Assessment of rice crop biophysical parameters using Sentinel-1 C-band SAR data

Kushwaha, Amit; Dave, Rucha; Kumar, G. Senthil; Saha, Koushik; Khan, Armugha

doi:10.1016/j.asr.2022.02.021

Cited by 12 publications

(5 citation statements)

References 35 publications

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“…Multiple regression yields the best R 2 values, but they are low. Similar behaviour has been also observed for regression with the backscatter parameters for rice (Kushwaha et al, 2022).…”

Section: Resultssupporting

confidence: 83%

“…The reason for this may be the fact that, at this stage the C-band SAR signal can interact with the full length of the crop. Better correlations at this growth stage have been also observed in earlier studies for regression with various SAR parameters (Harfenmeister et al, 2021;Kushwaha et al, 2022).…”

Section: Resultssupporting

confidence: 82%

“…available. For most agricultural applications, the SAR parameter that have been commonly used are the backscatter coefficients sigma naught (σ 0 ) (Vreugdenhil et al, 2018;Dineshkumar and Satishkumar 2019;Sharma et al, 2019;Kushwaha et al, 2022).…”

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confidence: 99%

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Analysing the potential of polarimetric decomposition parameters of Sentinel–1 dual-pol SAR data for estimation of rice crop biophysical parameters

et al. 2023

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The potential of dual-polarization Sentinel–1 polarimetric decomposition parameters i.e., entropy, anisotropy and alpha angle, to monitor the biophysical parameters of rice crop namely, fresh biomass, dry biomass, vegetation water content (VWC) and plant height is investigated in this study. Multi-temporal Sentinel–1A dataset during critical growth stages of rice was considered for regression analysis between the polarimetric decomposition parameters and the biophysical parameters using linear and logarithmic models. The best correlations are obtained at early vegetation stages of the crop i.e. between tillering to booting stages. The maximum R2 value obtained is 0.6 for linear regression of entropy and VWC followed by other biophysical parameters. The correlation of polarimetric decomposition parameters with biophysical parameters is not as high as that of VH backscatter coefficient. Multiple regression using various Sentinel–1 parameters yields a better correlation than regression using individual parameters. The maximum R2 value of 0.765 is obtained for the multiple linear regression for VWC. Multiple regression using the backscatter coefficients and polarimetric decomposition parameters together gives a better correlation than that using only the backscatter coefficients. This indicate that the polarimetric decomposition parameters are sensitive to biophysical parameters and can be used as additional parameters along with the backscatter parameters for rice crop monitoring.

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“…Multiple regression yields the best R 2 values, but they are low. Similar behaviour has been also observed for regression with the backscatter parameters for rice (Kushwaha et al, 2022).…”

Section: Resultssupporting

confidence: 83%

Section: Resultssupporting

confidence: 82%

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Analysing the potential of polarimetric decomposition parameters of Sentinel–1 dual-pol SAR data for estimation of rice crop biophysical parameters

et al. 2023

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“…Wheat reached RVI saturation at the prebooting stage, while maize reached saturation at the pre-jointing stage. Previous studies by Kushwaha et al (2022) found that the SAR signal exhibited the strongest correlation with plant height during the BBCH 21-49 stages of rice, with estimated values ranging from 40-80 cm. Similarly, Nasirzadehdizaji et al (2019) and Liao et al (2018) discovered through correlation analysis that the optimal wheat height is below 53 cm and the sensitivity of SAR parameters begins to decline when maize height exceeds 150 cm.…”

Section: Impact Of Population Growth Differences On Dynamic Monitorin...mentioning

confidence: 90%

“…The growth cycle of a crop involves changes in its structure, crop cover, and appearance, which in turn affect how it responds to vegetation indices. Previous studies (Kushwaha et al, 2022;Mandal et al, 2021;Nasirzadehdizaji et al, 2019) have shown that the SAR signal interacts more effectively with the entire crop structure during the pre-growth phase when there is less canopy cover. However, when the crop has a dense canopy cover, this interaction with the SAR signal is limited.…”

Section: Introductionmentioning

confidence: 99%

Exploring the effects of crop growth differences on radar vegetation index response and crop height estimation using dynamic monitoring model

Wang,

Liu,

Sheng

et al. 2024

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Synthetic aperture radar (SAR) has emerged as a promising technology for monitoring crop plant height due to its ability to capture the geometric properties of crops. Radar vegetation index (RVI) has been extensively utilized for qualitative and quantitative remote sensing monitoring of vegetation growth dynamics. However, the combination of crop, growing environment, and temporal dynamics makes crop monitoring data a complex task. Despite the relatively simple underlying mechanisms of this phenomenon, there is still a need for more research to identify specific vegetation structures that correspond to changes in the response of vegetation indices. Building upon this premise, this study utilized a dynamic monitoring model to conduct dynamic monitoring of plant height for three common crops: rice, wheat, and maize. The findings revealed that (1) models developed for specific spatial and temporal scales of particular crop varieties may not accurately predict crop growth in different regions or with different varieties in a timely manner, due to growth variations; (2) these models maintain accuracy over a range of plant heights, such as rice at around 70cm, wheat at around 50cm, and maize at around 150cm; (3) among the three crops, planting density was identified as the main factor influencing the differences in RVI response. This research contributes to our comprehension of the dynamic response of RVI to different growth conditions in crops, and offers valuable insights and references for agricultural monitoring.

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Quantifying rice dry biomass to determine the influence of straw burning on BC and NO2 emissions in the Hanoi metropolitan region

Pham,

2024

Environ Monit Assess

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Assessment of rice crop biophysical parameters using Sentinel-1 C-band SAR data

Cited by 12 publications

References 35 publications

Analysing the potential of polarimetric decomposition parameters of Sentinel–1 dual-pol SAR data for estimation of rice crop biophysical parameters

Analysing the potential of polarimetric decomposition parameters of Sentinel–1 dual-pol SAR data for estimation of rice crop biophysical parameters

Exploring the effects of crop growth differences on radar vegetation index response and crop height estimation using dynamic monitoring model

Quantifying rice dry biomass to determine the influence of straw burning on BC and NO2 emissions in the Hanoi metropolitan region

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