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Ocean satellite remote sensing plays important roles in the observations of physical, biological and biogeochemical features in inland, coastal, and global ocean waters, with high temporal and spatial resolution. The satellite-measured ocean products are used for near-real-time ocean monitoring and climate data records to understand short-/long-term variabilities in marine environments and ecosystems as well as for decision making tools to manage social, economic, and environmental benefits. Validation/evaluation including a combination of field measurements and inter-satellite comparison is an essential step in providing more accurate satellite-derived ocean products. In this Special Issue, 14 papers have been published and include research on validation/evaluation, retrieval algorithms of ocean geophysical and biogeochemical parameters, and application of the satellite ocean products in the regional and global ocean. Subjects treated include: Sea Surface Temperature; Sea Ice Surface Temperature from VIIRS thermal infrared sensor; Sea Ice Detection from Spectroradiometer; Sea Surface Winds from HY-2A Scatterometer and GNSS—Reflectometry; Wave Height from Sentinel-3A SAR; Retrievals of Sea Surface Salinity, Chlorophyll-a, Particulate Organic Carbon, Particulate Backscattering, Marine Fishery resource, and Submesoscale Eddies from multiple Ocean Colour sensors.

Research & information: general --- sea ice --- ice surface temperature --- Suomi NPP --- JPSS --- remote sensing --- leads --- MODIS --- ocean color --- algorithm --- chlorophyll --- HPLC --- fluorometry --- particulate organic carbon --- southern ocean --- ocean colour --- satellite-derived chlorophyll-a concentration --- algorithm evaluation --- Northwest Atlantic --- Northeast Pacific --- Japanese common squid --- Todarodes pacificus --- habitat suitability index (HSI) --- the Yellow Sea --- the South Sea of South Korea --- spaceborne GNSS-R --- DDM --- ocean surface wind speed --- GMF --- CYGNSS --- HY-2A --- scatterometer --- sea surface wind field --- evaluation --- satellite altimetry --- significant wave height --- SAR --- wave buoy observations --- validation --- southwest England --- coastal altimetry --- Sentinel-3A --- SRAL --- particulate optical backscattering --- Raman scattering --- QAA algorithm --- ESA OC-CCI --- steric height --- sea level variability --- interferometric altimeter validation --- high-frequency radar --- MODIS ocean color patterns --- submesoscale eddies --- sea surface salinity estimation --- Changjiang diluted water --- neural network --- GOCI application --- sea surface temperature --- global gridded dataset --- Yellow Sea --- bias correction --- chlorophyll-a --- phytoplankton --- East/Japan Sea

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• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Ocean satellite remote sensing plays important roles in the observations of physical, biological and biogeochemical features in inland, coastal, and global ocean waters, with high temporal and spatial resolution. The satellite-measured ocean products are used for near-real-time ocean monitoring and climate data records to understand short-/long-term variabilities in marine environments and ecosystems as well as for decision making tools to manage social, economic, and environmental benefits. Validation/evaluation including a combination of field measurements and inter-satellite comparison is an essential step in providing more accurate satellite-derived ocean products. In this Special Issue, 14 papers have been published and include research on validation/evaluation, retrieval algorithms of ocean geophysical and biogeochemical parameters, and application of the satellite ocean products in the regional and global ocean. Subjects treated include: Sea Surface Temperature; Sea Ice Surface Temperature from VIIRS thermal infrared sensor; Sea Ice Detection from Spectroradiometer; Sea Surface Winds from HY-2A Scatterometer and GNSS—Reflectometry; Wave Height from Sentinel-3A SAR; Retrievals of Sea Surface Salinity, Chlorophyll-a, Particulate Organic Carbon, Particulate Backscattering, Marine Fishery resource, and Submesoscale Eddies from multiple Ocean Colour sensors.

Research & information: general --- sea ice --- ice surface temperature --- Suomi NPP --- JPSS --- remote sensing --- leads --- MODIS --- ocean color --- algorithm --- chlorophyll --- HPLC --- fluorometry --- particulate organic carbon --- southern ocean --- ocean colour --- satellite-derived chlorophyll-a concentration --- algorithm evaluation --- Northwest Atlantic --- Northeast Pacific --- Japanese common squid --- Todarodes pacificus --- habitat suitability index (HSI) --- the Yellow Sea --- the South Sea of South Korea --- spaceborne GNSS-R --- DDM --- ocean surface wind speed --- GMF --- CYGNSS --- HY-2A --- scatterometer --- sea surface wind field --- evaluation --- satellite altimetry --- significant wave height --- SAR --- wave buoy observations --- validation --- southwest England --- coastal altimetry --- Sentinel-3A --- SRAL --- particulate optical backscattering --- Raman scattering --- QAA algorithm --- ESA OC-CCI --- steric height --- sea level variability --- interferometric altimeter validation --- high-frequency radar --- MODIS ocean color patterns --- submesoscale eddies --- sea surface salinity estimation --- Changjiang diluted water --- neural network --- GOCI application --- sea surface temperature --- global gridded dataset --- Yellow Sea --- bias correction --- chlorophyll-a --- phytoplankton --- East/Japan Sea

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• Reference Manager
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Agriculture, as the main source of alimentation and the most important economic activity globally, is being affected by the impacts of climate change. To maintain and increase our global food system production, to reduce biodiversity loss and preserve our natural ecosystem, new practices and technologies are required. This book focuses on the latest advances in remote sensing technology and agricultural engineering leading to the sustainable agriculture practices. Earth observation data, in situ and proxy-remote sensing data are the main source of information for monitoring and analyzing agriculture activities. Particular attention is given to earth observation satellites and the Internet of Things for data collection, to multispectral and hyperspectral data analysis using machine learning and deep learning, to WebGIS and the Internet of Things for sharing and publication of the results, among others.

Research & information: general --- Geography --- geographic information system (GIS) --- pocket beaches --- coastal management --- Interreg --- climate change --- remote sensing --- drone --- Sicily --- Malta --- Gozo --- Comino --- systematic literature review --- anomaly intrusion detection --- deep learning --- IoT --- resource constraint --- IDS --- evapotranspiration --- penman-monteith equation --- artificial neural network --- canopy conductance --- Ziz basin --- water quality --- satellite image analysis --- modeling approach --- nitrate --- dissolved oxygen --- chlorophyll a --- time series analysis --- environmental monitoring --- water extraction --- modified normalized difference water index (MNDWI) --- machine learning algorithm --- hyperspectral --- proximal sensing --- panicle initiation --- normalized difference vegetation index (NDVI) --- green ring --- internode-elongation --- Sentinel 1 and 2 --- Copernicus Sentinels --- crop classification --- food security --- agricultural monitoring --- data analysis --- SAR --- random forest --- 3D bale wrapping method --- equal bale dimensions --- mathematical model --- minimal film consumption --- optimal bale dimensions --- round bales --- Sentinel-2 --- SVM --- RF --- Boufakrane River watershed --- irrigation requirements --- water resources --- sustainable land use --- agriculture --- invasive plants --- precision agriculture --- rice farming --- site-specific weed management --- nitrogen prediction --- 1D convolution neural networks --- cucumber --- crop yield improvement --- mango leaf --- CCA --- vein pattern --- leaf disease --- cubic SVM --- chlorophyll-a concentration --- transfer learning --- overfitting --- data augmentation --- guava disease --- plant disease detection --- crops diseases --- entropy --- features fusion --- machine learning --- object-based classification --- density estimation --- histogram --- land use --- crop fields --- soil tillage --- data fusion --- multispectral --- sensor --- probe --- temperature profile --- forest roads --- simulation --- autonomous robots --- smart agriculture --- environmental protection --- photogrammetry --- path planning --- internet of things --- modeling --- convolutional neural networks --- machine vision --- computer vision --- modular robot --- selective spraying --- vision-based crop and weed detection --- Faster R-CNN --- YOLOv5 --- band selection --- CNN --- NDVI --- hyperspectral imaging --- crops --- urban flood --- Sentinel-1a --- Synthetic Aperture Radar (SAR) --- 3D Convolutional Neural Network --- multi-temporal data --- land use classification --- GIS --- Coatzacoalcos --- algorithms --- clustering --- pest control --- site-specific --- virtual pests --- rice plant --- weed --- hyperspectral imagery --- sustainable agriculture --- green technologies --- Internet of Things --- natural resources --- sustainable environment --- IoT ecosystem --- hyperspectral remoting sensing --- crop mapping --- image classification --- deep transfer learning --- hyperparameter optimization --- metaheuristic --- soil attribute --- ordinary Kriging --- rational sampling numbers --- spatial heterogeneity --- sampling --- soil pH --- spatial variation --- ordinary kriging --- Land Use/Land Cover --- LISS-III --- Landsat --- Vision Transformer --- Bidirectional long-short term memory --- Google Earth Engine --- Explainable Artificial Intelligence