The technology based on spectral analysis of high resolution satellite crop images which enables to monitor vegetation developments, soil temperature, humidity and to reveal problem areas on the field. Satellite crop monitoring is also suitable to precise weather forecast based on concrete field coordinates and to recall historical weather data retrospection.
Along with the development of remote sensing applications, satellite monitoring data has become the uppermost data source to monitor large-scale crop condition based on vegetation index analysis. Vegetation images show crop growth from planting through to harvest, changes as the season progresses and abnormalities such as weed patches, soil compaction, watering problems etc. A georeferenced and orthorectified image can locate these problem areas as well as the size of the area affected can be easily determined. Satellite crop monitoring and vegetation control help the farmer make informed decisions about the most feasible solution. In addition to highlighting problematic areas, images will also help monitor the effectiveness of any corrective actions which may be implemented. Images can act as an early indicator of crop yield. This early predictor of yield can aid the farmer in making marketing decisions as well as the allocation of resources.
To gain the benefits from satellite crop monitoring data farmers, managers, consultants and technicians must understand and be able to interpret the image. There are a wide range of enhancement tools available which can help make an image more interpretable for specific applications. Enhancement and classification tools are often used to highlight features. The techniques employed will depend on the type of remote sensed data as well as the objectives of the end user.
The satellite provides imagery data at different spatial, spectral and temporal resolutions for agriculture 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 geospatial information is valuable when used in conjunction with ancillary data to define management zones for a field (1).