A5050018534- Remote Sensing in Agriculture
- Spectroscopy and Chemometric Analyses
- Leaf Properties and Growth Measurement
- Soil Moisture and Remote Sensing
- Remote-Sensing Image Classification
- Synthetic Aperture Radar (SAR) Applications and Techniques
- Precipitation Measurement and Analysis
- Land Use and Ecosystem Services
- Remote Sensing and LiDAR Applications
Banaras Hindu University
2020-2023
Indian Institute of Technology BHU
2020-2023
The leaf chlorophyll content (LCC) is a vital parameter that indicates plant production, stress, and nutrient availability. It critically needed for precision farming. There are several multispectral images available freely, but their applicability restricted due to low spectral resolution, whereas hyperspectral which have high resolution very limited in In this work, imagery (AVIRIS-NG) simulated using image (Sentinel-2) reconstruction method, namely, the universal pattern decomposition...
With the development in sensor technology, there is a spectroradiometer with resolution as high 1nm and data capture extending from 350nm-2500nm; it helps viewing spectral variability of subject interest. The advantage such instruments opens up many opportunities for hyperspectral analysis precision agriculture. In presented work, estimation Leaf Area Index (LAI) done inversion technique using Transformed Vegetation (TVI), SR (Simple Ratio), NDVI (Normalized difference ratio index)...
The high dimensional hyperspectral data due to its narrow bands ranging between 250-3500 nm, becomes a serious issue for processing and analysis. Selection of optimal absorbance spectral from original spectra brings great possibility in removing the redundancy, quantifying pigments such as chlorophyll, carotenoids, anthocyanin retrieval biophysical variables LAI, Biomass corresponding crops using advance derivative techniques. As part this study atmospheric corrected reflectance AVIRIS-NG...