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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Bulletin of Electrical Engineering and Informatics
Gaikwad, Abhay N.
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Computer Science & IT Electrical & Electronics Engineering Engineering

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Soil moisture estimation using ground scatterometer and Sentinel-1 data Desai, Geeta T.; Gaikwad, Abhay N.
Bulletin of Electrical Engineering and Informatics Vol 13, No 3: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i3.6433

Abstract

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.
Crop classification using object-oriented method and Google Earth Engine Desai, Geeta T.; Gaikwad, Abhay N.
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i2.pp1271-1280

Abstract

Agriculture crop monitoring in real-time is crucial in formulating effective agricultural practices and management policies. The primary goal of the investigation is to explore how the utilization of Sentinel-1 data and its fusion with Sentinel-2 impact crop classification accuracy in a fragmented agricultural landscape in the Yavatmal District of Maharashtra, India. Pixel based classification and object-oriented classification approaches were implemented on Google Earth Engine (GEE), and obtained results were compared for different combinations of optical and microwave features. The research revealed that the object-based technique performed better than the pixel-based approach, with a 3.5% increase in overall accuracy. For 2022, crop-type mapping was generated with overall accuracies varying from 85.5% to 61% and a kappa coefficient between 0.77 and 0.37. These overall accuracies imply that joint use of optical and radar data has given a 24% improvement in overall accuracy compared to use of only optical data. In addition, the temporal change in the backscatter coefficients and different vegetation indices for different crops were examined over crop growth cycle. This work demonstrates the fusion of Sentinel-1 and Sentinel-2 data to classify wheat, chickpea, other crops, water and urban areas.
Co-Authors Desai, Geeta T.