Precision agriculture can significantly benefit from remote sensing in numerous practices, including seeding, fertilization, protection, and cultivation. Remote sensing provides valuable crop health and growth data, soil conditions, and environmental factors. This information can be used to develop improved management strategies to increase yields and profits.
Farmers and agricultural researchers can monitor agricultural land using multitemporal remote-sensing imagery to gain insights into the effectiveness of farming practices like irrigation and fertilization. It helps in making decisions to optimize crop yield and reduce waste. The approach is also helpful in managing agricultural land on a larger scale, ensuring accurate prediction of cropland suitability and control of farming subsidies.
Unlike other indices, MSAVI is an effective vegetation index that works during seed germination and leaf development stages. This tool helps to track seedlings, especially in fields with a large amount of exposed soil. Seed development is threatened by various risks, such as unstable growth, cold and heat stress, abnormal rainfall, and differences in elevation.
Calculating the MSAVI index helps farmers to identify uneven seed growth. When combined with reliable weather data, the impact of extreme weather on plant health can also be assessed. An in-depth understanding of early crop development enables adjusting field management practices and improving yields.
The provided index is intended to replace NDVI and NDRE in cases where they are unable to provide precise information because of insufficient vegetation or a lack of chlorophyll in the plants.
In the early stages of growth, when the seeds are sprouting and the leaves are developing, there tends to be a significant amount of bare soil between the young plants. At this stage, both NDVI and NDRE indicate that the vegetation is in poor condition.
Modified Soil Adjusted Vegetation Index comes to aid by reducing the effect of soil on the calculation of vegetation density in the field. Some crop types experience a similar situation at the end of the season, just before harvesting. The density of vegetation is decreasing, causing a reduction in chlorophyll content and resulting in more bare earth.
In such a case, only MSAVI can help to accurately distinguish crop problems from factors that indicate crop readiness for harvest. Other indices under such conditions indicate low vegetation density and chlorophyll content.
How to Calculate
The SAVI index was created to reduce the impact of soil on vegetation data. It was achieved by incorporating a soil correction factor L in the NDVI equation’s denominator.
To accurately consider the impact of soil, the L coefficient should decrease in correlation with an increase in vegetation. One way to obtain the L function is through either inductive methods or by using the product of NDVI and weighted difference vegetation index.
MSAVI boosts the vegetation signal by reducing the impact of soil interference in the background. This results in increased sensitivity to vegetation, which is calculated by comparing the strength of the vegetation signal to the level of soil noise.
The calculation of the given vegetation index involves taking the ratio between the R and NIR values and applying an inductive L function to minimize the influence of soil on the vegetation signal effectively. The MSAVI formula looks like this:
MSAVI & NDVI: What’s the Difference?
The Normalized Difference Vegetation Index is a widely used method for assessing crop health. It measures the level of crops’ “greenness” or photosynthetic activity to determine the index, making it a valuable tool for growers and researchers.
The NDVI is a calculation based on the difference between an image’s red and near infrared bands, done per pixel. While it’s a helpful indicator of crop health, it only works perfectly for some crops, growth stages, or vigor levels.
MSAVI focuses on the early stage of crop growth before the canopy has fully formed. It is when the crop has just emerged. When using NDVI, it can often be misinterpreted as indicating no vegetation and therefore give null results. The application of MSAVI in vegetation tracking has its own advantage. Bare soil’s presence is considered and corrected in the calculation of this index. It provides more accurate readings of yield changes in young or sparse crops.
MSAVI In EOSDA Crop Monitoring
EOS Data Analytics is a trusted provider of satellite imagery analytics for more than 20 industries, including agriculture. The company has also developed the EOSDA Crop Monitoring software, which enables farmers to improve farm management and yields through data-driven decision-making.
This precision-farming solution, based on remote sensing technologies, gives users access to valuable data on soil and vegetation conditions. Field monitoring using vegetation index calculations allows for the timely detection of crop problems, minimizing or preventing damage, and rationally allocating available resources according to plant needs.
The platform uses satellite data and vegetation indices to generate maps for better crop management, reducing costs and maximizing yield. EOSDA Crop Monitoring provides a 14-day weather forecast that can help to anticipate extreme temperatures in their fields and stay alert. By tracking the temperatures and MSAVI as curves on a graph, growers can react timely when the index drops.
Young plants are vulnerable to the effects of both excess and insufficient rainfall. Reviewing the precipitation data on the software graphs makes it possible to anticipate and prepare for droughts or floods. Additionally, users can apply the MSAVI tool to determine if abnormal precipitation has caused any harm to their newly planted crops.