Browsing by Author "Rucha Dave"

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Crop type discrimination using Geo-Stat Endmember extraction and machine learning algorithms
(Elsevier Ltd, 2024) Prachi Singh; Prashant K. Srivastava; Dharambhai Shah; Manish K. Pandey; Akash Anand; Rajendra Prasad; Rucha Dave; Jochem Verrelst; Bimal K. Bhattacharya; A.S. Raghubanshi
The identification of crop diversity in today's world is very crucial to ensure adaptation of the crop with changing climate for better productivity as well as food security. Towards this, Hyperspectral Remote Sensing (HRS) is an efficient technique that offers the opportunity to discriminate crop types based on morphological as well as physiological features due to availability of contiguous spectral bands. The current work utilized the benefits of Airborne Visible Infrared Imaging Spectrometer- New Generation (AVIRIS-NG) data and explored the techniques for classification and identification of crop types. The endmembers were identified using the Geo-Stat Endmember Extraction (GSEE) algorithm for pure pixels identification and to generate the spectral library of the different crop types. Spectral feature comparison was done among AVIRIS-NG, Analytical Spectral Device (ASD)-Spectroradiometer and Continuum Removed (CR) spectra. The best-fit spectra obtained with the Reference ASD-Spectroradiometer and Pure Pixel spectral library were then used for crop discrimination using the ten supervised classifiers namely Spectral Angle Mapper (SAM), Spectral Information Divergence (SID), Support Vector Machine (SVM), Minimum Distance Classifier (MDC), Binary Encoding, deep learning-based Convolution Neural Network (CNN) and different algorithms of Ensemble learning such as Tree Bag, AdaBoost (Adaptive Boosting), Discriminant and RUSBoost (Random Under Sampling). In total, nine crop types were identified, namely, wheat, maize, tobacco, sorghum, linseed, castor, pigeon pea, fennel and chickpea. The performance evaluation of the classifiers was made using various metrics like Overall Accuracy, Kappa Coefficient, Precision, Recall and F1 score. The classifier 2D-CNN was found to be the best with Overall Accuracy, Kappa Coefficient, Precision, Recall and F1 score values of 89.065 %, 0.871, 87.565%, 89.541% and 88.678% respectively. The output of this work can be utilized for large scale mapping of crop types at the species level in a short interval of time with high accuracy. © 2022 COSPAR
Leaf chlorophyll content retrieval for AVIRIS-NG imagery using different feature selection and wavelet analysis
(Elsevier Ltd, 2024) Bhagyashree Verma; Prachi Singh; Rajendra Prasad; Prashant K. Srivastava; Rucha Dave
The Leaf Chlorophyll Content (LCC) is a crucial indicator of plant vitality. It plays a crucial role in photosynthetic processes and regulates metabolic activities in plants. Thus, it is an important task for the scientific community to estimate its precise quantity. In this study, we used AVIRIS-NG imagery, a wavelet analysis, and a number of feature selection approaches to estimate the chlorophyll content of several agricultural species. Eight different Discrete Wavelet Transform (DWT) methods, including Daubechies (db), Biorthogonal (bior), Reverse biorthogonal (rbio), etc., were employed to generate denoised vegetation spectra, in which bior produced the best approximation. Recursive Feature Elimination (RFE), Regularized Random Forest (RRF), Least Absolute Shrinkage and Selection Operator (LASSO), and Partial Least Square (PLS) were used to select features from the approximated signals, and the top three bands were chosen to be used in the creation of new indices for LCC retrieval. In order to estimate LCC, linear regression models were developed using these indicators. The best results were obtained by the PLS-based LCC retrieval model, with a correlation of r = 0.948, a Root Mean Square Error (RMSE) of 5.464, and a bias of 3.305. The LASSO model yielded the worst results. Hence, for any hyperspectral images such as of AVIRIS-NG, the chlorophyll content may be reliably estimated using a PLS model combined with wavelet analysis. © 2023 COSPAR
Operational 500 m surface soil moisture product using EOS-04 C-band SAR over Indian agricultural croplands
(Indian Academy of Sciences, 2024) Dharmendra Kumar Pandey; Prashant Kumar Srivastava; Rucha Dave; Raj K. Setia; Ompal; Rajiv Sinha; Muddu Sekhar; Manish Parmar; Shubham Gupta; Deepak Putrevu; Raghav Mehra; V. Ramanujam; Bimal Kumar Bhattacharya; Raj Kumar
Surface soil moisture (SSM) at high spatial resolution is an essential land parameter for agricultural applications like irrigation mapping, scheduling, crop water stress assessment, etc. However, available satellite derived soil moisture products are inadequate for meeting the requirements of agricultural applications due to coarse scale soil moisture (~10–40 km). In this article, we developed an operational framework for first of its kind sub-km (~500 m) operational soil moisture product over India by utilizing ISRO’s EOS-04 C-band synthetic aperture radar (SAR) data based on active-passive approach. The potential of EOS-04 SAR for sub-km scale is demonstrated and tested over major cropland sites covering highly heterogeneous and dynamic crop conditions in different agro-climatic regions over India which shows a good agreement with in situ datasets with mean ubRMSE, ranging from 0.051 to 0.078 m3/m3. © (2024), (Indian Academy of Sciences). All rights reserved.