Rice is one of the important staple food crop worldwide, especially in India. Accurate and timely prediction of rice phenology plays a significant role in the management of water resource, administrative planning, and food security. Apart from the conventional method of rice yield estimate, remotely sensed time series data can provide the necessary estimation of rice phenological stages over a large region. Thus, the present study utilizes the 16-day composite Enhanced Vegetation Index (EVI) product with a spatial resolution of 250 m from the moderate resolution imaging spectroradiometer (MODIS) to monitor the rice phenological stages over Karur district of Tamil Nadu, India using the Google Earth Engine (GEE) platform. The rice fields in the study area were classified using the machine learning algorithm in GEE. The ground truth was obtained from the paddy fields during crop production which was used for classifying the paddy grown area. After classification of paddy fields, local maxima, and local minima present in each pixel of time series EVI product was used to determine the paddy growing stages in the study area. The results shows that in the initial stage, the pixel value of EVI in the paddy field shows local minima (0.23) whereas local maxima (0.41) were obtained during the peak vegetative stage. The results derived from the present study using MODIS data was cross-validated using the field data.

Rice is the most essential and nutritional staple food crop worldwide. There is a need for accurate and timely rice mapping and monitoring which is a pre-requisite for crop management and food security. Recent studies utilize Sentinel-1 data for mapping and monitoring rice grown area. The present study was carried out in the Google Earth Engine (GEE), where the Sentinel-1data were used for monitoring the rice grown area over Kulithalai taluk of Karur district, located along the Cauvery delta region. Normally, the production of rice in the study area starts in the late Samba Season where the long duration variety Cr1009 (130 days) is extensively grown. The results exhibit a low backscattering values during the transplanting stage of VV and VH polarisation (-15.19 db and -24.6 db), whereas maximum backscattering is experienced at peak vegetation stage of VV and VH polarisation (-7.42 and -16.9 db) and there is a decrease in the backscattering values after attaining the maturity stage. Amongst VH and VV polarisation, VH polarisation provides a consistently increasing trend in backscatter coefficients from the panicle initiation phase to the early milking phase after which the crop attains its maturity phase, whereas in the VV polarisation early peak of backscatter coefficients are seen at much earlier during the flowering phase itself. Thus, in this study, VV polarisation gives better interpretation than VH Polarisation in the selected rice crop fields. The obtained results were cross-validated by collecting the ground truth values during the satellite data acquisition time, throughout the crop growing period from the selected rice fields.

Soil erosion is a serious environmental threat amongst the prevailing major natural hazards which affects the livelihood of millions of people around the world. The deterioration of nutrient-rich topsoil can affect the sustainability of agriculture and various ecosystems by decreasing soil productivity. Conservation measures should be implemented in those regions which are critical to soil erosion. Identification of areas susceptible to soil erosion through prioritization of watershed can help in proper planning and implementation of suitable conservational measures. Therefore, in this study, prioritization of 23 micro-watersheds present in the Dnyanganga watershed of Tapti River basin is carried out based on morphometric parameters and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). TanDEM-X 90m openly accessible DEM generated from SAR interferometry is obtained through DLR is used for determining the morphometric parameters. These parameters are grouped into linear, areal and relief aspects. Initially, the relative weights of various morphometric parameters used in TOPSIS were determined using Saaty’s Analytical Hierarchy Process (AHP). Thereafter, the MCDM package in R software was utilized to implement TOPSIS. The micro-watersheds were classified into very high (0.459 - 0.357), high (0.326 – 0.240), moderate (0.213 – 0.098), and low (0.096 – 0.088) prioritisation levels based on the TOPSIS highest closeness (C_i⁺) to ideal solution. It is evident from the results that micro-watersheds (MW10, MW18, MW19, MW2, MW11, and MW17) are highly susceptible to soil erosion and thus, conservation measures can be carried out in these micro-watersheds on priority to ensure the sustainability of future agriculture by preventing excessive soil loss through erosion.

SAR Interferometry technique generates digital elevation models (DEMs) and is being used by various agencies widely. The recently released TanDEM-X DEM by DLR at 90m spatial resolution is available for free download to users. This paper examines the accuracy of TanDEM-X DEM at different experimental sites having different topographic characteristics. Three sites were chosen namely Kendrapara, Orissa; Jaipur, Rajasthan and Dehradun, Uttarakhand with plain, moderate and highly undulating terrain conditions. The RMSE were calculated using ground control points (GCPs) collected by Differential GPS method for experimental sites at Dehradun, Jaipur and Kendrapara. The accuracy of TanDEM-X 90m datasets is compared with other openly accessible optically derived DEMs (ASTER GDEM V2, CartoDEM V3 R1, AW3D30) and InSAR derived DEMs (SRTM, ALOS PALSAR RTC HR). The RMSEs reveals that at Jaipur site with moderate terrain having urban and agriculture as major LULU classes, the results of TanDEM-X 90m DEM has higher accuracy than ALOS PALSAR RTC HR DEM. However, it is observed that in predominantly plain region having agriculture practice (Kendrapara site, Orissa) and rugged region (Dehradun site, Uttarakhan) with mixed land use land cover (e.g., forest, urban, streams, and agriculture) the results of ALOS PALSAR RTC HR data are having higher accuracy than TanDEM-X 90m DEM. Further, the study indicates that for relatively plain site at Kendrapara, Orissa; CartoDEM V3 R1 DEM has best performance with an RMSE of 1.96m, which is least among all DEMs utilized in the study.

Hyperspectral datasets provides explicit ground covers with a number of contiguous bands. Filtering of high - dimensional hyperspectral datasets will pave way for further processing with the dataset in a better way. In order to discriminate surface features potentially, the number of spectral bands need to be minimized without losing the original information from the hyperspectral dataset. This technique is termed as ‘dimensionality reduction’. Several approaches are available for reducing higher order dimension to low order dimension of hyperspectral sensor datasets. In this paper, two major dimensionality reduction techniques such as Principal Component Analysis (PCA) and Minimum Noise Fraction (MNF) have been applied to reduce the dimension of spectral features. The dataset used for the study is AVIRIS NG hyperspectral imagery having a total number of 425 spectral bands covering the wavelengths from 356 nm to 2500 nm. Dimensionality reduction techniques are applied to acquire highly informative bands favoring urban landscape of Kalaburagi, Karnataka. The redundant bands are based on a fact that neighboring bands are highly correlated with each other thus sharing similar information. The benefits of utilizing dimensionality reduction methods are to slacken the complexity of the data during processing and transform original data to remove correlation among the bands. Performance evaluations for dimensionality reduction techniques are assessed and bands that are highly uncorrelated are considered for further processing.

Flash floods in the Himalayan region resulted in hundreds of death and causes sudden hazard in a minimum period of time. Himalayan rivers mostly flooded due to large amount of precipitation and resulting in the melting of snow on the mountains which causes floods. The high intensify rainfall over a long time, combined with runoff and finally causing high floods over the downstream channels. Most of the cloud burst events occurred in the Indian Himalayas with sudden heavy deluge of precipitation in a short time interval over a small region. The present paper contributes two catchments in Uttarakhand region namely Tehri dam and Srinagar, to map the flood prone areas which are more vulnerable and uses SAC Hydro Simulated discharged data combined with morphometric parameters for assessing extreme weather events. The data were analysed by both hydrological and geographical information techniques for basin delineation, stream ordering and digital elevation model. The developing methodology of “Analytical Hierarchy Process” integrated into a Geographical Information system in order to produce a flood vulnerable map. By using these data, the model uses seven parameters namely discharge, rainfall, slope, drainage density, geology, relief ratio and stream frequency. Priority weights were assigned to each criterion based on Saaty’s nine point scale of preference and weights were normalized through AHP. By using these techniques, flood vulnerable regions are mapped along the rivers of Bhagirathi and Alaknanda causes extensive damages at the local towns surrounded by these catchments. Along with these results, Sentinel 1A data used to analyze the flooded and non-flooded region of these catchments.

Land use / Land Cover (LULC) is a significant factor which plays a vital role in defining an urban ecosystem. Interpretations of LULC are eased in recent times by utilizing hyperspectral and multispectral datasets obtained from airborne and spaceborne platforms. In this study, an attempt has been made to comparatively assess the potentiality of AVIRIS NG hyperspectral data with Sentinel 2 multispectral data through applied classification techniques for Kalaburagi urban sphere. Hyperspectral data being acquired airborne consists of 425 bands covering wavelength from 356 nm to 2500 nm are analyzed for dimensionality reduction transform and are further classified. Meanwhile Sentinel 2 multispectral dataset being spaceborne and less expensive having a minimal ground sampling distance of 10 meter are classified. Spectral responses of both multispectral and hyperspectral bands were analyzed to derive reflectance spectra for considered spectral bands that are well – distributed among all the other spectral bands. The most relevant information which significantly constitutes urban land cover is considered thus avoiding redundancy among the bands. This paper focuses on applying standard supervised classification algorithms associated with dimensionality reduction techniques. Spectral resampling of hyperspectral to multispectral data product have been used to assign user – defined classes. For performance evaluation, the results are validated in order to check which of the given datasets outperforms well and provides better classified results.

Advanced techniques using high resolution hyperspectral remote sensing data has recently evolved as an emerging tool having potential to aid mineral exploration. In this study, pertinently, five mosaicked scenes of Airborne Visible InfraRed Imaging Spectrometer-Next Generation (AVIRIS-NG) hyperspectral data of southeastern parts of Aravalli Fold belt in Jahazpur area, Rajasthan have been processed. The exposed Proterozoic rocks in this area is of immense economic and scientific interest because of richness of poly-metallic mineral resources and their unique metallogenesis. Analysis of high resolution multispectral satellite image reveals that there are many prominent lineaments which acted as potential conduits of hydrothermal fluid emanation, some of which resulted in altering the country rock. This study takes cues from studying those altered minerals to enrich our knowledge base on mineralized zones. In this imaging spectroscopic study, we have, thus, identified different hydrothermally altered minerals consisting of hydroxyl, carbonate and iron-bearing species. Spectral signatures (image based) of minerals such as Kaosmec, Talc, Kaolinite, Dolomite and Montmorillonite were derived in SWIR (Short wave infrared) region while Iron bearing minerals such as Goethite and Limonite were identified in VNIR (Visible and Near Infrared) region of electromagnetic spectrum. Validation of the target minerals were done by subsequent ground truthing and XRD analysis. The altered end members are further mapped by Spectral Angle Mapper (SAM) and Adaptive Coherence Estimator (ACE) techniques to detect target minerals. Accuracy assessment is reported to be 86.82% and 77.75% for SAM and ACE respectively. This study, hence, confirms that the AVIRIS-NG hyperspectral data provides better solution for identification of endmember minerals.

Seismological research importance around the globe is very clear, therefore new tools and algorithms are needed in order to predict magnitude, time and geographic location, as well as found out relationships that allow us to understand better this phenomenon and thus be able to save countless human lives. However, given the highly random nature of the earthquakes and the complexity in obtaining an efficient mathematical model, until now the efforts are insufficient and new methods capable of contributing to this challenge are needed.

In this work a novel prediction method is proposed, which is based on the composition of a known system whose behavior is governed according to the measurements of more than two decades of seismic events and is modeled as a non-linear system using machine Learning, specifically a recurrent neural network, architecture based on long-short term memory (LSTM) cells.

Many Bayesian statistical procedures are used to observe the probability distributions that an event will have. In this paper, we use Bayesian method to obtain important information about the magnitude and time in which earthquakes occur in areas where seismic events occur frequently.

We analyze the region of Italy bounded by the longitudes 12.3-13.6 and the latitudes 41.6-44. We consider the events with magnitude greater than 4. The results show that the Bayesian based methods are much better than the classical statistical method.