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Past research has comprehensively assessed the capabilities of satellite sensors operating at microwave frequencies, both active (SAR, scatterometers) and passive (radiometers), for the remote sensing of Earth’s surface. Besides brightness temperature and backscattering coefficient, microwave indices, defined as a combination of data collected at different frequencies and polarizations, revealed a good sensitivity to hydrological cycle parameters such as surface soil moisture, vegetation water content, and snow depth and its water equivalent. The differences between microwave backscattering and emission at more frequencies and polarizations have been well established in relation to these parameters, enabling operational retrieval algorithms based on microwave indices to be developed. This Special Issue aims at providing an overview of microwave signal capabilities in estimating the main land parameters of the hydrological cycle, e.g., soil moisture, vegetation water content, and snow water equivalent, on both local and global scales, with a particular focus on the applications of microwave indices.

active microwaves --- snow --- sea ice --- co-pol ratio --- dual-frequency ratios --- snow water equivalent --- passive microwave --- radar --- snow correlation length --- soil moisture --- SAR --- scatterometer --- data fusion --- scale gap --- soil moisture --- Radarsat-2 --- SAR --- water-cloud model --- vegetation descriptor --- Sentinel-1 --- vegetation water content --- microwave indices --- crops --- AMSR2 --- passive microwave soil moisture --- soil moisture downscaling --- microwaves --- radiometer --- radar --- vegetation index --- soil scattering --- roughness --- soil moisture --- SMAP --- SMOS --- microwave radiometry --- microwave indices --- soil moisture content --- vegetation biomass --- snow cover characteristics --- Sentinel-1 backscatter --- polarization --- Terra MODIS --- NDVI --- soil moisture --- Sentinel-1 and Sentinel-2 --- time series analysis --- start of season, harvest, mountain region --- Microwave Radiometry --- Microwave Indices --- Soil Moisture Content --- Vegetation Biomass --- Snow Depth and Snow Water Equivalent

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Changes in sea surface roughness are usually associated with a change in the sea surface wind field. This interaction has been exploited to measure sea surface wind speed by scatterometry. A number of features on the sea surface associated with changes in roughness can be observed by synthetic aperture radar (SAR) because of the change in Bragg backscatter of the radar signal by damping of the resonant ocean capillary waves. With various radar frequencies, resolutions, and modes of polarization, sea surface features have been analyzed in numerous campaigns, bringing various datasets together, thus allowing for new insights into small-scale processes at a larger areal coverage. This Special Issue aims at investigating sea surface features detected by high spatial resolution radar systems, such as SAR.

SAR --- Sentinel-1 --- wave height --- wind speed --- Copernicus --- CMEMS --- Baltic Sea --- hurricane internal dynamical process --- synthetic aperture radar (SAR) --- eyewall replacement cycles --- ocean winds --- ocean surface wind speed retrieval --- synthetic aperture radar (SAR) --- quad-polarized SAR --- synthetic aperture radar (SAR) --- typhoon/hurricane-generated wind waves --- fetch- and duration-limited wave growth relationships --- synthetic aperture radar (SAR) --- hurricane --- rain --- wind --- dual-polarization --- synthetic aperture radar --- GF-3 --- coast and ocean observation --- sea surface roughness --- compact polarization (CP) --- RADARSAT Constellation Mission (RCM) --- geophysical model function (GMF) --- wind retrieval --- CoVe-Pol and CoHo-Pol models --- right circular horizontal polarization model --- right circular vertical polarization model --- oceans --- Synthetic Aperture Radar --- polarimetry --- co-polarized phase difference --- Doppler radar --- radar --- sea surface roughness --- air-sea interaction --- proper orthogonal decomposition --- ocean surface waves --- dispersion curve filtering --- marine X-band radar --- phase-resolved wave fields --- Sentinel-1 --- cross-polarization --- wind retrieval --- SMAP --- Wake detection --- Synthetic Aperture Radar --- support vector machines --- detectability model --- n/a

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In a rapidly changing world, there is an ever-increasing need to monitor the Earth’s resources and manage it sustainably for future generations. Earth observation from satellites is critical to provide information required for informed and timely decision making in this regard. Satellite-based earth observation has advanced rapidly over the last 50 years, and there is a plethora of satellite sensors imaging the Earth at finer spatial and spectral resolutions as well as high temporal resolutions. The amount of data available for any single location on the Earth is now at the petabyte-scale. An ever-increasing capacity and computing power is needed to handle such large datasets. The Google Earth Engine (GEE) is a cloud-based computing platform that was established by Google to support such data processing. This facility allows for the storage, processing and analysis of spatial data using centralized high-power computing resources, allowing scientists, researchers, hobbyists and anyone else interested in such fields to mine this data and understand the changes occurring on the Earth’s surface. This book presents research that applies the Google Earth Engine in mining, storing, retrieving and processing spatial data for a variety of applications that include vegetation monitoring, cropland mapping, ecosystem assessment, and gross primary productivity, among others. Datasets used range from coarse spatial resolution data, such as MODIS, to medium resolution datasets (Worldview -2), and the studies cover the entire globe at varying spatial and temporal scales.

Google Earth Engine --- NDVI --- vegetation index --- Landsat --- remote sensing --- phenology --- surface reflectance --- cropland mapping --- cropland areas --- 30-m --- Landsat-8 --- Sentinel-2 --- Random Forest --- Support Vector Machines --- segmentation --- RHSeg --- Google Earth Engine --- Africa --- remote sensing --- semi-arid --- ecosystem assessment --- land use change --- image classification --- seasonal vegetation --- carbon cycle --- Google Earth Engine --- crop yield --- gross primary productivity (GPP) --- data fusion --- Landsat --- MODIS --- MODIS --- Random Forest --- pasture mapping --- Brazilian pasturelands dynamics --- Google Earth Engine --- crop classification --- multi-classifier --- cloud computing --- time series --- high spatial resolution --- BACI --- Enhanced Vegetation Index --- Google Earth Engine --- cloud-based geo-processing --- satellite-derived bathymetry --- image composition --- pseudo-invariant features --- sun glint correction --- empirical --- spatial error --- Google Earth Engine --- low cost in situ --- Sentinel-2 --- Mediterranean --- burn severity --- change detection --- Landsat --- dNBR --- RdNBR --- RBR --- composite burn index (CBI) --- MTBS --- lower mekong basin --- landsat collection --- suspended sediment concentration --- online application --- google earth engine --- Landsat --- Google Earth Engine --- protected area --- forest and land use mapping --- machine learning classification --- China --- temporal compositing --- image time series --- multitemporal analysis --- change detection --- cloud masking --- Landsat-8 --- Google Earth Engine (GEE) --- Google Earth Engine --- LAI --- FVC --- FAPAR --- CWC --- plant traits --- random forests --- PROSAIL --- small-scale mining --- industrial mining --- google engine --- image classification --- land-use cover change --- seagrass --- habitat mapping --- image composition --- machine learning --- support vector machines --- Google Earth Engine --- Sentinel-2 --- Aegean --- Ionian --- global scale --- soil moisture --- Soil Moisture Ocean Salinity --- Soil Moisture Active Passive --- Google Earth Engine --- drought --- cloud computing --- remote sensing --- snow hydrology --- water resources --- Google Earth Engine --- user assessment --- MODIS --- snow cover --- flood --- disaster prevention --- emergency response --- decision making --- Google Earth Engine --- land cover --- deforestation --- Brazilian Amazon --- Bayesian statistics --- BULC-U --- Mato Grosso --- spatial resolution --- Landsat --- GlobCover --- SDG --- surface urban heat island --- Geo Big Data --- Google Earth Engine --- global monitoring service --- Google Earth Engine --- web portal --- satellite imagery --- trends --- earth observation --- wetland --- Google Earth Engine --- Sentinel-1 --- Sentinel-2 --- random forest --- cloud computing --- geo-big data --- cloud computing --- big data analytics --- long term monitoring --- data archival --- early warning systems