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>

scholarly journals Novel Clustering Schemes for Full and Compact Polarimetric SAR Data: An Application for Rice Phenology Characterization

2020 ◽  
Author(s):  
Subhadip Dey ◽  
Avik Bhattacharya ◽  
Debanshu Ratha ◽  
Dipankar Mandal ◽  
Heather McNairn ◽  
...  
Keyword(s):  
Temporal Analysis ◽  
Test Site ◽  
Unsupervised Clustering ◽  
Degree Of Polarization ◽  
Phenological Stages ◽  
Sar Images ◽  
Growing Period ◽  
Entropy Parameter ◽  
Sar Data ◽  
Type Parameter

<div>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. 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</div><div>stages.</div>

scholarly journals Novel Clustering Schemes for Full and Compact Polarimetric SAR Data: An Application for Rice Phenology Characterization

2020 ◽  
Author(s):  
Subhadip Dey ◽  
Avik Bhattacharya ◽  
Debanshu Ratha ◽  
Dipankar Mandal ◽  
Heather McNairn ◽  
...  
Keyword(s):  
Temporal Analysis ◽  
Test Site ◽  
Unsupervised Clustering ◽  
Degree Of Polarization ◽  
Phenological Stages ◽  
Sar Images ◽  
Growing Period ◽  
Entropy Parameter ◽  
Sar Data ◽  
Type Parameter

<div>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. 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</div><div>stages.</div>

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>

scholarly journals Deriving crop calendar using NDVI time-series

2014 ◽  
Vol XL-8 ◽  
pp. 869-873 ◽  
Author(s):  
J. H. Patel ◽  
M. P. Oza
Keyword(s):  
Vegetation Index ◽  
Spectral Response ◽  
Temporal Analysis ◽  
Growth Cycle ◽  
Crop Growth ◽  
Data Series ◽  
Time Interval ◽  
Growth Stages ◽  
Seasonal Behavior ◽  
Crop Calendar

Agricultural intensification is defined in terms as cropping intensity, which is the numbers of crops (single, double and triple) per year in a unit cropland area. Information about crop calendar (i.e. number of crops in a parcel of land and their planting & harvesting dates and date of peak vegetative stage) is essential for proper management of agriculture. Remote sensing sensors provide a regular, consistent and reliable measurement of vegetation response at various growth stages of crop. Therefore it is ideally suited for monitoring purpose. The spectral response of vegetation, as measured by the Normalized Difference Vegetation Index (NDVI) and its profiles, can provide a new dimension for describing vegetation growth cycle. The analysis based on values of NDVI at regular time interval provides useful information about various crop growth stages and performance of crop in a season. However, the NDVI data series has considerable amount of local fluctuation in time domain and needs to be smoothed so that dominant seasonal behavior is enhanced. Based on temporal analysis of smoothed NDVI series, it is possible to extract number of crop cycles per year and their crop calendar. <br><br> In the present study, a methodology is developed to extract key elements of crop growth cycle (i.e. number of crops per year and their planting – peak - harvesting dates). This is illustrated by analysing MODIS-NDVI data series of one agricultural year (from June 2012 to May 2013) over Gujarat. Such an analysis is very useful for analysing dynamics of kharif and rabi crops.

scholarly journals Differentially Deep Subspace Representation for Unsupervised Change Detection of SAR Images

2019 ◽  
Vol 11 (23) ◽  
pp. 2740 ◽  
Author(s):  
Bin Luo ◽  
Chudi Hu ◽  
Xin Su ◽  
Yajun Wang
Keyword(s):  
Principal Component Analysis ◽  
Change Detection ◽  
Principal Component ◽  
Temporal Analysis ◽  
Component Analysis ◽  
Sar Images ◽  
Linear Change ◽  
Non Linear ◽  
Multi Temporal ◽  
Sar Data

Temporal analysis of synthetic aperture radar (SAR) time series is a basic and significant issue in the remote sensing field. Change detection as well as other interpretation tasks of SAR images always involves non-linear/non-convex problems. Complex (non-linear) change criteria or models have thus been proposed for SAR images, instead of direct difference (e.g., change vector analysis) with/without linear transform (e.g., Principal Component Analysis, Slow Feature Analysis) used in optical image change detection. In this paper, inspired by the powerful deep learning techniques, we present a deep autoencoder (AE) based non-linear subspace representation for unsupervised change detection with multi-temporal SAR images. The proposed architecture is built upon an autoencoder-like (AE-like) network, which non-linearly maps the input SAR data into a latent space. Unlike normal AE networks, a self-expressive layer performing like principal component analysis (PCA) is added between the encoder and the decoder, which further transforms the mapped SAR data to mutually orthogonal subspaces. To make the proposed architecture more efficient at change detection tasks, the parameters are trained to minimize the representation difference of unchanged pixels in the deep subspace. Thus, the proposed architecture is namely the Differentially Deep Subspace Representation (DDSR) network for multi-temporal SAR images change detection. Experimental results on real datasets validate the effectiveness and superiority of the proposed architecture.

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>

scholarly journals Joint Use of Scattered and Received Wave Polarization Information for Target Characterization and Scattering Power Decomposition

2020 ◽  
Author(s):  
Alejandro C. Frery ◽  
Debanshu Ratha ◽  
Avik Bhattacharya ◽  
Dipankar Mandal ◽  
Subhadip Dey
Keyword(s):  
Covariance Matrix ◽  
Scattered Wave ◽  
Degree Of Polarization ◽  
Total Power ◽  
Geometrical Factor ◽  
Wave Polarization ◽  
Decomposition Techniques ◽  
Target Characterization ◽  
Sar Data ◽  
Type Parameter

This manuscript was submitted on 31 December 2019 to IEEE Transactions on Geoscience and Remote Sensing.<br><br>Abstract: Incoherent target decomposition techniques provide unique scattering information from polarimetric SAR data either by fitting appropriate scattering models or by optimizing the ``received" wave intensity through the diagonalization of the coherency (or covariance) matrix. Hence, the information provided by the ``scattered" wave might be neglected. This scattered wave information can be well utilized to gain complete polarimetric information for numerous applications. In this study, a new roll-invariant scattering-type parameter is introduced, which jointly uses the degree of polarization as the ``scattered" wave information and the elements of the covariance matrix as the ``received" wave information from both full-polarimetric (FP) and compact-polarimetric (CP) SAR data. This scattering-type parameter, which is comparable to that of the Cloude $\alpha$ for FP SAR data and the ellipticity parameter $\chi$ for CP SAR data, can be well utilized to characterize various targets. Furthermore, this new scattering-type parameter is adequately utilized to obtain a non-model based three-component scattering power decomposition technique. The double-bounce and the odd-bounce scattering powers are obtained by modulating the total polarized power by a proper geometrical factor easily derived using the new scattering-type parameter for both FP and CP SAR data. Moreover, due to its natural and direct formulation, the decomposition scattering powers are non-negative and roll-invariant while the total power is conserved. The proposed method is qualitatively and quantitatively assessed utilizing the L-band ALOS-2 and C-band Radarsat-2 FP and the associated simulated CP SAR data.

Seasonal Nonstructural Carbohydrate Patterns in Dewberry (Rubus spp.) Roots

Weed Science ◽  
2021 ◽  
pp. 1-23
Author(s):  
Katherine M. Ghantous ◽  
Hilary A. Sandler
Keyword(s):  
Weed Management ◽  
Management Practices ◽  
Soluble Sugars ◽  
Leaf Expansion ◽  
Control Measures ◽  
Growth Stages ◽  
Phenological Stages ◽  
Long Term Storage ◽  
Nonstructural Carbohydrate ◽  
Fruit Maturity

Abstract Applying control measures when carbohydrate levels are low can decrease the likelihood of plant survival, but little is known about the carbohydrate cycles of dewberry (Rubus spp.), a problematic weed group on cranberry farms. Weedy Rubus plants were collected from areas adjacent to production beds on commercial cranberry farms in Massachusetts, two locations per year for two years. For each site and year, four entire plants were collected at five phenological stages: budbreak, full leaf expansion, flowering, fruit maturity, and after onset of dormancy. Root sections were analyzed for total nonstructural carbohydrate (TNC; starch, sucrose, fructose, and glucose). Overall trends for all sites and years showed TNC were lowest at full leaf expansion or flowering; when sampled at dormancy, TNC concentrations were greater than or equal to those measured at budbreak. Starch, a carbohydrate form associated with long-term storage, had low levels at budbreak, leaf expansion and/or flowering with a significant increase at fruit maturity and the onset of dormancy, ending at levels higher than those found at budbreak. The concentration of soluble sugars, carbohydrate forms readily usable by plants, was highest at budbreak compared to the other four phenological samplings. Overall, our findings supported the hypothesis that TNC levels within the roots of weedy Rubus plants can be predicted based on different phenological growth stages in Massachusetts. However, recommendations for timing management practices cannot be based on TNC cycles alone; other factors such as temporal proximity to dormancy may also impact Rubus plants recovery and further research is warranted. Late-season damage should allow less time for plants to replenish carbohydrate reserves (prior to the onset of dormancy), thereby likely enhancing weed management tactics effectiveness over time. Future studies should consider tracking the relationship between environmental conditions, phenological stages, and carbohydrate trends.

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