Data obtained during a ground-based SAR experiment and an associated field campaign have been exploited to study the rate and sources of decorrelation in an agricultural test site in the conditions of observation of a geosynchronous SAR. It was found that the scene is less affected by temporal decorrelation when the primary image is acquired during night time or early morning. Additionally, a periodic oscillation on a sub-daily scale was observed when creating coherence time series with increasing temporal baseline. Two factors which strongly contribute to these oscillations are the daily cycles of soil moisture and evapotranspiration.

A dataset of sub-daily C-band data, acquired with a ground-based synthetic aperture radar, has been used to study soil and vegetation dynamics during a complete growing season in a controlled agricultural test site. The data have been exploited to analyse the rate and sources of decorrelation in the scene, as well as the consequences of the observation conditions of a sub-daily satellite (with either low, medium or geosynchronous orbit): short revisit times, availability of multiple acquisitions during a single day, and shallow observations at some incidence angles. Repeat-pass coherence is found to be less affected by temporal decorrelation when the primary image is acquired during nighttime or the last hours predawn. Regarding the incidence angle, VV has increased sensitivity to certain phenological stages as the incidence angle increases. Additionally, a periodic oscillation on a sub-daily scale is observed when creating coherence time series with increasing temporal baseline. Factors which strongly contribute to these oscillations are the daily cycles of temperature, soil moisture and vegetation water dynamics.

Biophysical parameters are descriptors of crop growth and production estimates. Retrieval of these biophysical parameters from synthetic aperture radar sensors at operational scales is highly interesting given the increase in access to data from radar missions. Vegetation backscattering can be simulated using the water cloud model (WCM). Crop biophysical parameters are obtained by inverting this model. However, the inversion problem is ill-posed, and existing methods, which include the lookup table (LUT) and iterative search algorithms, are often computationally intensive and lack good generalization capacity. This might make retrieval of the biophysical parameters computationally intensive for large study areas. In addition, the new generation of operational missions, which are often associated with a large volume of data, poses a challenge for estimating crop parameters. In this work, we use the cloud computing potentials of the Google Earth Engine (GEE) to demonstrate a unified processing pipeline for WCM inversion. The processing pipeline (GEE4Bio) uses Sentinel-1 radar measurements for WCM inversion and subsequently produces crop biophysical maps. Inversion is achieved by employing Random Forest regression, which is trained with radar backscatter measurements at Vertical transmit and vertical receive (VV) and Vertical transmit and horizontal receive (VH) channels. The model is trained and validated with independent calibration and validation datasets consisting of ground measurements for five major crops over the Joint Experiment for Crop Assessment and Monitoring–Carman test site in Canada. The inversion accuracies indicate strong correlation coefficients (r) of 0.83 and 0.87, with the estimated and in situ measured plant area index and wet biomass, respectively, with low root mean square error values. The GEE4Bio processing chain produced crop inventory maps with a reasonable time and apprehended the variability in plant growth across the test site.

Sentinel-1 Synthetic Aperture Radar (SAR) data have provided an unprecedented opportunity for crop monitoring due to its high revisit frequency and wide spatial coverage. The dual-pol (VV-VH) Sentinel-1 SAR data are being utilized for the European Common Agricultural Policy (CAP) as well as for other national projects, which are providing Sentinel derived information to support crop monitoring networks. Among the Earth observation products identified for agriculture monitoring, indicators of vegetation status are deemed critical by end-user communities. In literature, several experiments usually utilize the backscatter intensities to characterize crops. In this study, we have jointly utilized the scattering information in terms of the degree of polarization and the eigenvalue spectrum to derive a new vegetation index from dual-pol (DpRVI) SAR data. We assess the utility of this index as an indicator of plant growth dynamics for canola, soybean, and wheat, over a test site in Canada. A temporal analysis of DpRVI with crop biophysical variables (viz., Plant Area Index (PAI), Vegetation Water Content (VWC), and dry biomass (DB)) at different phenological stages confirms its trend with plant growth dynamics. For each crop type, the DpRVI is compared with the cross and co-pol ratio (σ_VH⁰/σ_VV⁰) and dual-pol Radar Vegetation Index (RVI = 4σ_VH⁰/(σ_VV⁰ + σ_VH⁰)), Polarimetric Radar Vegetation Index (PRVI), and the Dual Polarization SAR Vegetation Index (DPSVI). Statistical analysis with biophysical variables shows that the DpRVI outperformed the other four vegetation indices, yielding significant correlations for all three crops. Correlations between DpRVI and biophysical variables are highest for canola, with coefficients of determination (R²) of 0.79 (PAI), 0.82 (VWC), and 0.75 (DB). DpRVI had a moderate correlation (R²≳ 0.6) with the biophysical parameters of wheat and soybean. Good retrieval accuracies of crop biophysical parameters are also observed for all three crops.

Estimation of bio-and geophysical parameters from Earth observation (EO) data is essential for developing applications on crop growth monitoring. High spatio-temporal resolution and wide spatial coverage provided by EO satellite data are key inputs for operational crop monitoring. In Synthetic Aperture Radar (SAR) applications, a semi-empirical model (viz., Water Cloud Model (WCM)) is often used to estimate vegetation descriptors individually. However, a simultaneous estimation of these vegetation descriptors would be logical given their inherent correlation, which is seldom preserved in the estimation of individual descriptors by separate inversion models. This functional relationship between biophysical parameters is essential for crop yield models, given that their variations often follow different distribution throughout crop development stages. However, estimating individual parameters with independent inversion models presume a simple relationship (potentially linear) between the biophysical parameters. Alternatively, a multi-target inversion approach would be more effective for this aspect of model inversion compared to an individual estimation approach. In the present research, the multi-output support vector regression (MSVR) technique is used for inversion of the WCM from C-band dual-pol Sentinel-1 SAR data. Plant Area Index (PAI, m² m⁻²) and wet biomass (W, kg m⁻²) are used as the vegetation descriptors in the WCM. The performance of the inversion approach is evaluated with in-situ measurements collected over the test site in Manitoba (Canada), which is a super-site in the Joint Experiment for Crop Assessment and Monitoring (JECAM) SAR inter-comparison experiment network. The validation results indicate a good correlation with acceptable error estimates (normalized root mean square error–nRMSE and mean absolute error–MAE) for both PAI and wet biomass for the MSVR approach and a better estimation with MSVR than single-target models (support vector regression–SVR). Furthermore, the correlation between PAI and wet biomass is assessed using the MSVR and SVR model. Contrary to the single output SVR, the correlation between biophysical parameters is adequately taken into account in MSVR based simultaneous inversion technique. Finally, the spatio-temporal maps for PAI and W at different growth stages indicate their variability with crop development over the test site.