scholarly journals Dual-polarimetric descriptors from Sentinel-1 GRD SAR data for crop growth assessment

2021 ◽  
Vol 178 ◽  
pp. 20-35
Author(s):  
Narayanarao Bhogapurapu ◽  
Subhadip Dey ◽  
Avik Bhattacharya ◽  
Dipankar Mandal ◽  
Juan M. Lopez-Sanchez ◽  
...  
Keyword(s):  
Crop Growth ◽  
Sar Data ◽  
Growth Assessment

scholarly journals Spatial Rice Yield Estimation Based on MODIS and Sentinel-1 SAR Data and ORYZA Crop Growth Model

2018 ◽  
Vol 10 (2) ◽  
pp. 293 ◽  
Author(s):  
Tri Setiyono ◽  
Emma Quicho ◽  
Luca Gatti ◽  
Manuel Campos-Taberner ◽  
Lorenzo Busetto ◽  
...  
Keyword(s):  
Growth Model ◽  
Rice Yield ◽  
Crop Growth ◽  
Yield Estimation ◽  
Crop Growth Model ◽  
Sar Data

Estimation of winter wheat crop growth parameters using time series Sentinel-1A SAR data

2017 ◽  
Vol 33 (9) ◽  
pp. 942-956 ◽  
Author(s):  
P. Kumar ◽  
R. Prasad ◽  
D. K. Gupta ◽  
V. N. Mishra ◽  
A. K. Vishwakarma ◽  
...  
Keyword(s):  
Time Series ◽  
Winter Wheat ◽  
Growth Parameters ◽  
Crop Growth ◽  
Wheat Crop ◽  
Winter Wheat Crop ◽  
Sar Data

scholarly journals Dual-polarimetric descriptors from Sentinel-1 GRD SAR data for crop growth assessment

2021 ◽  
Author(s):  
Narayanarao Bhogapurapu ◽  
Subhadip Dey ◽  
Avik Bhattacharya ◽  
Dipankar Mandal ◽  
Juan M Lopez Sanchez ◽  
...  
Keyword(s):  
Leaf Development ◽  
Development Stage ◽  
Crop Growth ◽  
Google Earth ◽  
Growth Monitoring ◽  
Flowering Stage ◽  
High Entropy ◽  
Sampling Points ◽  
Sar Data ◽  
Type Parameter

Accurate and high-resolution spatio-temporal information about crop phenology obtained from Synthetic Aperture Radar (SAR) data is an essential component for crop management and yield estimation at a local scale. Crop growth monitoring studies seldom exploit complete polarimetric information contained in dual-pol GRD SAR data. In this study, we propose three polarimetric descriptors: the pseudo scattering-type parameter (θc), the pseudo scattering entropy parameter (Hc), and the co-pol purity parameter (mc) from dual-pol S1 GRD SAR data. We also introduce a novel unsupervised clustering framework using Hc and θc with six clustering zones to represent various scattering mechanisms. We implemented the proposed algorithm on the cloud-based Google Earth Engine (GEE) platform for Sentinel-1 SAR data. We have shown the sensitivity of these descriptors over a time series of data for wheat and canola crops at a test site in Canada. From the leaf development stage to the flowering stage for both crops, the pseudo scattering-type parameter θc changes by approximately 17°. Moreover, within the entire phenology window, both mc and Hc varies by about 0.6. The effectiveness of θc and Hc to cluster the phenological stages for the two crops is also evident from the clustering plot. During the leaf development stage, about 90 % of the sampling points were clustered into the low to medium entropy scattering zone for both the crops. Throughout the flowering stage, the entire cluster shifted into the high entropy vegetation scattering zone. Finally, during the ripening stage, the clusters of sample points were split between the high entropy vegetation scattering zone and the high entropy distributed scattering zone, with > 55 % of the sampling points in the high entropy distributed scattering zone. This innovative clustering framework will facilitate<br>the operational use of S1 GRD SAR data for agricultural applications.<div><b><br></b></div><div>This article is submitted to ISPRS Journal of Photogrammetry and Remote Sensing<br><br></div>

Monitoring Wheat Crop Growth Using a New Vegetation Index from Sentinel-1 GRD SAR Data

2021 ◽  
Author(s):  
Narayanarao Bhogapurapu ◽  
Subhadip Dey ◽  
Dipankar Mandal ◽  
Avik Bhattacharya ◽  
Y. S. Rao
Keyword(s):  
Vegetation Index ◽  
Crop Growth ◽  
Wheat Crop ◽  
Sar Data

scholarly journals Dual-polarimetric descriptors from Sentinel-1 GRD SAR data for crop growth assessment

2021 ◽  
Author(s):  
Narayanarao Bhogapurapu ◽  
Subhadip Dey ◽  
Avik Bhattacharya ◽  
Dipankar Mandal ◽  
Juan M Lopez Sanchez ◽  
...  
Keyword(s):  
Leaf Development ◽  
Development Stage ◽  
Crop Growth ◽  
Google Earth ◽  
Growth Monitoring ◽  
Flowering Stage ◽  
High Entropy ◽  
Sampling Points ◽  
Sar Data ◽  
Type Parameter

Accurate and high-resolution spatio-temporal information about crop phenology obtained from Synthetic Aperture Radar (SAR) data is an essential component for crop management and yield estimation at a local scale. Crop growth monitoring studies seldom exploit complete polarimetric information contained in dual-pol GRD SAR data. In this study, we propose three polarimetric descriptors: the pseudo scattering-type parameter (θc), the pseudo scattering entropy parameter (Hc), and the co-pol purity parameter (mc) from dual-pol S1 GRD SAR data. We also introduce a novel unsupervised clustering framework using Hc and θc with six clustering zones to represent various scattering mechanisms. We implemented the proposed algorithm on the cloud-based Google Earth Engine (GEE) platform for Sentinel-1 SAR data. We have shown the sensitivity of these descriptors over a time series of data for wheat and canola crops at a test site in Canada. From the leaf development stage to the flowering stage for both crops, the pseudo scattering-type parameter θc changes by approximately 17°. Moreover, within the entire phenology window, both mc and Hc varies by about 0.6. The effectiveness of θc and Hc to cluster the phenological stages for the two crops is also evident from the clustering plot. During the leaf development stage, about 90 % of the sampling points were clustered into the low to medium entropy scattering zone for both the crops. Throughout the flowering stage, the entire cluster shifted into the high entropy vegetation scattering zone. Finally, during the ripening stage, the clusters of sample points were split between the high entropy vegetation scattering zone and the high entropy distributed scattering zone, with > 55 % of the sampling points in the high entropy distributed scattering zone. This innovative clustering framework will facilitate<br>the operational use of S1 GRD SAR data for agricultural applications.<div><b><br></b></div><div>This article is submitted to ISPRS Journal of Photogrammetry and Remote Sensing<br><br></div>

Novel Clustering Schemes for Full and Compact Polarimetric SAR Data: A Case Study for Rice Phenology Characterization

2020 ◽  
Author(s):  
Subhadip Dey ◽  
Avik Bhattacharya ◽  
Debanshu Ratha ◽  
Dipankar Mandal ◽  
Heather McNairn ◽  
...  
Keyword(s):  
Morphological Characteristics ◽  
Temporal Analysis ◽  
Crop Growth ◽  
Unsupervised Clustering ◽  
Degree Of Polarization ◽  
Growth Stages ◽  
Phenological Stages ◽  
Sar Images ◽  
Sar Data ◽  
Type Parameter

Information on rice phenological stages from Synthetic Aperture Radar (SAR)images is of prime interest for in-season monitoring. Often, prior in-situ measurements of phenology are not available. In such situations, unsupervised clustering of SAR images might help in discriminating phenological stages of a crop throughout its growing period. Among the existing unsupervised clustering techniques using full-polarimetric (FP) SAR images, the eigenvalue-eigenvector based roll-invariant scattering-type parameter, and the scattering entropy parameter are widely used in the literature. In this study, we utilize a unique target scattering-type parameter, which jointly uses the Barakat degree of polarization and the elements of the polarimetric coherency matrix. In particular, the degree of polarization attributes to scattering randomness from a target. The scattering randomness in crops increases with advancements in its growth stages due to the development of branches and foliage. Hence, the degree of polarization varies with changes in the crop growth stages. Besides, the elements of the coherency matrices are directly related to the crop geometry as well as soil and crop water content. There-fore, this complementarity information captures the scattering randomness at each crop growth stage while taking into account diverse crop morphological characteristics. Likewise, we also utilize an equivalent parameter proposed for compact-polarimetric (CP) SAR data. These scattering-type parameters are analogous to the Cloude-Pottier’s parameter for FP SAR data and the ellipticity parameter for CP SAR data. Besides this, we also introduce new clustering schemes for both FP and CP SAR data for segmenting diverse scattering mechanisms across the phenological stages of rice. In this study, we use the RADARSAT-2 FP and simulated CP SAR data acquired over the Indian test site of Vijayawada under the Joint Experiment for Crop Assessment and Monitoring (JECAM) initiative. The temporal analysis of the scattering-type parameters and the new clustering schemes help us to investigate detailed scattering characteristics from rice across its phenological stages.<div>(Submitted to ISPRS journal)</div>

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