Maize Growth and Condition Monitoring with Multisensor Remotely Sensed Time Series

Abstract

Crop growth and condition monitoring is important for estimating crop production and supporting agricultural management. We used several remotely sensed datasets for mapping phenometrics and assessing maize condition. For the derivation of phenometrics, MODIS data were integrated with Landsat-8 and Sentinel-2 to create synthetic time series in order to fill the missing values in original data. For crop condition monitoring, indicators were derived based on vegetation index time series and Land Surface Temperature (LST). The synthetic data could estimate the start and the end of the season with 3-12 days deviation. Vegetation indices were sensitive to crop condition during growing seasons with unfavorable hydroclimatic conditions. LST was a useful indicator of drought-induced crop stress with prediction rates over 80%. The developed approach can be further used to assess crop condition on field level in areas vulnerable to extreme events such as droughts.