Identification of agricultural row features using optical data for scattering and reflectance modeling over periodic surfaces

Various forward models have been developed for measuring agricultural biophysical parameters from the microwave or optical data, but their application to periodic surfaces (e.g., periodic crop rows and plowed soil rows) still suffers from the need of row-feature descriptions. Accordingly, an operational method was proposed herein to estimate the orientation and period of row features from very high spatial resolution optical data, as input to multi-scale scattering models. The periodic features were estimated from the frequency domain using the Fourier magnitude spectrum by scanning in azimuth to identify the presence/absence of periodic features and the main direction. The frequency representing the dominant periodic features was then determined from the main direction of the magnitude spectrum. Moreover, the tillage type of the bare soil surface was classified into one of three types (sinusoidal, sinusoidal bench, or bench) according to the magnitudes of dominant periodic features in the main direction. The method was evaluated using two data sets consisting of various agricultural features in regional Melbourne, Australia. The retrieval Root Mean Square Error (RMSE) in orientation and period was < 3° and 0.1 m. The overall accuracy of detecting the periodic features and tillage type classification was >95% and 70.59%, respectively. The proposed method and the retrieved periodic features are expected to partly solve the ill-posed nature of microwave data inversion over periodic surfaces.