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Agriculture Crop Management And Production Improved By Satellite Remote Sensing Technology And Geographic Information Systems (GIS)

Scientists for many years have been using satellite remote sensing technology, utilising low and medium resolution sensors to improve water balance and farming yield assessment on large geographical scales around the world.

(June 22). With the availability of high resolution satellite sensors such as IKONOS, QuickBird and soon GeoEye-1, the current remote sensing NDVI algorithms utilised have become more accurate and reliable, providing detailed crop information for agriculture management to improve production and crop health.

FAO (Food and Agriculture Organization of the United Nations) data indicate that annually 2500 km3 of freshwater is used for agricultural production, which amounts to 70% of the water resources that the world population consumes in a year. China is now consuming more than twice as much as what its ecosystems can supply sustainably, having doubled its needs since the 1960s, as indicated in a new WWF report. With the global population continuing to grow at a high pace, it is essential to optimise the use of water resources and to increase agricultural production in view of the prospect of having to feed 8 billion humans by 2030.

Agriculture resources are among the most important renewable, dynamic natural resources. Comprehensive, reliable and timely information on agricultural resources is very much necessary for countries whose main source of the economy is agriculture. Agriculture surveys are conducted through the nation in order to gather information and statistics on crops, rangeland, livestock and other related agricultural resources. This data is most important for the implementation of effective management decisions.

Satellite images can show variations in organic matter and drainage patterns. Soils higher in organic matter can be differentiated from lighter sandier soil that has a lower organic matter content. “Satellite image data have the potential to provide real-time analysis for large areas of attributes of a growing crop that can assist in making timely management decisions that affect the outcome of the current crop” said Leopold J. Romeijn, President of Satellite Imaging Corporation of Houston, Texas. However, like other precision agriculture technologies the information gained from satellite imagery are more meaningful when used with other available data and visualised and analysed with a 2D/3D Geographical Information Systems (GIS).

Satellite Imagery analysis for agriculture production allows for:

* Fast and accurate overview
* Quantitative green vegetation assessment
* Underlying soil characteristics
* TreeGrading

Remote sensing satellite imaging is an evolving technology with the potential for contributing to studies for land cover and change detection by making globally comprehensive evaluations of many environmental and human actions possible. These changes, in turn, influence management and policy decision-making. Satellite image data enable direct observation of the land surface at repetitive intervals and therefore allow mapping of the extent and monitoring and assessment of:

* Crop health
* Storm Water Runoff
* Change detection
* Air Quality
* Environmental analysis
* Energy Savings
* Irrigated landscape mapping
* Carbon Storage and Avoidance
* Yield determination
* Soils and Fertility Analysis

Normalized Difference Vegetation Index (NDVI)

The Normalized Difference Vegetation Index (NDVI) is a simple numerical indicator that can be used to analyse remote sensing measurements from a space or airborne platform, and assess whether the target being observed contains live green vegetation or not. AgroWatch (*) algorithms were developed by DigitalGlobe to enhance the NDVI results.

High or medium resolution satellite image data products help quantify crop status, soil conditions and rates of crop change throughout the field as small as 2’ x 2’. NDVI products reduce the field time by 50% by quickly identifying the problem areas – often before they are visible to the naked eye and to provide a solution to the problem which can significantly boost field productivity and crop quality, while reducing costs.

Green Vegetation Index – Colorized Map

The Green Vegetation Index – Colorized Map (GVC) colourises the green vegetation index (GVI) values to show the spatial distribution of remotely sensed vegetation. The index is related to crop vigour, vegetation amount or biomass, resulting from inputs, environmental, physical and cultural factors affecting crops. The NDVI algorithm is applied to calibrate satellite images to separate the reflectance of vegetation from variation caused by underlying soils or water. The product is produced for a given field as well as for a region of interest.

Green Vegetation Index – Sharpened Map (GVS) is a superior product which combines pansharpened information and GVC values to improve manual image interpretation intended to facilitate the identification and mapping of significant spatial features. Information about green biomass density is contained in the original GVC product, which uses colours to show various levels in increments of 5 (on a scale from 0 to 100). GVS uses the registered panchromatic image (collected to make this a visible pan image) to modulate the brightness of each GVC colour. The result has the excellent properties of a pansharpened image, but with quantitative numbers that are close (within 2 units) of the original GVC numbers.

Soil Zone Index

To develop a Soil Zone Index map, satellite images of the agriculture fields are calibrated and then spectral algorithms are applied that isolate soil components from vegetation. The final satellite image shows what the soil surface of your field looks like, including irrigation patterns, sand streaks, clay lenses, and organic matter and crop residue variations. If the crop has less than 50 percent canopy cover, the NDVI algorithms sees it all, and the Soil Zone map shows only the underlying soil. With a Soil Zone map, you can clearly see landscape variations. Lighter colours indicate dry, salty or coarsely textured soils, while darker colours indicate wet or organic soils. Often, variations in colour indicate topographic variations across fields, which can greatly impact your crop management strategies and zone creation for precision agriculture management applications.


The TreeGrading product provides an assessment of each individual tree in an orchard to help growers manage trees for top production. High resolution QuickBird, IKONOS or SPOT-5 satellite image data can be collected in support of Agriculture Management developing TreeGrading Maps to reveal the location and extent of each tree canopy determined by using a proprietary spectral algorithm. The properties of the GVI satellite images within each polygon are extracted to an industry standard Geographic Information Systems (GIS) database. The GIS software is then used to view and analyse the data. A satellite image and GIS map of missing trees is also created so the manager can plan for replacement.

Satellite Imaging Corporation (SIC) provides archived and new IKONOS, QuickBird, SPOT-5, ALOS and other Satellite Image data for many areas around the Globe and utilises advanced Remote Sensing techniques, Colour and Panchromatic image data processing services, orthorectification, culture and feature extractions, pan sharpening with image data fusion from different sensors and resolutions, enhancements, georeferencing, mosaicing and colour/grayscale balancing for GIS and other geospatial applications.

(source: Satellite Imaging Corporation and Spacenews)