Precision Agriculture Mapping

Precision Agriculture Mapping

The Food and Agriculture Organization of the United Nations, the world’s population will reach 9.1 billion, 34 percent higher than today’s population, by 2050. Due to this expected growth, there is pressure throughout the world for higher agricultural production and reliable statistics. To achieve these objectives, improved management of the world's agricultural resources are required, especially in developing countries.

(*) (NIR2 - Red) / (NIR2 + RED) = WV-VI

To make this happen it is first necessary to obtain reliable data and information on not only the types, health status, quality, quantity and location of these resources. Geographic Information Systems (GIS) tools can help farmers to conduct crop forecasting and manage their agriculture production by utilizing multispectral imagery collected by Satellites, fix wing Aircraft or Unmanned Aerial Vehicles (UAV's) and processed to provide critical statistics, NDVI and other vegetation/ soil indices, together with crop stress field reports. This data is used in regional GIS management systems. The ability of GIS to analyze and visualize agricultural environments and workflows has proven to be very beneficial to those involved in the farming industry.

When agriculture management software is not in place, vegetation and soil index imagery can be reviewed in Google Earth™ for regional farmers to identify areas in the fields, requiring a closer crop/soil analysis and review of the crop stress report and statistics, to decide if additional irrigation or fertilization of the crop is required.

High and Medium Resolution Satellite Imagery

Satellite Imagery is very effective for larger areas with no requirements for permitting, mobilization/ demobilization and avoid any security issues. The Satellite sensors deliver 16-Bit 4-Band (B,G,R,N) or 8-Band (C,B,G,Y,R,RE,N,N2) Multispectral pixel resolutions from 1.2m to 5m. Pansharpened vegetation indices can be delivered with a resolution of 30cm, 40cm or 50cm, providing greater details for analysis. The high and medium resolution Satellite sensors used for precision agriculture, acquire new Imagery when orders are placed, with minimum commitments of 50 to 100 Km², per area.

To download various NDVI samples and save or open the KMZ file in Google Earth™, please click on one of the links below:

Pivot ID-66 / NDVI (WV-VI) 1.6m

Pivot ID-40 / Natural Color 40cm:

Pivot ID-40 / Pansharpened NDVI Index 40cm

ESA Sentinel-2A Satellite Sensor

The Sentinel-2A Satellite sensor acquires 10m 4-Band (BGRN) Multispectral Imagery every 10 days providing a choice for a good selection to search for cloud free Imagery for the Area Of Interest (AOI). This Imagery is very suitable to deliver 10m resolution NDVI Imagery in KMZ format, crop stress field reports and statistics. The KMZ vegetation index image files can be imported into Google Earth™, similar to the Images below. Because the Sentinel-2A Satellite Imagery, provided by the European Space Agency (ESA), can be downloaded for free and requires only vegetaion index image processing, generate statistics and provide a crop stress field report, providing a cost effective Ag solution, covering large areas around the globe, were 10m resolution is acceptable or desired due to limited financial resources.

The fees for Sentinel-2A produced vegetation index Imagery (NDVI, TSAVI etc.) are starting at US $ 0.20/ Ac or US $ 0.49/ Ha for each Imagery date, with a minimum commitment of 1,000 Ac or 405 Ha and 2 months of service, including the delivery of up to six (6) Sentinel-2A scenes in Natural Color and Color InfraRed (CIR), vegetation index scenes in GeoTIFF, IMG or KMZ format. Crop stress reports and statistics, in shapefile format, can be delivered at an additional cost. For this service, the outline of the farm fields are required in KMZ or shapefile format.

To download a Tutorial to create farm field polygon files for a single or multiple fields in Google Earth Pro™, please click HERE.

Google Earth™ / Sentinel-2A NDVI** Vegetation Index - 20160719 - Michigan, U.S.A.

(**) (NIR - Red) / (NIR + Red) = NDVI

When the KMZ or shapefiles for the producing farm fields cannot be provided, SIC can deliver the Sentinel-2A Satellite Imagery and vegetation index (NDVI or SAVI) were crop stress can be identified on the Index and polygons can be produced and entered into a GPS tablet or handheld to locate the areas to be analyzed and treated in the field.

NDVI Scale from -1 Low Reflectance to +1 High Reflectance

The vegetation indices provided will cover multispectral reflectance values from 0.5 to 1.0 or any other suitable range.

Google Earth™/ Sentinel-2A Soil Adjusted Vegetation Index (SAVI***) - 20160711 - Alexandria, Egypt

(***) (NIR - Red) / (NIR + Red) x (1 + L) = SAVI L= 0.5 Constant soil factor for intermediate vegetation cover.

For areas throughout Africa and other regions around the World, experiencing agriculture production problems in quality or quantity, the Sentinel-2A Satellite sensor can support the regional farmers and farm cooperatives and other organizations, with timely and critical information.

For more information in how SIC can assist, please CONTACT US.

Normalized Difference Vegetation Index (NDVI)

The agriculture, forestry and environmental industry are using the standard NDVI index for many years but with the availability of hyperspectral sensors and high resolution Satellite sensors, such as WorldView-2 and WorldView-3, utilizing an expanded Multispectral reflectance range, SIC can provide a variety of vegetation indices to filter the correct band combinations for vegetation, soil and environmental analysis to support crop, forest and environmental project management.

SIC utilizes many different vegetation and soil analysis and other algorithms to provide value-added information products that help farmers, growers, consultants, fertilizing companies and other decision makers to quantify crop status, soil conditions and rates of soil and crop change throughout the field to achieve optimal yields.

Managed Canopy Assessment

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