A processing chain for estimating crop biophysical parameters using temporal Sentinel-1 synthetic aperture radar data in cloud computing framework

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.Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Mathematical Geodesy and Positionin

A comparison between support vector machine and water cloud model for estimating crop leaf area index

The water cloud model (WCM) can be inverted to estimate leaf area index (LAI) using the intensity of backscatter from synthetic aperture radar (SAR) sensors. Published studies have demonstrated that the WCM can accurately estimate LAI if the model is effectively calibrated. However, calibration of this model requires access to field measures of LAI as well as soil moisture. In contrast, machine learning (ML) algorithms can be trained to estimate LAI from satellite data, even if field moisture measures are not available. In this study, a support vector machine (SVM) was trained to estimate the LAI for corn, soybeans, rice, and wheat crops. These results were compared to LAI estimates from the WCM. To complete this comparison, in situ and satellite data were collected from seven Joint Experiment for Crop Assessment and Monitoring (JECAM) sites located in Argentina, Canada, Germany, India, Poland, Ukraine and the United States of America (U.S.A.). The models used C-Band backscatter intensity for two polarizations (like-polarization (VV) and cross-polarization (VH)) acquired by the RADARSAT-2 and Sentinel-1 SAR satellites. Both the WCM and SVM models performed well in estimating the LAI of corn. For the SVM, the correlation (R) between estimated LAI for corn and LAI measured in situ was reported as 0.93, with a root mean square error (RMSE) of 0.64 m2m-2 and mean absolute error (MAE) of 0.51 m2m-2. The WCM produced an R-value of 0.89, with only slightly higher errors (RMSE of 0.75 m2m-2 and MAE of 0.61 m2m-2) when estimating corn LAI. For rice, only the SVM model was tested, given the lack of soil moisture measures for this crop. In this case, both high correlations and low errors were observed in estimating the LAI of rice using SVM (R of 0.96, RMSE of 0.41 m2m-2 and MAE of 0.30 m2m-2). However, the results demonstrated that when the calibration points were limited (in this case for soybeans), the WCM outperformed the SVM model. This study demonstrates the importance of testing different modeling approaches over diverse agro-ecosystems to increase confidence in model performance.Water Resource