Crop biophysical parameter retrieval from Sentinel-1 SAR data with a multi-target inversion of Water Cloud Model

Citation

Mandal, D., Kumar, V., Lopez-Sanchez, J.M., Bhattacharya, A., McNairn, H., Rao, Y.S. (2020). Crop biophysical parameter retrieval from Sentinel-1 SAR data with a multi-target inversion of Water Cloud Model. International Journal of Remote Sensing, [online] 41(14), 5503-5524. http://dx.doi.org/10.1080/01431161.2020.1734261

Plain language summary

A number of researchers have used the semi-empirical Water Cloud Model (WCM) to estimate biophysical crop parameters from Synthetic Aperture Radar (SAR) satellite imagery. These crop parameters include Plant Area Index (PAI) and wet above ground biomass (W). Typically, once the WCM is parameterized, the model is inverted using a Look Up Table (LUT), and PAI or W are estimated from the radar scattering intensity. However, this inversion method does not permit the retrieval of PAI and W simultaneously. In this 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 estimated from the WCM. The performance of the inversion approach is evaluated with in-situ field measurements collected over the test site in Manitoba (Canada). 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. The WCM was used to produce geospatial maps for PAI and W at different growth stages. These maps for the Manitoba test site track the variability in crop development, as expected. This method offers an interesting approach to retrieve indicators of crop productivity using space based SAR imagery.

Abstract

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.

Publication date

2020-07-17

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