Time series analysis of co-polarization phase difference (PPD) for winter field crops using polarimetric C-band SAR data

Haldar, Dipanwita; Rana, Pooja; Yadav, Manoj; Hooda, R. S.; Chakraborty, Manab

doi:10.1080/01431161.2016.1204024

Cited by 13 publications

(5 citation statements)

References 15 publications

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“…PR is more persistent than the backscattering coefficient for both polarizations, VV and VH. Previous studies [18] have confirmed that PR reduces the effect of soil roughness and gives a more accurate estimation of SM.…”

Section: Polarization Ratiomentioning

confidence: 66%

“…where ∅𝑐 is the relative phase difference and is equal to the absolute phase difference between co-polarized HH and VV polarization, the HH and VV phase difference consists of vital information at par with cross-polarized phase difference in two polarization channels [18]. The investigation focuses on relating the changes in phase difference to SM.…”

Section: Polarization Phase Differencementioning

confidence: 99%

“…Wang and Mo [17] used co-polarization phase difference (PPD) for corn and orchid fields. Haldar et al [18] used PPD for wheat crops using Sentinel-1 data. In this investigation, we have employed the polarization ratio (PR) and PPD for SM estimation.…”

Section: Introductionmentioning

confidence: 99%

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Soil moisture estimation using ground scatterometer and Sentinel-1 data

Desai,

Gaikwad

2024

Bulletin EEI

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Soil moisture (SM) is a crucial criterion for agronomics and the management of water resources, particularly in areas where the socio-economic status and significant source of income depend upon agriculture and related sectors. This paper intends to estimate SM over the vegetative area using a generalized regression neural network (GRNN) and ground scatterometer and compare the results with SM retrieved using Sentinel-1 data. At the same time, random forest regression (RFR) and support vector regression (SVR) models are used for SM estimation. Correlation analysis results concluded that L-band HV-polarization at 300 incidence angle showed the highest correlation with the measured field parameters. This study investigated backscattering coefficients, VV/VH polarization ratio and polarization phase difference over wheat’s entire growth phase to estimate SM. The results indicate that the GRNN with backscattering coefficients and polarization ratio provided the highest accuracy compared to the random forest (RF) and SVR with the root mean square error of 0.093 over the Yavatmal District, Maharashtra, India.

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Section: Polarization Ratiomentioning

confidence: 66%

Section: Polarization Phase Differencementioning

confidence: 99%

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Soil moisture estimation using ground scatterometer and Sentinel-1 data

Desai,

Gaikwad

2024

Bulletin EEI

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“…Spaceborne polarimetric SAR has been found to improve accuracies compared to the less complex backscatter models (Haldar et al, 2016;Busquier et al, 2020). However, at this time the applications for fully polarimetric SAR imagery are somewhat limited by the small image swath sizes caused by satellite power constraints (Villano et al, 2015).…”

Section: Sar Signal Characteristicsmentioning

confidence: 99%

“…While backscatter intensity for crop parameter extraction is commonly used, there is also an interest in polarimetric methods (Lee et al, 2001;Brown et al, 2003;Haldar et al, 2016;Busquier et al, 2020) and coherent scattering models (Liu et al, 2019). Polarimetric methods have successfully been applied to crops such as cereals (Lee et al, 2001;Busquier et al, 2020), cotton (Cui et al, 2020), and rice (Lopez-Sanchez et al, 2012).…”

Section: Applications Of Multifrequency Sarmentioning

confidence: 99%

Evaluating Multifrequency SAR for Crop Classification in Saskatchewan, Canada and Georgia, USA

Lunger

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Synthetic Aperture Radar (SAR) is a useful tool for crop monitoring programs, which are critical components of agricultural supply chain management. Multifrequency SAR has the potential to close knowledge gaps that can occur using radar data with a single frequency.However, it may also add noise to a classification, depending on when and where the imagery was collected. This study applied Random Forest and Support Vector Machine classifiers to study areas in Saskatchewan, Canada and Georgia, USA to test whether multifrequency SAR could improve crop classification accuracies. Combined L-, C-, & X-band imagery provided the best overall accuracies when multiple crop types were being studied. Different crops had different responses to each frequency at different points in the growing season. Regions with complex cropping calendars pose extra challenges for choosing image acquisition timing. This research found that multifrequency SAR provided accuracies over 85% for both areas of interest.

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