Precision Agriculture Mapping

Precision Agriculture Mapping

The Food and Agriculture Organization (FAO) 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 worldwide for higher agricultural production and reliable crop status information.

To achieve these objectives, improved management of the world's agricultural resources is required, especially in developing countries.

Utilizing high-resolution, multispectral satellite or aerial imagery and Artificial Intelligence (AI), Machine Learning (ML), and Computer Vision (CV) algorithms, image data is collected and transferred into management solutions for crop health and improved production targets.

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

To make this happen it is first necessary to obtain reliable crop status information on not only the types, health status, quality, quantity, and location of these resources.

Geographic Information Systems (GIS) tools and online web resources can help farmers to conduct crop forecasting and manage their agriculture production by utilizing multispectral imagery collected by satellites, fix wing aircraft or uUnmanned aerial vehicles (UAV's), and processed to provide NDVI and other vegetation soil indices, to identify crop stress. This data is used in regional GIS or CAD management systems and web portals. 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 Pro™ for regional farmers to identify areas in the fields, requiring a closer analysis, to decide if additional irrigation or fertilization of the crop is required.

View article on Agriculture Crop Management and Production Improved by Satellite Remote Sensing Technology and Geographic Information Systems (GIS)

WorldView-2 NDVI Index and Moisture Map - South Africa

Image Copyright © MAXAR. All rights reserved.

Satellite images are very effective for larger areas with no requirements for permitting, mobilization/demobilization, and avoid any security issues. Satellite sensors can deliver 16-Bit 4-Band (B,G,R,N) or 8-Band (C,B,G,Y,R,RE,N,N2) multispectral pixel resolutions from 1.2-meter to 5-meters. 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 Pro™, 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-2 Satellite Constellation

The Sentinel-2 Satellite sensors acquire 10m 4-band (BGRN) multispectral, 20-meter, 4-band RedEdge, and 2-band Short Wave InfraRed (SWIR) and 60m 3-band Coastal Aerosol, Water Vapour, and SWIR Cirrus imagery 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 10-meters resolution NDVI or other vegetation index Imagery and moisture maps in KMZ format. The KMZ vegetation index image files can be imported into Google Earth™, similar to the Images below. Because the Sentinel-2 satellite imagery, provided by the European Space Agency (ESA), can be downloaded for free and requires only vegetation and soil index image processing, providing a cost-effective Ag solution, covering large areas around the globe, where 10-meter resolution is acceptable or desired due to limited financial resources.

The satellites in the SENTINEL-2 constellation will provide a revisit time of 5 days at the equator in cloud-free conditions.

To download a Tutorial to create polygon KML/KMZ files for a single or multiple areas in Google Earth Pro™, please click HERE.

Google Earth™ / Sentinel-2 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-2 satellite imagery and vegetation index (NDVI or SAVI) where 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-2 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-2 satellite sensor can support the regional farmers, farm cooperatives, and other organizations with timely and critical information.

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

Normalized Difference Vegetation Index (NDVI)

The agriculture, forestry, and environmental industries are using the standard NDVI index for many years, but with the availability of multipectral 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.

Managed Canopy Assessment

Image Copyright © DigitalGlobe. All rights reserved.

Vegetation and Soil Index Maps