Agriculture remote sensing basics of investing
This growing population is expected to require 35 per cent more food by Close proximity to high growth Asian markets and a reputation for clean, high-quality agricultural products makes Australia an important part of the global food supply chain, and well-positioned to help meet increasing demand. The Australian agriculture industry has the capacity to produce food for million people, thus supporting a population approximately five times larger than our own. Over the long term, farmland — as a finite asset — has consistently appreciated in value.
Agriculture is increasingly harnessing technology such as robotics, artificial intelligence and remote sensing, and sustainable land management practices, to reduce costs and improve productivity. Investment in agriculture may produce returns in the form of both income and capital growth and is a proven tool to protect investment portfolios against inflation.
With the current inflationary pressures caused by fiscal and monetary stimulus, supply chain disruption, and geo-political events, it is timely to consider assets which might offer inflation protection. Access to agriculture investments has historically been limited to large scale investors and institutions. However, accessibility is now improving, with diversified managed funds offering exposure to horticulture, viticulture, irrigated or dryland crops, livestock, and fiber assets such as cotton, wool and timber.
Based on this review, the authors conclude that to benefit from this technological advancement and bridge the gap between technical analysts and policy makers, some key points are fundamental: capacity building, political will and institutional commitment, public-private partnership, and proofs of concept.
Introduction Africa remains the world's only region that is a net importer of food products, but all studies show that the continent has sufficient resources in manpower, land, and water to be a breadbasket. While agriculture is central to Africa's economies, its performance in this sector has traditionally lagged compared to that of other regions. Weak productivity has been caused by a wide range of factors, including poverty e. This concern is reflected in international initiatives, such as the AUDA African Union Development Agency , private investments, and the conception of new agricultural policies to reform land and resource access, to build infrastructure and to develop services such as research, extension, credit and insurance.
More than half of the seventeen Sustainable Development Goals for the — period are directly or indirectly connected with farming. Consequently, there is a strong need for information about agricultural issues in SSA, which often lacks basic data due to the poor condition of the African national systems of statistics. These systems often suffer not only from weak financial and material resources and technical skill gaps but also from the complexity of the cropping systems and, possibly, from the mistrust of the farmers.
The lack of reliable and timely basic information is a problem, and any approach that can streamline the monitoring and improvement of the existing systems is an important step. One promising focus of research has centered on the potential of satellite technologies and the new sources of information that they offer for decision-making.
There are still few examples of these scientific and technical advances in the realm of the operational uses of Earth Observation EO data for agricultural public policy at the national or regional scale. The available examples include the control of subsidies e. The scientific studies linking land changes to agricultural public policies are also weak compared to those dedicated to environmental policies e. Based on classifications of high resolution images e.
Despite these interesting examples, there is a consensus on the global underutilization of remote sensing data for public policy, resulting in a real asymmetry in information, with private traders having access to better and more information than governments 1 The development of large international initiatives in the domain of agriculture attests to the fact that there is an urgent need to further mobilize remote sensing data for public decision making.
These initiatives concern the production of off-the-shelf global land products e. However, currently, these initiatives cannot totally fulfill the government needs for better agricultural and land management and planning. Using the specific context of West Africa as an entry point, this paper aims to analyze the gap between the technical aspects of the remote sensing sciences and the pragmatic need for relevant data to address agricultural policies in Sub-Saharan Africa, and to produce operational recommendations.
To achieve this goal, the authors 1 determine the information, and in particular the geoinformation, needs to develop, implement, and evaluate agricultural public policies, 2 clarify the role of remote sensing as a public policy tool through a brief review of the existing remote sensing products and services in the Sub-Saharan African context, and 3 propose an analysis of the existing gap between the remote sensing research community and the policy makers.
Agricultural Public Policy Information Challenges for African Agriculture To achieve the Sustainable Development Goals and produce more food using limited resources via innovative practices, while preserving the food quality and the environment as much as possible, the development of both productive and sustainable farming became a priority in Africa Shimeles et al.
In addition, the African agricultural system will have to increase the competitiveness of its agricultural exports to contribute more efficiently to the global trade system and thus to the continental economic growth. These challenging goals must be addressed in a sustainable way that secures the productive capacity of the natural resources for present and future needs Goyal and Nash, , no matter how complex and diverse the African agriculture types are Collier and Dercon, After the shrinking of international aid during the'80 and '90s and the food crisis, a consensus now exists: a major investment in agriculture pays off both in terms of food security and national economic development.
African nations must devise coherent public resources, strategies and policies such as infrastructures, energy, land regulations, agronomic research, the availability of credit, mechanisms to fight price volatility and manage risks, the reinforcement of agricultural organizations, and training Jayne et al. Renewed interest in agriculture and agriculture policies has been strong Pingali, Initiatives to design strategies for the development of African agriculture have been multiplied, in the regional and continental level.
Many countries have applied these general orientations in national programs by developing investment plans, agricultural public policies, and partnerships. Agricultural Public Policy: Information Needs An agricultural policy is a set of laws, regulatory measures, structural arrangements, and financial and human means implemented by a government to enable agricultural production Ribier, Governments usually implement agricultural policy with the goal of increasing food and raw material production Pisani and Chatellier, However, in recent years, new agricultural goals have been set: raising the standard of living through rural population employment, food and nutrition security, poverty reduction, climate resilience and water, soil, and biodiversity conservation Felix, Policy making process is a matter of ongoing debate.
Nowadays, the debate focuses on the best way to modernize production systems, green revolution Woodhouse, ; Horlings and Marsden, vs. With which actors, large-scale commercial agro-industries specialized in crops and animal production Woodhouse, vs. So designing public agricultural policies is a difficult task, mainly to reconcile various objectives Howlett, Surely, there is neither a simple solution to Africa's underproduction of foodstuffs, nor a single answer, that will bring food security to everyone.
Agricultural policies must be adapted to diversity, and information is needed to enable this adaptation. According to knowledge-based decision-making Hale et al. Examples of information needs for agriculture-related public policies have been gathered and listed in Table 1. What information and what EO products, for what policy? Until 90s, data had been usually provided by the agricultural censuses every 10 years for some countries and surveys.
In the two last decades, the situation of the African national systems of agricultural statistics has been strongly criticized Leif et al. The goal is now to develop more efficient tools, which are integrated into national statistics systems GSARS, Digital technologies, such as Earth Observation, mobile devices, and web services, offer new possibilities in this regard.
Geoinformation to Support Agricultural Policies Thanks to its capacity to observe the Earth at local, regional, and global scales and with different thematic focuses, remote sensing is an essential technology for providing geoinformation in support to land and agricultural policies.
The main EO variables and products identified in Table 1 to support agricultural public policies can be categorized as follows: - The baseline maps, to ensure spatial consistency within heterogeneous datasets; - The land use and land cover maps produced at high spatial resolutions and updated on a regular basis, to monitor the land changes evaluation, prospective , support the statistical sampling protocols agricultural census , or manage the agricultural risks and opportunities; - The biogeophysical maps of crop phenology, biomass, growth anomalies, etc.
Even though the combination of these three categories of products provides relevant elements to support and evaluate public policies through baseline setting, targeting, monitoring and evaluation, it is not sufficient. Most of the time, EO data must be completed by inquiries, ancillary data, and local expertise in order to derive products in accordance with the real needs of each policy.
Remote Sensing Data and Products for Agricultural Applications In this section, we present a non-exhaustive overview of the existing products and agriculture-related services based on Earth-Observation data in Africa, in the challenging context of smallholder agriculture in global South.
Overview of the EO Products Available for Agricultural Monitoring Baseline Maps Metric and sub-metric resolution images are used to produce baseline maps that provide geographically localized information and offer a shared vision for a territory. To date, there are no baseline maps at the global scale, but projects to produce these maps at national scales are multiplying throughout the world. In West Africa, Benin 9 has recently joined the circle of those countries Senegal, Mali and Burkina Faso 10 with an up-to-date and homogeneous national topographic database and the tools to exploit them.
The maps were produced at a scale between and , ranging from mosaics of satellite high resolution orthoimages e. These projects, funded by the European Union, were implemented with the national geographic institutes of the countries. The mosaic is packaged and distributed to government departments, universities, and research institutes.
Although these land cover maps have been largely used for global analysis, several studies highlighted large discrepancies between them, both in area estimation and the location of the main land cover classes, and concluded that there is generally no consensus concerning the cropland classes in Africa Fritz et al.
The reasons for the discrepancies between products are mainly related to the specificities of smallholder agriculture in the African context see section The African agriculture challenges for remote sensing. Waldner et al. At the national scale, land cover maps have been produced in several African countries, mainly in the context of North-South partnership projects or national land cover programs. For instance, in the framework of the Africover and the Global Land Cover Network projects overseen by the Food and Agriculture Organization FAO , more than fifteen countries, mainly in East Africa, have been mapped through visual interpretation of Landsat images Latham et al.
Recently, the Sen2-Agri project has developed a platform to produce monthly dynamic cropland masks and cultivated crop type maps at a 10 m resolution twice during the agricultural season based on Sentinel-2 and Landsat-8 data Defourny et al. Finally, at the subnational scale, many isolated land cover projects were conducted based on the need for research projects or thematic expertise e.
Biogeophysical Products For agricultural applications, information about the crop growing conditions is essential to detect and manage potential vegetation stress or drought. Hence, international programs have developed off-the-shelf global biogeophysical products based on satellite imagery. The global biogeophysical products are categorized based on the land cover, vegetation status vegetation indices, productivity, leaf area index, fraction of absorbed photosynthetically active radiation, and fires , water cycle rainfall, evapotranspiration, soil moisture, and water bodies , energy budget albedo, reflectance, and radiation , and topography indices from digital elevation models.
The spatial resolutions of these products range from 20 m to km, while the temporal resolutions range from 15 min to annual, depending on the biogeophysical variables. The African Agriculture Challenges for Remote Sensing Despite the widely acknowledge potential of remote sensing for agricultural monitoring, and the existing EO-derived products, the complexity and variety of African agriculture types Collier and Dercon, , the complex interactions between weather and geography where diverse rainfall patterns may exist even in nearby areas Becker-Reshef et al.
High Spatial and Temporal Heterogeneities of the Cropping Systems The soil quality, climate, population density, infrastructure, markets, financial instruments, governance, etc. However, except Southern Africa, Maghreb, Egypt and few coastal West African countries, small-scale farming is the prevalent form of agriculture in Africa Dixon et al.
Small-scale agriculture is characterized by small to very small plot sizes with high inter- and intra-plot variability, making it difficult to produce agricultural land use maps Arias and Inglada, ; Fritz et al. The spatial heterogeneity of agricultural lands is often enhanced by the presence of trees within plots e. The crop-fallow rotation is also widely used in African small-scale agriculture as mean to restore soil fertility, but until recently was poorly detected by remote sensing Tong et al.
Most of decision support products for agriculture rely on the processing of satellite image time series, as they allow to account for land surface phenology which is the cornerstone for the discrimination of the different land use type e.
Unfavorable Weather Conditions The high cloud cover during rainy season is a major constraint to the rainfed crop monitoring that dominate in African cropping systems. Recent studies Whitcraft et al. This frequency increases to 3—6 days in July and September. In addition to the low spatial resolution satellites daily time frequency , the ESA's Sentinel-2 constellation with a 5-day revisit period offers a revisit frequency closed to that required to cover the entire growing season.
For instance, atmospheric information from ground station are needed to calibrate correction model during the preprocessing phase of satellite images. Besides, information related to topography, soil type, climate, or agricultural statistics are very useful for image processing and evaluation of resulting products.
However, since the 's weather systems and agricultural statistics systems of several African countries underwent an ongoing decrease of quality, due to a lack of resources and institutional coordination World Bank et al. This results in the paucity of ground databases, which is critical for developing and assessing the accuracy of remote sensing based indicators and methods Becker-Reshef et al.
Existing high-resolution satellite archives are heterogeneous. Landsat repositories are not completed. SPOT repositories are highly heterogeneous depending on region and time periods considered. Hence, until the launch of the Sentinel-2 constellations and , mainly images acquired during the dry season are available. Examples of Current Earth Observation-Based Services in Africa Despite the challenges posed by the African agricultural systems, services focusing on agricultural policy information have been developed based on EO-derived products.
These information services produce regular, standardized information that is easy to access and use. They comply with precise specifications, according to the needs of the end users. Among the few services implemented in Africa, three thematic domains are presented below as examples. Some of the off-the-shelf products are routinely used in the early warning systems for food security [see Fritz et al. For West Africa, a complete system to monitor rainfall, crop water requirement satisfaction and prospective yields, and the progress of the vegetative development and its seasonal and interannual variations has been developed by the AGRHYMET Regional Center from ground and satellite data Traore et al.
However, despite the very large improvement in data availability, processing methods, and the stream of dissemination, these systems still suffer from insufficient spatial input data especially accurate cropland maps, crop calendars, and meteorological data and methods for better yield predictions Fritz et al. NDVI and precipitation products are also largely used for crop and pasture yield forecasting and estimation. Many African countries have developed their own forecasting systems based on different datasets and models and produce monthly or seasonal bulletins.
In South Africa, the official crop forecast for summer and winter crops is released monthly by the Crop Estimates Committee.
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The relationship between reflected, absorbed and transmitted energy is used to determine spectral signatures of individual plants. Spectral signatures are unique to plant species. Remote sensing is used to identify stressed areas in fields by first establishing the spectral signatures of healthy plants.
The spectral signatures of stressed plants appear altered from those of healthy plants. Figure 3 compares the spectral signatures of healthy and stressed sugarbeets. Figure 2. Spectral signatures of crops and soil Kyllo, Figure 3. Spectral signatures of healthy and stressed sugarbeets Kyllo, Stressed sugarbeets have a higher reflectance value in the visible region of the spectrum from nm.
This pattern is reversed for stressed sugarbeets in the nonvisible range from about nm. The visible pattern is repeated in the higher reflectance range from about nm. Interpreting the reflectance values at various wavelengths of energy can be used to assess crop health.
The comparison of the reflectance values at different wavelengths, called a vegetative index, is commonly used to determine plant vigor. The most common vegetative index is the normalized difference vegetative index NDVI. The NDVI value of each area on an image helps identify areas of varying levels of plant vigor within fields. How Does Remote Sensing Work? There are several types of remote sensing systems used in agriculture but the most common is a passive system that senses the electromagnetic energy reflected from plants.
The sun is the most common source of energy for passive systems. Passive system sensors can be mounted on satellites, manned or unmanned aircraft, or directly on farm equipment. There are several factors to consider when choosing a remote sensing system for a particular application, including spatial resolution, spectral resolution, radiometric resolution, and temporal resolution.
Spatial resolution refers to the size of the smallest object that can be detected in an image. The basic unit in an image is called a pixel. One-meter spatial resolution means each pixel image represents an area of one square meter. The smaller an area represented by one pixel, the higher the resolution of the image. Spectral resolution refers to the number of bands and the wavelength width of each band. A band is a narrow portion of the electromagnetic spectrum. Shorter wavelength widths can be distinguished in higher spectral resolution images.
Multi-spectral imagery can measure several wavelength bands such as visible green or NIR. Landsat, Quickbird and Spot satellites use multi-spectral sensors. Hyperspectral imagery measures energy in narrower and more numerous bands than multi-spectral imagery. The narrow bands of hyperspectral imagery are more sensitive to variations in energy wavelengths and therefore have a greater potential to detect crop stress than multi-spectral imagery.
Multi-spectral and hyperspectral imagery are used together to provide a more complete picture of crop conditions. Radiometric resolution refers to the sensitivity of a remote sensor to variations in the reflectance levels. The higher the radiometric resolution of a remote sensor, the more sensitive it is to detecting small differences in reflectance values. Higher radiometric resolution allows a remote sensor to provide a more precise picture of a specific portion of the electromagnetic spectrum.
Temporal resolution refers to how often a remote sensing platform can provide coverage of an area. Geo-stationary satellites can provide continuous sensing while normal orbiting satellites can only provide data each time they pass over an area. Remote sensing taken from cameras mounted on airplanes is often used to provide data for applications requiring more frequent sensing.
Cloud cover can interfere with the data from a scheduled remotely sensed data system. Remote sensors located in fields or attached to agricultural equipment can provide the most frequent temporal resolution. Remote Sensing: The Complete Process Figure 4 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 analyzed F and displayed on field maps G. There are types of remote sensing in agriculture. The location, area, status, and conversion information of farmlands are vital if you want to understand how human activities will affect the lithosphere, hydrosphere, and biosphere. Additionally, you can also formulate sustainable agricultural development policies and study the simulation of the carbon-nitrogen cycle.
Hence, understanding remote sensing and its application in agriculture are vital. Applications of Remote Sensing In Agriculture Remote sensing technology has found numerous applications in fields like forestry, geology, surveying, and photography. However, the use of remote sensing in agriculture is where it has been found most useful.
Some of the many applications of agriculture and remote sensing include the following. Observing and Monitoring Crops A critical role of remote sensing in agriculture is monitoring the health of crops. Optical VIR sensing allows one to see beyond visible wavelengths, like infrared; in this case, the wavelengths are very sensitive to crop vigor, damage, and stress.
Recent advances in this technology have allowed farmers to observe their fields and make timely crop management decisions. Crop identification using remote sensing also helps identify crops affected by conditions related to weather, pests, etc. Some critical soil parameters to optimize crop management include soil organic matter SOM , soil texture, soil pH level, moisture content, etc. Remote sensing technology in agriculture will also provide canopy health, growth stage, yield, biomass, and vegetative density.
If you want to investigate the crop growth pattern changes, you need to emphasize the link between crop performance and soil conditions. Monitoring Water Conditions Due to population growth and food demand, it is expected that irrigated lands will double by It will decrease water availability, contribute to climate change, and cause other environmental changes.
Hence, monitoring and assessing agricultural water resources are critical to achieving sustainable food security and development. Remote sensing in precision farming has successfully provided accurate and timely information like water bodies, irrigated cropland, crop and soil water status, and various scales. Predicting Weather Conditions Climate and weather data systems are essential if you want to make crop management decisions and schedule irrigation.
Additionally, this data can also help you prepare against natural disasters. This application of remote sensing in precision farming has provided spatial coverage to predict upcoming weather conditions successfully. Observing Air Quality Different types of crops thrive in different air conditions.
Some may do well against windy air conditions, while others are more suited for calmer environments. Remote sensing in plant protectionhas allowed us to determine air conditions in specific locations. This data can help you predict upcoming air conditions to take the necessary precautions in case of unfavorable weather conditions.
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How Advance is Agriculture of America (Chile) - Remote Sensing In Agriculture - Agricultural VlogAlternative investments are generally considered to be uncorrelated with more commonly held assets such as shares, cash, property and fixed income.
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| Agriculture remote sensing basics of investing | The data processing methods are often inadequate for the African agricultural systems, which are more diversified and much less documented than agricultural systems in industrialized countries. The available examples include the control of subsidies e. The mosaic is packaged and distributed to government departments, universities, and research institutes. Many countries have applied these general orientations in national programs by developing investment plans, agricultural public policies, and partnerships. Both commercial and our open-source solutions are characterized to help final users to make a sound selection, considering their needs and budget. Every column or sub-grid was identified with a number from 1 to 4 located in Fig. Many African countries have developed their own forecasting systems based on different datasets and models and produce monthly or seasonal bulletins. |
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This signature is unique to different plant species. Remote sensing farming helps identify stressed areas by determining the spectral signatures of plants that are healthy. Agriculture is one of the most significant land-use activities around the world. Apart from changing the land cover, agriculture also profoundly impacts the sustainable development of the social economy, carbon cycle, climate change, ecosystem services, food security, etc. There are types of remote sensing in agriculture.
The location, area, status, and conversion information of farmlands are vital if you want to understand how human activities will affect the lithosphere, hydrosphere, and biosphere. Additionally, you can also formulate sustainable agricultural development policies and study the simulation of the carbon-nitrogen cycle. Hence, understanding remote sensing and its application in agriculture are vital. Applications of Remote Sensing In Agriculture Remote sensing technology has found numerous applications in fields like forestry, geology, surveying, and photography.
However, the use of remote sensing in agriculture is where it has been found most useful. Some of the many applications of agriculture and remote sensing include the following. Observing and Monitoring Crops A critical role of remote sensing in agriculture is monitoring the health of crops. Optical VIR sensing allows one to see beyond visible wavelengths, like infrared; in this case, the wavelengths are very sensitive to crop vigor, damage, and stress. Recent advances in this technology have allowed farmers to observe their fields and make timely crop management decisions.
Crop identification using remote sensing also helps identify crops affected by conditions related to weather, pests, etc. Some critical soil parameters to optimize crop management include soil organic matter SOM , soil texture, soil pH level, moisture content, etc. Remote sensing technology in agriculture will also provide canopy health, growth stage, yield, biomass, and vegetative density.
If you want to investigate the crop growth pattern changes, you need to emphasize the link between crop performance and soil conditions. Monitoring Water Conditions Due to population growth and food demand, it is expected that irrigated lands will double by It will decrease water availability, contribute to climate change, and cause other environmental changes.
Hence, monitoring and assessing agricultural water resources are critical to achieving sustainable food security and development. Remote sensing in precision farming has successfully provided accurate and timely information like water bodies, irrigated cropland, crop and soil water status, and various scales. Predicting Weather Conditions Climate and weather data systems are essential if you want to make crop management decisions and schedule irrigation.
Additionally, this data can also help you prepare against natural disasters. This application of remote sensing in precision farming has provided spatial coverage to predict upcoming weather conditions successfully. A plant looks green because the chlorophyll in the leaves absorbs much of the energy in the visible wavelengths and the green color is reflected.
Sunlight that is not reflected or absorbed is transmitted through the leaves to the ground. Interactions between reflected, absorbed, and transmitted energy can be detected by remote sensing. The differences in leaf colors, textures, shapes or even how the leaves are attached to plants, determine how much energy will be reflected, absorbed or transmitted. The relationship between reflected, absorbed and transmitted energy is used to determine spectral signatures of individual plants.
Spectral signatures are unique to plant species. Remote sensing is used to identify stressed areas in fields by first establishing the spectral signatures of healthy plants. The spectral signatures of stressed plants appear altered from those of healthy plants. Figure 3 compares the spectral signatures of healthy and stressed sugarbeets.
Figure 2. Spectral signatures of crops and soil Kyllo, Figure 3. Spectral signatures of healthy and stressed sugarbeets Kyllo, Stressed sugarbeets have a higher reflectance value in the visible region of the spectrum from nm. This pattern is reversed for stressed sugarbeets in the nonvisible range from about nm. The visible pattern is repeated in the higher reflectance range from about nm.
Interpreting the reflectance values at various wavelengths of energy can be used to assess crop health. The comparison of the reflectance values at different wavelengths, called a vegetative index, is commonly used to determine plant vigor. The most common vegetative index is the normalized difference vegetative index NDVI.
The NDVI value of each area on an image helps identify areas of varying levels of plant vigor within fields. How Does Remote Sensing Work? There are several types of remote sensing systems used in agriculture but the most common is a passive system that senses the electromagnetic energy reflected from plants. The sun is the most common source of energy for passive systems. Passive system sensors can be mounted on satellites, manned or unmanned aircraft, or directly on farm equipment.
There are several factors to consider when choosing a remote sensing system for a particular application, including spatial resolution, spectral resolution, radiometric resolution, and temporal resolution. Spatial resolution refers to the size of the smallest object that can be detected in an image. The basic unit in an image is called a pixel.
One-meter spatial resolution means each pixel image represents an area of one square meter. The smaller an area represented by one pixel, the higher the resolution of the image. Spectral resolution refers to the number of bands and the wavelength width of each band. A band is a narrow portion of the electromagnetic spectrum. Shorter wavelength widths can be distinguished in higher spectral resolution images.
Multi-spectral imagery can measure several wavelength bands such as visible green or NIR. Landsat, Quickbird and Spot satellites use multi-spectral sensors. Hyperspectral imagery measures energy in narrower and more numerous bands than multi-spectral imagery.
The narrow bands of hyperspectral imagery are more sensitive to variations in energy wavelengths and therefore have a greater potential to detect crop stress than multi-spectral imagery. Multi-spectral and hyperspectral imagery are used together to provide a more complete picture of crop conditions. Radiometric resolution refers to the sensitivity of a remote sensor to variations in the reflectance levels. The higher the radiometric resolution of a remote sensor, the more sensitive it is to detecting small differences in reflectance values.
Higher radiometric resolution allows a remote sensor to provide a more precise picture of a specific portion of the electromagnetic spectrum. Temporal resolution refers to how often a remote sensing platform can provide coverage of an area. Geo-stationary satellites can provide continuous sensing while normal orbiting satellites can only provide data each time they pass over an area.
Remote sensing taken from cameras mounted on airplanes is often used to provide data for applications requiring more frequent sensing. Cloud cover can interfere with the data from a scheduled remotely sensed data system. Remote sensors located in fields or attached to agricultural equipment can provide the most frequent temporal resolution.
Remote Sensing: The Complete Process Figure 4 illustrates a satellite remote sensing process as applied to agricultural monitoring processes. The sun A emits electromagnetic energy B to plants C.
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