Many progressive farmers in the world are constantly looking for ways to maximize their returns. Some advanced technologies like Geographic Information Systems (GIS), Remote Sensing and Global Positioning Systems (GPS) may provide new ways needed for farmers to maximize the economic and environmental benefits of precision farming.
Unfortunately, most farmers do not have the knowledge to utilize these technologies effectively. But with time and friendly computer applications, there are many possibilities to overcome this problem. Even though technology has the potential to help alleviate the problem facing future generations, an integrated approach is needed to promote its use among farmers.
Remote sensing is the science of acquiring data about the earth’s surface without being really in contact with it. We can sense and record reflected or emitted energy and do further processing, analyzing and applying that data. We need the sun (1), an energy source which illuminates or provides electromagnetic energy to the ground (3). Energy travels from the sun to the ground through the atmosphere (2), where they react. There is also an interaction between the ground and solar radiation, the intensity of which depends on the properties of both. Energy is scattered or emitted by the ground and we can record that electromagnetic radiation with satellite sensors (4). We transmit, receive and process data through different equipment (5) and finally that data are interpreted and analyzed (6). The final results of the process are useful interpretations of imagery (7). Those are steps in satellite remote sensing, which is useful in many situations. On the other hand, there is a broader definition which says that remote sensing includes ultrasound, speed radar, graduation photos, sonars, magnetic resonance imaging, x-rays (in hospitals) etc.
Satellite and airborne images and, of course, their interpretations can be used for the following applications: crop type classification; plant health estimation through the leaf area index, leaf chlorophyll content and leaf water content; crop yield estimation; mapping of soil characteristics and predicting fertilizer needs; combining plant, weather and soil data; mapping of soil management practices, compliance monitoring.
For just one of many examples of using remote sensing satellite data, we can mention one of the well-known remote sensing products: Normalized Difference Vegetation Index (NDVI), which is a simple graphical indicator of plant photosynthetic activity. NDVI has an extremely wide (and growing) range of applications. It is used to monitor vegetation conditions and therefore provide early warning on droughts.
NDVI is calculated for every cell (pixel) of satellite images through the following formula:
where NIR is the near-infrared band value and RED is red band value from the cell. We got all of that from multi-spectral data coming from satellite imaging.
NDVI takes values between -1 and 1, with values 0.5 indicating dense vegetation and values <0 indicating no vegetation. We use the following legend for NDVI:
Water bodies (e.g., oceans, seas, lakes and rivers) which have a rather low reflectance in both spectral bands result in very low positive or even slightly negative NDVI values. Soils which generally exhibit a near-infrared spectral reflectance somewhat larger than the red, and thus tend to also generate rather small positive NDVI values (say 0.1 to 0.2)
The Copernicus project’s Sentinel satellites are revolutionizing earth observation. In the near future (2016!), they will free, full and open access to data with very short revisit times. We will get satellite images every five days with 10 m spatial resolution and a good spectral resolution of 13 bands. The portfolio of possible products is vast. Use-cases of such a service range from plant health monitoring, land and water body change, flood monitoring, disaster mapping and more.
Scientists and computer engineers just have to lower the current gap between Sentinel source data and their end users. That means that terabytes of data per week and complex images with 13 bands should be translated to interpreted data that is useful to the end user. The approach of all these experts combines cloud-based GIS technologies, parallel processing and fully automated procedures. End users, farmers, would use all these data on the field and would identify crop health problems and stress early, reduce environmental impact through cost-effective and eco-friendly plant nutrition and improve crop yield. We just need to say yes to all these technologies and follow best practices in the field of advanced farming.
Janez Avsec, Datalab, Geo-information systems specialist
Janez Avsec is in charge of integration of geo-information systems (GIS) into PANTHEON Farming modules, such as agroclimatology, phenology, crop optimizing, pest monitoring, disease monitoring, fruit growing, wine growing and animal husbandry. He integrates decision support models (e.g. irrigation, fertilization) with the help of data from sensors in the ground and integrated in farming machinery on the basis of international standards (ISOBUS).