From industry consolidation to investment to business agreements, a lot of big dollar decisions are being made. But how well do you truly understand the technology around remote sensing in agriculture?
Here are four key concepts that anyone doing business in the sector should understand. To learn more, you can download our recent whitepaper: Understanding and Evaluating Satellite Remote Sensing Technology in Agriculture.
1. Revolutions and Resolution
Distinguishing between pixel size and other characteristics is important, as they are often forgotten but significant details. When satellite imagery is referred to as “high resolution” it is most likely referring to the spatial resolution – and as a trade-off, the resulting temporal resolution is low.
Currently, no one can offer high spatial and high temporal resolution – so the tradeoff is required. Understanding the effect of the various resolutions helps to evaluate the value of the data based on the needs.
Spatial Resolution: Refers to the smallest object that can be identified on the ground and varies based on the position of the sensors relative to the target. Therefore, pixels within the same satellite image can have different spatial resolutions.
Temporal Resolution: The amount of time between image acquisitions, or time revisit and the frequency at which data is downloaded to a ground segment.
What to look for when buying your first application drone
High spatial, low temporal is best for precision tools that require intrafield analysis. But the type of crop being evaluated effects the value of the spatial resolution. For example, there is little value in ultra-high spatial resolution for large production crops since the equipment used to manage these crops cannot apply treatments at that level of precision. By comparison, an orchard that manages its crop on a plant-by-plant basis will extrapolate more value from the ultra-high spatial resolution data.
Low spatial, high temporal is best for crop monitoring. The more acres and growers an agronomist manages, the more important it becomes to identify which fields need attention quickly. By receiving daily data on a large scale, time management is more efficient and effective.
2. The Spectrum
Radiometers on satellites measure wavelengths of electromagnetic radiation being reflected by targets on Earth – known as the spectral response – based on the electromagnetic spectrum. Different objects have different spectral signatures, which provide information on what is being observed.
Because each remote sensing tool (satellite, drone, etc.) has its own spectral band, they do not see the same exact colors for a given surface. This means data being collected from multiple sources cannot be compared to one another without carefully cross-calibrating the data.
3. From Wavelengths to Crop Health
The wavelengths and intensity of the spectral response provide the information needed for vegetation index calculations. Vegetation indices provide an indication of the relative density and health of vegetation for each pixel in a satellite image.
The most common is the Normalized Difference Vegetation Index (NDVI): Sensitive to both biomass and chlorophyll activity, providing a data range from -1 to +1. Since there is more reflected radiation in near-infrared wavelengths than in visible wavelengths, the closer to +1 indicates healthier vegetation. If there is little difference, the vegetation may be stressed or dead – or the data captured is of bare soil.
Ultimately each pixel captured provides a wealth of information that can be used in a variety of calculations to provide multiple data points. The more cloud-free pixels you can capture, the more information you have available and the better the resulting data will be.
4. The Power of Processing
When it comes to agricultural application, satellite imagery is not suitable in its raw form. The information captured from the satellite sensors must be processed before it can be used for analyses such as NDVI. There are several factors that affect imagery captured by a single satellite that must be corrected, including (but not limited to):
The atmosphere
Cloud cover
Shadows due to sun blockage
Varying spatial resolutions based on the distance from the NADIR
Varying angles of sunlight based on the curvature of the Earth
Viewing angle of the sensor
Topographical distortions
Environmental effect
Most of these distortions can be removed automatically, given the right algorithms and metadata, but others can be a rather labor intensive process. This is what makes the difference between a pretty, colorful map and actual data that users can capitalize on.
While using remote sensing data doesn’t require an in-depth understanding of how the technology works, having a working knowledge of a few key concepts helps to understand better what is feasible. REAL Agri
Advantages of Remote Sensing and GIS in Agriculture
Crop Sown Area Estimation
One of the critical uses of remote sensing in horticulture is the assessment of the Crop sown region. Data from aerial and satellite sensors give a precise analysis of planted regions and helps with risk evaluation if there ought to be an event of disaster or catastrophe.
Crop Disease Identification
Gis remote sensing in agriculture makes it simpler to recognize contaminations and pest attacks in crops over huge areas at starting stages. This gives producers an adequate opportunity to apply any counter means to safeguard the harvests from any tremendous losses. This becomes possible through satellite imaging and examination.
Soil Properties
Perhaps the primary element in ensuring a sound yield of harvests is the appropriate support of soil. It directly influences the harvest. Any progressions in farm management or farming system cause soil changes, which in turn influence soil capacity of production. Characteristics, for instance, Soil salinity, Soil pH, organic substance level, and soil texture can be recognized using remote detection and that data can be analyzed to carry out any significant soil treatment. Soil moisture mapping gives a precise assessment of water content in the soil which can help with carrying out any upgrades in the irrigation system structure.
Flood Impact
Remote Sensing through satellite-based sensors and the data assembled through ground sensors can help with giving a ton of definite information to decide an accurate loss assessment. In case of flooding due to excess rainfall, the areas of land with poor drainage frameworks are at risk of waterlogging which causes basic loss of harvests and yield. The loss assessment can help with further planning for the damage control and countermeasures for keeping losses to a minimum.
NATCAT Modeling
Remote Sensing in agriculture can assist with assessing current and forecasting Natural Catastrophe hazards. Utilizing the information relayed by sensors and the behavior of regular risks. This requires risk mapping and calculating hazards through estimating hazards which are finished by PC simulated disaster models. Remote sensing maps prepared with the assistance of historic information and present information gathered from various sensors help in assigning areas of high capability of flooding with high hazard ratings. This aids in farming as regions with higher risk ratings are not planted on and get treated for better flood protection for the subsequent season.
Drone Image Analysis for Crop Damage Assessment
Drone picture analysis is utilized in crop assessment for damages because of hailstorms, tree counting, and invasions. The drone imagery is as accurate as the input spatial resolution. Which can be increased as per the requirement.
