SatTech Plus Ground Data For Agri-Advisors In Portugal

Overview: Cross-Validating Remote Sensing And Ground Data

Precision agriculture relies on advanced agritech solutions that are closely intertwined with one another. The ability to come up with insightful analytics and suggestions depends on an advisor’s ability to obtain reliable data. Quite often, the utmost precision is achieved by cross-validating the data from ground probes with satellite imagery data.

One of EOSDA’s clients works together with agricultural producers in Portugal, Spain, and Chile through the commercialization, development, and integration of precision technologies to achieve the highest yield results. The company is also dedicated to providing services and consultancy that allows the producer to manage their plots in an efficient, profitable, and sustainable way.

The company manages the installation of ground sensors and provides aerial imaging from drones and weather stations, but the agritech implementation doesn’t stop there. They also ensure technical support throughout the whole campaign cycle, converting the obtained data into actionable recommendations. EOSDA Crop Monitoring platform has been added to the range of the services the client’s company provides to increase the value proposition and enhance efficiency, which we will discuss further in this case study.

Overall, by combining ground sensors, remote sensors, and data analytics, the client has been able to achieve the highest degree of precision in agricultural consultancy. Below, we will review challenges that they could tackle by extending the range of analytical data into applied space imagery and analytics from the EOSDA Crop Monitoring platform.

Challenge: Climate Conditions, Increased Cost Of Production

The established agribusiness processes and climate conditions in Mediterranean Europe have naturally led to sharpening the client’s expertise in advising on growing crops like tomatoes, olives, almonds, maize, wheat, and rice.

One of the most important considerations for their region of operations is dealing with specific climate conditions. For instance, there is sunny weather 80% of the days during dry years in Portugal. The rains that occur in winter do not get preserved in the lower layers of the land. Despite the fact that the local soils are rich in sand, which absorbs the water quite well, the natural abilities of soil permeability are not enough to sustain the fields with the winter rainwater throughout the dry seasons. On the other hand, high relative humidity in mild temperatures during winter may ignite crop diseases. Consequently, climate conditions lead to the necessity of dealing with ongoing agribusiness challenges.

The cost of production increases when producers have to spend more resources on activities like irrigation during dry years, while the yields that they get are roughly the same every other year. In order to provide the maximum value to growers, the client had to deliver suggestions on how to increase the cost-benefit ratio, while decreasing dependency on factors that are outside the producers’ influence (like weather and seasonal changes).

Solution: Tracking Vegetation, Combining Remote Sensing With Ground Sensing

Research shows that to reach the statistical characteristics of satellite data, one should install seven ground probes per 100 sq.m. ^{De Nijs, A., & De Jeu, R. (2023, January 8). Planetary variables: Soil water content (SWC). Planet Labs.} That’s a whopping number of 700 probes per hectare just to reach the same level of accuracy! Ground probes also need constant calibration, depending on elevation and installation method. To optimize the cost for analytics, the client used satellite data analytics from EOSDA Crop Monitoring to install ground probes only in locations that required cross reference. Furthermore, they added vegetation status reports as an additional service which complemented the existing practice with ground probes installation and simplified reporting.

To identify the appropriate locations for installing ground probes, EOSDA’s client monitored vegetation indices of the areas of interest, compared the present data with historical data (e.g. a year ago), and assessed weather forecasts for better timing of their research and field trips.

In accordance with the area-specific crop types, the company focused on three types of vegetation indices provided by EOSDA Crop Monitoring:

• NDVI (Normalized Difference Vegetation Index) for photosynthetically active biomass monitoring
• RECI (Red-Edge Chlorophyll Vegetation Index) for chlorophyll content detection
• MSAVI (Modified Soil-Adjusted Vegetation Index) for early stages of the growth, bare soil-adjusted

For example, during the irrigation season, they monitored vegetation indices every day, with an average frequency of revisit of 2 to 3 times a day. NDVI, Contrast View, and Weather Data were assessed before visiting the field and installing ground probes for soil moisture measurements. Thanks to the whole area coverage obtained from the comfort of their office, the technical staff could save time on analytics, and go directly where they needed to go when scouting the fields.

Using the EOSDA Crop Monitoring mobile app came in handy when the researchers and technical specialists were identifying zones in the field that required checking. Also, by sharing account access with producers, they could demonstrate the visual representation of crop development.

Outcome: Best Probe Locations, Efficient Analytics, Enhanced Time-To-Value

When using EOSDA Crop Monitoring, the client achieved spending less time on analytics while being more productive in the fields. Right now, 30% less time is spent analyzing the fields which increases the effectiveness of analytics and helps to deliver high-precision consultations in a timely manner.

As an example of the shown efficiency, the company of the client used the Weather tab in the EOSDA Crop Monitoring platform to analyze one of the fields where they assumed the development of crop disease. The subsequent visit to the field has confirmed the remote estimation as the rice disease was identified exactly in those areas. Agriculture experts could react timely, helping the producer to prevent the disease from further development and save the crops. During that vegetation season, the combination of the above-mentioned tactics helped to increase the rice yields by 20% in the monitored areas.