spectral crop coefficient” (K sp ) approach for estimating the water used by crops, pastures, and natural vegetation. In the K sp approach, the daily water use (WU) of vegetation is estimated as follows, WU = PET fc ∙ GC ∙ F s where PET fc is the potential evapotranspiration of the vegetation under full-canopy conditions calculated from standard weather observations, GC is the ground cover of the vegetation, and F s is a stress factor (Rajan et al., 2010). For irrigated crops or non-irrigated vegetation (such as dryland crops or pastures) that are acclimated to their environment, F s ≈ 1. The value of GC represents K sp and can be estimated from multispectral remote sensing data from a variety of sources (Maas and Rajan, 2008; Rajan and Maas, 2009). Daily estimates of WU can be summed to produce estimates of seasonal WU. WU can be estimated for a variety of spatial scales depending on the spatial resolution of the remote sensing data. As shown in the figure below, the spatial distribution of WU across an individual field can be estimated from highor medium-resolution imagery. The WU of individual fields within an agricultural region can be estimated from medium-resolution imagery like that from Landsat. Individual-field estimates can be statistically aggregated to produce regional values for watersheds or geopolitical regions such as counties. Alternatively, regional WU values can be estimated from re-sampled medium-resolution image data or directly from low-resolution imagery (such as MODIS). The K sp approach represents a practical method for operationally estimating WU for a variety of realworld water management applications. Spatial distribution of seasonal crop water use (inches) across a 120-acre center pivot-irrigated cotton field in the Texas High Plains estimated using Landsat multispectral imagery. Further reading Maas, S. J., and N. Rajan. 2008. Estimating ground cover of field crops using medium-resolution multispectral satellite imagery. Agronomy Journal. 100(2):320-327. Rajan, N., and S. Maas. 2009. Mapping crop ground cover using airborne multispectral digital imagery. Precision Agriculture. 10:304-318. Rajan, N., S. J. Maas, and J. Kathilankal. 2010. Estimating crop water use of cotton in the Texas High Plains. Agronomy Journal. 102(6):1641-1651.
11
Daily evapotranspiration by combining remote sensing with ground observations: Study from Maricopa, Arizona USA A. French and D. Hunsaker Presenting author’s e-mail:
[email protected] Affiliation: U.S. Arid Land Agricultural Research Center, USDA/ARS, Maricopa, AZ USA Objective To evaluate the potential for merging remotely sensed data with ground-based observations over irrigated crops to obtain spatially distributed time continuous estimates of evapotranspiration (ET). Highlights • • • • •
Estimation of ET over crops is needed to improve water management under heat stress. ET estimates from remote sensing are synoptic, but frequently unavailable. By combining episodic remote sensing data with time-continuous ground observations these shortcomings can be resolved at weekly time steps. Remotely sensed data can be merged using surface energy balance to yield daily ET. The approach was tested with experimental data from cotton grown in 2003 at Maricopa, Arizona. ET 7-day forecasts were accurate to within 1.5 mm/day (French et al. 2010).
Further reading French, A. Hunsaker, D., Clarke, T. 2012. Forecasting spatially distributed cotton evapotranspiration by assimilating remotely sensed and ground-based observations. J. Irrig. Drain. Eng. 138:984-992.
12
Remote sensing data over cotton (A) provide spatial information to constrain hourly land temperatures (B) and crop cover (C) to obtain estimates of daily ET (D). Modeled ET are shown in blue, observed ET based on soil moisture is in pink. Modeling resolves multiple time scales. Early to mid-season modeling was accurate to
