Browsing by Author "Keerthana, N."

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An optimum datasets analysis for monitoring crops using remotely sensed Sentinel-1A SAR data
(Taylor and Francis Ltd., 2023) Salma, S.; Keerthana, N.; Dodamani, B.M.
To effectively monitor crops, it is necessary to select extremely redundant satellite images and to know the number of acquisitions required for a specific period to analyse cropping patterns, thereby reducing analysis time. In this paper, we have examined an empirical analysis for the optimum dataset (OptD) selection required to monitor the crops. Sentinel-1 dual-polarized SAR datasets were used in this study to illustrate the effectiveness of optimum datasets required for the considered crops (ginger, tobacco, rice, cabbage, and pumpkin). In this work, at first, the entropy and alpha bands were treated as cluster centres for crop decomposition and its scattering mechanism using the cluster-based K-means unsupervised classification technique. The clusters are plotted on the H-α plane to get the H-α plot of dual-polarization SAR data for target decomposition. To understand the dominance of scattering type with crop growth stage, the obtained scattering distribution from the H-alpha plot is scaled to a percentage analysis. Based on qualitative observations of the percent scattering distribution of crop pixels over a h-alpha plot and backscattering coefficient behaviour at different crop growth stages, an empirical approach has been used to select dataset reduction. It has been suggested that the combination of successive repeated data with similar scattering analysis of combined h-alpha plots and backscattering analysis is the best reduced dataset selection for effective crop monitoring. From the analysis, the optimum dataset required for monitoring Ginger (from the flourishing stage), Tobacco, Paddy, Cabbage, and Pumpkin has been identified, and found that the tobacco crop requires fewer datasets, whereas the rice crop requires a greater number of datasets for monitoring. Despite the challenges associated with, p-bias for the crops was achieved at good levels, given that, lowering the datasets to obtain the optimal number without significantly reducing the accuracy of the data. © 2023 Informa UK Limited, trading as Taylor & Francis Group.
Identifying Rice Crop Flooding Patterns Using Sentinel-1 SAR Data
(Springer, 2022) Keerthana, N.; Salma, S.; Dodamani, B.M.
In India, the majority of the population relies heavily on rice as it is their primary source of nutrition. Rice crop yield productivity depends on seasonal variations and mainly depends on hydrological conditions. Long-term water clogging in rice fields for an extended period causes crop flooding and reduces production in terms of quality and quantity. This study deals with the identification of rice crop fields and their flooding due to surface irrigation using Sentinel-1 SAR data. The identification of rice fields was attempted by classifying the image data using a random forest algorithm. For crop flooding analysis, the temporal backscatter of the corresponding fields has been extracted from SAR data and local thresholding is used. The temporal analysis of the SAR backscattering showed a similar tendency in terms of crop growth. The overall accuracy of rice crop classification for VH and VV is 97.30% and 92.24% with RMSE errors of 0.0143 and 0.0145, respectively, obtained at the peak stage of the crop. From the crop flooding analysis, it is observed that crop fields have been flooded at the growth stage due to surface irrigation and rainfall. We identified crop flooding even at the crop mature stage. In the analysis, it has been observed that the flooding is not due to irrigation water but is due to the precipitation water. © 2022, Indian Society of Remote Sensing.
Target decomposition using dual-polarization sentinel-1 SAR data: Study on crop growth analysis
(Elsevier B.V., 2022) Salma, S.; Keerthana, N.; Dodamani, B.M.
Decomposition of synthetic aperture radar data has been carried out using fully polarised microwave bands. Considering the economical point of view, fully polarimetric SAR data is expensive to use for many applications like soil and agriculture, where, it is important to monitor frequently. With the advancement of human civilization, new agricultural techniques and crops are being developed to meet global food demand. With the development of crop growth, crop texture and dielectric properties varies, which is depicted in the backscattering values along with the crop growth. In this study, Sentinel-1 SAR data is used, which is freely available in dual polarimetric mode with a temporal revisit of 12 days with respect to the transmitter polarizations. In this work, we attempt to decompose the targets from dual polarised SAR data using entropy and alpha bands of H-A-α decomposition. The entropy and alpha band clusters are obtained by using K-means unsupervised classification is utilized for target decomposition. The clustering process is repeated 30 times for 100 iterations to obtain the optimum grouping of pixels. The clusters are plotted on the H-α plane to get an H-α plot of dual-polarization SAR data for target decomposition. The obtained H-α plot is used to identify the crop stages and its scattering mechanism at different growth stages. Crops grown in selected crop fields during the considered period include ginger, tobacco, rice, cabbage, and pumpkin. An attempt is made by plotting the time-series trends of early and late-planted crops with peak mature stages in terms of backscattering analysis, and the results were compared to the H-α plot to gain a better understanding of crop growth scattering mechanisms at various growth stages. Although the backscattering values for the VH and VV polarizations of crops are different, the temporal backscattering study showed the same trend for both polarizations with good similarity of VH polarization than VV for analyzing crop growth stages. The crop growth scattering mechanism on the H-α plane produced similar results to the temporal analysis. © 2022 Elsevier B.V.