Vegetation Indices And Their Usage
On August 23, EOS Data Analytics, a global provider of AI-powered satellite imagery analytics, and QOLDAU digital business platform provider hosted a free webinar on using vegetation indices in land remote sensing during the spring field and harvesting works in Kazakhstan.
The participants talked about difficulties in assessing yields and planning agricultural operations and how using satellite data facilitates solving these and other tasks related to growing crops. The guests also learned about EOS Data Analytics, the EOSDA Crop Monitoring platform’s functionality, particularly vegetation indices, and working with them using examples of the country’s agricultural lands.
The online event was attended by farmers, food producers, suppliers of fertilizers and agricultural machinery, and everyone interested in how the latest technologies are changing agriculture in Kazakhstan.
The following speakers made presentations:
- Vitalii Vyshniak, Business Development Manager at EOS Data Analytics
- Aleksandr Kryvoshein, Senior Researcher at EOS Data Analytics
- Kairat Sultanbekov, QOLDAU representative
Over 30,000 farms in the country grow crops on 1 to 10 thousand hectares. The difficulty that farmers usually face is the need to continuously control the crops’ state and the correctness of field work performed on large areas of arable land. Factors preventing them from effectively monitoring the situation in the fields and quickly responding to changes are:
- Lack of personnel (scouts, EOSDA Crop Monitoring and precision agriculture specialists, and more)
- Not always favorable and predictable climate and weather conditions
- Insufficient quantity of agricultural machinery
- Lack of internet in remote parts of the country
Automation of field monitoring via remote sensing — the study of Earth’s surface using optical and radar satellites — will allow farmers to get updates on the situation in any part of the field without the need for personal presence.
The main advantage of this research method lies in the monitoring coverage. Satellite imagery makes it possible to obtain prompt and detailed information about the state of large areas, unlike ground-based spot measurements.
Remote sensing in agriculture is based on the reflectivity of plants. Measuring the degree of reflection of electromagnetic radiation by plants (in the spectral ranges visible and invisible to the human eye) makes it possible to evaluate crop health at different stages of development with vegetation indices.
Vegetation index is a mathematical fractional linear combination of two or more spectral bands that enhances the contrast between vegetation (which is highly reflective) and unvegetated soil, buildings, and more.
Satellite image analysis with vegetation indices permits tracking crop growth and soil conditions accurately without in-person inspections. Convenience and the ability to regularly receive accurate information about crops make this technology in demand among farmers around the world. I am sure that specialists from our country will also appreciate its advantages.
Speakers discussed several vegetation indices available on the EOSDA Crop Monitoring and their use cases in detail.
NDVI is the most popular index used to estimate the intensity of crop growth during the season. The most accurate results are obtained in the middle of the season when the plants are developing the most actively.
MSAVI was developed to mitigate soil effects on EOSDA Crop Monitoring results. Therefore, the index is applied when NDVI can’t provide accurate values, particularly with a high percentage of bare soil, scarce vegetation, or low plant chlorophyll content.
ReCl is used for evaluating chlorophyll content in leaves. In other words, the index allows for analyzing a plant’s health.
The presenters also explained what lower index values could indicate, what causes them, and what must be done to improve soil condition.
The webinar’s final part was dedicated to reviewing real-life use cases for satellite data and vegetation indices to solve problems in Kazakhstan’s agricultural lands. The speakers reviewed these tasks:
- Segmentation of fields of non-standard complex forms
- Estimation of snow cover area
- Assessment of the area of development of weed vegetation before the sowing campaign
- Evaluation of the germination of the recently sown crops
- Determination of field productivity on a specific date or for the growing season
- Definition of AOI for scouting
- Estimation of maturation of spring grain crops
- Harvest field monitoring
We wanted to show the attendees that there are various scenarios for applying satellite imagery analysis with vegetation indices at all stages of the growing season.
EOSDA considered several options for adopting the technology so that potential partners can choose the best option in terms of budget, implementation time, availability of expertise in software development, and strategic goals. This can be either the purchase of a subscription to the EOSDA Crop Monitoring precision farming platform, or the integration of data through the API, white label product, or an individual solution.