Investigating capability of open archive multispectral and SAR datasets for Wheat crop monitoring and acreage estimation studies

Upreti, Manjari; Kumar, Deepak

doi:10.1007/s12145-021-00656-9

Cited by 4 publications

(1 citation statement)

References 27 publications

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“…where value (i) is the original DN value, and Ai is the backscatter coefficient denoted as sigma nought (σ0). After that, since the SAR images are acquired in burst mode or beam mode, to further radiometrically correct the SAR imagery, it is deburst [43]. The deburst operation is followed by the multi-look operation where square pixels are generated from the SAR image having different spatial resolutions in range and azimuth directions (5 m × 20 m to 14 m × 14 m).…”

Section: Sar Data Pre-processingmentioning

confidence: 99%

Estimating Land Subsidence and Gravimetric Anomaly Induced by Aquifer Overexploitation in the Chandigarh Tri-City Region, India by Coupling Remote Sensing with a Deep Learning Neural Network Model

Reshi

Sandhu

Cherubini

et al. 2023

Water

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This study utilizes surface displacement data from Persistent Scatterer SAR Interferometry (PSInSAR) of Sentinel-1 satellite and groundwater storage change data from the Gravity Recovery and Climate Experiment (GRACE) satellite mission to understand land subsidence in the Chandigarh tri-city region. The satellite datasets are used along with the groundwater level data obtained from wells over the study area. Since the GRACE data are available at a much coarser spatial resolution of 1o by 1o, challenges remain in correlating the dataset with PSInSAR displacement that has been multi-looked at 14 m by 14 m resolution. Therefore, multiple sources of data (i.e., the monthly average of GRACE data, groundwater storage change and monthly average PSInSAR displacement per pixel, and interpolated groundwater level data from wells for 2017 to 2022) have been deployed into a deep learning multi-layer perceptron (DLMLP) model to estimate the groundwater storage change at the urban level. This has an indirect downscaling method that is carried out successfully using the DLMLP model for the estimation of groundwater storage changes at the urban level, which is usually complicated by applying direct downscaling methods on the GRACE data. Thus, the DLMLP model developed here is a distinctive approach considered for estimating the changes in groundwater storage using PSInSAR displacement, groundwater data from wells, and GRACE data. The DLMLP model gives an R2-statistics value of 0.91 and 0.89 in the training and testing phases, respectively, and has a mean absolute error (MAE) of 1.23 and root mean square error (RMSE) of 0.87.

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Section: Sar Data Pre-processingmentioning

confidence: 99%