A multi-year cross-validation experiment for estimating rice plant area index (PAI) over the JECAM-India test site from simulated RADARSAT constellation mission (RCM) compact polarimetric SAR data

ABSTRACT

Using the cross-validation approach, strategies for estimating biophysical parameters are still pre-operational with synthetic aperture radar (SAR) data. In this regard, the Joint Experiment for Crop Assessment and Monitoring (JECAM) SAR inter-comparison experiments provide an opportunity for the potential implementation of cross-validation strategies for biophysical parameters retrieval utilizing the next-generation compact polarimetric (CP) modes available from the RADARSAT Constellation Mission (RCM). This work first uses the conventional semi-empirical Water Cloud Model (WCM) modified by exploiting the scattering power decompositions of CP measurements to estimate the Plant Area Index (PAI) for rice. The modified WCM (MWCM) is then inverted using the scattering power components from the $iS-\mathrm{\Omega }$ decomposition. We compare the PAI estimates using MWCM-$iS-\mathrm{\Omega }$ between the estimates obtained from (1) the conventional WCM using the RH and RV backscatter intensities and (2) MWCM-$m-\chi$ decomposition scattering powers. We exploit a time series of simulated compact-pol SAR data over the JECAM test site in Vijayawada, India, throughout 2018 and 2019. We use the C-band RADARSAT-2 full-pol data to simulate the RADARSAT Constellation Mission (RCM) compact-pol mode data. Utilizing the advantage of systematically collected multi-year SAR data and in-situ measurements, the present research also assesses the calibrated model transferability performances to another data set and cross-validation of a model in a multi-year experiment setting. The comparative analysis indicates potential improvements in PAI estimation with MWCM-$iS-\mathrm{\Omega }$ scattering powers. A high range of correlation coefficient ($r=0.84\pm 0.06$) between the estimated and observed PAI is observed with good Root Mean Square Error (RMSE) of $0.778\pm 0.107$ m² m⁻², and Mean Absolute Error (MAE) of $0.601\pm 0.103$ m² m⁻².

Acknowledgements

The authors would like to thank the Canadian Space Agency and MAXAR Technologies Ltd. (formerly MDA) for providing the RADARSAT-2 images through the Joint Experiment for Crop Assessment and Monitoring (JECAM) SAR Inter-comparison experiment network. The authors are also thankful to the Andhra Pradesh Space Application Centre (APSAC), ITE & C Department, Government of Andhra Pradesh for support during the field campaigns. The authors acknowledge the GEO-AWS Earth Observation Cloud Credits Program, which supported the computation on AWS cloud platform through the project “AWS4AgriSAR-Crop inventory mapping from SAR data on cloud computing platform.” This work was supported in part by the Shastri Indo Canadian Institute, New Delhi, India, through the Shastri Research Student Fellowship (2018–2019) grant for the project “Estimation of crop biophysical parameters using Polarimetric Synthetic Aperture Radar (SAR) Remote Sensing Data.”

Disclosure statement

No potential conflict of interest was reported by the author(s).

Codes

We made available the code that supports the reproducibility and replicability of this work in a Github repository: https://github.com/dipankar05/IJRS-MWCM.

Data availability statement (DAS)

Due to the nature of this research, participants of this study did not agree for their data to be shared publicly, so supporting data is not available.

Additional information

Funding

This work was supported by the Shastri Indo-Canadian Institute.