Use of HANTS algorithm in Sentinel-2 images for interpolation of missing data: a case-study for soy agricultural areas in periods of growth and harvesting

Abstract

Information about the current state of an agricultural culture can be fundamental in decision making, varying from quantity of fertilizers to be used in an area to estimating the productivity of a harvest in a period to establish credits payment. Satellite images with reasonable spatial resolution like Sentinel-2 are a powerful tool to visualize agricultural areas production, especially with the application of multispectral indexes like the Normalized Difference Vegetation Index (NDVI). Yet, due to the limitation of temporal resolution of satellites and periods with large cloudy weather can make the analysis difficult. This study proposed the use of the harmonic analysis time series algorithm (HANTS) to aid the incompleteness of time series obtained by the available satellite images in a case study of soy culture in Brazil. By applying the algorithm, a harmonic interpolation is obtained to produce a full daily NDVI time series from the beginning to end of the study period, facilitating further analysis to obtain metrics of interest. In this study, time series with gaps on specified periods are created based on full time series, then interpolated and compared with the real ones based on root mean squared error (RMSE) to access its accuracy.