Field Campaigns & Activities


Test Site: TSBWN
Test Site: Barddhaman, WB, INDIA
Test Site: Vijayawada, A.P., INDIA
Test Site: Vijayawada, A.P., INDIA
SMAPVEX16-MB
Test Site: Manitoba, CANADA

 


Research Activities


Monitoring Rice Crop using Time-series Sentinel-1 Data in Google Earth Engine Platform

This research work aims to explore the efficiency of using the Google Earth Engine (GEE) platform for temporal analysis of rice with the potential to apply the platform for a larger scale.

 

 

Crop Biophysical Parameters Retrieval from Polarimetric SAR data

This research work aims to assess an inversion algorithm for the semi-empirical water cloud model of vegetation based on radar backscattering power. The multi-target SVR has implemented for the simultaneous inversion of Leaf Area Index (LAI) and crop wet biomass over L-band E-SAR/AgriSAR 2006 data for winter barley crop.

 

 

 

This research work aims to explore the efficiency of using the Google Earth Engine (GEE) platform for temporal analysis of rice with the potential to apply the platform for a larger scale.

 

Recent work


[caption id="attachment_205" align="alignnone" width="785"] Study area at Bardhaman district of West Bengal, India[/caption]

 

[caption id="attachment_204" align="alignnone" width="785"] Temporal RGB composite with Sentinel-1 IW mode data.[/caption] [caption id="attachment_206" align="alignnone" width="785"] Temporal response of cross-pol (HV) backscatter coefficients for different sites of each Block.[/caption]

 

Team Members:
Dipankar Mandal, PhD Research Scholar, CSRE, IIT Bombay
Vineet Kumar, PhD Research Scholar, CSRE, IIT Bombay
Dr. Avik Bhattacharya, Associate Professor, CSRE, IIT Bombay
Dr. Y. S. Rao, Professor, CSRE, IIT Bombay

 

Publication

Mandal D., Kumar V., Bhattacharya A., Rao Y.S. (2017) Monitoring Rice Crop using Time Series Sentinel-1 Data in Google Earth Engine Platform. 38th Asian Conference on Remote Sensing, ACRS 2016, New Delhi, India, Paper ID: 624.

 

This research work aims to assess an inversion algorithm for the semi-empirical water cloud model of vegetation based on radar backscattering power. However, ill-posed inverse problem arises during the retrieval of biophysical parameter using such model. Regularization can cope up with such ill-posed inverse problem with the ease of support vector regression (SVR). The SVR has implemented for the simultaneous inversion of Leaf Area Index (LAI) and crop wet biomass over L-band E-SAR/AgriSAR 2006 data for winter barley crop.

Recent work


 

Temporal variation in LAI and biomass of Wheat crop.

 

[caption id="attachment_199" align="alignnone" width="785"] Semi empirical Water cloud model (Prevot et al., 1993)[/caption]

 


Team Members:
Dipankar Mandal, PhD Research Scholar, CSRE, IIT Bombay
Vineet Kumar, PhD Research Scholar, CSRE, IIT Bombay
Dr. Avik Bhattacharya, Associate Profesror, CSRE, IIT Bombay
Dr. Y. S. Rao, Profesror, CSRE, IIT Bombay

Acknowledgement:
We would like to thank European Space Agency (ESA) and German Aerospace Centre (Deutsches Zentrum für Luft- und Raumfahrt; DLR) for providing AgriSAR 2006 campaign (No. 19974/06/I-LG) data through Earth Observation Project (EOP) ID 14114.


Publications

Mandal D., Kumar V., and Rao Y.S. (2017) Winter Barley Biophysical Parameters Retrieval using Multi-output Support Vector Regression from Polarimetric SAR Data. ESA POLinSAR 2017, Rome, Italy.