Satellite imagery is becoming an increasingly important tool for farmers and decision-making authorities everywhere, allowing them to monitor and evaluate the health and status of their crops. Through the accurate and timely information derived from multitemporal satellite data applications like monitoring and managing of crops, forecasting of crop production, measuring and managing water use and crop needs as well as fighting crop insurance fraud can be easily and accurately issued.
The long archive of Landsat program (dated from 1984) allows us to perform robust time series analysis and examine the crop phenology in order to differentiate specific crop types. In addition, recent satellite data from Landsat 8 give us the ability to provide near real-time estimation of crops health, pinpoint signs of crop stress, monitor vegetation growth as well determine actual rates of evaporation.
Crop yield monitoring rely primarily on vegetation indices, such as the Normalized Differential Vegetation Index (NDVI), in order to monitor crop phenology. By examining and analyzing multitemporal values of the NDVI indices it is feasible to monitor the vegetation growth, the fruit/seed status and the maturity of each crop.
During this study we performed a multitemporal NDVI analysis exploiting the products derived from Spaceye, in the region of Illinois USA, in order to identify the different crop species and study their phenological features in relation to their growth.
From the above chart we can see that the Soybeans during the summer period are on their highest growth, whereas the Fallow cropland are on their lowest growth. It can also be observed that it is feasible to accurately discriminate all the different types of crops in at least 3 different time periods between the time range of 05-2015 to 12-2015
Automated classification of the different crop types (right image) by exploiting the extracted phenological features from multitemporal NDVI values (left image) and machine learning algorithms