Drones Vs. Satellites For The Agri-Sector Use

Drones And Satellites In Farming And Field Observing

The technology of remote sensing (satellite vs. UAV) has become more affordable recently, and the scope of its applications embraces a growing number of spheres. Agriculturalists also face the necessity to use remote sensing sooner or later, so they are eagerly exploring the benefits of using a drone or satellite for farm management to boost the productivity of their fields.

Different types of satellites can perform multiple monitoring tasks, including current weather and forecasts or crop health analytics.

Flying low, drones vs. satellites can capture a thief’s face or distinguish pest species and signs of crop disease. The narrow angle of drone flights allows compiling 3D interactive maps and models.
Satellites vs. drones capture vast territories and don’t depend on rains or winds. So, space-retrieved imagery can be a cheaper option when high precision is unnecessary.

Each method has its advantages and disadvantages of use. Thus, the wisest and most cost-effective option is to combine drones with satellite imagery whenever feasible.

Use Of Drones And Satellites In Agriculture

Aerial imagery shows what is happening on the farms by collecting data from the sky. In some cases, space-retrieved data is more cost-efficient than operating drones, but drones also have their peculiarities of use. This is why drones and satellites can’t fully replace but successfully complement each other.

On the one hand, satellites are helpful when it comes to:

• Precipitation forecasts to schedule irrigation events according to crop needs, save resources with upcoming rains, and prevent waterlogging.
• Assessment of crop development at all growth stages on space-based software for precision agriculture.
• Analysis of soil fertility and health through vegetation indices to determine the necessity and amounts of fertilization.
• Soil moisture monitoring (surface or root zone) to timely measure wilting points in crops and prevent water stresses.
• Yield prediction and further field productivity assessment through satellite imagery processing and analysis of vegetation indices.
• Harvest time estimation with agricultural software algorithms for space-retrieved field images and crop analytics.
• Remote sensing of canopy structure to measure vegetation biomass, plant height, phenological stage, LAI, and develop fertilization and fertigation plans.
• Remote sensing of canopy surface to measure chlorophyll content, plant damage from pests or crop diseases, suppression by weeds. The reasons for low chlorophyll content or productivity can be exactly identified after scouting and addressed correspondingly.
• Animal tracking and monitoring through space-retrieved imagery archives to analyze cattle productivity on different pastures in the past years and choose the most suitable areas for grazing in the nearest future.

On the other hand, the use of drones in agriculture is justified when the capturing angle matters. Despite the latest innovations, satellite imagery is somewhat flat. Another significant feature of a UAV or drone for agriculture is physical crop treatment like crop dusting and spraying, controlling pests, or managing weeds. However, this function can be performed not only by all ag drones but heavy lift payload UAVs only. The average drone payload varies from 0.3 to 2 kg, which means that vast fields require multiple rounds or expensive heavy-lift payload UAVs. Either option involves additional expenses.

Difference Between Drone And Satellite

Apart from similar functions satellites or drones can do for agri-business, there are also specific ones, this is why the situations to apply them are also different. When choosing between these aerial imagery sources, there are several aspects to consider.

Autonomy

Drones require operators to fly. Besides, some countries like the U.S. impose drone operators to have their devices within the scope of visibility. Once reaching its orbit, satellites vs. drones revolve around our planet on their own, retrieving and transmitting data to the ground station.

Accessibility

Some areas like mountains or forests are difficult to cover with drones and present a risk of losing equipment. Also, drones need to fly near the controller, and their route distance depends on the battery capacity.

At the same time, satellite vs. drone flights are not limited by landscape specifics so that they can capture nearly any spot, including remote or hard-to-reach destinations.

Scalability

The most important difference between satellite and drone is the scalability of image capturing. Thus, satellites are more suitable for monitoring large terrains and change detection by comparing a series of historical images. Instead of repeatedly setting up a drone, you can use online field monitoring platforms with all data required. Marking the field contours on the application, you will get a stack of space-retrieved analytics that is easy and convenient to use.

Restricting Legal Regulations

Many countries induce legal restrictions on how to utilize drones for farming, and these restrictions in each country are different. Growers need to make sure they obey the laws and comply with all subsequent amendments before setting up a drone. The prohibition primarily refers to flying drones near strategic objects like military bases or airports, and UAV operating requires licenses in many countries ^{Operation of Small Unmanned Aircraft Systems Over People. Federal Aviation Administration (FAA) and Office of the Secretary of Transportation (OST), Department of Transportation (DOT)}.

Being in space, satellites vs. drones don’t depend on territorial restrictions. The probability of legal restrictions on field images on data-driven platforms is very low.

Price

An ag drone operation includes its price or rent expenses and the operator’s fees. The costs are higher if the fields are vast, which involves additional expenses with every single flight. The retrieved images need GIS specialists’ interpretations, which are also not free.

To access satellite imagery, one often just needs to pay the subscription for specialized crop monitoring services, e.g., EOSDA Crop Monitoring. Furthermore, the data format on such analytical platforms is easy-to-understand for the end user. Space-retrieved images for each available date don’t involve extra payment. Additionally, the platform offers other useful features for crop state analysis like vegetation and productivity maps, scouting tasks, weather forecast and monitoring, historical data, recording and tracking field activities, etc.

Alongside existing imagery sources, EOS Data Analytics has sent to space its own multi-purpose EOS SAT-1 satellite with a specific focus on agriculture, forestry, natural disaster management, and urban monitoring. EOS SAT-1-retrieved imagery ensures more precise data for even more comprehensive field analytics, especially for small-area fields.

Dependence On Weather Conditions

Optical satellite imagery is sensitive to cloud cover unlike low-flying drones, yet a drone for agriculture is of no use under a lack of light, heavy rains, or winds. Because broken skies disrupt picture quality, it’s better to use unmanned aerial vehicles when the sky is either fully clear or overcast ^{Kashiyama, Takehiro et al. “Sky Monitoring System for Flying Object Detection Using 4K Resolution Camera.” Sensors (Basel, Switzerland) vol. 20,24 7071. 10 Dec. 2020, doi:10.3390/s20247071}. At the same time, radar sensors of SAR satellites penetrate through clouds, so they can retrieve images even on cloudy days.

With Cloud-Free NDVI, field analytics on EOSDA Crop Monitoring is possible on cloudy days, too. The platform algorithm allows restoring cloudy images between two cloudless images. This feature gives an opportunity to analyze the so-called “blind period”. Even though drones are insensitive to cloud cover, they can’t fly under heavy rains or strong gusts of wind. Conversely, satellites still can provide data under these conditions.

Specifics Of Data Processing

Drone imagery is but a raw data source with no managerial value because users need to process it themselves with external software or hire a GIS specialist for interpretation. Cloud farming software often includes necessary tools for satellite imagery analytics. Agricultural platforms offer users already interpreted data suitable for decision-making. In particular, satellite analytics on EOSDA Crop Monitoring includes vegetation maps, productivity maps, actual and historical data on soil moisture, precipitation level, temperature, etc. Thus, the processing of drone imagery adds to costs, while interpretation of satellite images is already paid in the subscription.

Comparing Satellite Imagery To UAV Data

Both data acquisition media provide information on the fields, but they have certain specifics. The major difference between drone and satellite imagery is the following:

• expenses involved;
• spectral bands used;
• spatial resolution;
• types of satellite or drone sensors for agriculture;
• ability to retrieve quality images in bad weather;
• data archives.

Typically, drones are equipped with RGB cameras that capture RGB images. Advanced drones with thermal sensors retrieve thermal drone imagery for agriculture with thermal sensors. Thermal imagery detects stressed or dead crops, which are warmer due to low evapotranspiration ^{Stutsel, Bonny, et al. “Detecting plant stress using thermal and optical imagery from an unoccupied aerial vehicle.” Frontiers in plant science (2021): 2225.}. Edgy dual models allow toggling between RGB and thermal view, but they are way more expensive.

Satellite imagery vs. drone imagery usually has a lower resolution, which is typically sufficient for farming, especially when fields are large. There is no need to film individual plants; it is more important to understand the whole picture. However, when high details and precision matter, users can analyze high-resolution satellite images.

Agriculture drone images and satellite data can complement each other in some cases. For example, when satellites detect a problematic area, it can be additionally inspected with higher resolution and precision of a drone image vs. a satellite image. Still, drone image processing and storing on a large scale is challenging.

EOSDA Crop Monitoring For Agri-Sector Needs

EOSDA Crop Monitoring analytics supports thoughtful decisions in crop insurance, finances and banking, IT, chemical industry, and other spheres. For these agri-business stakeholders, it is important to have comprehensive data on the field and see the full picture not only currently but also in the past years.

Satellite imagery vs. drone one suits these purposes better. For example, by analyzing space-retrieved historical data on the field, insurance companies can easily track if agricultural activities are carried out timely, including precision irrigation or fertigation to address moisture or nutrient deficiency. They can do it in the Field Activity Log on our platform. If the yield losses happen because the farmer has failed to irrigate, fertilize, or harvest crops on time, insurance claims compensations are under question.

By integrating EOSDA Crop Monitoring into their businesses and providing access to their clients, insurers can structure and track field activities. Companies’ user accounts will comprise all insurance-covered farmlands, and the users will be informed of progress or problem in each field. At the same time, the farmers will access the same information solely for their fields.

UAV vs. satellite imagery for farm surveys has some disadvantages. It is expensive, time-consuming, has a low duration of continuous use, and drone imagery data vs. satellite imagery data requires additional processing. Based on the latest cloudless image, satellite analytics will provide data on the field state or the history of its treatment in just a few minutes.

Such databases can be also helpful to agri-consulting agencies. Additionally, EOSDA Crop Monitoring can be used for customer attraction when fields are hard to reach. It is important when agri-consultants can’t access the field physically or its inspection involves additional expenses due to remote locations (e.g., in another country or state).

You can choose any field on the planet if you have its coordinates. With vegetation indices and other satellite-based functionalities on our platform, you can make a commercial offer on how to increase the productivity of individual fields.

For example, you can easily compare field data with the Split View mode on the platform, taking NDVI values for two different dates and detecting changes in crop state during the selected period.

The EOSDA Crop Monitoring capabilities far exceed NDVI. By default, users can avail of other vegetation indices, including MSAVI, NDRE, ReCl, and NDMI, each of which is useful at a certain time and in a specific way.

Upon detecting crop health deviances with satellite-based vegetation indices analytics, you can actualize the problem not only with drones but with scouts as well. The Scouting feature on EOSDA Crop Monitoring allows users to send a scout to the critical zone just in a couple of clicks by tagging the area and tasking a scout.

Additionally, with available historical satellite data on the product, users can compare changes in the field through decades without extra expenses. By using vegetation maps on EOSDA Crop Monitoring, growers can allocate VRТ applications with precision agriculture drones and other machinery. This approach saves costs yet increases yields and prevents nature pollution, contributing to sustainable farming.

Can Drones Replace Satellites In Agriculture?

No, they can’t – particularly, when it comes to large companies and vast fields. Surveying big territories with drones is economically unjustified because satellite data is much cheaper, especially when available at farming platforms like EOSDA Crop Monitoring. Also, big farming enterprises need to access historical data archives that allow analyzing field treatment in the previous seasons and choosing the correct crop rotation sequence and events to improve soil fertility in the nearest future.

The combination of satellites and drones with further data processing will cover each technology’s “blind zones” and get maximal information. For example, when satellites can’t give a full picture of the field due to clouds, the situation becomes clear with a UAV. Integration of data from both sources will allow assessing crop state in the whole field. On the other hand, if the sky is clear and satellite imagery is available, there is no need for satellite replacement with UAVs.

While drone updates involve additional costs, space-retrieved imagery becomes more affordable and accessible year by year. This factor simplifies satellite analytics implementation in the agri-sector and prompts managing risks by addressing crop health issues quickly.