Recently, GNSS remote sensing (RS) using GNSS reflectometry (GNSS–R), GNSS radio occultation (GNSS–RO), and positioning techniques are rapidly growing worldwide in applications in number of areas of environmental and disaster monitoring and management. This chapter includes reviews on the principles and applications of these three GNSS remote sensing techniques in environment and disaster assessment and management. GNSS–R RS emerged as successful alternative of conventional microwave RS techniques such as synthetic aperture radar (SAR) and radiometry for the retrieval of soil moisture content (SMC). Several empirical and physical models are developed for the retrieval of SMC using space-borne and ground-based GNSS–R derived parameters, viz soil surface reflectivity and Fresnel reflection coefficient, peak amplitude of signal delay Doppler map (DDM), and signal-to-noise ratio (SNR) as inputs. GNSS–R developed as supplementary RS technique for the estimation of several vegetation parameters such as above-ground biomass (AGB), tree height, and vegetation water content (VWC) which are important for forest fire disaster and sustainable forest management. Space-borne GNSS–R signature parameters, viz SNR, polarimetric ratio, waveforms’ trailing-edge slope (TES), DDM, and leading-edge slope (LES), found useful as inputs for empirical, physical, and artificial neural network (ANN)-based machine learning (ML) models for the local and global scales estimation of vegetation AGB and tree height. Space-borne GNSS–R observable parameters (surface reflectivity, DDM, SNR, and TES) successfully applied worldwide for the detection, mapping, and monitoring of flood inundated areas. Limited studies showed the potential of GNSS–R RS in cryospheric applications in estimation of snow/ice thickness, and characterization and monitoring of dry and wet snows in parts of Antarctica. Last two decades, GNSS–RO RS measured zenith tropospheric delay data was widely used for the accurate retrieval of atmospheric precipitable water vapor (PWV), profile of temperature, vapor pressure, and humidity. Number of research studies showed the significant improvement of regional and global short term as well as long term weather and flash flood predictions using numerical weather prediction (NWP) models for by inclusion of GNSS–RO derived atmospheric profiles and PWV as inputs. Climate change indicators, e.g., temperature and height of geo-potential tropopause of the atmosphere which are important parameters of climate change studies could also be obtained by the low Earth orbit (LEO) satellites based GNSS–RO remote sensing observations. Ionosphere total electron content (TEC) anomaly which is widely used as one of the earthquake precursors for reliable prediction of earthquake is accurately derived from GNSS–RO measured ionospheric delay. Globally, several geological disasters/hazards such as landslide, land subsidence, earthquake, and tsunami are effectively monitored and managed by the use of real-time precise GNSS positioning RS alone or combinations of other techniques. Several research studies showed successful applications of GNSS PPP (Precise point positioning) data alone and in combination with interferometry SAR (InSAR) techniques for cost effective and rapid for monitoring spatial variability of land subsidence. GNSS–PPP and real-time kinematic (RTK) alone and combination (PPR + RTK) observations have been operationally used for real-time local to regional scales landslides monitoring. Precise real-time GNSS 3D positional data from space-borne and ground-based Continuous Operating Reference System) systems provide vital information in pre-, co-, and post-seismic stages which are very helpful for seismic risk zonation, likelihood of future occurrences of earthquake, and finding location and plate movement associated with surface deformation. GNSS positioning remote sensing is also playing vital role in operational tsunami early warning system in different countries of the world.