04788nam 2201285z- 450 991055769180332120210501(CKB)5400000000044613(oapen)https://directory.doabooks.org/handle/20.500.12854/68321(oapen)doab68321(EXLCZ)99540000000004461320202105d2021 |y 0engurmn|---annantxtrdacontentcrdamediacrrdacarrierHyperspectral Remote Sensing of Agriculture and VegetationBasel, SwitzerlandMDPI - Multidisciplinary Digital Publishing Institute20211 online resource (266 p.)3-03943-907-3 3-03943-908-1 This book shows recent and innovative applications of the use of hyperspectral technology for optimal quantification of crop, vegetation, and soil biophysical variables at various spatial scales, which can be an important aspect in agricultural management practices and monitoring. The articles collected inside the book are intended to help researchers and farmers involved in precision agriculture techniques and practices, as well as in plant nutrient prediction, to a higher comprehension of strengths and limitations of the application of hyperspectral imaging to agriculture and vegetation. Hyperspectral remote sensing for studying agriculture and natural vegetation is a challenging research topic that will remain of great interest for different sciences communities in decades.Environmental economicsbicsscResearch & information: generalbicsscabaxialadaxialanalytical methodsAOTFartificial intelligencebiodiversityBRDFcanopy spectrachlorophyll contentclassificationclassification of agricultural featurescontinuous wavelet transform (CWT)correlation coefficientcrop propertiesdiscriminationDLARIEragrostis tefEthiopiaexpansive speciesfeature selectionfield spectroscopyfuture hyperspectral missionsgrapevineheavy metalshigh-resolution spectroscopy for agricultural soils and vegetationhyperspectralhyperspectral data as input for modelling soil, crop, and vegetationhyperspectral databases for agricultural soils and vegetationhyperspectral imaginghyperspectral imaging for vegetationhyperspectral LiDARhyperspectral remote sensinghyperspectral remote sensing for soil and crops in agricultureinvasive speciesleaf chlorophyll contentmacronutrientMDATTmicronutrientMLRmulti-angle observationNatura 2000new hyperspectral technologiesobject-oriented segmentationpartial least square regression (PLSR)partial least squarespeanutplantplant traitsplatforms and sensorsPLSprecision agricultureproduct validationproximal sensing dataproximal sensorrandom forestRed Edgeremote sensingreplicabilityreproducibilitysoil characteristicsspectraspectral reflectancespectroscopysupport vector machineSVMvegetationvegetation classificationvegetation parameterswaveband selectionEnvironmental economicsResearch & information: generalPascucci Simoneedt1311963Pignatti StefanoedtCasa RaffaeleedtDarvishzadeh RoshanakedtHuang WenjiangedtPascucci SimoneothPignatti StefanoothCasa RaffaeleothDarvishzadeh RoshanakothHuang WenjiangothBOOK9910557691803321Hyperspectral Remote Sensing of Agriculture and Vegetation3030627UNINA