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Remote sensing in Agriculture

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C Heerema

on 11 December 2014

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Transcript of Remote sensing in Agriculture

Remote Sensing in Agriculture
Current use
“Remote sensing is the science (and to some extent, art) of
acquiring information
about the Earth's surface
actually being in
with it. This is done by sensing and recording reflected or emitted
electromagnetic radiation
and processing, analyzing, and applying that information” CCRS

How it works
The key is the different interaction of materials within the electromagnetic spectrum

Gives unique
photographs (VIS)
spectral signatures (VIS-MIR)

Crop identification

Imaging Spectroscopy
Used to map specific materials by detecting specific chemical bonds. (USGS, 2002)

Simultaneous registration of ‘reflectance’ images. (Wageningen UR, 2014)
Spectral Signatures
Kate Heerema
Samuel Welsh

R.S. In Agriculture
A tool to help obtain data in order to
identify and monitor crops
in agricultural practices

An understanding of the
temporal and spatial

of agricultural land cover

Important in the
decision making
of agricultural strategies
Precision farming
“Information and technology-based agricultural management system to improve crop production efficiency.” (Metternicht, 2006)

Case Studies
Resolution (Spatial and Temporal)
Data error

Lag time
Heterogeneity of cropland
Data availability
User knowledge/usability
Not universal/appropriate
Awareness of limitations

Global food demand

Advances in technologies
Multi-sensor studies
Improve hardware, software
Implanting nano-chips

Real-time fertilization

Biogeochemical fluxes

Future directions

Atzberger, C. (2013) Advances in remote sensing of agriculture: Context description, existing operational monitoring systems and major information needs. Remote Sensing, 5(2), 949-981.

Canada Centre for Remote Sensing (CCRS), Natural Resources Canada 2013 Tutorial: Fundamentals of Remote Sensing. http://www.nrcan.gc.ca/earth-sciences/geomatics/satellite-imagery-air-photos/satellite-imagery-products/educational-resources/9309 [Accessed 27 November 2014]

Dunn, B. W. et al (2002) The potential of near-infrared reflectance spectroscopy for soil analysis—a case study from the Riverine Plain of south-eastern Australia. Animal Production Science, 42(5), 607-614.

Kyllo, K. P. (2003) NASA funded research on agricultural remote sensing, Department of Space Studies, University of North Dakota.

Leroux, L. et al. (2014) How Reliable is the MODIS Land Cover Product for Crop Mapping Sub-Saharan Agricultural Landscapes? Remote Sensing, 6(9), 8541-8564

Metternicht, G. (2006) Presentation: Use of remote sensing and GNSS in precision agriculture. [ONLINE] Available at: http://www.oosa.unvienna.org/pdf/sap/2006/zambia/presentations/04-01-01.pdf. [Accessed 27 November 2014]

Mulianga, B. et al. (2013) Forecasting Regional Sugarcane Yield Based on Time Integral and Spatial Aggregation of MODIS NDVI Remote Sensing 2013, 5(5), 2184-2199
Need to
Establish ground truth
Visualize: e.g. Normalize data using Vegetation Indices.

Yield estimation and prediction

Recognizing site variability through enhanced imagery and multi- temporal analysis

Determining the cause of such variability and ‘problem areas’ in crop production

Cost efficient/effective tool

Additionally helps protect the environment

“A Production Efficiency Model-Based Method for Satellite Estimates of Corn and Soybean Yields in the Midwestern US.”
(Xin et al, 2013)

“Politics & technology: U.S. polices restricting unmanned aerial systems in agriculture.”
(Freeman et al, 2014)

“Forecasting Regional Sugarcane Yield Based on Time Integral and Spatial Aggregation of MODIS NDVI.” (
Mulianga et al, 2013)

“How Reliable is the MODIS Land Cover Product for Crop Mapping Sub-Saharan Agricultural Landscapes.”
(Leroux et al, 2014)

of Remote Sensing since it's beginning has led to the following in the
More frequent use

Murray, P. (2013) Precision Agriculture – High Technology Invades The Farm | Singularity HUB. [ONLINE] Available at: http://singularityhub.com/2011/03/13/precision-agriculture-high-technology-invades-the-farm/. [Accessed 27 November 2014]

National Agricultural Statistics Survey (NASS) (2009) NASS Surveys - Remotely Sensed Data for Crop Acreage. [ONLINE] Available at: http://nass.usda.gov/Surveys/Remotely_Sensed_Data_Crop_Acreage/index.asp. [Accessed 27 November 2014]

Precision Agriculture (2014) Variable Rate Applications | Precision Agriculture. [ONLINE] Available at: http://www.precisionagriculture.com.au/variable-rate-applications.php. [Accessed 27 November 2014]

Satellite Imaging Corporation and SPOT Image Corporation (2005) SPOT 5 false colour composite of agricultural fields in Saxony, Germany, near Berzdorf Lake. http://www.seos-project.eu/modules/agriculture/agriculture-c02-p02.html [Accessed 27 November 2014]

SEOS-project (Agriculture) (2014) Remote Sensing and Geo-Information Technologies in Agriculture. [ONLINE] Available at: http://www.seos-project.eu/modules/agriculture/agriculture-c00-p01.html. [Accessed 27 November 2014]

USGS (2002) About Imaging Spectroscopy, http://speclab.cr.usgs.gov [Accessed 27 November 2014]

Wageningen UR (2014) Remote Sensing, http://scomp5063.wur.nl/courses/grs20306/ [Accessed 27 November 2014]
policy decisions
financial benefits
trading industry - import/export
strategies in farming operations

The Evolution of...
Kyllo, 2003
Wageningen UR, 2014
Robinson, 2012
Murray, 2011
Better understanding
More reliable results
However, there are still
to overcome
Full transcript