Machine Kudzidza algorithm
Product Information
Zvinotsanangurwa
- Product Name: Remote Sensing Article
- Munyori: Larissa Patricio-Valerio, Thomas
Schroeder, Michelle J. Devlin, Yi Qin, Scott Smithers - Zuva Rekuburitswa: 21 Chikunguru 2022
- Keywords: Himawari-8, ruvara rwegungwa, rakagadzirwa
neural network, Great Barrier Reef, mvura yemahombekombe, yakazara
zvakamiswa zvakasimba, kudzidza muchina, kunaka kwemvura
Mirayiridzo Yekushandiswa Kwechigadzirwa
1. Nhanganyaya
Iyo Remote Sensing Chinyorwa chinopa muono mukushandiswa kwe
muchina kudzidza algorithms ekudzoreredza yakazara yakamiswa solids
muGreat Barrier Reef uchishandisa data kubva kuHimawari-8. Chinyorwa
inokurukura matambudziko uye mabhenefiti ekushandisa geostationary
Earth orbit satellites yekuenderera mberi kwekutarisa kwemahombekombe
nzvimbo.
2. Nzira yekudzorera
Chinyorwa chinoratidza kukosha kwe geostationary
satellites seHimawari-8 mukutora pedyo-chaiyo-nguva data pa
nzira dzemahombekombe. Inosimbisa miganhu yepasi pasi orbit
masatellite ekugadzirisa kusiyanisa kwenguva pfupi kana ichienzaniswa
geostationary satellites.
3. Ocean Color Sensors
Chinyorwa chinotaura kukosha kwemasensa emavara egungwa pa
masatellite ekuwana ruzivo rwenzvimbo ine chekuita nemvura
quality. Inotaura nezvesimba renguva rinoonekwa ne
geostationary satellites uye maitiro avo pakutarisa mahombekombe
phenomena.
Mibvunzo Inowanzo bvunzwa (FAQ)
Mubvunzo: Chii chinonyanya kutariswa cheRemote Sensing Article?
A: Chinangwa chikuru chiri pakushandisa muchina wekudzidza algorithm nawo
Himawari-8 dhata yekudzoreredza yakazara yakamiswa yakasimba muGreat
Barrier Reef.
Mubvunzo: Sei geostationary satellites ichisarudzwa kumahombekombe
monitoring?
A: Geostationary satellites inopa pedyo nekuenderera kwekutarisa kwe
nzvimbo huru dzine frequency yakakwirira, zvichibvumira kuongororwa kuri nani
yekukurumidza kuchinja nzira dzemahombekombe.
remote sensing
Article
Muchina Kudzidza Algorithm yeHimawari-8 Yese Yakasimudzwa Solids Kudzoserwa muGreat Barrier Reef.
Larissa Patricio-Valerio 1,2,* , Thomas Schroeder 2, Michelle J. Devlin 3 , Yi Qin 4 naScott Smithers 1
1 Koreji yeSainzi neUinjiniya, James Cook University, Townsville, QLD 4811, Australia; scott.smithers@jcu.edu.au
2 Commonwealth Scientific and Industrial Research Organisation, Oceans and Atmosphere, GPO Box 2583, Brisbane, QLD 4001, Australia; thomas.schroeder@csiro.au
3 Center for Environmental Fisheries uye Aquaculture Science, Parkfield Road, Lowestoft, Suffolk NR33 0HT, UK; michelle.devlin@cefas.co.uk
4 Commonwealth Scientific and Industrial Research Organisation, Oceans and Atmosphere, GPO Box 1700, Canberra, ACT 2601, Australia; yi.qin@csiro.au
* Kunyorerana: larissa.patriciovalerio@my.jcu.edu.au
Citation: Patricio-Valerio, L.; Schroeder, T.; Devlin, MJ; Qin, Y.; Smithers, S. A Muchina Kudzidza Algorithm yeHimawari-8 Yese Yakasimudzwa Solids Retrievals muGreat Barrier Reef. Remote Sens. 2022, 14, 3503. https://doi.org/ 10.3390/rs14143503
Academic Editor: Chris Roelfsema
Yakagamuchirwa: 15 Chivabvu 2022 Yakagamuchirwa: 19 Chikunguru 2022 Yakabudiswa: 21 Chikunguru 2022
Muparidzi Cherechedzo: MDPI inoramba isina kwayakarerekera maererano nezvichemo zvekutonga mumepu dzakaburitswa uye masangano akabatana.
Copyright: © 2022 nevanyori. Rezinesi MDPI, Basel, Switzerland. Chinyorwa ichi chinyorwa chekupinda pachena chakagoverwa pasi pemitemo nemamiriro erezenisi reCreative Commons Attribution (CC BY) (https:// creativecommons.org/licenses/by/ 4.0/).
Abstract: Kunzwa kure kure kweruvara rwegungwa kwave kwakakosha pakuenzanisa-chiyero chekutarisa kunaka kwemvura yemugungwa muGreat Barrier Reef (GBR). Nekudaro, ma sensors eruvara rwegungwa ari paboard low orbit satellite, senge Sentinel-3 boka renyeredzi, haana kukwana kudzokorora kugona kugadzirisa zvizere kusiyanisa kwemasikati munzvimbo dzine simba dzemahombekombe. Kuti ukunde chipingamupinyi ichi, basa iri rinopa fizikisi-yakavakirwa pamhenderekedzo yegungwa ruvara algorithm yeAdvanced Himawari Imager pabhodhi reHimawari-8 geostationary satellite. Kunyangwe ichigadzirirwa kushandiswa kwemamiriro ekunze, Himawari-8 inopa mukana wekufungidzira ruvara rwemakungwa maminetsi ega ega gumi, mumabhendi mana akafaranuka uye ari pedyo-infrared spectral, uye pa10 km1 spatial resolution. Yakabatanidzwa yegungwa remumhepo inopenya yekuchinjisa simulations emabhendi eHimawari-2 akaitwa kune yechokwadi huwandu hwemukati memvura uye mumhepo optical zvimiro zveGBR uye kune dzakasiyana siyana dzezuva uye yekutarisa geometries. Iyo data yakaedzerwa yakashandiswa kugadzira inverse modhi yakavakirwa pane artificial neural network matekiniki ekufungidzira yakazara yakamisikidzwa solids (TSS) yakamira zvakananga kubva kuHimawari-8 kumusoro-kwe-atmosphere spectral reflectance observations. Iyo algorithm yakasimbiswa pamwe chete mune situ data mhiri kwemahombekombe GBR uye miganho yekuona yakaongororwa. TSS retrievals yakaratidza kukanganisa kwakasiyana kusvika ku8% uye zvikanganiso zvakakwana zve 75 mg L-2 mukati mehutano hwekusimbisa 1 kusvika 0.14 mg L-24, ine muganhu wekuona we 1 mg L-0.25. Isu tinokurukura zvingangoshandiswa zveHimawari-1 diurnal TSS zvigadzirwa zvekuvandudzwa kwekutarisa uye manejimendi emhando yemvura muGBR.
Keywords: Himawari-8; ruvara rwegungwa; artificial neural network; Great Barrier Reef; mvura yegungwa; zvachose yakamiswa yakasimba; kudzidza muchina; kunaka kwemvura
1. Nhanganyaya Ocean color sensors onboard low Earth orbit (LEO) setiraiti, seMODIS/Aqua,
VIIRS/Suomi-NPP, uye OLCI/Sentinel-3, vakapa marekodhi enguva refu ekucherechedza kwakakosha uye kusingadhuri kuti vaongorore zuva nezuva kusvika pakati pegore nepakati pemvura yemhando yemvura muGreat Barrier Reef (GBR) [1]. Iyo LEO setiraiti inoongorora iyo yakafanana geographic nharaunda mukati mezuva rimwe kana maviri zvakanakisa; zvisinei, nguva-yenguva pakati penzira mbiri dzakatevedzana uye dzakafanana (kureva, revisit periodicity) inowanzosiyana pakati pevhiki imwe kusvika kumana. Pamusoro pezvo, mufananidzo wemuvara wegungwa unogona kukanganiswa zvakanyanya nekuvapo kwemakore uye kupenya kwezuva, zvichitadzisa kudzoserwa kwemhando yepamusoro yekutarisa [5]. Izvi zvinogona kuda seti yevhiki nemwedzi seti yemifananidzo yemazuva ese kubva munzvimbo imwe chete kugadzira inoumbwa isina makore. view yegungwa. Nekuda kweizvozvo, kugona kwekanguva kwemasatelliti e LEO hakuna kukwana kugadzira hurongwa hwekutarisa hwakazara uye kunyatso tarisisa maitiro enguva pfupi ekuchinja kwemahombekombe, senge phytoplankton diel cycles, kufambira mberi kwemazuva ese kwemafashama, uye.
Remote Sens. 2022, 14, 3503. https://doi.org/10.3390/rs14143503
https://www.mdpi.com/journal/remotesensing
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kusimuka kwemhepo uye kunofambiswa nemhepo [7]. Vatsvakurudzi uye vatungamiri vezvakatipoteredza vachiri
vimba ne LEO ocean color produ-cts yekuwana inodhura-inoshanda ruzivo rwenzvimbo- mu
Coastal GBR [10,11], asi ziva magumo ehunyanzvi uhu kugadzirisa nguva pfupi-pfupi.
kusiyana-siyana.
-
Satellite pane g-eostationary Earth orbit (GEO), zvikasadaro, bvumira pedyo nekuenderera
kucherechedzwa kwenzvimbo dzakakura dzepasi panguva yepamusoro-soro (maminitsi kusvika kumaawa) zvichienzaniswa
kune iri pedyo zuva nezuva revisiting frequency yeLEO mapuratifomu, kunyanya pamusoro penzvimbo dzinopisa [9]. The
nyika yekutanga Geostationary Ocean Color Imager (GOCI-I), yakatangwa muna 2010, yakaratidza
The temporal dynamics yekukurumidza kuchinja kwemahombekombe-maitiro muNortheast Asia, senge
ye turbidity plumes uye inokuvadza algal blooms [12,13]. Kubudirira kwayo kwakapa nyaya inobatsira
yekuvandudza mune ramangwana repasi rose GEO ocean color mishoni [14]; zvisinei, hapana
mishoni dzakarongwa kutangwa mukati memakore gumi anotevera dzakagadzirirwa kucherechedzwa
Mvura yeAustralia. Zvakangodaro, masetiraiti eGEO anoshandirwa pasi rose kuitira meteorological ob-.
masevhisi uye kufambira mberi kwetekinoroji kwakawedzera kugona kwavo kuunganidza data pamusoro pemakungwa, zvichibvumira maitiro ane simba kuti aonekwe kubva muchadenga [-15].
Tofhbe annedxst-ignentheera-vtiiosinblGe EspOemctreutemor(o2loogri3cailnssetenasdorosfaorenleyq1uibpapnedd)
nenhamba yakawedzerwa yakasanganiswa neyakavandudzwa
ragreadendovtisaoltyTnmahctpieeeortsornAivaacdlirsldvyoeiawnnpnsgoeciitdednivd,itui-ftHooryfnri-(mavsthliiagemewwnfieaaatlrr-etsi-totouIrm-tno-nimplaoorgiegsee,eci-rceara(daA-lnteioeHnobat)Ires)a-edtnorrdrnvueabeovtoniicaosboirntoldosafr-uHroedrvqiemecudarealvAni-wbicusraiauesrtstiair-ol[8ai1ns/l8ia9ac]ta.,iGopinnEacbOloiulfsidtaEiietnaesrglt[lih9tth]e.–feriTosGhmcBeusRrae-.
Himawa-ri-8 yakamira pa140.7E pamusoro peequator uye ine 10 min scan rate, inotora zvingangoita 48 fu-ll-disk zvakaonekwa mukati mezuva (8 am kusvika 4 pm nguva yemuno). Nepo chiridzwa cheAHI chakagadzirirwa mameteorological application, chinooneka uye chiri pedyo-mu-frared
(VNIR) mabhendi (Mufananidzo 1 uye Tafura 1) inogonesa kuonekwa kwezvinhu zvegungwa zvine simba.
masaini masaini, akadai seaya anobva mumvura ine mhepo ine mhepo [19]. Mukuwedzera, Himawari-21
ultra-high- tempo-ral resolution yekutarisa inobvumira kuongororwa kwezvinhu zvegungwa kubva
sub-hourly kupinda mukati-r-yepagore s-makero eiyo yose GBR lagoon uye iri padyo negungwa
basin without inter-orbital data g-aps.
wFiigthurtehe1.trHainmsmawisas-riio-n8
spectral mhinduro mabasa eiyo inooneka uye infrared mabhendi (yakasimba chena mitsara) yemhepo yemhepo (grey yakazadzwa mutsara) uye kutapurirana ne ozone (tsvuku
yakasimba mutsetse) pakati pe400 ne1000 nm.
Huwandu hwakakura hwemashandisirwo ekutarisa uye manejimendi enzvimbo dzemakungwa ane mukana wekutorwa kubva kwaari-awari-8, kusanganisira yegungwa ruvara - [22,23]. Zvidzidzo zvenguva pfupi yapfuura zvakaratidza kugona kwekuona kweHima-wari-8 pakuona kwezvakarembera zvakamira (TSS) mumvura yemahombekombe [17,24] uye chloroph-yll-a concen-tration (CHL) mugungwa rakashama [22]. Mhedzisiro iyi inopa mukana unonakidza wemon-itoring yakakwira-kakawanda-yepamusoro uye ine simba maitiro mumhenderekedzo yeGBR. Zvisineyi, kunyangwe s-everal ocean color algorithms ingavepo pakutora setiraiti yemhando yemhando yemvura yemahombekombe, inogona kunge isina kukodzera kuoma kwemaziso eGBR kana kusashanda kune Himawari-8 zvakaonekwa.
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--
Tleanbglteh1s.anHdimbaanwdawrii-d8-thA, dasvsaoncciaetdedHsipmaatiwalarreisIomluatgioenr.vSiisgibnlael-aton-dni-oni-esearr–aintiforsar(SeNd Rb)anfrdosmcerfitcpearlstmwane.
Bhendi # (Zita) #1 (bhuruu) #2 (girinhi) #3 (tsvuku) #4 (NIR)
Band Center (Width) 470.64 (45.37) nm 510.00 (37.41) nm 639.15 (90.02) nm 856.69 (42.40) nm
Spatial Resolution 1 km 1 km 0.5 km 1 km
SNR @100% Albedo 585 (641.5) 645 (601.9) 459 (519.3) 420 (309.3)
Model-b-ased ocean color algorithms inoshandisa radiative kutamisa simulations yakaratidza hukuru hwekushanda kwekushandisa mune akawanda-temporal ari kure ekunzwa zvidzidzo zvemvura yemahombekombe zvichienzaniswa neempirical algorithms [26]. Kunyanya, neural network inzira yekombuta inoshanda yekushandura kure kure sensing application mumvura yakaoma yegungwa nekuda kwekugona kwavo kufungidzira kusiri-mutsara inoshanda rela-tionship [27]. Iri bepa rinotsanangura kugadzirwa kwemodhi-based neural network ocean color algorithm (Mufananidzo 35) yeHimawari-2-uye yakamisikidzwa yemvura yemahombekombe eGBR. Iyo imwe-nhanho-inversion algorithm yakagadziridzwa kufungidzira TSS yakananga kubva kuHimawari-8 kumusoro-kwe-atmosphere (TOA) yekutarisa ine multilayer perceptron, kirasi yeartificial neural network (ANN). Chekutanga, iyo spectral angular kugovera kweTOA inoratidza RTOA() sr-8 yakateedzerwa pamabhendi eVNIR Himawari-1 ane iripo yakasanganiswa oceanatmosphere radiative transfer (RT) modhi (yemberi modhi). Iyo RT simulations yaisanganisira kusiyanisa kwechokwadi mumhando yemvura paramita, uye mamiriro emuchadenga uye ekuvhenekesa. Zviyedzo zvakati wandei zveANN (mamodheru akasiyana) zvakabva zvasaina, kudzidziswa, uye kuyedzwa kuti vatore TSS pamabhendi eHimawari-8 zvichibva pane akatedzerwa TOA radiances. Chekupedzisira, iyo Himawari-8 yakadzoreredza TSS zvabuda zvakaongororwa nenhamba zvichipesana neyakafanana in situ mvura yemhando yedata muGBR uye zvipimo zvealgorithm yakasarudzwa zvakaongororwa.
Mufananidzo 2. Kuyerera dhayagiramu yemuenzaniso-based ocean color algorithm yakagadziridzwa yeHimawari–8.
2. Methods The parameterisation yeradiative transfer simulations uye dhizaini ye
ANN inverse modhi inotsanangurwa muzvikamu zvinotevera. Iyo yekumberi uye yakapesana modhi parameterisations inotevera nzira yakambogadzirwa kuEurope yemahombekombe egungwa [36] asi yakagadziridzwa muchidzidzo ichi f-kana mukati-mvura optical mamiriro eGBR [38]. Pamusoro pezvo, iyo H-imawari-39 yekutora, kugadzirisa uye masking maitiro, uye yegungwa ruvara processor inotsanangurwa th-e modhi-yakavakirwa algorithm yakagadziriswa pano. Iyo yekusimbisa protocol uye nzira dzekuongorora kwealgorithm yekugumira-s inounzwa, pamwe nemhedzisiro yekutanga yeTSS yekutarisisa muGBR.
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2.1. The Forward Model
Mubasa iri, scalar vhezheni yeMatrix-Operator MODEL (MOMO) [40,41] yakashandiswa kune akabatanidzwa oceanatmosphere radiative kutamisa simulations emabhendi eHimawari-8 VNIR (Table 1). Kuregeredza polarization yemhepo kunogona kutungamirira kune zvikanganiso zve1% paTOA, iyo inogamuchirwa kune kushandiswa kwemvura yegungwa [2]. Iyo Himawari-42 RTOA() yakayedzerwa kune yechokwadi huwandu hwemukati-mvura uye mumhepo yekuona zvimiro zveGBR.
Iyo yakateedzerwa oceanatmosphere system yakamisikidzwa mune akati wandei akatwasuka homogeneous plane-parallel layers uko kwakatsanangurwa mhando uye kutarisisa kwemvura uye mumhepo optical constituents inotariswa. Kureba kwemhepo yakasimudzwa (TOA) yakakora makiromita makumi mashanu uye yakakamurwa kuita gumi nerimwe zvidimbu apo yakatwasuka pro.files yekumanikidza, tembiricha, uye hunyoro zvinotevera US Standard Atmosphere [43]. Iko kuderedzwa neRayleigh kupararira kunoverengerwa neaviri barometric pamusoro pekumanikidza kwe980 hPa uye 1040 hPa. Mamiriro ekunze akapatsanurwa kuita muganhu wechikamu (0 km), yemahara troposphere (2 km), uye stratosphere (2 km). Muchikamu chega chega, zviyereso zvacho zvakaitirwa zvisere zvakasiyana-siyana zveaerosol akaungana ane akasiyana siyana eiyo aerosol optical ukobvu (a) pa12 nm pakati pe12 ne50. Imwe neimwe aerosol assemblage inoumbwa nemhando nhatu huru dzeaerosol, modhi yemugungwa mumuganhu, modhi yekondinendi mune yemahara troposphere, uye sulfuric acid modhi mu stratosphere, pane hunyoro huri pakati pe550% ne0.015%. Rudzi rwakanga rwakatemwa kubva kune akawanda-pagore Level 1.0 zuva-photometer yakatarisa yeAERONET [70] chiteshi paLucinda Jetty Coastal Observatory (LJCO) iri pakati peGBR [99S, 2E]. Ongororo yeanowirirana Ångström coefficients [44,45] pakati pe18.52 ne146.39 nm pachiteshi cheLJCO AERONET inosimbisa musanganiswa wemhando dzemugungwa nekondinendi aerosol inoenderana neanoshandiswa mumifananidzo yeRT.
Kufambiswa kwemagasi emuchadenga (kunze kweO3) kwakatorwa kubva kuHighResolution Transmission Molecular Absorption (HITRAN) dhatabhesi [47] uye yakashandiswa mukufambiswa kwemagetsi emagetsi kuburikidza neyakagadziriswa k-kugovera modhi yeBennartz naFischer [48]. Iyo radiative kutamisa simulations yakaitwa ichitora nguva dzose ozone kurodha ye344 Dobson Units (DU) [43]. Mabhendi eHimawari-8 akateedzerwa kwemakona gumi nemanomwe ezuva uye ekuona uye 17 akaenzana akaenzana azimuth angles. Mamienzaniso acho akaitirwa kushanduka kwechokwadi kwemhando yemvura, inomiririrwa neyakasarudzika yakasarudzika yakasarudzika yeCHL, TSS, uye yero zvinhu (YEL), iyo yakazodaidzwa kunzi yekusungwa katatu. Iwo marenji eakateedzerwa ekutepfenyura katatu akatsanangurwa zvichibva pakuparadzirwa kwe in situ correlated concentrations inowanikwa muGBR, ichitevera nzira yaZhang et al. [25]. Iwo akateedzerwa evasungwa katatu akagovaniswa zvakaenzana munzvimbo yelogarithmic, saka yega yega yehukuru yakamiririrwa nenzira yakafanana ichinzvenga mifanidzo yakadzokororwa.
Iyo yakazara spectral kunyura kwemvura yegungwa a() yakaenzanisirwa nechina-chikamu bio-optical modhi accounting yeyakachena mvura absorption (aw), kutorwa kwe phytoplankton uye zvese zvakafa organic zvinhu (kureva, detritus) ap1 sebasa reCHL [0.01, 15, 2] ye-non-sss particle ye asp-ss particle ye aspgal particle [0.01, 100.0], uye kutorwa kweyero zvinhu ay pa443 nm [0.002, 2.5]. Mucherechedzo wekunyura wemvura yakachena (aw) wakafananidzwa maererano naPapa uye Fry [50] yeHimawari-8 inooneka mabhendi 1 uye neHale uye Querry [3] yebhendi 51. The spectral absorption ye phytoplankton uye detritus ap4 yakatevera parameterisation yeBricaud et al. [1], nepo kutorwa kweasiri-algal particles ap52 kwakaiswa parameterised maererano naBabin et al. [2], ine kuremerwa kweSp53 ye2 iyo yakatorwa kubva in situ bio-optical data s.ampyakatungamira muGBR pakati pa 2002 na 2013. Iyo spectral absorption coefficient yeyero zvinhu ay yakaumbwa maererano naBabin et al. [53], ine mutserendende Sy ye0.015 iyo yakatorwawo kubva mune situ zvakaonekwa kubva kuGBR [39].
Iyo yakazara spectral kupararira kwemvura yegungwa (b ()) yakafananidzwa ne-two-component bio-optical model [53] accounting yekupararira kwemvura yakachena (bw) uye kuparadzira kana organic uye inorganic particles bp sebasa reTSS. Mvura yegungwa yakachena ichipararira
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coefficient yakaratidzwa sewavelength inotsamira mutemo wesimba wakavakirwa muMorel [54],
inotsanangurwa yeavhareji yemunyu yepasi rose ye35 PSU. Mupiro wekuparadzira we organic uye
inorganic particles yakasanganiswa kuti itore iyo yakazara particulate yekuparadzira coefficient bp zvichitevera parameterisation yeBabin et al. [55]. Iyo yakawanda chaiyo yekuparadzira coefficient
yeTSS particles bp ye0.31 m2 g-1 yakaverengerwa mvura yeGBR, ichitevera Babin et al. [55]. A backscattering probability model for Case 2 mvura yakashandiswa [49,56] to
kuverenga uye sarudza mukati-mvura yekuparadzira chikamu mabasa (, ) zvichienderana nechiyero cheTSS neYEL. Mienzaniso yakaitwa nokuda kwenhamba huru yekusagadzikana
katatu uye mamiriro emuchadenga, sezvakambotaurwa, kuvaka yakazara
dhatabhesi yeazimuthally yakagadziriswa Himawari-8 RTOA(). Kubva pane iyi database, statistically
kudzidziswa kwemumiriri uye bvunzo subsets dzakatorwa zvisina tsarukano kugadzira inverse
model. Iyo yekudzidziswa uye bvunzo subsets imwe neimwe yaive ne100,000 ekuisa mavector
x
zvine
iyo: yakatevedzwa RTOA pa470, 510, 640, uye 856 nm mabhendi, gungwa mwero wemhepo yemhepo pakati pe980 ne1040 hPa, solar zenith angle (s), kucherechedza zenith (v), uye hama azimuth ().
2.2. The Inverse Model
Muchidzidzo ichi, a multilayer perceptron (MLP), kirasi yefeed-forward artificial neural network (ANN) [57], yakaitwa seyakasiyana modhi yakavakirwa paNeural Network Simulator C-chirongwa chakagadzirwa neMalthouse [58], kufungidzira hukama hwekushanda pakati peHimawari-8 RTOA () uye TSS concentration. Iyo MLP iripo inosanganisira yekuisa layer, yakavanzika layer, uye inobuda layer ye neurons. Neuron yega yega yakabatana neuron yega yega yechikamu chinotevera nehuremu. Iyo inotariswa muchina kudzidza kana maitiro ekudzidzisa anogona kurondedzerwa seanotevera:
·
Maneuroni ekupinza (ni) anogashira vheta yekupinda
x
, ine zvinofananidzira zvinoratidzira
uye iyo ancillary data yakatsanangurwa pamusoro, uye inoiparadzira kune yakavanzika layer neurons
(nh).
· Muchikamu chakavigwa, ma<em>artificial neurons anopfupikisa masiginecha akaremerwa uye opfuudza izvi kuburikidza neasina mutsara wekufambisa basa uye wozoendesa zvabuda.
kune yakabuda layer neurons (kwete).
· Mutengo wekuita (kureva, zvikanganiso zvakapetwa kaviri, MSE-Equation (1)) pakati pe sim-
yakabatanidzwa chinangwa chakabuda yt uye iyo ANN computed zvabuda yc inoverengerwa yese dataset yekudzidziswa (N = 100,000), uye huremu hwemukati (W1, W2) hwenetiweki hunogadziriswa.
· Kudzidziswa kweANN kunodzokororwa kudzamara mutengo unoshanda pakati pekubuda uye kukosha kwechinangwa waderedzwa.
MSE = y c – y t /N
(1)
Mutengo webasa unodzikiswa nekugadzirisa uremu matrices (W1, W2) iteratively uchishandisa Limited Memory BroydenFletcherGoldfarbShanno optimization algorithm [59]. Kune matatu-layer MLP yekuvaka, iyo yakazara analytic basa inopiwa neEquation (2):
yc
=
S2
×
W2 × S1
W1 × x
(2)
uko S1 uye S2 ndiyo isiri-mutsara (Equation (3)) uye mutsara wekutamisa mabasa anoshandiswa mukubuda uye yakavanzika layer, zvichiteerana.
S(x) = 1 + ex -1
(3)
Huwandu hwemaneuroni muzvikamu zvekupinza uye zvekubuda zvakatemwa nenhamba yekupinza uye yekubuda paramita yedambudziko, nepo kuedza kwakati wandei.
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zvaidiwa kuti zvitarise huwandu hwakakwana hwemaneuroni mune yakavanzika layer. The
kuyedza kwakagadzirwa nekusiyanisa huwandu hweyakavanzika layer neurons kubva pa10 kusvika ku100,
increments of 10. A random but for all tested seed yakashandiswa kutanga -
uremu gadziriso yema network. Kuedza kwaisanganisira chinhu chikuru
ongororo (PCA) sedanho rekutanga-kugadzirisa danho rekushongedza iyo RTOA () yekupinda. Pamusoro pezvo, zviedzo zvakagadzirirwa ne 0.8% inotaridzika isingabatanidzwe chiratidzo-inotsamira rando-m - ruzha rwakawedzerwa kune iyo RTOA yekupinza mubhendi rega rega. Zviyedzo zveANN zvakadzidziswa uye zvakayedzwa nechikamu che100,000 ekuisa mavector akatorwa zvisina tsarukano kubva mukufambiswa kweradiative.
simulated dataset. Imwe neimwe vhekita yekupinza yaisanganiswa nelogarithmic TSS concentration, - iyo yakasarudzwa sechinangwa chekubuda kuti chienzaniswe neanotariswa kudzidza.
nzira. Zvese zviedzo zvakadzidziswa 1000 iterations uye kuderedzwa kwemutengo
basa (Equation (1)) yakaverengerwa pamusoro pedhata rese rekudzidzisa panguva imwe neimwe iteration. An
Yakazvimirira test dataset yeN = 100,000 vectors yakashandiswa kutarisa kudzidziswa kwetiweki
kuita uye kudzivirira kudarika-kukodzera.
-
2.3.
TBhaesHicipmrawoceasrsi-in8- gOscteeapns
Color Processing yeHimawari-8 mbishi
data
kupinda
TSS
zvigadzirwa
vari
kuratidzwa
in
Mufananidzo
3.
Level 1 (L1) yakazara disk Himawari-8 VNIR mabhandi akawanikwa, akatorwa pamusoro penzvimbo yeGBR -
(10 S, 29 S, 140 E, 157 E), geolocated, a-uye kufamba kwakagadziriswa. Iyo geolocated mbishi data
akashandurwa kuita Level 1b (L1b) TOA radiances (LTOA() W m-2sr-1µm-1) kuburikidza -
tghreidawppalsicraetsiaomnpolfedpofrsot-mlau0.n5ckhmuptoda1tkedmctaolimbraattcihonthceoreefsfiocliuetn-itosn[o60f ]t.heTahseso6c4i0atnemd VbNanIRd
bands. Iyo L1b yakarongedzerwa LTOA() yakajairwa neyekunze-yepasi solar irradiance F () W -m-2 yebhendi rega rega. F () yakaverengerwa sebasa rezuva regore
uye kushandisa zvinorehwa nekuwedzera kwezuva irradiance F tsika b-ased paKurucz [61] uye yakagadziridzwa kumabhendi eHimawari-8 [62]. Mhedzisiro TOA inoratidza-ances RTOA() sr-1 paVNIR Himawari-8 mabhendi akachengetwa semapoinzi kunzira ye-inver-sion. Mukuwedzera, the
s, v, and values were calculated for each pixel of the satellite image as a function of latitude, longitude, and local time, following existing procedures [63], and converted into
Cartesian coordinates (x, y, z).
Mufananidzo 3. Himawari-8-Ocean Color Processing flowchart. HSD inoreva Himawari-8 Stan-dard Data, GBR inoreva Great Barrier Reef, VNIR inoreva Himawari-8 inoonekwa an-d padyo nemabhendi e infrared (470, 510, 640, uye 856 nm), uye ANN inoreva Artificial Neural Network.
the
ACulsoturadlimanasckoinntginoenf tHainmdaswuarrroi–u8nodbisnegrvwaatitoenrss.
aiva The
yakagadzirwa naQin et al. [64] ye2 km resolution gore mask yaive
resampzvinotungamirira kuguruva noutsi
1plkummHesimfraowmabrii-o8mg-raisds
uye inosanganisira masking emapikseli akasvibiswa nekupisa. Saizvozvo, pixels akaonekwa seakabuda
nzvimbo, senge nzvimbo dzemakondinendi, zvitsuwa, uye mashools, aive akafukidzwa zvichienderana nechimirofiles
inowanikwa kubva kuGreat Barrier Reef Marine Park Authority [65] dhatabhesi. A sun-glint
-
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mask yakagadzirwa nekuverenga kurongeka kwenzvimbo huru yekupenya kwezuva (PPS) sebasa rezuva regore (solar inclination), awa yemunharaunda, latitude, uye longitude [66], pa 1 km spatial resolution. Iyo contour yezuva disk yakavharwa kune denderedzwa radius ye1300 km kubva kumakongisheni ePPS. Iyo radius saizi yakasarudzwa mushure meakatevedzana ekuona bvunzo akashandiswa kuti ave nechokwadi chekuvharika kwenzvimbo huru yezuva disk.
Kucherechedzwa kweHimawari-8 kwaive kwakajairwa pixel-ne-pixel uye kwebhendi rega rega rine pedyo-pamwe chete setiraiti data yeiyo yakazara column ozone yakatorwa kubva kuOzone Yese kubva Kuongororwa kweStratospheric uye Tropospheric Satellite components (TOAST) chigadzirwa [67] isati yashandurwa. Iyo TOAST chigadzirwa, ine spatial resolution ye1.25 ne1 madhigirii uye zuva nezuva kugadzirisa kwechinguvana, yaive res.ampinotungamira kune 1 km yekutevedzera iyo Himawari-8 grid. Kucherechedzwa kweHimawari-8 kwakagadziridzwa pabhendi rega rega nereshiyo pakati pekufambiswa kweTOAST-yakatorwa ozone kune kutapurirana kweiyo yakateedzerwa ozone column density ye344 DU. Pamusoro pezvo, iyo yepakati pegungwa remumhepo yekumanikidza data kubva kuNCEP/NCAR `Reanalysis 2' PaRt2m [68] yakashandiswa seyakagadziriswa yekushandurwa kweHimawari-70 zvakaonekwa. Iyo `Reanalysis 8′ data inoverengerwa maawa matanhatu ega ega (2, 6, 0, 6 UTC) uye s.ampinotungamirwa pane yenguva dzose gidhi yepasi rose ye2.5 madhigirii epamhepo resolution [71]. Iyo yepedyo yePaRt2m data yakawanikwa uye resampinotungamira kune 1 km Himawari-8 grid. Iyo TSS yakadzoserwa, masks akabatana, uye metadata zvakachengetwa muNetCDF file, kusanganisira mireza yakabatana nepixel yekunze-kwe-range yekupinda uye zvinobuda. Iwo marenji ezvakakodzera zvekupinza uye zvakabuda zvakatsanangurwa zvichibva pane RT simulated dataset. Semuyenzaniso, kana imwe pixel yekupinda uye/kana yekubuda parameter yakapfuura mirango yakafananidzirwa, pixel yakapihwa mureza unoenderana. Iyo yekupinza uye yekubuda mireza yakapfupikiswa kune yega pixel yeHimawari-8 grid. Iyo mireza yekunze-yemhando yakashandiswa kune zvigadzirwa zvemhando yemvura isati yatevera kusimbiswa uye kuongororwa kwekushandisa.
2.4. Great Barrier Reef muSitu Data
In situ TSS yakayerwa pakati pa2015 na2018 neAustralia Institute of Marine Sciences (AIMS) uye Commonwealth Scientific and Industrial Research Organisation (CSIRO) yakawanikwa kubva kuIMOS Bio-optical Database [72] kuburikidza neAustralian Ocean Data Network (AODN) portal. Ose CSIRO neAIMS anoshandisa nzira yegiravimetric kuona TSS kusungwa mumvura yegungwa. Nzira iyi inosanganisira kuyera huremu hwakaoma hwezvakarembera kubva pahuwandu hunozivikanwa hwemvura yegungwa s.ample mushure mekunge yave vacuum yakasefa pane pre-yakayerwa membrane sefa. Rumwe ruzivo nezve nzira inoshandiswa neAIMS neCSIRO inotsanangurwa muGreat Barrier Reef Marine Park Authority [73] uye Soja-Woz'niak et al. [74] zvichiteerana. Zvisinei neAIMS neCSIRO marabhoritari vachishandisa nzira dzakasiyana zvishoma kuona TSS (kureva, nhamba yezvakadzokororwa, mafirita mapedhi, ekuringa, nezvimwewo), aya madhata akasanganiswa muchiitwa ichi chekusimbisa. Zvose zve 347 in situ data points neTSS kubva ku0.01 kusvika ku85 mg L-1 uye zvinoreva 3.5 mg L-1 zvakaonekwa. In situ data points mukati me1 km kubva kumhenderekedzo yegungwa kana matombo akaiswa kunze kwekuongorora kuderedza kusava nechokwadi nekuda kwemigumisiro yepedyo [75]. Takabatanidza zvese mu situ seawater sampzvishoma zvakatorwa pamusoro (<0.5 m kudzika) kwezviteshi zviri munzvimbo dzakadzika dzemvura dzakasiyana (1.5 m kusvika 40 m), nenzvimbo isina kudzika yedata inoburitsa TSS> 10 mg L-1.
2.5. Validation Protocol
Iyo yekusimbisa protocol yakashandiswa muchidzidzo ichi inotevera chiitiko chekare chechokwadi maekisesaizi egungwa ruvara rwekure kure kunzwa muAustralia, kusanganisira mumahombekombe GBR [27,76,77]. Zvidzidzo izvi zvakatsanangura nhanho dzekugadzirisa kutorwa kwezvakaonekwa setiraiti panguva imwe chete nezviyero zvemu situ mumhenderekedzo yeGBR, pamwe chete nemaitiro anobatsira enhamba.
Yakawanda Himawari-8 yekutarisa inogona kusanganiswa mukati menguva yakatarwa (kureva, hourly) kubvisa zvinogona kubuda kunze uye kuderedza sensor uye ruzha rwezvakatipoteredza, zvichida kuvandudza fungidziro uye maitiro ekusimbisa [7,9,16]. Naizvozvo, zvese zviripo Himawari-8 zvakatariswa mukati me ± 30 min kubva pane yakarekodhwa in situ nguva yakawanikwa yeichi chiitiko chekusimbisa. Yakasarudzwa uye yakagadziriswa 10 min Himawari-8 zvakaonekwa paVNIR
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mabhendi ane zuva rakabatana uye yekutarisa geometry akaiswa ku3-by-3-pixe-l bo-x-es,
yakatarisana nekurongeka kweimwe neimwe panguva imwe chete mu situ data point. Saizvozvowo, 3-by-3-pixel subsets yemasiki panguva imwe chete (kureva, makore, lan-d, reef, uye kupenya kwezuva) uye data rekuwedzera (kureva, - ozone uye pressure) zvakaburitswa. Pedyo-echokwadi mavara anoumba akasarudzwa Himawari-8 -
zvakacherechedzwa zvakaongororwa nemaziso kuti zvibvise matchups mumvura ine yakapinza yakatwasuka
gradients mu optical properties (kureva, turbidity fronts) kana makore ari pedyo.
-
Hourly maumbirwo emaseti anoshanda akaverengerwa neavhareji yenguva, kusaremekedza -
mapikisheni akafukidzwa. The hourly akaunganidzwa- ma-su-bsets akagadziriswa neiyo ANN inversion
algorithms uye akafukidzwa kune kunze-kwe-ranji kukosha. Pakupedzisira, iyo yepakati uye yakajairwa kutsauka
zve hourly TSS subsets dzakaverengerwa, tisingabatanidzi m-akabvunzwa pixels. Iwo chete ma subset ane maviri kana mashoma pixels akafukidzwa papixel-bhokisi akaonekwa seanoshanda pa matchup. Iye ANN
zvakabuda zvakaverengerwa muchiyero chelogarithmic (log10) uye iyo yakabatana in situ TSS yakashandurwa kuti iongororwe nhamba. An overview yenzira yekusimbisa inoratidzirwa
muFigure 4. Zviitwa zvakaongororwa maererano nemudzi wavo zvinoreva square square kukanganisa
(RMSE-kana zvachose kukanganisa), kurerekera, kureva chikamu chakakwanatage kukanganisa (MAPE-kana hama kukanganisa), uye coefficient yekutsunga (R2). Bias, R2, uye RMSE zvakaverengerwa mulog10
-
nzvimbo uye MAPE yakaverengerwa mumutsara kuyerwa uye p iyo satellite-yakatorwa
psproadceu,cftowlloitwhi-nNgtEhqeunautimonbser(4o)f(v7a)l,iwd hmearetcmhuispsth. e
RMSE = 1/N (m -p)2
(4)
MAPE = 100/N |(m -p)|/p 2
(5)
R2 =
N
N(mp)- ( m)( p) m2 – ( m)2 N p2 – (
p)2
(6)
Rusarura = 1/N (m -p)
(7)
Iyo ANN match-up- zviedzo zvakaiswa pachiyero zvichienderana nenhamba yemetrics yakatsanangurwa - pamusoro. Kufarirwa kwakapihwa kune izvo zviedzo zvine yakaderera RMSE nekuti iyi manhamba parameter ibasa remutengo rinodzikiswa panguva yekudzidziswa kweANN. Kuedza kwepamusoro-soro nenhamba yakaderera yeneuroni muchikamu chakavanzika chakasarudzwa, kuderedza kuedza kwekombuta kwekushandura Himawari-8 kucherechedza - pamusoro peGBR yose.
Mufananidzo 4. A yakareruka pamusoroview yealgorithm yekusimbisa maitiro.
2.6. Kuongororwa kweMitemo
Iyo chiratidzo-ton-oise ratios (SNR) yakaverengerwa kune inooneka uye iri pedyo-inf-rared.
HEaimstearwnaSrti-a-8ndLTaOrdA
(Tim) oeb–seArvEaStTio)nast
scanned yakasarudzwa
pakati pe 08:00 kusvika 16:00 mazuva emunharaunda uye cloud-free-ee nzvimbo
nguva (yeAustralia yeCoral Sea
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(16.25S, 151E uye pa20.60S, 153.53E). Chete mushure meChikunguru 2017 zvakacherechedzwa pakuongorora uku, zvichipihwa kuti yavo calibration coefficients yakagadziriswa kune yakabatana uye yakatwasuka striping ruzha [63,78]. Echokwadi mavara emifananidzo anowanikwa kuburikidza neHimawari-8 Monitor P-Tree System [79] akabhurwa kuti asarudze nzvimbo yakanangwa uye kuve nechokwadi kuti idzi dzaive yunifomu yenzvimbo uye zvisingaite kukanganiswa nemakore, kupenya kwezuva, bio-optical features, uye hutsi hwehutsi kubva mukutsva kwepasi [80,81]. Izvo zvakasarudzwa zveHimawari-8 zvakacherechedzwa zvakashandurwa kubva pazviverengero zvakasvibirira kuenda kuzvikamu zvemuviri nekushandisa calibration coefficients [60], ine subsets ye51-by-51-pixels yakatorwa uye yakanangana nekurongeka kwematunhu anofarira. Uye zvakare, iwo ma subsets, akabatana masiki, uye geometric paramita aive hourly aggregated. Iyo 10 min uye hourly aggregated subsets akafukidzwa kumakore, nyika, matombo, uye kupenya kwezuva, uye iwo ari pedyo-echokwadi mavara macomposites akaongororwa kuti asaonekwe maficha akadai semakorari cays, reef, cloud shadows, uye sensor artefacts.
Iyo SNR yakaverengerwa yega yega Himawari-8 bhendi rinotevera Equation (8) [80]. Avhareji yeLTOA () yemapikseli ese anoshanda mukati menzvimbo yakanangwa inopa Ltypical (), uye kutora yakajairwa kutsauka () mukati menzvimbo imwe chete inopa ruzha rwakafanana neradiance (Lnoise ()). Iyo SNR inoverengerwa sereshiyo pakati peLtypical neLnoise pabhendi rega rega:
SNR() = Ltypical ()/Lnoise() = LTOA()/(LTOA())
(8)
Iyo diurnal musiyano uye hukuru mutsauko pakati peSNR computed ne10 min uye hourly yakaunganidzwa Himawari-8 zvakaonekwa (SNRSING() uye SNRAGG(), zvichiteerana) zvakaongororwa pabhendi rega rega. Mukuwedzera, maitiro avo ekuona akaongororwa kune mitsara ye s nokuti mazinga eruzha anozivikanwa kuti anosiyana nekukwirira kwezuva [80]. Pakupedzisira, chikamu chakabatanatagmazinga eruzha (% Noise) akaverengerwa s = 45 ± 1 uye akashandiswa kuongorora kunzwisiswa kwealgorithm kuHimawari-8 mazinga eruzha.
Iyo TSS algorithm yakagadziridzwa muchidzidzo ichi yakadzidziswa ne spectrally flat (uncorrelated) photon noise (0.8%) iyo yakawedzerwa kune yekudzidziswa dhataset, zvichitora ruzivo rushoma rwemasensor performance performance pane eceanic targets. Kuongorora kugadzikana kweiyo inversion uye nekupa yekutanga senitivity kuongororwa kweTSS algorithm, inotaridzika flat photon ruzha rwe0.1, 1.0, uye 10 uye 50% yakawedzerwa kune yekuyedza dhata uye inverted. Pamusoro pezvo, iyo %Noise yakabatana nemabhendi eHimawari-8 yakawedzerwa kune dataset yekuyedza kuverengera mhedzisiro yemazinga anotsamira eruzha pakurongeka kweTSS kutora. Kugadzikana kwekudzorera kwakadudzirwa maererano nekuwedzera kwekuwedzera kweRMSE kune zvakasiyana-siyana zveTSS (0.01 kusvika 100 mg L-1) zvakaenzana muzvikamu zvelogarithmic. Pamusoro pezvo, kurebesa kutenderera kwezvigadzirwa zveTSS zvakatorwa mumvura ine homogeneous uye isina makore yemahombekombe eGBR uye muGungwa reCoral yakaongororwa pachikero chepixel kuti iongororwe yemhando yeruzha rweHimawari-8.
3. Migumisiro
3.1. Algorithm Validation
Manetiweki akawanda akadzidziswa neakasiyana magadzirirwo ezvivakwa uye yakanyanya kunaka network ine yakaderera inogoneka RMSE uye yakaderera nhamba yemaneuroni mune yakavanzika layer yakasarudzirwa inversions. Chiyedzo chakasarudzwa, chine 50 neurons muchikamu chakavanzika, chakadzora TSS kubva 0.14 kusvika 24 mg L-1, ine yakanaka R2 uye bias ye0.014 mg L-1, MAPE ye75.5%, uye 10RMSE ye2.08 mg L-1, sezvakaratidzwa mumufananidzo 5.
Remote Sens. 2022, 14, 3503
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Mufananidzo 5. In situ uye Himawari-8-de- r-ved TSS ine b-est-performing ANN chiedzo, ine in situ TSS makoshero eruvara-code-d muchikero chelogarithmic. Mabhawa ekukanganisa anomiririra-t the intra-pix-el standard deviation yeTSS mukati me3-by-3-pi-xe-l bhokisi. Zviratidzo zvakasiyana zvinoratidza mune situ data yakaunganidzwa neAIMS
uye neCSIRO kuLJCO.
-
3.2. Himawari-8 Yese Yakamiswa S-olids yeGreat Barrier Ree-f
Mufananidzo 6 unoratidza pedo-yechokwadi ruvara rwemusanganiswa weHimawari-8 (kuruboshwe panhivi) yakatorwa 27 Gumiguru 2017 ove-r iyo GBR nharaunda, uye inoenderana TSS chigadzirwa pa10 min temporal resolution (kurudyi panhi). Mvura dziri mukati medhamu reGBR dzine TSS kazhinji iri kana pamusoro pe1 mg L-1, nepo mvura dzemhiri kwegungwa iyo GBR iripo pasi pe1 mg L-1. Chigadzirwa cheTSS chakaratidza kunyura kwakasimba uye ruzha rwemitsetse munzvimbo dzakavhurika dzegungwa reCoral Sea.
Mufananidzo 6. Muvara wepadyo-wechokwadi Himaw-ari-8 mufananidzo weGBR wakawanikwa musi wa27 Gumiguru 2017 nenguva dza15:00 AEST (paneru yekuruboshwe) uye yakabatana TSS p- roduct [mg L-1] (parudyi). Mapikisi akafukidzwa mutema nekuda kwegore-s a-uye kunze-kwe-renji kukosha.
Himawari-8 TSS kuchinja kwakaongororwa Burdekin River muromo uye pamusoro yekumaodzanyemba GBR.
yemahombekombe emvura reef matrix (Mufananidzo 7
saunrdroaunn-imdinatgiothnes
mu link). Chiitiko chemafashama eBurdekin chemusi wa12 Kukadzi 2019 chakaburitsa mhungu iyo
yakabata matombo ekunze (50 km kubva pamuromo) pakati pe3 kusvika 4 pm, neTSS> 20 mg L-1.
-
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-
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Th- e Burdekin River sediment plume yakagadzirwa panguva yemafungu anouya ane huwandu hwe0.3 m pakati pemvura yakaderera uye yakakwirira. Mvura yegungwa iri pedyo nematombo yakawana kuwedzera kwekukura kweTSS (3.6, 26.4 mg-L-1) mukati me-s-emi-diurnal tidal cycle (muchinjiko mumufananidzo 7 (kuruboshwe) uye Mufananidzo 8a). Matombo akafukidzwa nemvura yemafashamo akaiswa kuTSS ~ 40 nguva dzakakwirira kudarika nhungamiro ye0.7- mg L-1 [82]. Nzvimbo uko-e TSS yakapfuura 100 mg- L-1, pedyo nemuromo, yakavharwa (nzvimbo dema) as- ou- t-of-range values (ANN mireza). Mhuka yekuchinja kweTSS kunotevera chiitiko chikuru chekuburitsa inowanikwa muMufananidzo S1.
Mufananidzo 7. Mafashamo emvura ari kubuda kubva muRwizi rweBurdekin, Kukadzi 2019 (kuruboshwe). TSS tidal jets mukati meGBR reef matrix munaNovember 2016 (kurudyi panhi). Cherechedza mitsara yakasiyana-siyana muchikamu chimwe nechimwe. Mapikisi akafukidzwa mutema anokonzerwa nekubuda-kwe-ra-nge TSS kukosha.
Nepo zviitiko zvikuru zvemafashamo zvichiratidza zvakajeka maTSS mumahombekombe eGBR, majeti epasi-meso-scale tidal jets anocherechedzwa akatenderedza matrix ematombo asina kudzika uye akanyura mumvura kumaodzanyemba kweGBR (Mufananidzo 7 (panhivi yekurudyi)), zvichiratidza kuti mamiriro aya akasiyana anopesvedzera sei kusiyana kwenguva pfupi- TSS. Mhuka yakapihwa muMufananidzo S2 inotaridza mafambiro ekuchinja kweTSS, uko mafungu epamusoro (4 m) uye akaderera (0.2 m) akaitika na10 am na6 pm, zvakateerana (Mufananidzo 8b). Mazinga eTSS ari pedyo neHeralds Reef (muchinjiko wakamisikidzwa) aichinja-chinja pamusoro peodha imwe muhukuru mukati mezuva (0.3, 2.0 mg L-1), ine hutsika hunodarika nhungamiro yehutano hwemvura inokurudzirwa kune yakavhurika yemahombekombe GBR (0.7 mg L-1). -
Mufananidzo 8. Nguva dzakatevedzana dze10 min Himawa-ri–8-derived TSS pamuromo weRwizi rweBurdekin panguva yemafashamo aFebruary 2019 (a) uye kumaodzanyemba kweGBR reef matrix muna Mbudzi 2016 (b), sezvakaratidzwa mumufananidzo 7. Mabhawa ekukanganisa anomiririra intr-a-pixel standard deviations. Nhungamiro yezvikumbaridzo zveinshore (2.0 mg L– 1) uye yepakati--sherefu (0.7 mg-L-1) mvura inoiswa mutsvuku. Cherechedza nguva dzakasiyana-siyana mumufananidzo wega wega.
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3.3. Detection Limits Iyo SNR yakaverengerwa kubva kumaseti maviri eHimawari-8 yekutarisa inoratidzwa mune
magirafu eMufananidzo 9. Maonero mashoma mashoma akapotsa nekuda kwekuvharika kwegore, kunyanya musi wa 06 Gunyana 2017, uye zvakakonzera kusiyana kwedata munhevedzano yenguva. SNRSING uye SNRAGG yakaratidza kujeka kwe diurnal kuchinjika, nepamusoro SNR inoitika kune yakaderera s (<30), pakati pe11 am na12 pm Hukuru uye diurnal kusiyanisa kwaive kwakakwira kune SNRAGG uye pamabhendi ebhuruu negirinhi (470 uye 510 nm), kana ichienzaniswa nemakosiyumu eSNRUSING. Iyo SNR yakaverengerwa iyo 640 nm uye 856 nm mabhendi aive akaderera zvakapetwa katatu pane iyo SNR yakaverengerwa mabhendi ebhuruu uye egirinhi, aine zvidiki diurnal musiyano. Kuchinja kwezuva nezuva kweSNR pakati pemazuva nenzvimbo kwaive kwakasiyana, kunyanya yeblue bhendi uye kubva kuSNRAGG. Pana 06 Gunyana 2017 (zvinoreva v ~ 22), iyo SNRAGG mumabhandi ebhuruu uye magirini aive akafanana muhukuru (Mufananidzo 9b). Pana 25 Gunyana 2017 (pane imwe nzvimbo ine zvinoreva v ~ 28), bhendi rebhuruu rakaratidza SNRSING yakapetwa kaviri pakukwirira kwegirinhi bhendi (Mufananidzo 9d).
Mufananidzo 9. Nguva dzakatevedzana dze sig-na-l-to-noise ratios (SNR, akisi yekurudyi) computed for single (SNRSING) (a,c) uye yeaggregated (SNRAGG) zvakaonekwa (b,d) zvine chokuita s (kuruboshwe axis). Iyo S-NR ndiyo
color-coded nebhendi.
Mapoka e
spectral variability of s, apo mwero
SNRSING uye SNRAGG inoratidzwa kutsauka mukati meboka rega rega raive
muMufananidzo wakarongwa se
10 yecapped
kukanganisa katatu
mabara. Iko kutariswa kumwe chete kwaiwanzo buritsa yakaderera SNR pane yakaunganidzwa yekutarisa-kuona
mumabhendi ese, uye SNR ndiyo yaive yepamusoro-soro paMufananidzo 9. Kutsauswa kwakajairwa kweSNR
s <30, mukubvumirana nedata rakaverengerwa kune imwechete uye yakaunganidzwa
zvinoratidzwa mukucherechedzwa
wforeresm>o4r0epartotnhoeubnlcueedbfoanr dsp>re4s0enatendd
pamabhandi eblue negirini. Kutsauswa kwakajairika kwe27 uye ye
SNR yakaverengerwa 51 yeSNRSING
uye SNRAGG kutsauka kwe
, zvichiteerana, nepo 13 uye 26, zvichiteerana.
SNR yakaverengerwa yegirinhi bhendi yakaratidzwa chiyero Uku kutsauka kungangove kwakabatana neiyo var-iable
mamiriro emuchadenga enzvimbo imwe neimwe, ayo anowedzerwa pamabhandi ebhuruu negirini
uye pakakwirira mumhepo nzira.
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Mufananidzo 10. Kupararira kwezviyero zve si-gn-al-to-noise zvakaverengerwa kune imwe chete (SNRSING) (-a) uye
aggregated cherechedzo (SNRAGG) (b), uye akaiswa mumapoka seyakajairwa kutsauka kweSNR mukati meboka rega rega
zve s.
tatu
ranges
of
s.
Error
mabara
vaiva
computed
TgcorhemegpaSTtNuehtdReedASoGNbfGsoRerrvAvaaGallGluti,seoitsnnhsgceolwLemtioytphpbiicslaeesldr,=vaiann4t5diToaLn±bnsol e1iwse2iawtwhnederersaec=saosob4m5copiuaitlt±eetddw1ipinceweTrcaaeebsrnelhetiai2ngg.chelLunaikdsoeeitswdheeifs(oc%eor, NtrchrooeeimssSpepN)oaf–nRordiSrsIiNaongnGg-. SNRSING, kunze kwebhendi dzvuku. Zvakadaro, ruzha rwakakura muhutsvuku (~3%) uye mu tshigenNalIRdebsapnidtest(h~e5%eff)oinrtdsiicnataevtohiadtinthgeeSnNviRroAnGmG emnataylbceonmdoistitolynsafifnecitmedagbeystehleesicpt-IR kunyanya ehhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhB bhandi, uko mvura inosiya kupenya kunoonekwa sekusina basa mumvura yakashama yegungwa.
-
-
-
Tafura 2. Inooneka uye iri pedyo-infrared Himawari-8 Ltypical uye Lnoise W m-2sr-1µm-1 uye yakabatana
percentage ruzha (% Noise) yeSNRAGG pa s = 45 ± 1. Yakaverengerwa SNRSING pa s = 45 ± 1 kukosha
akawedzerwa kuenzanisa.
Bhendi 470 510 640 865
Ltypical 59.5 38.3 13.8 3.4
Noise 0.26 0.29 0.41 0.18
Noise
0.44 0.76 3.02 5.26
-
SNRAGG 223 130 33 19
SNRSING 100 74 28 8
dalegpoerTnithdhemenopturptechosoemntoetsns ronefaorsiesotenriaiesbvililenlurgestTtrrSiaeStve(ad0l.0pin1erttfhooer1m0g0raamnpcgheisLcfs-o1or)fTwFSiSigthautrsoepre1ac1tb.roaI-vnlleyb0ofl.t1ahtmasgcnedL-n-sap1r,-ieoe-csxtr,catehlplyet
apo 50% ye spectrally flat photon ruzha rwakawedzerwa Zvichakadaro, zvikanganiso zvakakura (> 300%) zvakawanikwa.
kuHimawari-8 yeTSS retriev-als
mabhandi pazasi
(Mufananidzo-e 0.1 mg
11a). L-1,
zvisinei nerudzi rweruzha uye mwero. Pane m-ore realistic scenario, kana inotsamira-ndent
photon ruzha (kureva, % Noise kubva Tafura 2) -inowedzerwa kumabhendi eHimawari-8, zvikanganiso zviri
kazhinji pasi pe100% - yeTSS > ~ 0.25 mg L-1 (Mufananidzo 11 (panerudyi)). Naizvozvo, kuti uwane
kudzoserwa kwakavimbika kubva kuHimawari-8 ine curr-ent TSS algorithm, yekuona li-mit ye0.25 mg L-1 yakasarudzwa. Kuenzanisa, t-iye yekuona miganhu yeTSS yekudzosa yakaverengerwa
kubva mumhepo yakagadziriswa Himawari-8, semuDorji uye Fearns [17], inomiririrwa se
vertical dashed line pa 0.15 mg L-1.
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Mufananidzo 11. Kudzorera zvikanganiso zveRMSE (mu mg- L-1) ye spectrally flat (kuruboshwe panel) uye spectrally dependent (kurudyi panel) photon ruzha mazinga. Radia-tive transfer (RT) TSS uye yakabatana RMSE val-ues inounzwa mune logarithmic chiyero. The vertical dashed line pa 0.15 m- g L-1 ndiyo yekuona muganhu adapte-d kubva kuDorji uye Fearns [17], 2018. Mutsara we vertical dashed pa 0.25 m- -g L- 1 ndiyo muganhu wekuona wezvino nzira.
Kuongorora kwekuona kwemazinga eruzha kwakaratidza kusimba kwakakomba uye mitsetse yakachinjika iogttitnnruhbraeraHstnbhneTsiierumdeSvlcCSaaactAtostwoiiGroaooaaGnsnfrl-tiTsaSw-(-h8lSeTaoaSaTSswSrS(SIesmNSSeeaIGdNvaspgeGra(iroenT)nendcSdlatruySnaescras> Sdei~r(nAdouF1GwicomgmeGpsu-da,egtrsaFnei-knLki1goie-an2ucn1g)ger,)geba.parne1raIetno2rgwwt)uaeaeacantd-eeutrdeenddlarciis1T-rtllill5oSwkushata1
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Mufananidzo 12. Nzvimbo ye transects (magenta arrows) yakatorwa yeTSSSING (a) uye TSSAGG (b). Cherechedzai
kuunganidza makore masiki muTSSAGG.Himawari-8 zvakaonekwa zvakatorwa musi wa9 Gunyana 2017 pakati pe-n
10:00 uye 10:50 nguva yemunharaunda (AEST).
-
The transect sampinotungamirwa pakati pe19S ne20Sin the Coral Sea (Mufananidzo 13a) yakafanotumirwa
TSSSING neTSSAGG tsika dzakanyanya dziri pasi pemiganhu yekuona yenzira (0.25 mg L–1), iyo inogona kuunza kukanganisa kukanganisa kudarika 100%. TSSSING yakaratidza masipikisi kana maodha akasiyana-siyana ehukuru hunoitika zvakatevedzana pachiyero chepixel (kana ithin 1 km). As
mhedzisiro, misiyano inosvika 0.3 mg L-1 yakaonekwa pakati peevakidzani pixel-s,
sezvinoratidzwa nekutumirwa kwakapfava
pplioxtela-tnon-potixaetilovnasriantiFonigsu(r~e0.1036am. gMLe-an1)w. ShuilbetltehdeifafsesroencicaetsedweTrSeSoAbGsGerpvered-
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-
-
-
-
between TSSSING and T-SSAGG in the transects taken in the coastal GBR (Figure 13b), particularly for TSS -> 1 mg L-1. However, with increasing distance from the coast, TSS dropped below 1 mg L-1 and differences between TSSSING and TSSAGG were enhance-d. Although- most TSSSING pixels of Figure 13b were abo-ve-detection limits (0.25 mg L-1), they presented- poor spatial coherency in the coast-to-ocean transition area (151.4 to 152-.0E). Because TSSSING and TSSAGG provide comparable results for TSS > ~1 mg L-1, both may be appropriate for monitoring the coastal GBR. However, TSSAGG presents overall better spatia-l coherency and may be preferred over TSSSING, depending on the area of application.
Mufananidzo 13. Transects yeHimawari-8-yakatorwa TSS (mg L-1) inotorwa muCoral Sea (a) uye mukati megungwa.
yemahombekombe GBR mvura (b) kubva kuTSSSING (madotsi ebhuruu) uye TSSAGG (madotsi matsvuku). Iwo magepu edata anomiririra mapikseli akafukidzwa kumakore, nyika, kupenya kwezuva, kana mireza yeANN, pazvinenge zvakakodzera. Iyo TSS yakanyorwa (mumiseve nhema) inoratidza pixel-top-ixel kukosha uye girini yakachinjika mutsara inotaridza muganhu wekuona.
nzira.
4. Kukurukurirana
Synoptic yekutarisa kunaka kwemvura mune yakakura uye yakaomesesa yakaoma GBR ndiyo inonyanya kukosha, inopa dambudziko kune vatariri vezvakatipoteredza nevaongorori [2,83]-. Kunyange zvazvo ruvara rwegungwa rwuri kure rwunonzwa ruine radiometric uye spectral zvinodiwa, Himawari–8 inopa nhamba isati yamboonekwa yekutarisa kwepamusoro pemvura yekutarisa kweGBR. Iri bepa rinopa yekutanga yepamberi sensing- algorithm munharaunda yakarongedzwa uye yakasimbiswa kune synoptic yekutarisa yemhando yemvura pazvikero zve diurnal muGBR.
4.1. Algorithm Kuvandudza uye Kusimbiswa
Iwo akabatanidzwa oceanatmosphere radiative kutamisa simulations akapa yakakura uye
trhoebuospttdicaatlavbaasrieaobfilRityTOoAf dthisetrGibBuRt.ioTnhienmthaechHinime alewaarrnii–n8gVANNIRNbaalngdosr,itphamramdeevteelroispeeddfoinr
A(ptart0hhenrN.flieo0saev1Nadcwittdtvmoraoeaenr1dontkcr0st-eic0paeasogvhm,lnleaeoifiglnrcwsidoLcwceem-ocndh1ompc)irtea,chprhrweieaencirdtttdehtihhodtieoroeneuwatcqt[crteu2acal7iudalnn,lri3wivteat6iexyico,tr3pynhos7laifi,otoc8latfin4hmrt]tgea.hoeetefttmtDrhaRofiooeiuTndnssOtppaespAlidhbut-eieatatnrsosliHvgecfeddroicrmoerosomirirtaniorhvwensmtecihamtisraeiso.iu-iwnsM8nluavpisobtdeprerjeoredeesccocritettoavedrtnnesaoutrgloi,renteltfh-ihg.moweeTfdiahaataTtaciletsgStcariSuops-olerrrneviateatssacsh,vyleamuitnnnheot’dgesss—–f
kusimba kwekupinza kusangana zvishoma
rnaodisioemweatsriecsrpeeqcuiairlelymaednvtsanotfaogceeoaunsccoolnosuirdseerninsgo-rHs iamnadweanrvii-r8odnomeesnntoatl
ruzha, kunyanya kubva mumhepo, runogona kukanganisa matorerwo. Izvi zvabuda
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yakakurudzira kumwe kushandiswa kwe Himawari-8 yekutarisa kuti isimbiswe inopesana ne in situ mvura yemhando data muGBR.
Iyo yakadzoserwa Himawari-8 TSS matchup kukanganisa yakaenzaniswa zvakanaka nezvinangwa zvemishini zvinotsanangurwa kune mamwe majenera egungwa, seSentinel-3 muCase 2 mvura [85], kunyanya yeTSS pamusoro pe0.1 mg L-1. Kuitwa kweiyo algorithm yemazuva ano inofananidzwa zvakanaka nevaya vanoshandisa mumhepo yakagadziriswa Himawari-8 zvakacherechedzwa [17,24], zvichiratidza kukodzera kwekutora TSS yegungwa nemuenzaniso-based one-step inversions. Kururamisa kwakajeka kwemuchadenga kunogona kuvandudza kudzoreredzwa kweyepasi TSS siyana (<~ 1 mg L-1), iyo inogona kukanganiswa neinotonga yemhepo nzira yekupenya uye yakaderera radiometric performance yeHimawari-8.
Kuvandudzwa kwemaitiro kunoda dhatabhesi yakakura uye yakazara yezviyero zve in situ bio-optical inovhara zviyero zvepakati uye zvenguva zvekusiyana. Zvakare, dzakaomarara kuyerwa maprotocol anofanirwa kuteverwa kudzikisa kusavimbika kwakabatana nealgorithm parameterisation uye kusimbiswa mumvura yemahombekombe. Semuenzaniso, katatu sampLes inokurudzirwa kutsunga kweTSS neiyo gravimetric nzira. Mukuwedzera, kusimbiswa sampLes inofanira kutorwa mumvura ine optically homogeneous [86], iyo inonyanya kuoma mumamiriro egungwa ane simba. Zvakangodaro, zviyero zve in situ zvakaitwa kuti zviwanikwe neakawanda masangano ekutsvagisa ane akasiyana ekutanga esainzi anoshandisa akasiyana s.ampLing uye nzira dzekuongorora. Pamusoro pezvo, maitiro emuviri uye ezvakatipoteredza, senge pasi kuratidza, fluorescence, bidirectional reflectance, polarisation, uye anokuvadza algal blooms, haana kuverengerwa asi anogona zvakare kubatsira mukukanganisa kudzoreredza matchup.
4.2. Himawari-8 Yese Yakamiswa Solids yeGreat Barrier Reef
Himawari-8 yakabvumira iyo yepedyo-chaiyo nguva yekutarisisa kwechiitiko cheepisodical mafashamo muGBR, ichiratidza kurongeka kwehukuru hweTSS kuwedzera mukati mezuva. Chiitiko ichi chakacherechedzwa mumwaka wemvura apo Burdekin yakaburitsa pakati pe0.5 ne1.5 miriyoni ML/zuva kwemazuva gumi akatevedzana (Burdekin River paClare station [10]). Kuchinja kweTSS kubva kumvura yemafashamo yeBurdekin kwaive pamusoro pemvura inotungamira kukosha kwe87 mg L-2 yemvura yakavhurika yemahombekombe uye yepakati pesherufu, pamwe ne1 mg L-0.7 yemvura yekumahombekombe yeGBR [1]. Mvura yemafashamo yakawedzera makiromita makumi mashanu mukati mematombo ekunze, uye kukura kwayo kwezuva nezuva kwakateverwa nhanho-ne-nhanho ne82 min Himawari-50-yakatorwa TSS. Naizvozvo, Himawari-10 yakapa huwandu husati hwamboonekwa hwekutarisa kwemhando uye kuwanda kwekutarisa kwezviitiko zvemafashamo muGBR. Mapikisi akafukidzwa mumvura yemafashamo anoratidza kukosha kunodarika 8 mg L-8, zvichireva kuti simulation range inofanira kuwedzerwa kune zvakakosha pamusoro peiyi muganhu pakutsvaga munguva yemafashamo muGBR.
Iwo maTSS maficha ekumaodzanyemba reef matrix angangodaro anokonzerwa neapfupi-akararama sub-mesoscale resuspension eddies (1 km dhayamita), anowanzo kunzi tidal jets. Kumaodzanyemba kweGBR, mafungu mahombe (10 m) anoita mafungu akasimba [5], achisunda mvura nemumigero nhete uye isina kudzika [10]. Aya akaoma hydrodynamics anokurudzira kumiswa uye jekiseni reTSS kubva pasherufu inoputsika mureef matrix, uye TSS kutariswa munzvimbo idzi kungangove kwakazvimiririra kune epasi masosi [88,89]. Majeti emvura akave akabatanidzwa nekusimudzirwa kwenzvimbo uye kuchinjana kwekudya pakati peCoral Sea neGBR lagoon [90], iri nzira inokosha yekufambisa uye kusanganiswa kwemarara, kudya, uye phytoplankton kugadzirwa [91]. Zvisinei, nzvimbo uye kuitika kwemajeti emvura hazvitsananguriki nekuda kwekushayikwa kwekutarisa kwakakodzera kwepakati uye kwechinguva [92,93]. Himawari-94 yakabvumira kuzivikanwa uye kurondwa kwezvinhu zvakadaro mukati meGBR, pakugadziriswa kwechinguvana kwekugadzirisa maitiro emahombekombe enguva pfupi.
4.3. Zvisingakwanisi
Himawari-8 inopa yakaderera SNR kana ichienzaniswa neyekare uye parizvino inoshanda yegungwa color sensors [80], uye kunzwisiswa kwayo kuri pazasi pezvishoma zvinodiwa zvekushandiswa kwemavara egungwa, kunyanya pamusoro pemvura yegungwa yakashama [9,97]. Zvisinei, Himawari-
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8's ine mwero radiometric resolution ye11 bits haigone kuzara pamusoro pezvinangwa zvakajeka, semakore [80], uye pamusoro pemvura yakanyanyisa kuyerera yemahombekombe (TSS ~ 100 mg L-1), ichipa kunzwisiswa kwakakwana kupa mwero unonzwisisika we discretisation pamusoro pemvura yakachena (> 0.25 mg L-1). Noise mazinga akaverengerwa kubva paaggregated cherechedzo aiwanzove akaderera pane ayo kubva kune imwe chete yekutarisa mumabhendi ese, achisimbisa kukodzera kwekusvibisa kugadzirisa kwechinguva kuvandudza kunaka kwemufananidzo [7,16]. Kunyangwe diurnal kuchinja kweSNR kuri kunyanya kugadziridzwa nemakona ekumusoro ezuva, kutsamira kwekutarisa kunoreva kuti kwakaringana kunobva ruzha rwekupinza (3% mumabhendi matsvuku neNIR) mumvura yegungwa yakavhurika inogona kubva mumhepo [5]. Zvakangodaro, muganho wekuona weiyo nzira yezvino (80 mg L-0.25) inofananidzwa nevaya vanoshandisa yakajeka kururamisa kwemuchadenga kune inversion yemeteorological data [1].
Muganho wekuona we0.25 mg L-1 uri pedyo nemuganhu wekutsvaga we in situ TSS yakayerwa ne gravimetric nzira ye ~ 0.4 mg L-1, yeAIMS neCSIRO. Kusave nechokwadi kwemaitiro e gravimetric nzira inosanganiswa neprotocol yekuyera inoshandiswa nemarabhoritari akasiyana-siyana, ayo anosanganisira kusiyana kwemhando dzemasefa, kusarura kwevashandi, rinsing yemunyu, nezvimwewo [99,100]. Semuenzaniso, makristasi emunyu akavharirwa mugirazi fiber mafirita anonyanya kukanganisa zviyero zveTSS uye munyu unofanirwa kubviswa nekusuka midziyo yekusefa [101,102]. Asi, zvikanganiso zvakakura se30% zvakawanikwa zvichishandisa nzira dzakasiyana-siyana dzekuchenesa munyu, zvichidzivisa kunyatsogadzirisa TSS yakaderera pane 1 mg [101]. Nokudaro, miganhu yekuona uye kusava nechokwadi kwezviyero zve in situ uye Himawari-8-yakatorwa TSS inofananidzwa nechidzidzo chezvino. Mhedzisiro iyi inoratidza kuti Himawari-8 inopa mukana wekutarisa nemazvo kusiyana kwe diurnal yemhando yemvura mumhenderekedzo yeGBR, yeTSS pakati pe0.25 ne100 mg L-1.
Himawari-8-yakatorwa TSS zvigadzirwa zvakaratidza yakarongeka yakachinjika mitsetse, ine saizi inowanzoenderana neyega yakachinjika scans (500 km), sezvakambozivikanwa naMurakami [22]. Iyo mitsetse yakaguma nekusiyana kwe detector-to-detector calibration slopes kubva kune solar diffuser kucherechedzwa kwemabhendi anooneka [103,104]. Kunyange zvazvo macalibration coefficients akashandiswa kune mushure meChikunguru 2017 zvakaonekwa, maitiro akakomberedzwa emitsara akanga achiripo mumvura yegungwa uye neTSS <1 mg L-1. Pamusoro pezvo, granulation yakaoma yakaonekwa muTSS zvigadzirwa zvakatorwa yega yega 10 min, ingangove yakabatana neiyo yakaderera radiometric kuita kweHimawari-8 sensor pamusoro pezvinangwa zvemvura [17,22]. Nekudaro, ruzha rwekuona rwakanyanya kuderedzwa nekuunganidzwa kwechinguva kwekakati wandei kucherechedzwa kwemunhu muhourly-yakatorwa TSS zvigadzirwa [16]. Sezvineiwo, ruzha rwegranulated rwakanga rusina basa mumvura yemahombekombe uye ine mwero ine turbid mvura (TSS> 1 mg L-1), ingave kubva ku10 min kana kubva ho.urly TSS zvigadzirwa. Ichi chigumisiro chinogona kusanganiswa nekuwedzera kwekudzokera shure kwezvikamu zvakasimirirwa, izvo zvinowedzera kupenya kwemvura-kubuda uye kukurira ruzha rwephoton [105]. Nekuda kweizvozvo, Himawari-8-yakatorwa TSS inogona kunyatso kudzoserwa pamusoro pemvura yegungwa ine mhepo ine mhepo ine mhepo kupfuura pamusoro pegungwa rakashama, ichitsigira kuongororwa kwemiganhu yekuona.
Pixel-to-pixel kusiyana munzvimbo dzegungwa dzakavhurika (TSS <0.25 mg L-1) zvingangove zvine chekuita negranulated mapatani akaonekwa nekuongororwa kwekuona, nekuda kwekuderera kwekunzwa kweHimawari-8 sensor pa 10 min resolution. Ruzha rweradiometric rweTSS pazasi 0.25 mg L-1 rwakanyanya kuderedzwa muTSS yakaunganidzwa, ichisimbisa kunzwisiswa uye kuongorora kwekuona. Sezvineiwo, kuvandudzwa kwenzvimbo yakabatana kwakaonekwa mumhenderekedzo yeGBR transect yeTSS> 1 mg L-1. Nekuda kweizvozvo, Himawari-8 10 min-derived TSS inogona kushandiswa nekuvimba kwakawanda seTSS yakabva kuneurly zvakaunganidzwa zvakaonekwa munzvimbo dzemahombekombe. Kuwana TSS pamaminetsi gumi ega ega mumahombekombe eGBR kunovandudza rusarura rwekuchinja kuri kukurumidza kuchinja kwemhando yemvura mukati meawa. Nekudaro, iyi yepedyo-chaiyo nguva yenguva frequency inoda yakakura kugadzirisa uye kuchengetedza kugona izvo zvingave zvisingagoneke kune iyo GBR yese. Kugadzira hourly TSS, zvikasadaro, haingonatsiridza mareti ekugadzirisa uye kugona kuchengetedza asiwo inobatsira kubvisa kunze uye kuwedzera huchokwadi hwezvigadzirwa zveTSS.
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5. Mhedziso uye Maonero Anouya
In-situ monitoring uye LEO satellite data yakapa ruzivo rwedu rwakawanda pamapombi emvura anopinda muGBR [4,106]. Nekudaro, kusatarisika uye kushomeka kwenzvimbo kwakatadzisa kunzwisiswa kwakazara kwekukura kwepombi uye shanduko pamusoro pezvikero zvenguva pfupi. Ichi chidzidzo chakaratidza kukodzera kweHimawari-108 kune yakavimbika TSS kudzoreredza mumahombekombe eGBR uye nemafashama plumes mepu, kuronda, nekutarisa. Kekutanga, mahombekombe eTSS maficha akaverengerwa zvakavimbika kune iyo GBR yese, pamitengo inongogoneka ne biogeochemical uye hydrodynamic modhi [8]. Himawari-109 TSS zvigadzirwa zvinounza kugona kuratidza uye kugadzirisa nguva nenguva uye yenguva pfupi zviitiko pane zvisati zvamboitika spatiotemporal resolution. Zvigadzirwa izvi zvichave zvinobatsira kune vanoongorora, mamoderi, uye vane chekuita vachiongorora maitiro emhando yemvura muGBR ecosystems parizvino vachishandisa LEO orbit ocean color products [8]. Shanduko dzemazuva ese uye madhiraivha ekuchinja kwemhando yemvura anofanirwa kuongororwa zvakare muGBR uchishandisa zvigadzirwa zveHimawari-109 TSS uye data yemaitiro emahombekombe semafungu, mhepo, uye kubuda kwemvura yakachena. Uyezve, iyo algorithm inoratidzwa muchidzidzo ichi inogona kushandiswa zvakananga kune yakafanana Himawari-8 AHI sensor, iyo yakarongwa kuti ibudirire Himawari-9 ne 8. Chizvarwa chinotevera cheHimawari mission (Himawari-2029) chiri muchikamu chekugadzirira uye mamwe migero munharaunda inooneka, pamwe nekuvandudzwa kwekunzwa uye kugadzirisa nzvimbo, inogoneka. Aya maitiro aizonyanya kufambisira mberi kugona kwemugungwa color algorithms ye geostationary sensors, zvichibvumira kudzoreredza kwakaringana mumvura yemahombekombe panguva yezvikero zvemasikati. Saizvozvovo, iyo Advanced Meteorological Imager (AMI) iri muchikepe GEOKOMPSAT-10A, pamwe neGOCI-II (GEOKOMPSAT-2B), parizvino iri kuona Australia neEast Asia, uye yakafanana muchina kudzidza algorithm inogona kugadzirwa kuti ishandise aya makuru uye akawanda dataset munguva iri pedyo-chaiyo. Muchirevo chechinyorwa chino, chidzidzo chazvino chinopa algorithm yepamberi uye tarisiro yezvingangoita mashandisirwo ekugadzirwa kana yegungwa ma sensors ari paboard geostationary mapuratifomu anove echokwadi kuAustralia.
Supplementary Materials: Izvi zvinotevera zvinowanikwa online pa https://www.mdpi.com/article/ 10.3390/rs14143503/s1, Mufananidzo S1: Diurnal kusiyanisa kweZvakasimudzwa Solids pamusoro peBurdekin River muromo muna Kukadzi 2019 kubva ku10 min Himawariguurre Spended Kuonekwa kwe8: iyo Southern Great Barrier Reef pedyo neHeralds Reef muna Mbudzi 2 kubva ku2016 min Himawari-10 zvakaonekwa.
Munyori Mipiro: Conceptualization, LP-V. uye TS; nzira, LP-V. uye TS; software, LP-V., TS uye YQ; kusimbiswa, LP-V.; kuongororwa kwepamutemo, LP-V.; data curation, LP-V., TS uye YQ; kunyora-yekutanga kugadzirira, LP-V.; kunyora-review uye kugadzirisa, TS, MJD, SS uye YQ; kutarisira, TS, MJD uye SS; kuwana mari, LP-V. Vese vanyori vakaverenga uye vakabvumirana neshanduro yakadhindwa yezvinyorwa.
Mari: Tsvagiridzo iyi yakatsigirwa neNational Council for Scientific and Technological Development (CNPq) Foundation yeBrazil Federal Government kuburikidza neScience pasina Border Chirongwa, nhamba yekupa 206339/2014-3.
Chirevo cheKuwanikwa kweData: Iyo data yakapihwa muchidzidzo ichi inowanikwa pakukumbira kubva kumunyori anoenderana.
Kutenda: Tinobvuma Juergen Fischer naMichael Schaale (Institute of Space Sayenzi, Dhipatimendi rePasi Sainzi, Freie Universität Berlin) nekupa mukana weMOMO radio radio transfer code uye ye inverse modelling tool. Britta Schaffelke, Michele Skuza, naRenee Gruber (AIMS) vanobvumwa nekupa ruzivo rwakakosha munzvimbo yakaunganidzwa sechikamu cheMarine Monitoring Chirongwa cheInshore Water Quality, kubatana pakati peGreat Barrier Reef Marine Park Authority, Australia Institute of Marine Science, James Cook University, uye Cape York Water Monitoring Partnership. Iyo Japan Meteorological Agency inotenderwa kushanda kweHimawari-8 uye kugovera data kuburikidza neAustralia Bureau yeMeteorology. The Australian Bureau of Meteorology inobvumwa nekupa data rekufanotaura kwemvura. In situ data yakatorwa kubva kuAustralia Integrated Marine Observing System (IMOS)–IMOS inogoneswa neNational Collaborative Research Infrastructure Strategy (NCRIS). NCRIS (IMOS) uye CSIRO
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vanotenderwa kupa mari Lucinda Jetty Coastal Observatory. Tsvagiridzo iyi yakaitwa nerubatsiro rwezviwanikwa kubva kuNational Computational Infrastructure (NCI Australia), iyo NCRIS-inogonesa kugona inotsigirwa neHurumende yeAustralia.
Kupokana kweKufarira: Vanyori vanozivisa kuti hapana kupesana kwechido.
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