In this research work, we have developed a new image encryption‐based algorithm for Landsat 8 satellite images. Image statistical parameters related to first‐order image statistics, that is, mean (μ), SD (σ), and variance (σ²) are used to obtain the feature values for both original images and their encrypted versions. Multispectral satellite images generally contain a wide range of bands, which is 9 to 11 specifically for the Landsat 8 satellite. Hence, the information content of these images is richer than typical images. Through this algorithm, we aim to reduce the image content by randomly selecting only 10% of the total image pixels. These pixels are used to create new images, the so‐called encrypted images. The visual presentation of the encrypted image resembles a noisy picture. First‐order image statistics are calculated for both original images and their encrypted counterparts. These parameters show a similarity in their numerical values for both original and encrypted images. Thus, noisy pictures can be used for transmission in place of the original image when an outcome is decided through statistical image features. The visual similarity in the original images and encrypted images is obtained from the histogram signature plot for both original and encrypted images. The proposed algorithm can hence be used to convert image data for secure transmission and privacy.

This paper investigates the performance of visual texture features based on the gray-level co-occurrence matrix to analyze multispectral remotely sensed Landsat images. Different case studies related to urbanization, flood, and drought are investigated in this research work. The changing land use/land cover pattern caused by urbanization, floods, and droughts is examined through quantitative assessment. Texture visual features, i.e., correlation, contrast, angular second moment or energy, and homogeneity, are derived from the gray-level co-occurrence matrix. These features are used to develop a pattern for the changing texture of land use/land cover. Human visual perception of smoothness and coarseness is related to the texture features and is later used to describe the texture features’ changing behavior. The quantitative assessment of texture features in terms of smoothness and coarseness establishes a novel pattern between pre- and postimages of urbanization, flood, and drought.

This letter aims to develop an inversion model to estimate soil moisture using Navigation with Indian Constellation (NavIC) L-band signal. Several research works suggest that microwave signal property gets affected after reflecting from the soil surface. The nature of the reflected microwave signal depends on the signal’s penetration depth, which is the factor of water content present in the soil surface. NavIC multipath signal can be used for the estimation of soil moisture using this property. The carrier to noise ratio $(C/N_{\mathrm {o}})$ of NavIC signal is used for this purpose. The model proposed in the letter is based on the relationship of estimated multipath phase value with the volumetric moisture content present in the soil surface. The output of the developed model is highly encouraging. A linear relation relationship with a high correlation coefficient value of 0.902 and root mean square error (RMSE) of 4.03% is obtained between ground truth soil moisture and retrieved soil moisture from developed algorithm.

For various precise navigational applications using Indian NavIC system, it is imperative to have an accurate estimation of the ionospheric line‐of‐sight total electron content (TEC). In dual frequency TEC estimation from code and carrier phase measurements, the error is mainly caused by the satellite and receiver differential instrumental biases which can be several TEC units. The reason for the existence of these biases is that the signals experience different delays within the satellite and receiver hardware at the two frequencies S1 and L5 of NavIC system. These biases are not stable over the time, may be due to aging or change in the environment of the satellites and receiver. In this paper, the dynamics of the estimated biases of different satellites of NavIC system and receiver are presented, based on the data collected over 19 months time at Dehradun (Lat.31.26^°N, Long.77.99^°E), India. Five state Kalman filter algorithm was used for the estimation of differential biases. The initial receiver bias required to run Kalman Filter algorithm was determined by applying fitted receiver bias (FRB) technique. The initial satellite biases needed for the purpose were supplied by Space Applications Centre (SAC), Indian Space Research Organization (ISRO), Ahmedabad, India.

With the Precise Point Positioning (PPP) technique, we can accurately determine the location of any user or navigational satellite system receiver within a range of a few centimeters or millimeters. Since PPP is a fundamental requirement in many crucial applications, such as defense for securing sensitive border areas, precision farming for spraying agrochemicals and fertilizers using drones, tourism and navigation in hilly areas, study of natural calamities like seismic events, movement of tectonic plates, Glaciology (study of glaciers), geodetic studies, disaster and relief management. We need the precise x, y, and z positional information of every user or recipient. However, the location determined by GNSS satellites has a number of observational errors that can reduce the positional accuracy of the navigational satellite system by up to a few meters, the most major of which is ionospheric error. In addition to this, a number of other factors, such as low signal strength, low latitude (user or receiver position) lying between $0^{\circ}$ N/S and $30^{\circ}$ N/S, low elevation angles of satellites, and space weather events (solar cycle, solar flare, solar radio burst, geomagnetic storm, plasma bubble) end up causing ionospheric scintillations resulting in cycle-slip error or loss of lock of signal that introduces range errors. Cycle-slip errors should be detected and corrected during data pre-processing, and efforts to deal with this serious issue continue to develop new, effective solutions. PPP systems will be highly reliable as long as the GNSS carrier phase signals are continuously monitored. This paper offers a thorough examination of the statistical characteristics such as mean, median, mode, and standard deviation of latitude, longitude, and altitude enabling for the development of a new method that will increase the positional accuracy of GNSS receivers.