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Monitoring agriculture with remote sensing

  •  REMOTE sensing is the science (and to some extent an art) of acquiring information about the earth’s surface without actually being in contact with it. This is done by sensing and recording reflected or emitted energy and processing, analysing, and applying that information.

    In remote sensing, information transfer is accomplished by use of electromagnetic radiation (EMR). Electromagnetic radiation is a form of energy that reveals its presence by observable effects it produces when it strikes the matter. It is considered to span the spectrum of wavelengths from 10-10mm to cosmic rays up to 1010mm, the broadcast wavelengths, which extend from 0.30-15 mm.

    Remote Sensing: The complete process

    Figure illustrates a satellite remote sensing process as applied to agricultural monitoring processes. The sun (A) emits electromagnetic energy (B) to plants (C). A portion of the electromagnetic energy is transmitted through the leaves. The sensor on the satellite detects the reflected energy (D). The data is then transmitted to the ground station (E). The data is analysed (F) and displayed on field maps (G).

    There are two types of remote sensing, passive e.g. sun radiation, and active e.g. radar. In Pakistan mostly passive remote sensing is used and the Pakistan Upper Space and Atmosphere Research Commission (Suparco) is pioneer of introducing remote sensing in agriculture. Remote sensing makes it possible to collect data on dangerous or inaccessible areas. Remote sensing applications include monitoring deforestation, effects of global warming on glaciers and Arctic and Antarctic regions, and depth sounding of ocean depths. Remote sensing also replaces costly and slow collection on the ground, ensuring in the process that areas or objects are not disturbed.

    When farmers or ranchers observe their fields or pastures to assess their condition without physically touching them, it is remote sensing. Observing the colours of leaves or the overall appearances of plants can determine plant’s condition. Remotely sensed images taken from satellites and aircraft provide a means to assess field conditions without physically touching them from a point of view high above the field.

    Most remote sensors see the same visible wavelengths of light that are seen by human eye, although in most cases remote sensors can also detect energy from wavelengths that are undetectable to human eye. The remote view of the sensor and the ability to store, analyse, and display the sensed data on field maps are what make remote sensing a potentially important tool for agricultural producers. Remotely sensed images can be used to identify nutrient, diseases, water deficiency or surplus, weed infestations, insect, hail, wind, herbicide damage, and plant populations. Ranchers use remote sensing to identify prime grazing areas, overgrazed areas or areas of weed infestations. Land institutions use remote sensing data to evaluate the relative values of land by comparing archived images with those of surrounding fields.

    Remotely sensed images can be used in many applications, for example land use planning and monitoring the condition of forest and agricultural areas. Information from remote sensing can be used as base maps in variable rate applications of fertilisers and pesticides. Information from remotely sensed images allows farmers to treat only affected areas of a field. Problems within a field may be identified remotely before they can be visually identified. Farmers, land care workers and field officers, with their detailed knowledge of the vegetation and soils in their own paddocks or regions, can extract information on productivity from simple displays of the satellite image.

    Remote sensing relies on the fact that particular features of the landscape such as bush, crop, salt-affected land and water reflect light differently in different wavelengths. Grass looks green, for example, because it reflects green light and absorbs other visible wavelengths. This can be seen as a peak in the green band in the reflectance spectrum for green grass above. The spectrum also shows that grass reflects even more strongly in the infrared part of the spectrum. While this can’t be detected by human eye, it can be detected by an infrared sensor. This sensing provides a cost-effective method for mapping and monitoring broad areas, and has the advantage that the spread of diseases such as dieback is not enhanced by remote monitoring. Archived data can be used to monitor how areas have changed through time.

    Monitoring information can then be combined with landform information to help predict which areas are at risk from salinity in the future, allowing remedial action to be taken where it is needed the most. This system is also increasingly being used for large-scale environmental monitoring programmes. It is able to offer large-scale monitoring relatively cheaply and easily, and can provide a baseline for future monitoring.

    Remote sensing can provide data on agricultural activities in inaccessible areas, or simply obtain more accurate information than otherwise available. This information can be obtained from radar images, and interpreted using machine vision techniques to identify different agricultural regions or crop types. The back-scatter signature from each crop varies according to its characteristics, such as leaf moisture, plant separation and number of leaves per square meter.

    The growth models of crop predict the characteristics of different crops at given times, and provide inputs to radar models which estimate the back-scatter from each crop of interest. These predictions are used to interpret the image measurements. Techniques to provide robustness to calibration and unknown environmental factors are included at this stage. Such a system is found to be about 75 per cent successful in identifying five different crop types.

    Sensor systems in satellites or aircraft collect image data in various portions of the electromagnetic (EM) spectrum, and these data contain much information about conditions of agricultural fields. The conditions of interest may include crop health or potential yield, soil conditions, weed infestations, etc. Image data can yield vital information about the variability of these conditions with respect to location, and this kind of information can be used by agricultural producers to optimise their management decisions with respect to location, thus maximising profit and minimising environmental effects.

    Remote sensing offers the possibility of monitoring agricultural resources for rapid and continuous assessment of plant, soil, and water resources. The technology of aerial photography, side-looking airborne radar, and orbital multi spectral photography is considered, and several FAO case studies relevant to agriculture development and monitoring are discussed (including tropical forest cover monitoring, desert locust survey, flood monitoring, and soil survey). Particular attention is given to the use of Land sat imagery in tropical countries.

    This technology has proven a powerful “tool” for assessing the identity, characteristics, and growth potential of most kinds of vegetative matter at several levels (from biomes to individual plants). Vegetation behavior depends on the nature of the vegetation itself, its interactions with solar radiation and other climate factors, and availability of chemical nutrients and water within the host medium (usually soil, or water in marine environments).

    A common measure of the status of a given plant, such as a crop used for human consumption, is its potential productivity (one such parameter has units of bushels/acre or tons/hectare, or similar units). Productivity is sensitive to amounts of incoming solar radiation and precipitation (both influence the regional climate), soil chemistry, water retention factors, and plant type. Examine the diagram below to see how these interact, keeping in mind that various remote sensing systems (e.g., meteorological or earth-observing satellites) can provide inputs to productivity estimation.


    Key Reference:  Muhammad Ahmad, Muhammad Atif Majeed & Dr Rabia Haouala

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