Remote Sensing and Precision Agriculture in Physical Geography

This article discusses the usage of remote sensing and precision agriculture in physical geography for measuring crop productivity, crop protection, and more. It covers topics like thermography, fluorescence measurements, hyperspectral techniques, and identified knowledge gaps. The article also highlights the benefits of using remote sensing and GIS techniques for sustainable management of agriculture.

Running head: PHYSICAL GEOGRAPHY
Remote Sensing
Name of the Student:
Name of the University:
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Table of Contents
Introduction......................................................................................................................................2
Precision agriculture........................................................................................................................2
Remote sensing and its applications in agriculture..........................................................................3
Precision crop protection.................................................................................................................5
Thermography..............................................................................................................................5
Fluorescence measurements........................................................................................................6
Hyperspectral techniques.............................................................................................................6
Precision agriculture for measuring the crop productivity..............................................................6
Identified knowledge gaps...............................................................................................................7
Conclusion.......................................................................................................................................7
Reference.........................................................................................................................................8
Appendix........................................................................................................................................11

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Introduction
Agricultural production systems are facing difficulties due to the variation in the
topography and the climate of the different regions. For the purpose of sustainable management
of agricultural all the factors are needed to be analyzed and on a spatiotemporal basis. Advanced
techniques like the geographical information system, global positioning system (GPS) and the
remote sensing are used for their effective management and assessment. These technologies have
the multifaceted benefits and utilities like yield prediction, crop acreage estimation, precise
agriculture/site-specific management, computation crop evapotranspiration, soil moisture
estimation, crop inventory, stress detection, crop growth detection, and crop discrimination
(Hunt et al., 2014). These data provide the reliable information and timely information that are
beneficial both for the policymakers and the farmers. Such information on a regional basis is
provided through the GIS techniques and remote sensing. Both the GIS and the remote sensing
are used effectively for the analysis of land cover and its use. Remote sensing can be described
as a cheap alternative that provides a large amount of data over a large geographical area. In
remote sensing, the basic concept that is used for the data acquisition is the through the remote
sensing and this includes the measuring the characteristics of spectral reflectance from the
various surface areas. The invention of both the hyperspectral and the multispectral remote
sensing technology has broadened its application in the different fields and areas (Kingra,
Majumder & Singh 2016). This study is based on the usage of the remote sensing for measuring
the productivity and the health of the agricultural practices which is also called precision
agriculture.
Precision agriculture
Precision agriculture was developed during the middle of 1980s. The application of the
remote sensing in the field of precision agriculture initially started with the sensors for the soil
organic matter. This, however, later diversified into tractor mounted sensors or handheld sensors,
aerial sensors and satellite sensors. Initially, the wavelengths of the electromagnetic radiation
focused on the near or visible infrared regions. However, nowadays the electronic magnetic
radiation include the wavelengths that range from the microwave to the ultraviolet spectrum.
Thus, enabling the usage of the advanced applications of the thermal spectroscopy, fluorescence
spectroscopy and light detection and ranging (LiDAR), and this also includes the traditional

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applications of the near infrared and the visible spectrum (Wei et al., 2012). With the advent of
the hyperspectral spectroscopy, the spectral bandwidth has decreased dramatically and this
allows the improved analysis of the crop biochemical and the biophysical characteristics, crop
stress, molecular interactions and the improved analysis of some of the compounds. Currently,
rather than the normalized difference vegetation indices, the spectral indices exist for the
different types of applications in the precision agriculture. The satellite remote sensing and its
spatial resolution along with the aerial imagery have now improved from 100 of meters to just a
sub-meter accuracy. Thus, this allows the evaluation of the crop and the soil properties at the
finest spatial resolution that only utilizes an extra amount of storage and the other processing
essentials. Temporal frequency has also developed to great extent presently. Presently there is a
significant amount of interest in collecting the data of remote sensing at various intervals for
conducting pest management, crop and real-time soil management (Barnhart & Crosby, 2013).
Precision agriculture involves the collection of data, analysis of the data and the
information management. This also includes the sensor design, remote sensing, yield monitoring,
and field positioning and technological advances in the field of computer processing. It has been
found that the more than the 30 percent of the agribusiness in Agriculture came from the
precision agriculture adoption by the farmers (Santesteban et al., 2013).
Remote sensing and its applications in agriculture
Remote sensing application in agriculture is entirely dependent on the interaction
between electromagnetic radiation with the plant and soil material. This includes measurement of
the reflected radiation instead of absorbed and transmitted radiation. It utilizes the non-contact
measurements of the emitted and the reflected from the agricultural fields. In addition, it is
important to mention that apart from absorption, transmittance and reflectance that the plant
leaves emit the energy via the thermal emission or fluorescence. Thermal remote sensing is used
to measuring the water stress of the plants is entirely based on radiation of the emission with
respect to the temperature of the canopy and the leaf that varies with the rate of
evapotranspiration and air temperature (Peng & Gitelson 2012). The amount of that is absorbed
by the plant's pigments is inversely related to the radiation that is absorbed by the plants and this
radiation varies with the incident radiation wavelength. Chlorophyll is a plant pigment and it
absorbs the radiation strongly in the range of the visible spectrum of 400- 700 nm. For the

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chlorophyll a, the wavelength is 430 nm (blue) and for chlorophyll b is 660 nm. The other plant
pigments like the carotenoids and the anthocyanin are also vital for measurement (Chen et al.,
2013).
In the region of near infrared (700 to 1300 nm), the plant reflectance is high and due to
this the canopy structure and the leaf density effects of the data and the measurements. The sharp
differences in the near infrared and the reflectance in the red spectrum has led to the
development of the spectral lines that are based on the reflectance value ratios in the near
infrared and the visible regions. These specific spectral lines are used for the purpose of
assessing the various attributes of the N content, chlorophyll content, biomass, leaf area index,
plant canopies (Schlemmer et al., 2013).
In the field of agriculture, the applications of remote sensing are classified on the
platform type used for the sensor. The sensor is used on the ground-based platforms, aerial and
satellite platform. The imaging systems and their platforms are differentiated based on the
minimum return frequency in sequential imaging, the image spatial resolution and the altitude of
the platform. The spatial resolution of an image influences the smallest pixel area and the area
for analysis (Zhang, Walters & Kovacs, 2014). The decrease in area of the smallest pixel when
the spatial resolution increases and this result in the homogeneity of the crop or the soil
characteristics within specific pixel increases. When the spatial resolution is poor, the spatial
resolution accommodates larger pixels and this results in heterogeneity within the plant
characteristics and the soil characteristics. Return frequencies are vital for the assessment of the
temporal patterns in the plant and the soil characteristics. However, one of the major barriers is
the remote sensing data from the satellite and the aerial platforms are delimited by the cloud
cover. Whereas, the remote sensing that is ground-based are not affected by these limitations
(Maes & Steppe, 2012).
The applications of the remote sensing within the purview of agriculture, includes the
measuring of the soil properties like the pH, clay content, moisture, organic matter, plant disease,
infestations of weeds, water stress and crop nutrient, biomass and crop yield. The conventional
agriculture also utilizes remote sensing that has led to the utilization of the same in the precision
agriculture. As the spectral resolution and satellite imagery advanced, the reflectance data
became increasingly suitable to be used (Atzberger, 2013).

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Precision crop protection
Thermography
Thermography is one of the techniques that allow the surface imaging temperatures of the
crop canopies, plants and leaves. The infrared radiation emitted by the thermal infrared region
from the 8 to 12 micrometre can be detected via the thermographic cameras and it is illustrated
as the false colour images. The image pixel is related to the value of the temperature that the
objects measured. The performance of the thermographic cameras can be measured by the scan
speed, image resolution and thermal sensitivity. Thermography has application in both the near
and remote sensing. The non-destructive nature of the infrared thermal imaging is beneficial,
non-contact and the non-invasive having short period of time within which the data of the
surface temperature of an object can be collected (Mutka & Bart, 2015). The technique is
suitable for detection of modification due to diseases in a plant by measuring the water and the
transpiration status. The temperature of a plant leaf can be determined through the plant
respiration rate and environment. The leaf temperature increases when the rate of transpiration
decreases. Similarly, some abiotic factors like the pathogens can also affect the stomatal opening
which regulates the loss of water from the plant. The thermographic method that is used in the
detection of the disease can be described as the passive measurement and is measured by
tracking the amount of water transpired by the plant. The technique does not include any
influence of external temperature. The temporal and the spatial scales can be measured within
the tissue and of the tissue by measuring the transpiration data. Thus, further disease
development can also be measured on the different scales (Fang & Ramasamy, 2015).
It has been found that local temperature changes have been noticed either due to the plant
pathogens or due to the defence mechanisms of the plant. Hypersensitive responses have been
observed within the tobacco leaves when it gets infected by the Tobacco Mosaic Virus (TMV).
This results in the initial increase in the tissue size due to the salicylic acid accumulation.
Whereas in the leaves of sugar beet the same kind of infection leads to the development of cold
spots. It is important to note that the thermography utility is highly sensitive to the environmental
conditions and thus its usage is a bit limited (Schaefer et al., 2012).

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Fluorescence measurements
The fluorescence technology is used for the purpose of assessing the nitrogen demand
for the crops in the field. A chlorophyll fluorescence is affected when a pathogen affects the
enzymes of the Calvin cycle, electron transport chain, pigments and the photosynthetic apparatus
of a plant. These methods are sensitive in detecting the abnormalities in the photosynthesis. The
sensors needed to be put into the field and however is limited by the response time (Porcar-
Castell et al., 2014).
Hyperspectral techniques
In measuring the plant vigour, the hyperspectral techniques have been found to be useful. The
reflectance changes are noticed due to the changes in the biochemical and the biophysical
characteristics of the plant tissues. Diseases in plants result in changes in the crop canopy density
and its interaction with the solar radiation is plants, crop canopy morphology, transpiration rate,
leaf shape and tissue colour. Due to this, the optical properties of the leaves gets modified. The
reflectance of the leaves is dependent on the health of the plant and is thus sensitive to the
changes in the cell wall degradation, hypersensitive reaction, and pigmentation (Bioucas-Dias et
al., 2013).
Precision agriculture for measuring the crop productivity
Achieving the maximum yield at the lowest investment is one of the major and the
ultimate goal of the farmers. There are techniques like the Global positioning systems (GPS) and
Remote Sensing (RS) that are used explicitly for assessing the temporal variations in crop yield
and crop dynamics. The near-infrared regions and the visible portion of the electromagnetic
spectrum plays a major role in assessing the crop yield, nitrogen stress, soil moisture, crop health
and crop type. The advancement if the field of remote sensing has led to the usage of the
multispectral images as a tool for monitoring the vegetation conditions, crop yield conditions and
crop stress (Tittonell & Giller, 2013). The prediction of the crop yield is a major part here due to
the several agronomic variables like the disease, maturity, vigour and density and these are used
as yield indicators. It is thus important to mention that remote sensing has a crucial role to play
in closely assessing the health of the plant. The reflectance is however dependent on the crop
health, stage type and phenology. Several studies have shown that the in order to enhance the

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precision agriculture, the Normalized Difference Vegetation Index (NDVI) is used. The NDVI
method is used for measuring the primary productivity due to its straight line relation with the
Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) (Al-Gaadi et al., 2016).
Identified knowledge gaps
Advancement in the remote sensing area has led to the advances in agriculture as well.
The satellite imagery has improved with respect to the spectral resolution, return visit frequency
and spatial resolution. Certain aspects of precision farming still require further research which is
as follows:
 Without the requirement of the reference strips, the sensors must be able to
estimate the nutrient deficiency directly.
 The spectral indices must be developed continuously so that it can assess the
multiple crop characteristics and stresses.
 Emphasis must be put on to the spectral decomposition and chemometric
decomposition (Mulla, 2013).
Conclusion
Thus, from the above study, it can be concluded that the precision agriculture has been
made possible due to the advancement in the remote sensing arena. With the advent of the
hypersensitive sensors, the capability of the measuring the crop health, crop disease and the crop
yield is done remotely. A large number of data collected through the satellite provides the scope
for spectral analysis. The spectral resolution also plays a pivotal role in identifying the several
high resolution and detailed information. The Precision agriculture is both carried out regionally
and remotely and the technique of remote sensing provides the option of assessing large areas
remotely.

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Reference
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Appendix
Figure 1: Remote sensing application in agriculture (Mulla, 2013)
Figure 2: Components of remote sensing process (Kingra, Majumder & Singh, 2016)