AND MANAGEMENT
3.4.1 Crop classification
The capability of identifying, discriminating and classifying crops, is one of the basic performance required to satellite remote sensing images. Crops classification is an information of primary importance both at national, regional and local level [17]. Is in the frame of crop classification capabilities that the actual community for the Earth Observation for agriculture is focusing its attention, in particular in understanding how to improve the actual abilities in the spectral analysis of vegetation behavior [11].
3.4.2 Crop monitoring
Crop monitoring by means of satellite remote sensing data could be performed over a very wide range of crop features and under many points of view. At a first analysis, satellite remote sensing images could monitor every most relevant crop and soil characteristic, and the systematic and repetitive nature of this kind of observations results in a very interesting and complete service.
The capability of monitoring croplands is mainly based on the possibility of describing in a continuous, systematic, reliable and undisputable manner those biochemical and biophysical parameters previously listed. Typical parameters such as vegetation density, vegetation type, vegetation phenology (in particular, the biomass and the green biomass content) and also the chlorophyll content, form a set of variables which give a very detailed and accurate description of the possible targets of interest, a very complete overview of their principal and most interesting conditions [17].
In particular, monitoring the phenological state is very important; a good knowledge of this phenomenon could result in reliable and accurate predictions about crop potential yield and also about possible interventions.
Phenology refers to the study of periodic plant and animal life cycle events and how these are influenced by seasonal and inter-annual variations in climate, as well as habitat factors.
Chapter 3 - Remote Sensing Data Application for Agricultural Monitoring and Management
37 Each plant species has a specific phenological state which influence its specific spectral response. For appropriate agricultural management practices is necessary that farmers and other commercial users interested know very accurately the specific phenological stage in every moment of the year and also the almost exact moment at which another certain phenological stage will be reached. The identification of the different phenological stages allow a better study of vegetation canopy and a good quantification of the biomass. From a general point of view, there exist three fundamental phenological stages, which goes from sowing to harvest: the vegetative stage, the stage of reproduction and the stage of maturation [25].
The phenology of a crop has to be described at least on a weekly base especially in the summer period, although phenology information are important at every moment of the year [25]. From this point of view, thanks to quantitative relations which could be systematically derived, satellite remote sensing data offer a great occasion to monitor and describe this phenomenon.
An accurate monitoring of the dynamics of phenological stages and the consequent prediction and identification of the successive phenological stages, is fundamental for the definition and for the planning of optimum strategies of fertilization and irrigation. Hence, a good control of phenology dynamics could result in appropriate optimization of available resources, a significant increase of production efficiency and consequently in very large savings of agriculture costs [25].
In agricultural practices monitoring, it is finally necessary to comprehend the control of crop rotation cycles, that is very useful for purposes of crop diversification and of crop intensification programs, and mostly in the so called practice of “crop residue covering”. With crop residue we refer to those residual materials leaved on cultivated zones after sowing times. In fact, good management of those residual materials it’s important to improve the soil quality [17].
3.4.3 Crop mapping
Satellite remote sensing data are at the base for the creation of many types of maps (like those of figures 2.3 and 2.4) which show lands, crops and all their fundamental characteristics: their extension, their geographical distribution, their placement and the specific cultivations within any crop.
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Maps are also very useful to discriminate between vegetated and non- vegetated lands [17].
Information contained in satellite remote sensing derived maps could be added to traditional methods of census and ground surveying and so result in a significant increase of actual capabilities and amount of data thanks. Not only, maps obtained from remote sensing data could be very successfully used to identify and monitor also wastelands, which could be a characteristics for example very interesting for large emerging countries, such as India [26].
Within these categories of products are very important:
true color maps.
ground cover maps, which are color coded maps showing particularly the quantity of land covered by green leaf.
An example of true color map is given in Figure 3.4. The true color map reported is produced thanks to RapidEye constellation observation [27].
Chapter 3 - Remote Sensing Data Application for Agricultural Monitoring and Management
39 A color coded ground cover map is given in Figure 3.5. The ground color map is produced thanks to RapidEye constellation observations over Wurzburg, Germany. A map of this type is used to classify lands: agriculture and forest lands are indicated by green, water by blue, urban lands by violet and bare soil by red.
Figure 3.5: A ground cover map by RapidEye constellation over Wurzburg, Germany.
With satellite remote sensing spectral and spatial data is possible to create more accurate and specific maps, the so called “thematic maps”. Thematic maps are used to give an idea of spatial distribution of the most important and significant biochemical and biophysical parameters (like LAI, chlorophyll content, or hydric resources distribution) or of some other indicators (like NDVI) which are empirically related with previous parameters [27].
Figure 3.6 shows a thematic map produced thanks to RapidEye observation and devoted to describe spatial distribution of chlorophyll content.
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High content of chlorophyll in are indicated by green, while a poor chlorophyll content is highlighted by yellow and red.
Figure 3.6: A chlorophyll thematic map by RapidEye constellation.
Other useful thematic maps which could be obtained by satellite remote sensing data regard soils, like for example the “soil brightness” maps shown by Figure 3.7, again derived thanks to RapidEye observation.
Figure 3.7: A soil brightness thematic map.
In particular, this type of map, show several characteristics related specifically to the soil, like its use, its color, its structure and about the organic matter which is present. A map of this type could be for example used like input for the creation of management zones [27].
Chapter 3 - Remote Sensing Data Application for Agricultural Monitoring and Management
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3.4.4 Crop yielding
With remote sensing satellite is possible to make very accurate and reliable estimates of crop yielding, also valid for long periods of time, in order to estimate seasonal production. There exist many ways to estimate crop yielding from satellite remote sensing data. Of particular interest is the one based on statistical large-scale analysis. These statistical analysis are conducted on large areas and then are appropriately scaled to single small units; after this operation, scaled statistical analysis are also projected forward in time. Statistical analysis could be eventually coupled for example with weather forecasts, in order to have a very global idea of the situation. Typical time length of these yielding projections are of the order of 6 months [17]. To provide very reliable predictions on crop yield, satellite remote sensing images have to be characterized by a very high accuracy and precision. An high accuracy is very important for example in the practice of the identification of management zones [28].
Management zones are sub-regions of a field that express a relatively
homogeneous combination of yield-limiting factors and for which a single rate of a specific crop input is appropriate [28].
This division of the crop in specific management zones that require a particular different set of treatments one from each other could result in an unique occasion to monitor particularly problematic areas and to incentive their production by planning a suitable cycle of timely and targeted interventions [28]. An example of management zones division based on NDVI measurements is provided by Figure 3.8, where non-productive zones are identified by the red and problematic zones by yellow.
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3.5 SUMMARY 3.1
Potential services achievable with optical remote sensing observations in the frame of agricultural monitoring and management practices (Table 3.1).
POTENTIAL SERVICES From reflectance curves,
identification of the most relevant vegetation features
Vegetation type.
Vegetation density.
Vegetation phenological state.
Vegetation phytosanitary state.
Moisture content. Vegetation biochemical and
biophysical parameters
LAI.
Biochemical concentration.
Biomass.
Leaf Chlorophyll Content.
Vegetation Indices.
Crop description Acreage.
Conditions.
Changes and transformation.
Crop monitoring and management Monitoring.
Classification.
Mapping.
Yielding.
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