scholarly journals MULTI-TEMPORAL FEATURE EXTRACTION FOR PRECISE MAIZE AREA MAPPING USING TIME-SERIES SENTINEL 1A SAR DATA

2019 ◽  
Vol XLII-3/W6 ◽  
pp. 169-173
Author(s):  
M. Venkatesan ◽  
S. Pazhanivelan ◽  
N. S. Sudarmanian
Keyword(s):  
Time Series ◽  
Tamil Nadu ◽  
Growth Period ◽  
Maturity Stage ◽  
Kappa Score ◽  
Maximum Increase ◽  
Maize Crop ◽  
Temporal Features ◽  
Multi Temporal ◽  
Sar Data

<p><strong>Abstract.</strong> A research study was conducted to map maize area in Ariyalur and Perambalur districts of Tamil Nadu, India using multi-temporal features extracted from time-series Sentinel 1A SAR data. Multi-temporal Sentinel 1A GRD data at VV and VH polarizations and SLC products were acquired for the study area at 12 days interval and processed using MAPscape-RICE software. Multi-temporal Sentinel 1A data was used to identify the backscattering dB curve of maize crop. Analysis of temporal signatures of the crop showed minimum values at sowing period and maximum during the tasseling stage, which decreased during maturity stage of the crop. The maximum increase in the signature was observed during seedling to vegetative growth period. The signature derived from dB values for maize crop expressed a significant temporal behavior with the range of &amp;minus;21.26 to &amp;minus;13.18 in VH polarization and &amp;minus;14.05 to &amp;minus;6.54 in VV polarization. Considering the accuracy of SAR data to phenological variations of maize growing period, Multi-Temporal Features were extracted from multi-temporal dB images of VV and VH polarization and coherence images. Multi-Temporal Features viz., max, min, mean, max date, min date and span ratio were extracted from VV and VH polarizations of Sentinel 1A GRD and SLC data to classify maize pixels in the study area using parameterized classification approach. The overall classification accuracy was 91 percent with the kappa score of 0.82.</p>

scholarly journals INTEGRATING TIME-SERIES SAR DATA AND ORYZA CROP GROWTH MODEL IN RICE AREA MAPPING AND YIELD ESTIMATION FOR CROP INSURANCES

2019 ◽  
Vol XLII-3/W6 ◽  
pp. 239-243
Author(s):  
S. Pazhanivelan ◽  
K. P. Ragunath ◽  
N. S. Sudarmanian ◽  
R. Kumaraperumal ◽  
T. Setiyono ◽  
...  
Keyword(s):  
Time Series ◽  
Tamil Nadu ◽  
Crop Insurance ◽  
Crop Growth ◽  
Linear Diffusion ◽  
Temporal Features ◽  
Rice Area ◽  
Multi Temporal ◽  
Sar Data ◽  
Sar Imagery

<p><strong>Abstract.</strong> Lowland rice in tropical and subtropical regions can be detected precisely and its crop growth can be tracked effectively through Synthetic Aperture Radar (SAR) imagery, especially where cloud cover restricts the use of optical imagery. Parameterised classification with multi-temporal features derived from regularly acquired, C-band, VV and VH polarized Sentinel-1A SAR imagery was used for mapping rice area. A fully automated processing chain in MAPscape-Rice software was used to convert the multi-temporal SAR data into terrain-geocoded &amp;sigma;<sup>0</sup> values, which included strip mosaicking, co-registration of images acquired with the same observation geometry and mode, time-series speckle filtering, terrain geocoding, radiometric calibration and normalization. Further Anisotropic non-linear diffusion (ANLD) filtering was done to smoothen homogeneous targets, while enhancing the difference between neighbouring areas. Multi-Temporal Features viz., max, min, mean, max date, min date and span ratio were extracted from VV and VH polarizations to classify rice pixels. Rice detection was based on the analysis of temporal signature from SAR backscatter in relation to crop stages. About sixty images across four footprints covering 16 <i>samba</i> (<i>Rabi</i>) rice growing districts of Tamil Nadu, India were obtained between August 2017 and January 2018. In-season site visits were conducted across 280 monitoring locations in the footprints for classification purposes and more than 1665 field observations were made for accuracy assessment. A total rice area of 1.07 million ha was mapped with classification accuracy from 90.3 to 94.2 per cent with Kappa values ranging from 0.81 to 0.88. Using ORYZA2000, a weather driven process based crop growth simulation model developed by IRRI, yield estimates were made by integrating remote sensing products viz., seasonal rice area, start of season and backscatter time series. By generating average backscatter for each time series and dB stack for each SoS, LAI values were estimated. The model has generated rice yield estimate for each hectare which were aggregated at administrative boundary level and compared against CCE yield. Yield Simulation accuracy of more than 86&amp;ndash;91% at district level and 82&amp;ndash;97% at block level from the study indicates the suitability of these products for policy decisions. SAR products and yield information were used to meet the requirements of PMFBY crop insurance scheme in Tamil Nadu and helped in identifying or invoking prevented/failed sowing in 529 villages and total crop failure in 821 villages. In total 303703 farmers were benefitted by this technology in getting payouts of INR 9.94 billion through crop insurance. The satellite technology as an operational service has helped in getting quicker payouts.</p>

Rabi Groundnut Area Estimation using Synthetic Aperture Radar (SAR) in Thiruvannamalai District of Tamil Nadu

2021 ◽  
Author(s):  
S. Thirumeninathan ◽  
S. Pazhanivelan ◽  
N.S. Sudarmanian ◽  
K.P. Ragunath ◽  
A. Gurusamy ◽  
...  
Keyword(s):  
Food Production ◽  
Tamil Nadu ◽  
Growth Period ◽  
Crop Acreage ◽  
Maximum Likelihood Classification ◽  
Legume Crop ◽  
Temporal Features ◽  
Production Distribution ◽  
Multi Temporal ◽  
Food And Feed

Background: Groundnut, commonly known as peanut, is a significant oil, food and feed legume crop grown in all seasons in Tamil Nadu, including kharif, rabi and summer and it is cultivated both under irrigated and rainfed conditions in all the seasons at Thiruvannamlai district. One of the most important applications of remote sensing in agriculture is a Crop Acreage and Production Estimation (CAPE). The CAPE’s main goal is to estimate crop acreage and production of important crops, so that advanced food production, distribution and supply data were achieved. Methods: Multi-temporal Sentinel 1A SAR IW- GRD data with 20 m spatial resolution and 12 days temporal resolution of Vertical - Horizontal (V-H) polarization were downloaded for the period of 4th October 2020 to 8th January 2021 to have the full coverage during the crop growth period in the study area used for this work. Crop backscattering and multi-temporal features were extracted from MAPscape 5.2 automated pre-processing tool and its classified using supervised maximum likelihood classification for groundnut acreage extraction for Thiruvannamalai district. Result: The rabi groundnut area of Thiruvannamalai district of Tamil Nadu was estimated using SAR Sentinel-1A data as 32298 ha with a higher accuracy percentage of 87.4 and kappa coefficient of 0.75.

scholarly journals AREA ESTIMATION OF COTTON AND MAIZE CROPS IN PERAMBALUR DISTRICT OF TAMIL NADU USING MULTI DATE SENTINEL-1A SAR DATA &amp; OPTICAL DATA

2019 ◽  
Vol XLII-3/W6 ◽  
pp. 137-140
Author(s):  
K. Ramalingam ◽  
A. B. Ramathilagam ◽  
P. Murugesan
Keyword(s):  
Tamil Nadu ◽  
Unsupervised Classification ◽  
Optical Data ◽  
Landsat 8 ◽  
Maize Crop ◽  
Classification Approach ◽  
Area Estimation ◽  
Crop Calendar ◽  
Multi Temporal ◽  
Sar Data

<p><strong>Abstract.</strong> This study was carried out to estimate the area of cotton and maize crops in Permabalur district of Tamil Nadu using microwave and optical data. Permabalur was selected as the study area, as it is the largest cotton and maize producing district in Tamil Nadu. The multi-temporal Sentinel-1A SAR data was acquired from 09th July, 2016 to 17th January, 2017 as it coincides with the crop calendar of these crops. Both the Vertical-Vertical (VV) and Vertical-Horizontal (VH) polarized data were compared. The cloud free Landsat 8 data acquired on 7th October 2016 was fused with the Vertical–Vertical (VV) and Vertical-Horizontal (VH) polarized data of 13th October and classified. Unsupervised classification approach was adopted to classify the cotton and maize pixels. The highest accuracy of 72.73% and 76.24% were achieved in VV polarization + Landsat 8 data and VH polarization + Landsat 8 data respectively. The cotton and maize areas were estimated to be 20,218&amp;thinsp;ha and 28,032&amp;thinsp;ha respectively. It is also evident that VH polarization fused with optical data is better in discriminating the cotton and maize crop than VV polarization fused with optical data.</p>

scholarly journals ESTIMATION OF COTTON AND MAIZE CROP AREA IN PERAMBALUR DISTRICT OF TAMIL NADU USING MULTI-DATE SENTINEL-1A SAR DATA

2019 ◽  
Vol XLII-3/W6 ◽  
pp. 67-71
Author(s):  
M. Ashmitha Nihar ◽  
J. Mohammed Ahamed ◽  
S. Pazhanivelan ◽  
R. Kumaraperumal ◽  
K. Ganesha Raj
Keyword(s):  
Decision Tree ◽  
Tamil Nadu ◽  
Ground Truth ◽  
Decision Tree Classifier ◽  
Maize Crop ◽  
Ground Truth Data ◽  
Tree Classifier ◽  
Multi Temporal ◽  
Crop Classification ◽  
Sar Data

<p><strong>Abstract.</strong> Crop classification is a key issue for agricultural monitoring using remote sensing techniques. Synthetic Aperture Radar (SAR) data has an advantage in crop classification because of its all-weather imaging capabilities. The objective of this study was to investigate the capability of SAR data for estimation of cotton and maize area in Perambalur district of Tamil Nadu. The multi-temporal Sentinel-1 SAR data was acquired from 2nd September, 2017 to 24th January, 2018. Both the Vertical-Vertical (VV) and Vertical-Horizontal (VH) polarized data was used. Ground truth data collection was performed for cotton and maize during the vegetative, flowering and harvesting stages. Sixty per cent of the ground truth data were used for training and remaining forty per cent were utilized for validation. The temporal backscattering coefficient (&amp;sigma;0) for cotton and maize were extracted using the training datasets.. The mean backscattering values for cotton crop during the entire cropping period had a range from &amp;minus;11.729&amp;thinsp;dB to &amp;minus;8.827&amp;thinsp;dB and from &amp;minus;19.167&amp;thinsp;dB to &amp;minus;14.186 dB for VV and VH polarization respectively. For maize crop it ranged from &amp;minus;11.248&amp;thinsp;dB to &amp;minus;8.878&amp;thinsp;dB and from &amp;minus;19.043 dB to &amp;minus;14.753&amp;thinsp;dB for VV and VH polarized data respectively. The Spectral Angle Mapper (SAM) and Decision Tree classifier (DT) methods were adopted for cotton and maize area estimation. SAM classified 73259 and 51489 hectares (ha) as cotton and maize respectively in VV polarization. DT classified the area of 61501 and 64530&amp;thinsp;ha for cotton and maize respectively in VH polarization. The accuracy measures, such as overall accuracy, producer’s accuracy and user’s accuracy and kappa coefficient were estimated. SAM classifier exhibits the overall accuracy of 73.3% for VV Decision tree classifier reported the overall accuracy of 75.0% for VH. It is evident from the present study, that the multi-temporal Sentinel-1 SAR sensor can be well used for the discrimination of cotton and maize crops because of its high temporal resolution which captures the complete phenology of the crops during the cropping period.</p>

scholarly journals DCN-Based Spatial Features for Improving Parcel-Based Crop Classification Using High-Resolution Optical Images and Multi-Temporal SAR Data

2019 ◽  
Vol 11 (13) ◽  
pp. 1619 ◽  
Author(s):  
Zhou Ya’nan ◽  
Luo Jiancheng ◽  
Feng Li ◽  
Zhou Xiaocheng
Keyword(s):  
Time Series ◽  
High Resolution ◽  
Short Term Memory ◽  
Multivariate Time Series ◽  
Hunan Province ◽  
Spatial Features ◽  
Optical Images ◽  
Multi Temporal ◽  
Crop Classification ◽  
Sar Data

Spatial features retrieved from satellite data play an important role for improving crop classification. In this study, we proposed a deep-learning-based time-series analysis method to extract and organize spatial features to improve parcel-based crop classification using high-resolution optical images and multi-temporal synthetic aperture radar (SAR) data. Central to this method is the use of multiple deep convolutional networks (DCNs) to extract spatial features and to use the long short-term memory (LSTM) network to organize spatial features. First, a precise farmland parcel map was delineated from optical images. Second, hundreds of spatial features were retrieved using multiple DCNs from preprocessed SAR images and overlaid onto the parcel map to construct multivariate time-series of crop growth for parcels. Third, LSTM-based network structures for organizing these time-series features were constructed to produce a final parcel-based classification map. The method was applied to a dataset of high-resolution ZY-3 optical images and multi-temporal Sentinel-1A SAR data to classify crop types in the Hunan Province of China. The classification results, showing an improvement of greater than 5.0% in overall accuracy relative to methods without spatial features, demonstrated the effectiveness of the proposed method in extracting and organizing spatial features for improving parcel-based crop classification.

scholarly journals Sentinel 1A SAR Backscattering Signature of Maize and Cotton Crops

2017 ◽  
Vol 104 (.1-.4) ◽  
Author(s):  
Kumaraperumal R ◽  
◽  
Shama M ◽  
Balaji Kannan ◽  
Ragunath K P ◽  
...  
Keyword(s):  
Tamil Nadu ◽  
Ground Truth ◽  
Backscattering Coefficient ◽  
Ground Truth Data ◽  
Crop Monitoring ◽  
Multi Temporal ◽  
The Mean ◽  
Sar Data ◽  
Remote Sensing Techniques ◽  
Crop Discrimination

Crop discrimination is a key issue for agricultural monitoring using remote sensing techniques. Synthetic Aperture Radar (SAR) data are advantageous for crop monitoring and classification because of their all-weather imaging capabilities. The multi-temporal Sentinel 1A SAR data was acquired from 08th August, 2015 to 23rd January, 2016 at 12 days interval covering the extent of Perambalur district of Tamil Nadu. Both the Vertical - Vertical (VV) and Vertical-Horizontal (VH) polarized data are compared. The ground truth data collection was performed for cotton and maize during the vegetative, flowering and harvesting stages. The temporal backscattering coefficient (σ0 ) for cotton and maize are extracted using the training datasets. The mean backscattering values for cotton during the entire cropping period ranges from -10.58 dB to -6.28 dB and -20.59 dB to -14.53 dB for VV and VH polarized data respectively, and for maize it ranges from -11.08 dB to -7.07 dB and -19.85 dB to -14.14 dB for VV and VH polarized data respectively.

scholarly journals RICE AREA ESTIMATION USING PARAMETERIZED CLASSIFICATION OF SENTINEL 1A SAR DATA

2019 ◽  
Vol XLII-3/W6 ◽  
pp. 141-147 ◽  
Author(s):  
M. G. Raman ◽  
R. Kaliaperumal ◽  
S. Pazhanivelan ◽  
B. Kannan
Keyword(s):  
Satellite Data ◽  
Tamil Nadu ◽  
Accuracy Assessment ◽  
Kappa Index ◽  
Transplanted Rice ◽  
Cropping Season ◽  
Rice Area ◽  
Multi Temporal ◽  
Sar Data ◽  
Crop Parameters

<p><strong>Abstract.</strong> A research study was conducted during <i>Rabi</i> 2016 (Samba season) to estimate rice area using SAR data in Tiruvarur district of Tamil Nadu. Multi temporal Sentinel 1A satellite data with VV and VH polarization at 20&amp;thinsp;m spatial resolution was acquired between September 2016 and January 2017 at 12 days interval and processed using rule-based Parameterized classification in MAPscape-RICE software. Continuous monitoring for crop parameters and validation exercise was done for accuracy assessment. Spectral dB curve of rice was generated and the dB values ranged from &amp;minus;12.76 to &amp;minus;9.95 for VV and from &amp;minus;19.25 to &amp;minus;15.15 for VH polarization with an average primary variation of 1.3 and 2.5&amp;thinsp;dB respectively. Start of Season (SOS) map was derived from satellite data showing rice emergence dates for the cropping season. A total rice area of 106773&amp;thinsp;ha was estimated in Tiruvarur district using VV polarization with an overall accuracy of 79.5% and 0.59 kappa index, while in VH polarization, the rice area was estimated to be 91007&amp;thinsp;ha with 82.1% over all accuracy and 0.64 kappa index. The lesser accuracy in VV polarization was due to underestimate of direct seeded rice area and in VH polarization, it was due to underestimate in Transplanted rice area. The VV and VH rice area maps were then integrated to derive a VV-VH rice area map in MAPscape-RICE software and it recorded a total rice area of 124551 ha with an accuracy of 91.5% and 0.83-kappa index.</p>

scholarly journals Monitoring of Spectral Signatures of Maize Crop using Temporal SAR and Optical Remote Sensing data

2021 ◽  
Vol 12 (6) ◽  
pp. 745-750
Author(s):  
D. Anil Kumar ◽  
◽  
P. Srikanth ◽  
T. L. Neelima ◽  
M. Uma Devi ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Plant Density ◽  
Optical Data ◽  
Maturity Stage ◽  
Optical Remote Sensing ◽  
Maize Crop ◽  
Ground Truth Data ◽  
Backscatter Intensity ◽  
Multi Temporal ◽  
Sentinel 2A

A study was carried out using the temporal Sentinel-1B microwave data (June to November at 12 days interval) and Sentinel-2A/2B optical data (June to November) to discriminate the maize crop from other competing crops rice and cotton in Siddipet district, Telangana state, India during kharif, 2019 (June to November). The study utilized the data from multiple sources such as Multi-temporal VH backscatter intensity from Sentinel-1B SAR and NDVI values from Sentinel-2A/2B in combination with field data to discriminate the maize crop. Synchronous to satellite pass, ground truth data on crop parameters viz., crop stage, crop vigour, biomass, plant height, plant density, soil moisture, LAI and chlorophyll content were collected. Multi-temporal VH backscatter intensity and Normalized Difference Vegetation Index (NDVI) data were used to characterize backscatter and greenness behaviour of the maize crop. The backscatter intensity (dB) for maize crop ranged from -21.83 (the lowest backscatter values) at planting to -12.52 (the highest backscatter values) at peak growth stage. The NDVI values during vegetative and reproductive stages (August and September) were >0.6 and during senescence to harvesting the values were less than or equal to 0.52. The increase in backscatter intensity values from initial vegetative stage to peak stage was due to increased volume scattering of the maize crop canopy and a continuous decline in backscatter intensity values of VH band at maturity stage, was due to decrease in greenness and moisture content in leaves of the maize crop helped in maize crop discrimination from other dominant kharif crops in the study area.

scholarly journals Long-Term Deflection Monitoring for Bridges Using X and C-Band Time-Series SAR Interferometry

2019 ◽  
Vol 11 (11) ◽  
pp. 1258 ◽  
Author(s):  
Jungkyo Jung ◽  
Duk-jin Kim ◽  
Suresh Krishnan Palanisamy Vadivel ◽  
Sang-Ho Yun
Keyword(s):  
Time Series ◽  
Orientation Angle ◽  
Sar Interferometry ◽  
Interferometric Synthetic Aperture Radar ◽  
Vertical Deflection ◽  
Vertical Displacements ◽  
Multi Temporal ◽  
Sar Data ◽  
Thermal Dilation

This study aims to monitor the deformation of bridges, namely in the form of long-term deflection and thermal dilation, using multi-temporal interferometric synthetic aperture radar (InSAR) observations. To precisely estimate the vertical and longitudinal displacements, we used the InSAR time-series technique with multi-track stacks of Sentinel-1 SAR dataset and a single-track stack of COSMO-SkyMed SAR data over two extradosed bridge cases; Kimdaejung and Muyoung bridges between 2013 and 2017. The vertical and longitudinal displacements are estimated using multi-track Sentinel-1 SAR data and orientation angle of bridges, and we converted the displacements into thermal dilation and long-term vertical deflection. From COSMO-SkyMed data, we calculated the horizontal thermal dilation and long-term vertical deflection assuming that they dominantly contribute to the horizontal and vertical displacements, respectively. This assumption appeared reasonable based on the comparison with calculations from Sentinel-1 data. The deflection patterns exhibit downward movements at the mid-spans between towers. The results reveal that both bridges have been suffering long-term deflection over the observation period. Thus, this study verifies the potential to monitor the long-term deflection and implies that the bridges need to be monitored periodically.

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