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As computer and space technologies have been developed, geoscience information systems (GIS) and remote sensing (RS) technologies, which deal with the geospatial information, have been rapidly maturing. Moreover, over the last few decades, machine learning techniques including artificial neural network (ANN), deep learning, decision tree, and support vector machine (SVM) have been successfully applied to geospatial science and engineering research fields. The machine learning techniques have been widely applied to GIS and RS research fields and have recently produced valuable results in the areas of geoscience, environment, natural hazards, and natural resources. This book is a collection representing novel contributions detailing machine learning techniques as applied to geoscience information systems and remote sensing.

artificial neural network --- n/a --- model switching --- sensitivity analysis --- neural networks --- logit boost --- Qaidam Basin --- land subsidence --- land use/land cover (LULC) --- naïve Bayes --- multilayer perceptron --- convolutional neural networks --- single-class data descriptors --- logistic regression --- feature selection --- mapping --- particulate matter 10 (PM10) --- Bayes net --- gray-level co-occurrence matrix --- multi-scale --- Logistic Model Trees --- classification --- Panax notoginseng --- large scene --- coarse particle --- grayscale aerial image --- Gaofen-2 --- environmental variables --- variable selection --- spatial predictive models --- weights of evidence --- landslide prediction --- random forest --- boosted regression tree --- convolutional network --- Vietnam --- model validation --- colorization --- data mining techniques --- spatial predictions --- SCAI --- unmanned aerial vehicle --- high-resolution --- texture --- spatial sparse recovery --- landslide susceptibility map --- machine learning --- reproducible research --- constrained spatial smoothing --- support vector machine --- random forest regression --- model assessment --- information gain --- ALS point cloud --- bagging ensemble --- one-class classifiers --- leaf area index (LAI) --- landslide susceptibility --- landsat image --- ionospheric delay constraints --- spatial spline regression --- remote sensing image segmentation --- panchromatic --- Sentinel-2 --- remote sensing --- optical remote sensing --- materia medica resource --- GIS --- precise weighting --- change detection --- TRMM --- traffic CO --- crop --- training sample size --- convergence time --- object detection --- gully erosion --- deep learning --- classification-based learning --- transfer learning --- landslide --- traffic CO prediction --- hybrid model --- winter wheat spatial distribution --- logistic --- alternating direction method of multipliers --- hybrid structure convolutional neural networks --- geoherb --- predictive accuracy --- real-time precise point positioning --- spectral bands --- naïve Bayes

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Vegetation plays an essential role in the study of the environment through plant respiration and photosynthesis. Therefore, the assessment of the current vegetation status is critical to modeling terrestrial ecosystems and energy cycles. Canopy structure (LAI, fCover, plant height, biomass, leaf angle distribution) and biochemical parameters (leaf pigmentation and water content) have been employed to assess vegetation status and its dynamics at scales ranging from kilometric to decametric spatial resolutions thanks to methods based on remote sensing (RS) data.Optical RS retrieval methods are based on the radiative transfer processes of sunlight in vegetation, determining the amount of radiation that is measured by passive sensors in the visible and infrared channels. The increased availability of active RS (radar and LiDAR) data has fostered their use in many applications for the analysis of land surface properties and processes, thanks to their insensitivity to weather conditions and the ability to exploit rich structural and texture information. Optical and radar data fusion and multi-sensor integration approaches are pressing topics, which could fully exploit the information conveyed by both the optical and microwave parts of the electromagnetic spectrum.This Special Issue reprint reviews the state of the art in biophysical parameters retrieval and its usage in a wide variety of applications (e.g., ecology, carbon cycle, agriculture, forestry and food security).

hyperspectral --- spectroscopy --- equivalent water thickness --- canopy water content --- agriculture --- EnMAP --- LAI --- LCC --- FAPAR --- FVC --- CCC --- PROSAIL --- GPR --- machine learning --- active learning --- Landsat 8 --- surface reflectance --- LEDAPS --- LaSRC --- 6SV --- SREM --- NDVI --- artificial neural networks --- canopy chlorophyll content --- INFORM --- leaf area index --- SAIL --- fluorescence --- in vivo --- spectrometry --- ASD Field Spec --- lead ions --- remote sensing indices --- meteosat second generation (MSG) --- biophysical parameters (LAI --- FAPAR) --- SEVIRI --- climate data records (CDR) --- stochastic spectral mixture model (SSMM) --- Satellite Application Facility for Land Surface Analysis (LSA SAF) --- the fraction of radiation absorbed by photosynthetic components (FAPARgreen) --- triple-source --- leaf area index (LAI) --- woody area index (WAI) --- clumping index (CI) --- Moderate Resolution Imaging Spectroradiometer (MODIS) --- soil albedo --- unmanned aircraft vehicle --- multispectral sensor --- vegetation indices --- rapeseed crop --- site-specific farming --- Sentinel-2 --- forest --- vegetation radiative transfer model --- Discrete Anisotropic Radiative Transfer (DART) model --- MODIS --- fraction of photosynthetically active radiation absorbed by vegetation (FPAR) --- three-dimensional radiative transfer model (3D RTM) --- uncertainty assessment --- vertical foliage profile (VFP) --- terrestrial laser scanning (TLS) --- airborne laser scanning (ALS) --- spaceborne laser scanning (SLS) --- riparian --- invasive vegetation --- burn severity --- canopy loss --- wildfire

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Over the past decade, interest in plant biostimulants has been on the rise, compelled by the growing interest of researchers, extension specialists, private industries, and farmers in integrating these products in the array of environmentally friendly tools to secure improved crop performance, nutrient efficiency, product quality, and yield stability. Plant biostimulants include diverse organic and inorganic substances, natural compounds, and/or beneficial microorganisms such as humic acids, protein hydrolysates, seaweed and plant extracts, silicon, endophytic fungi like mycorrhizal fungi, and plant growth-promoting rhizobacteria belonging to the genera Azospirillum, Azotobacter, and Rhizobium. Other substances (e.g., chitosan and other biopolymers and inorganic compounds) can have biostimulant properties, but their classification within the group of biostimulants is still under consideration. Plant biostimulants are usually applied to high-value crops, mainly greenhouse crops, fruit trees and vines, open-field crops, flowers, and ornamentals to sustainably increase yield and product quality. The global biostimulant market is currently estimated at about $2.0 billion and is expected to reach $3.0 billion by 2021 at an annual growth rate of 13%. A growing interest in plant biostimulants from industries and scientists was demonstrated by the high number of published peer-reviewed articles, conferences, workshops, and symposia in the past ten years. This book compiles several original research articles, technology reports, methods, opinions, perspectives, and invited reviews and mini reviews dissecting the biostimulatory action of these natural compounds and substances and beneficial microorganisms on crops grown under optimal and suboptimal growing conditions (e.g., salinity, drought, nutrient deficiency and toxicity, heavy metal contaminations, waterlogging, and adverse soil pH conditions). Also included are contributions dealing with the effect as well as the molecular and physiological mechanisms of plant biostimulants on nutrient efficiency, product quality, and modulation of the microbial population both quantitatively and qualitatively. In addition, identification and understanding of the optimal method, time, rate of application and phenological stage for improving plant performance and resilience to stress as well as the best combinations of plant species/cultivar × environment × management practices are also reported. We strongly believe that high standard reflected in this compilation on the principles and practices of plant biostimulants will foster knowledge transfer among scientific communities, industries, and agronomists, and will enable a better understanding of the mode of action and application procedures of biostimulants in different cropping systems.

Research & information: general --- Biology, life sciences --- Technology, engineering, agriculture --- Crocus sativus L. --- biofertilization --- arbuscular mycorrhizal fungi --- antioxidant activity --- crocin --- picrocrocin --- polyphenols --- safranal --- Maize --- biostimulant --- root --- stress --- growth --- gene expression --- stem cuttings --- propagation --- root morphology traits --- indole-3-acetic acid (IAA) --- indole-3-butyric acid (IBA) --- gibberellins --- phenolic compounds --- nutrients --- nutraceutical potential --- soybean --- yield --- N organic fertilizer --- seaweed extract --- mycorrhizal inoculants --- phosphate-solubilizing microorganisms --- biofertilizers --- microorganism consortium --- biostimulants --- Crocus sativus --- Funneliformis mosseae --- glasshouse --- protected cultivation --- Rhizophagus intraradices --- substrate --- L-methionine --- L-tryptophan --- L-glycine --- lettuce --- nitrogen --- plant biostimulant --- environmental stress --- vegetables --- fruit quality --- plants biostimulants --- yielding --- Biostimulants --- Euglena gracilis --- algal polysaccharide --- β-glucan --- water stress --- tomato --- aeroponics --- Zea mays L --- lignohumate --- lignosulfonate --- biological activity --- nitrogen metabolism --- carbon metabolism --- proteins --- phenolics --- sugars --- Ascophyllum nodosum --- Solanum melongena --- heterostyly --- pollination efficiency --- soilless conditions --- abiotic stress --- alfalfa hydrolysate --- chitosan --- zinc --- ascorbic acid --- Fragaria x ananassa --- functional quality --- lycopene --- organic farming --- protein hydrolysate --- Solanum lycopersicum L. --- tropical plant extract --- fertilizer --- melatonin --- phytomelatonin --- plant protector --- plant stress --- Lactuca sativa L. --- legume-derived protein hydrolysate --- nitrate --- Septoria --- wheat --- Paraburkholderia phytofirmans --- thyme essential oil --- isotope --- phytoparasitic nematodes --- suppressiveness --- sustainable management --- anti-nutritional substances --- fat --- fibre --- morphotype --- protein --- corn --- imaging --- industrial crops --- maize --- next generation sequencing --- phenomics --- plant phenotyping --- row crops --- Bacillus subtilis --- carotenoids --- probiotics --- PGPR --- Mentha longifolia --- humic acid --- antioxidants --- arbuscular mycorrhizal symbiosis --- mycorrhizosphere --- AMF associated bacteria --- plant growth-promoting bacteria --- phosphate-solubilizing bacteria --- siderophore production --- soil enzymatic activity --- biological index fertility --- nitrogenase activity --- microelements fertilization (Ti, Si, B, Mo, Zn) --- seed coating --- cover crop --- vermicompost --- growth enhancement --- AM fungi --- PGPB --- water deficit --- common bean --- Glomus spp. --- organic acids --- pod quality --- seaweed extracts --- seed quality --- tocopherols --- total sugars --- bean --- amino acids --- phenols --- flavonoids --- microbial biostimulant --- non-microbial biostimulant --- Lactuca sativa L. var. longifolia --- mineral profile --- physiological mechanism --- photosynthesis --- biocontrol --- plant growth promotion --- soil inoculant --- Trichoderma --- Azotobacter --- Streptomyces --- deproteinized leaf juice --- fermentation --- lactic acid bacteria --- plant nutrition --- antioxidant capacity --- ornamental plants --- N fertilization --- nitrogen use efficiency --- leaf quality --- Spinacia oleracea L. --- sustainable agriculture --- Valerianella locusta L. --- isotopic labeling --- turfgrass --- humic acids --- leaf area index (LAI) --- specific leaf area (SLA) --- Soil Plant Analysis Development (SPAD) index --- tuber yield --- ultrasound-assisted water --- foliar spray --- Pterocladia capillacea --- bio-fertilizer --- growth parameters --- Jew’s Mallow --- CROPWAT model --- eco-friendly practices --- total ascorbic acid --- Mater-Bi® --- mineral composition --- SPAD index --- Bacillus thuringiensis --- Capsicum annuum --- microbiome --- strain-specific primer --- tracking --- sweet basil --- alfalfa brown juice --- biostimulation --- chlorophyll pigments --- histological changes --- humic substances --- protein hydrolysates --- silicon --- arbuscular mycorrhiza --- plant growth promoting rhizobacteria --- macroalgae --- microalgae --- abiotic stresses --- nutrient use efficiency --- physiological mechanisms

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• Reference Manager
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Over the past decade, interest in plant biostimulants has been on the rise, compelled by the growing interest of researchers, extension specialists, private industries, and farmers in integrating these products in the array of environmentally friendly tools to secure improved crop performance, nutrient efficiency, product quality, and yield stability. Plant biostimulants include diverse organic and inorganic substances, natural compounds, and/or beneficial microorganisms such as humic acids, protein hydrolysates, seaweed and plant extracts, silicon, endophytic fungi like mycorrhizal fungi, and plant growth-promoting rhizobacteria belonging to the genera Azospirillum, Azotobacter, and Rhizobium. Other substances (e.g., chitosan and other biopolymers and inorganic compounds) can have biostimulant properties, but their classification within the group of biostimulants is still under consideration. Plant biostimulants are usually applied to high-value crops, mainly greenhouse crops, fruit trees and vines, open-field crops, flowers, and ornamentals to sustainably increase yield and product quality. The global biostimulant market is currently estimated at about $2.0 billion and is expected to reach $3.0 billion by 2021 at an annual growth rate of 13%. A growing interest in plant biostimulants from industries and scientists was demonstrated by the high number of published peer-reviewed articles, conferences, workshops, and symposia in the past ten years. This book compiles several original research articles, technology reports, methods, opinions, perspectives, and invited reviews and mini reviews dissecting the biostimulatory action of these natural compounds and substances and beneficial microorganisms on crops grown under optimal and suboptimal growing conditions (e.g., salinity, drought, nutrient deficiency and toxicity, heavy metal contaminations, waterlogging, and adverse soil pH conditions). Also included are contributions dealing with the effect as well as the molecular and physiological mechanisms of plant biostimulants on nutrient efficiency, product quality, and modulation of the microbial population both quantitatively and qualitatively. In addition, identification and understanding of the optimal method, time, rate of application and phenological stage for improving plant performance and resilience to stress as well as the best combinations of plant species/cultivar × environment × management practices are also reported. We strongly believe that high standard reflected in this compilation on the principles and practices of plant biostimulants will foster knowledge transfer among scientific communities, industries, and agronomists, and will enable a better understanding of the mode of action and application procedures of biostimulants in different cropping systems.

Research & information: general --- Biology, life sciences --- Technology, engineering, agriculture --- Crocus sativus L. --- biofertilization --- arbuscular mycorrhizal fungi --- antioxidant activity --- crocin --- picrocrocin --- polyphenols --- safranal --- Maize --- biostimulant --- root --- stress --- growth --- gene expression --- stem cuttings --- propagation --- root morphology traits --- indole-3-acetic acid (IAA) --- indole-3-butyric acid (IBA) --- gibberellins --- phenolic compounds --- nutrients --- nutraceutical potential --- soybean --- yield --- N organic fertilizer --- seaweed extract --- mycorrhizal inoculants --- phosphate-solubilizing microorganisms --- biofertilizers --- microorganism consortium --- biostimulants --- Crocus sativus --- Funneliformis mosseae --- glasshouse --- protected cultivation --- Rhizophagus intraradices --- substrate --- L-methionine --- L-tryptophan --- L-glycine --- lettuce --- nitrogen --- plant biostimulant --- environmental stress --- vegetables --- fruit quality --- plants biostimulants --- yielding --- Biostimulants --- Euglena gracilis --- algal polysaccharide --- β-glucan --- water stress --- tomato --- aeroponics --- Zea mays L --- lignohumate --- lignosulfonate --- biological activity --- nitrogen metabolism --- carbon metabolism --- proteins --- phenolics --- sugars --- Ascophyllum nodosum --- Solanum melongena --- heterostyly --- pollination efficiency --- soilless conditions --- abiotic stress --- alfalfa hydrolysate --- chitosan --- zinc --- ascorbic acid --- Fragaria x ananassa --- functional quality --- lycopene --- organic farming --- protein hydrolysate --- Solanum lycopersicum L. --- tropical plant extract --- fertilizer --- melatonin --- phytomelatonin --- plant protector --- plant stress --- Lactuca sativa L. --- legume-derived protein hydrolysate --- nitrate --- Septoria --- wheat --- Paraburkholderia phytofirmans --- thyme essential oil --- isotope --- phytoparasitic nematodes --- suppressiveness --- sustainable management --- anti-nutritional substances --- fat --- fibre --- morphotype --- protein --- corn --- imaging --- industrial crops --- maize --- next generation sequencing --- phenomics --- plant phenotyping --- row crops --- Bacillus subtilis --- carotenoids --- probiotics --- PGPR --- Mentha longifolia --- humic acid --- antioxidants --- arbuscular mycorrhizal symbiosis --- mycorrhizosphere --- AMF associated bacteria --- plant growth-promoting bacteria --- phosphate-solubilizing bacteria --- siderophore production --- soil enzymatic activity --- biological index fertility --- nitrogenase activity --- microelements fertilization (Ti, Si, B, Mo, Zn) --- seed coating --- cover crop --- vermicompost --- growth enhancement --- AM fungi --- PGPB --- water deficit --- common bean --- Glomus spp. --- organic acids --- pod quality --- seaweed extracts --- seed quality --- tocopherols --- total sugars --- bean --- amino acids --- phenols --- flavonoids --- microbial biostimulant --- non-microbial biostimulant --- Lactuca sativa L. var. longifolia --- mineral profile --- physiological mechanism --- photosynthesis --- biocontrol --- plant growth promotion --- soil inoculant --- Trichoderma --- Azotobacter --- Streptomyces --- deproteinized leaf juice --- fermentation --- lactic acid bacteria --- plant nutrition --- antioxidant capacity --- ornamental plants --- N fertilization --- nitrogen use efficiency --- leaf quality --- Spinacia oleracea L. --- sustainable agriculture --- Valerianella locusta L. --- isotopic labeling --- turfgrass --- humic acids --- leaf area index (LAI) --- specific leaf area (SLA) --- Soil Plant Analysis Development (SPAD) index --- tuber yield --- ultrasound-assisted water --- foliar spray --- Pterocladia capillacea --- bio-fertilizer --- growth parameters --- Jew’s Mallow --- CROPWAT model --- eco-friendly practices --- total ascorbic acid --- Mater-Bi® --- mineral composition --- SPAD index --- Bacillus thuringiensis --- Capsicum annuum --- microbiome --- strain-specific primer --- tracking --- sweet basil --- alfalfa brown juice --- biostimulation --- chlorophyll pigments --- histological changes --- humic substances --- protein hydrolysates --- silicon --- arbuscular mycorrhiza --- plant growth promoting rhizobacteria --- macroalgae --- microalgae --- abiotic stresses --- nutrient use efficiency --- physiological mechanisms