Crop biomass estimation using multi regression analysis and neural networks from multitemporal L-band polarimetric synthetic aperture radar data

Biomass has a direct relationship with agricultural production and may help to predict crop yield. Earth observation technology can contribute significantly to monitoring given the availability of temporally frequent and high-resolution radar or optical satellite data. Polarimetric Synthetic Aperture Radar (PolSAR) has several advantages for operational monitoring given that at these longer wavelengths atmospheric and illumination conditions do not affect acquisitions and considering the sensitivity of microwaves to the structural properties of targets. Therefore, SARs are a promising source of data for crop mapping and monitoring. With increasing access to SARs the development of robust methods to monitor crop productivity is timely.

In this paper, we examine the use of machine learning and artificial intelligence approaches to analyze a time series of Polarimetric parameters for crop biomass estimation. In total, 14 polarimetric parameters from a time series of Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) airborne L-band data were used for biomass estimation for an intensively cropped site in western Canada. Then, Multiple linear regression (MR) and artificial neural network (ANN) models were developed and evaluated to estimate the biomass for canola, corn, and soybeans. According to the experimental results, the ANN provided more accurate biomass estimates compared to MR.

Canola biomass, in general, showed less sensibility to almost all the polarimetric parameters. Nevertheless, Freeman-Double combined with vertical-vertical backscattering (VV) delivered the correlation coefficient (r) of 0.72, and the root mean square error (RMSE) of 56.55 g m^?2of canola biomass. For corn, the highest correlation was observed between a pairing of horizontal- horizontal backscattering (HH) with Entropy (H) for biomass estimation yielding an r of 0.92 and RMSE of 196.71 g m^?2. Horizontal-vertical backscattering (HV) and Yamaguchi-Surface (O_Y) delivered the highest sensitivity for soybeans (r of 0.82 and RMSE of 13.48 g m^?2). If all crops are pooled, H combined with O_Y provided the most accurate estimates of biomass (r of 0.89 and RMSE of 135.31 g m^?2). These results demonstrated that models which make use of polarimetric parameters that characterize the multiple sources of scattering typical of vegetation canopies can be used to estimate crop biomass accurately. Such results bode well for agricultural monitoring considering the increasing number of satellite SAR sensors with various frequencies, imaging modes and revisit times. As such, the time series analysis and methods proposed in this study could be used to monitor crop development and productivity using SAR space technologies.