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

Dipankar Mandal, Vineet Kumar, Juan M. Lopez-Sanchez, Y. S. Rao, Heather McNairn, Avik Bhattacharya, Scott Mitchell

Research output: Chapter in Book/Conference proceedings/Edited volumeChapterScientificpeer-review

1 Citation (Scopus)
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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.

Original languageEnglish
Title of host publicationRadar Remote Sensing
Subtitle of host publicationApplications and Challenges
Number of pages17
ISBN (Electronic)9780128234570
ISBN (Print)9780128235942
Publication statusPublished - 2022

Bibliographical note

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.


  • Cloud computing
  • Crop
  • Google Earth Engine
  • Model inversion
  • PAI
  • Sentinel-1


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