Dual polarimetric radar vegetation index for crop growth monitoring using sentinel-1 SAR data

Mandal, Dipankar; Kumar, Vineet; Ratha, Debanshu; Dey, Subhadip; Bhattacharya, Avik; López-Sánchez, Juan M.; McNairn, Heather; Rao, Y. S.

doi:10.1016/j.rse.2020.111954

Cited by 227 publications

(134 citation statements)

References 59 publications

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“…Additionally, a smoothing of the time series could lead to better regression results, as found by Canisius et al [25]. Furthermore, the multiple regression analysis could be performed using additional parameters such as the Radar Vegetation Index (RVI) [61], Shannon entropy [35], or field statistics of SAR parameters, as was done by Holtgrave et al [16]. Additionally, it would be advantageous if existing field measurements are completed by additional parameters with the consideration of water content in different parts of the plant to catch also small water content differences, particularly during the ripening in the grains.…”

Section: Discussionmentioning

confidence: 99%

Agricultural Monitoring Using Polarimetric Decomposition Parameters of Sentinel-1 Data

et al. 2021

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The time series of synthetic aperture radar (SAR) data are commonly and successfully used to monitor the biophysical parameters of agricultural fields. Because, until now, mainly backscatter coefficients have been analysed, this study examines the potentials of entropy, anisotropy, and alpha angle derived from a dual-polarimetric decomposition of Sentinel-1 data to monitor crop development. The temporal profiles of these parameters are analysed for wheat and barley in the vegetation periods 2017 and 2018 for 13 fields in two test sites in Northeast Germany. The relation between polarimetric parameters and biophysical parameters observed in the field is investigated using linear and exponential regression models that are evaluated using the coefficient of determination (R2) and the root mean square error (RMSE). The performance of single regression models is furthermore compared to those of multiple regression models, including backscatter coefficients in VV and VH polarisation as well as polarimetric decomposition parameters entropy and alpha. Characteristic temporal profiles of entropy, anisotropy, and alpha reflecting the main phenological changes in plants as well as the meteorological differences between the two years are observed for both crop types. The regression models perform best for data from the phenological growth stages tillering to booting. The highest R2 values of the single regression models are reached for the plant height of wheat related to entropy and anisotropy with R2 values of 0.64 and 0.61, respectively. The multiple regression models of VH, VV, entropy, and alpha outperform single regression models in most cases. R2 values of multiple regression models of plant height (0.76), wet biomass (0.7), dry biomass (0.7), and vegetation water content (0.69) improve those of single regression models slightly by up to 0.05. Additionally, the RMSE values of the multiple regression models are around 10% lower compared to those of single regression models. The results indicate the capability of dual-polarimetric decomposition parameters in serving as meaningful input parameters for multiple regression models to improve the prediction of biophysical parameters. Additionally, their temporal profiles indicate phenological development dependent on meteorological conditions. Knowledge about biophysical parameter development and phenology is important for farmers to monitor crop growth variability during the vegetation period to adapt and to optimize field management.

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Section: Discussionmentioning

confidence: 99%

Agricultural Monitoring Using Polarimetric Decomposition Parameters of Sentinel-1 Data

et al. 2021

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“…The increasing RVI and σ o VH/VV at the beginning of the vegetation period indicates the attenuation of the radar signal by growing vegetation. Similar results have also been reported by Mandal et al (2020) for wheat crop during these growth stages. Furthermore, a high anisotropic scattering (>0.5) for low SF A values (<1) indicates two dominant scattering mechanisms with almost equal probability and a less significant third scattering mechanism.…”

Section: Relationship Between Satellite Metrics and Sf Asupporting

confidence: 91%

Crop lodging detection and susceptibility mapping: a multi-sensor approach

Chauhan¹

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“…This causes the generation of dual polarisation products from this satellite system. The concurrent synchronization of these two satellite orbits causes a 6-day repeat cycle to deliver a single or dual-polarization C-band SAR datasets in Single Look Complex (SLC) or Ground Range Detected (GRD) formats 38 . There is no phase information available with GRD product formats and are available in three modes such as full resolution (FR), high resolution (HR), and medium resolution (MR).…”

Section: Methodsmentioning

confidence: 99%

Urban objects detection from C-band synthetic aperture radar (SAR) satellite images through simulating filter properties

Kumar

2021

Sci Rep

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Satellite-based remote sensing has a key role in the monitoring earth features, but due to flaws like cloud penetration capability and selective duration for remote sensing in traditional remote sensing methods, now the attention has shifted towards the use of alternative methods such as microwave or radar sensing technology. Microwave remote sensing utilizes synthetic aperture radar (SAR) technology for remote sensing and it can operate in all weather conditions. Previous researchers have reported about effects of SAR pre-processing for urban objects detection and mapping. Preparing high accuracy urban maps are critical to disaster planning and response efforts, thus result from this study can help to users on the required pre-processing steps and its effects. Owing to the induced errors (such as calibration, geometric, speckle noise) in the radar images, these images are affected by several distortions, therefore these distortions need to be processed before any applications, as it causes issues in image interpretation and these can destroy valuable information about shapes, size, pattern and tone of various desired objects. The present work aims to utilize the sentinel-1 SAR datasets for urban studies (i.e. urban object detection through simulation of filter properties). The work uses C-band SAR datasets acquired from Sentinel-1A/B sensor, and the Google Earth datasets to validate the recognized objects. It was observed that the Refined-Lee filter performed well to provide detailed information about the various urban objects. It was established that the attempted approach cannot be generalised as one suitable method for sensing or identifying accurate urban objects from the C-band SAR images. Hence some more datasets in different polarisation combinations are required to be attempted.

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Dual polarimetric radar vegetation index for crop growth monitoring using sentinel-1 SAR data

Cited by 227 publications

References 59 publications

Agricultural Monitoring Using Polarimetric Decomposition Parameters of Sentinel-1 Data

Agricultural Monitoring Using Polarimetric Decomposition Parameters of Sentinel-1 Data

Crop lodging detection and susceptibility mapping: a multi-sensor approach

Urban objects detection from C-band synthetic aperture radar (SAR) satellite images through simulating filter properties

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