01092nam a2200313 i 4500991003450999707536080311s1999 it a 001 0 ita d8822062167b13693141-39ule_instDip.to Matematicaengitaeng510AMS 00AZDM H04LC QA93.H47Higgins, Peter M.524604Mathematics for the curious.Italian54869Divertirsi con la matematica :curiosità e stranezze del mondo dei numeri /Peter M. HigginsBari :Dedalo,c1999256 p. :ill. ;21 cmNuova biblioteca Dedalo ;216 Includes indexMathematicsPopular works.b1369314102-04-1411-03-08991003450999707536LE013 H04 HIG11 (1999)12013000207308le013pE13.00-l- 05150.i1469565017-03-08Mathematics for the curious54869UNISALENTOle01311-03-08ma -itait 0004096nam 22010213a 450 991036775160332120250203235433.09783038978213303897821310.3390/books978-3-03897-821-3(CKB)4100000010106201(oapen)https://directory.doabooks.org/handle/20.500.12854/53452(ScCtBLL)5be3c837-a520-4fcc-90d2-3fb8d1d3205c(OCoLC)1163809781(oapen)doab53452(EXLCZ)99410000001010620120250203i20192019 uu engurmn|---annantxtrdacontentcrdamediacrrdacarrierMicrowave Indices from Active and Passive Sensors for Remote Sensing ApplicationsSimonetta Paloscia, Emanuele SantiMDPI - Multidisciplinary Digital Publishing Institute2019Basel, Switzerland :MDPI,2019.1 electronic resource (224 p.)9783038978206 3038978205 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.Geographybicssctime series analysispassive microwave soil moistureSentinel-1 and Sentinel-2Snow Depth and Snow Water Equivalentsnow cover characteristicsvegetation biomassroughnesssea iceSMOSmicrowave radiometrysoil moisture downscalingVegetation Biomassvegetation indexTerra MODISSentinel-1Microwave Indicessoil moisture contentdual-frequency ratiosSMAPpassive microwavewater-cloud modelsnowSentinel-1 backscatterAMSR2data fusionmicrowavesmountain regionSARstart of seasoncropsNDVIscatterometerRadarsat-2polarizationvegetation water contentco-pol ratioactive microwavesmicrowave indicesharvestMicrowave Radiometrysoil moistureSoil Moisture Contentsnow correlation lengthradiometerradarsoil scatteringvegetation descriptorscale gapsnow water equivalentGeographyPaloscia Simonetta1787925Santi EmanueleScCtBLLScCtBLLBOOK9910367751603321Microwave Indices from Active and Passive Sensors for Remote Sensing Applications4322004UNINA