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Precipitation is a well-recognized pillar in the global water and energy balances. The accurate and timely understanding of its characteristics at the global, regional and local scales is indispensable for a clearer insight on the mechanisms underlying the Earth’s atmosphere-ocean complex system. Precipitation is one of the elements that is documented to be greatly affected by climate change. In its various forms, precipitation comprises the primary source of freshwater, which is vital for the sustainability of almost all human activities. Its socio-economic significance is fundamental in managing this natural resource effectively, in applications ranging from irrigation to industrial and household usage. Remote sensing of precipitation is pursued through a broad spectrum of continuously enriched and upgraded instrumentation, embracing sensors which can be ground-based (e.g., weather radars), satellite-borne (e.g., passive or active space-borne sensors), underwater (e.g., hydrophones), aerial, or ship-borne. This volume hosts original research contributions on several aspects of remote sensing of precipitation, including applications which embrace the use of remote sensing in tackling issues such as precipitation estimation, seasonal characteristics of precipitation and frequency analysis, assessment of satellite precipitation products, storm prediction, rain microphysics and microstructure, and the comparison of satellite and numerical weather prediction precipitation products.

Northern China --- raindrop size distribution (DSD) --- microphysical processes --- quantitative precipitation estimation (QPE) --- satellite-based precipitation --- elevation --- extreme events --- IMERG-V05B and V06A --- MSWEP --- ERA5 --- SM2RAIN --- precipitation estimation --- soil moisture --- SM2RAIN-CCI --- SM2RAIN-ASCAT --- multi-satellite precipitation analysis (TMPA) --- error decomposition --- complex topography --- diverse climate --- gauge data --- IMERG --- TAHMO --- morphing --- field displacement --- TIGGE --- precipitation --- numerical weather prediction --- satellite --- flood --- spring 2019 --- Iran --- GPM IMERG --- satellite precipitation --- spatiotemporal analysis --- statistical distribution --- validation --- Mainland China --- GSMaP_NRT --- GSMaP_Gauge_NRT --- raindrop size distribution --- radar reflectivity --- raindrop spectrometer --- semi-arid area --- assessment --- Taiwan --- data assimilation --- WRF model --- high-impact rainfall events --- GNSS ZTD --- optimum interpolation --- geographically weighted regression --- downscaling --- Tianshan Mountains --- satellite precipitation products --- evaluation --- daily rainfall --- hourly rainfall --- GPM --- TRMM --- GNSS --- GNSS antenna --- receiver antenna calibration --- relative calibration --- Phase Center Variation --- U-blox --- goGPS --- Zenith Tropospheric Delay --- ZED-F9P --- GSMaP --- Nepal --- cloud radar --- thunderstorm --- LDR --- hydrometeor --- hydrometeor classification --- lightning --- discharge --- remote sensing --- SEVIRI --- ground radar --- precipitation interpolation --- geographically and temporally weighted regression --- time weight function --- geographically and temporally weighted regression kriging --- extreme rainfall --- polarimetric radar signatures --- quantitative precipitation estimation --- southern china --- reanalysis --- linear trends --- mainland China --- EDBF algorithm --- geospatial predictor --- spatial pattern --- weighted precipitation --- Cyprus --- bias correction --- object-based method --- storm events --- Thies --- disdrometer --- weather circulations --- convective --- stratiform --- rain spectra --- radar reflectivity–rain rate relationship --- gridded precipitation products --- abrupt changes --- trends --- statistical indicators --- agriculture --- Pakistan --- rainfall --- radar --- extreme precipitation --- spatial bootstrap --- Louisiana --- annual maxima --- n/a --- radar reflectivity-rain rate relationship

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The rapid growth of the world population has resulted in an exponential expansion of both urban and agricultural areas. Identifying and managing such earthly changes in an automatic way poses a worth-addressing challenge, in which remote sensing technology can have a fundamental role to answer—at least partially—such demands. The recent advent of cutting-edge processing facilities has fostered the adoption of deep learning architectures owing to their generalization capabilities. In this respect, it seems evident that the pace of deep learning in the remote sensing domain remains somewhat lagging behind that of its computer vision counterpart. This is due to the scarce availability of ground truth information in comparison with other computer vision domains. In this book, we aim at advancing the state of the art in linking deep learning methodologies with remote sensing image processing by collecting 20 contributions from different worldwide scientists and laboratories. The book presents a wide range of methodological advancements in the deep learning field that come with different applications in the remote sensing landscape such as wildfire and postdisaster damage detection, urban forest mapping, vine disease and pavement marking detection, desert road mapping, road and building outline extraction, vehicle and vessel detection, water identification, and text-to-image matching.

Research & information: general --- synthetic aperture radar --- despeckling --- multi-scale --- LSTM --- sub-pixel --- high-resolution remote sensing imagery --- road extraction --- machine learning --- DenseUNet --- scene classification --- lifting scheme --- convolution --- CNN --- image classification --- deep features --- hand-crafted features --- Sinkhorn loss --- remote sensing --- text image matching --- triplet networks --- EfficientNets --- LSTM network --- convolutional neural network --- water identification --- water index --- semantic segmentation --- high-resolution remote sensing image --- pixel-wise classification --- result correction --- conditional random field (CRF) --- satellite --- object detection --- neural networks --- single-shot --- deep learning --- global convolution network --- feature fusion --- depthwise atrous convolution --- high-resolution representations --- ISPRS vaihingen --- Landsat-8 --- faster region-based convolutional neural network (FRCNN) --- single-shot multibox detector (SSD) --- super-resolution --- remote sensing imagery --- edge enhancement --- satellites --- open-set domain adaptation --- adversarial learning --- min-max entropy --- pareto ranking --- SAR --- Sentinel–1 --- Open Street Map --- U–Net --- desert --- road --- infrastructure --- mapping --- monitoring --- deep convolutional networks --- outline extraction --- misalignments --- nearest feature selector --- hyperspectral image classification --- two stream residual network --- Batch Normalization --- plant disease detection --- precision agriculture --- UAV multispectral images --- orthophotos registration --- 3D information --- orthophotos segmentation --- wildfire detection --- convolutional neural networks --- densenet --- generative adversarial networks --- CycleGAN --- data augmentation --- pavement markings --- visibility --- framework --- urban forests --- OUDN algorithm --- object-based --- high spatial resolution remote sensing --- Generative Adversarial Networks --- post-disaster --- building damage assessment --- anomaly detection --- Unmanned Aerial Vehicles (UAV) --- xBD --- feature engineering --- orthophoto --- unsupervised segmentation