What is NDVI and how to use crop imaging in remote sensing?
The most popular remote sensing measure used in agriculture is the NDVI index. NDVI stands for Normalized Difference Vegetation Index, which simply describes the plant’s development based on how a plant reflects different light waves. A healthy well-developed plant absorbs red light and reflects near-infrared light and the opposite happens to a diseased or poorly developed plant. NDVI measures the difference between near-infrared and red light. NDVI values range from 0 to +1. The value 0 indicates no vegetation, so most likely the surface is bare soil, snow or water etc. Values near +1 show healthy vegetation and dense coverage with green leaves.
Different palettes are used for remote sensing with NDVI. Green palette is the more common NDVI palette, where light green shows areas with low NDVI (poor vegetation) and dark green with high NDVI (healthy vegetation).
In order to clearly bring out problematic areas, there is contrast palette available. Contrast palette shows NDVI values from 0 to 0,07 as grey (very low index values). Low vegetation areas are coloured as red (0,07 to 0,3) on this palette.
NDVI images help a farmer to get a good overview of the development of the crop without actually having to visit the field. It is a tool that can help to identify any problem areas within the field. For example, when lower NDVI areas are at the same place every year there might be a problem with drainage, pH or even compaction. Low vegetation areas can also be caused by diseases, insect damage, lack of nitrogen or of drought-prone soil types. Or they can simply turn out to be obstacles on fields, such as big rocks or piles of stones. NDVI is helpful when taking care of large areas and on-site controlling is time-consuming. Changes in NDVI might be due to mismanaging application operations- e.g not applying fertilisers to all the field or making mistakes while drilling.
Other crop imaging indexes
NRI, Nitrogen Reflectance Index is used to indicate the nitrogen level in plants. Similarly to NDVI, the green colour shows high level of nitrogen and red means low level of N. Seeing red areas on the map might indicate nitrogen deficiency.
NDWI, Normalized Difference Water Index, shows the water level in the area. Green means high water level and red low water level which might indicate drought. If there is a dark green area on the field, it might indicate poor drainage. For farmers who use irrigation systems, early recognition of plant water stress can be critical to prevent lower crop production or crop failure.
Analysing weather data
Remote sensing also consists of analysing data about weather parameters. Air temperature has a great influence on both physical (growth) and chemical processes of plants (photosynthesis). In eAgronom NDVI module, the weather data is seen under the maps section of NDVI page. On every weather parameter tab, there is also yield history, which enables farmers to compare their yield data to weather parameters, which is necessary to look deeper into yield forming and make assumptions on why the yield number was as high or average or low as it was.
The first weather parameter to look at is the weather forecast, which is specific to the location of the field. It helps farmers to plan their tasks- e.g when spraying wind speed, air temperature and predicted rainfall must be taken into account.
Soil temperature is measured from the surface and depth of 10 cm. Comparing temperatures with past year can be enabled from graph’s legend. Soil temperature helps a farmer to decide drilling time. For example, it is very important for an organic farmer to put a seed into warm soil, so that the crop can germinate immediately and get a good start to compete with weeds.
Soil moisture graphs also provide valuable data for drilling. When soil moisture is low and the weather forecast doesn’t show rainfall in the near future, then it might be reasonable to adjust drilling depth a bit deeper so that the seed wouldn’t be left in dry conditions where it doesn’t germinate. Soil moisture also helps to plan irrigation scheduling.
Historical weather charts provide farmers with data about temperatures and rainfall in the past 3 months (the period can be adjusted). When was the last time it rained? What were the maximum, minimum and average temperatures on a specific date? When was the average temperature above 5 degrees, so the crops actively grew? Answers can be found by analysing the weather history charts.
Each plant species needs a sum of day degrees to reach a certain stage of development. The accumulated temperature chart below shows how much heat plants have accumulated to a specific date. This chart helps farmers to predict when crops reach certain growth stages and can also help in predicting harvesting time.
Accumulated temperature charts provide farmers with comparative data about previous years, so they can see where they stand in the current season. If there has been lower day degrees then the crops might mature later and the start of harvesting period will be delayed. The example below shows that autumn 2020 has been warmer than normal and this is reflected in the field where development growth of winter crops and NDVI levels are higher than normal. This is a positive indication that the 2021 winter crop harvest as of today has a higher potential than the last two years.
Accumulated precipitation graph also helps farmers to compare the current season with previous years. It helps farmers to see how much moisture has been available for crops and plan tasks according to that. It is again useful data for irrigation planning.
Crop based NDVI reports
Crop based NDVI report gives indications to potential yield when comparing NDVI with historical data about a similar crop in a similar year. If NDVI figures are higher than on comparative year, then it might indicate that there will be higher yield. The end result will always be affected by weather conditions.
In conclusion, remote sensing tools like NDVI and weather reports are useful tools in helping farmers to locate problems on fields, look for reasons behind the problems, plan tasks and predict their yield potential.
eAgronom has recently added NDVI to the platform. When used together with yield maps, soil type data, terrain maps, and weather data, the combination creates a powerful tool for assisting in pinpointing the reasons behind yield differentiations within fields and years.