With increasing population pressure throughout the world and the need for increased agricultural production, there is a need for improved management of the world's agricultural resources.
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. Satellite or Unmanned Aerial Vehicles (UAV's) imagery, crop stress field reports, statistics and analysis, for use in Geographic Information Systems (GIS) management systems will continue to be a very important factor in the improvement of the present systems of acquiring and generating precision agricultural crop data to detect crop disease, improve crop health and increase prodution.
For regional agriculture applications the utilization of UAV's has become popular, but limted to line of sight operations and require mobilization/demobilization.
High and medium resolution Satellite Imagery is very effective for larger areas with no requirements for permitting, mobilization/demobilization and delivers 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 10m. The 10m resolution Sentinel-2A Satellite sensor acquires Imagery every 10 days. This Imagery is very suitable to deliver NDVI and various other vegetation indices, crop field reports and statistics, covering large areas around the Globe, were 10m resolution is acceptable:
|CCCI - Canopy chlorophyll conent index||OMNBR - Optimal multiple narrow band reflectance index|
|CIRedEdge - Red-edge chlorophyll index||OSAVI - Optimized soil-adjusted vegetation index|
|CWSI - Crop water stress index||REDVI - Red-edge difference vegetation index|
|GNDVI - Green nomalized difference vegetative index||REIP - Red-edge inflation point|
|LAI - Leaf area index||RERDVI - Red-edge renormalized difference vegetation index|
|MRESAVI - Modified RESAVI||RERVI - Red-edge ration vegetation index|
|MTCI - MERIS terrestial chlorophyll index||RESAVI - Red-edge soil-adjusted vegetation index|
|NDRE - Normalized difference red edge||RVI - Ration vegetation index|
|NDVI - Normalized difference vegetative index||SAVI - Soil-adjusted vegetation index|
|NNI - Nitrogen nutrition index||TCARI - Transformed chlorophyll absorption reflection index|
Satellite Imaging Corporation (SIC) provides satellite image data at different spatial, spectral and temporal resolutions for agriculture mapping and crop assessment, crop health, change detection, environmental analysis, irrigated landscape mapping, yield determination and soils analysis. Scheduling and timing of image acquisition is very important and will hinge on the main goals and the type of information that the end user is hoping to gain. 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. This information is valuable when used in conjunction with ancillary data to define management zones for a field. Once data has been collected it can be implemented into a mapping environment such as GIS maps for management and control of agricultural resources.
Managed Canopy Assessment
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Normalized Difference Vegetation Index (NDVI)
SIC provides Normalized Difference Vegetation Index (NDVI) image processing to filter the correct band combinations for vegetation classification, soil analysis, crop management and tree grading. AgroWatch™ algorithms enhance the NDVI results.
SIC utilizes AgroWatch™ and other agriculture monitoring softwares and algorithms to provide value-added information products that help farmers, growers, consultants and other decision makers to quantify crop status, soil conditions and rates of soil and crop change throughout the field.
AgroWatch™ Index Maps
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Precision agriculture products can cut field-scouting time in half by identifying problem areas often before they are visible to the naked eye. Precision agricultural services SIC provides can quickly locate problem areas and help customers significantly boost field productivity and crop quality, often while reducing costs.
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