Observing The Hydrological Cycle Over Land - Eo4society
Observing the Hydrological Cycle over Landusing SMOSAhmad Al BitarPhD. Ing.Centre d’Etudes Spatiales de la Biosphère (CESBIO), Toulouse, FranceCentre National de Recherche Scientifique (CNRS), Paris, France
A simple water budgetSoilmoistureSurfacewaterGroundwaterdS SMdS SWdSGW P E QdtdtdtPrecipitationEvapotranspirationEnergy budgetDischarge
The surface layer as part of the Critical zoneCritical Zone (Chorover et. al. 2007)Surface Layer processes (ESA)
Motivation for soil moisture measurmentsJung et al., 2010, nature - (see also Mirales et al. 2016)
Is it a priority to observe the hydrological cycle ?The Global Risks Perception Survey from the World Economic Forum1234
Space measurements for the water cycleSMOS, SMAP,AMSR-E, SSMIS3, S1, JASON,Saral AltikaSWOTGroundwaterdS SMdS SWdSGW P E QdtdtdtGRACE,GRACE-FOTRMM, GPM,MeghaTropiques,IASIS2, CERESAIRS,MODISS3, Jason,Saral AltikaSWOTVis, NIR, TIRNot all components are observed at the desired resolution and accuracy
Microwave Radiometry for the hydrological CycleBascis ns
Microwave missions for global soil moisture mappingDedicatedSMMissionsL-BandSentinel 1
201020082012SkyLabNasa19772014L-Band eRadarSMOS (ESA CNES) (40 km / 3days / global )Aquarius (NASA) (100km / 8days / global)ALOS (JaXA)SMAP (NASA) (10-60 km / 3days / global)ALOS-2 (JaXA)SAOCOM-1/2NISARProposalsULID ?SMOS-HR (CNES) ? (1 km / 3days / global)Soil moisture products are also available from C-Band sensors (AMRSE1/2, ASCAT, RadarSat)Al Bitar,10 Cesbio
How is data organised (case of SMOS)L0Correlation, TelemetryL1cL2Brightness temperaturesPhysical variables(soil moisture, optical thickness )L3SM (time synthesis soil moisture)L3TB (time synthesis angle binned TB)L4High-end product obtained from models and other sensors
Microwave RadioMeter (MRM)?An MRM is: A passive remote sensing device (in contrast to an active radar). A highly sensitive receiver for thermal radiation.It measures Thermal radiance at a given frequency (called brightness temperature) TBp(p H, V) Thermal radiation is electromagnetic radiation generated by chargedparticles in matter that move due to kinetic energies associated withphysical temperature T. Airborne L-band MRMGround based L-band MRMSatteliteMike Schwank - SMOS training session, ESA-ESAC, 18 – 22 May 2015, Madrid (Spain)
Signal power levels in L-BandExample of a black body at 300 k observed between 1400 – 1427 MHzP k.T.BRayleigh-Jeans approximation of Planck’s lawEmissivity 1 (black-body)k 1.380658 10 23 JK-1 (Bolzmann constant)T 300K (Physical temperature)B 27MHz (bandwidth of protected part of L-band (1400 – 1427 MHz))P 1.1 10-13 WImportant features : Directed antenna with high gainLow-noise, narrow-band receiverHighly stable temperature controlInternal and external reference (calibration) noise-sources at known noisetemperaturesAdapted from Mike Schwank - SMOS training session, ESA-ESAC, 18 – 22 May 2015, Madrid (Spain)
Same frequency but different technologiesSMOSSMAPL-band PassiveL-Band Active (3 first months) and Passive2D Interferometric radiometer (std: 2.4 k)Mesh reflector antenna (std: 1.3 k)Multi-angular acquisitions (0 - 60 )One fixed angle (40 )3 days global coverage at 6 am and 6 pm3 days global coverage at 6 am and 6 pmSpatial resolution (27 -55 km)Passive Spatial resolution (51 X 47km)RFI mitigation at ground segmentSpectral filtering for RFI on board
Incidence angles and swath widthField of view914 km1515
ObjectiveSMOS HR L Band continuation Science and opertaional applications Improve spatial resolution and filtering 10 km, 3 day revisit, global Solution Factor 2 through concept improvement Factor 2 through signal processing Situation R&T study underway Phase 0 started Programmatic context Collaboration NASA, CAS, 16
Copernicus Imaging Microwave Radiometer(CIMR) Ø6m Mission ObjectiveRespond's directly to the Integrated EU Arctic PolicyClimate Change and Safeguarding the ArcticEnvironment Sustainable Development in and aroundthe ArcticInternational Cooperation on Arctic IssuesOperational Sea Ice Services and Global SST capability -Characteristics (To be Confirmed)Conically scanning multi-frequency microwave radiometerSingle satellite, Observation Zenith angle 55 1.5 Loose convoy flight with MetOp-SG(B) 360s separation 95% global coverage every day, mean 6 hourly-revisit in Arctic AreasIn Phase A/B1, Launch: 2025 Channels (GHz, all H&V*):1.4 6.9 10.65 18.7Resolution (km): 55 15 15 5 5NEΔT (K @150K): 0.3 0.2 0.3 0.3 0.7(g:3km)*Full Pol in discussionProducts (Performance TBC, P Primary, S Secondary)P1: Sea Ice Concentration ( 5 km, 5%)P2: Sea Surface Temperature (10 km, 0.2 K)S: Sea Ice Drift ( 25 km, 3 cm/s)S: Thin Sea Ice Thickness ( 40 km, 10%)S: Snow on Sea IceS: Snow Water EquivalentS: Sea Surface Salinity ( 40 km)S: Ice Type ( 5 km)S: Extreme WindAdditional tertiary products (eg. global soil moisture,atmospheric water load, lST, precipitation rate )36.5
SMOS brightness temperaturesFirst step, computing pseudo-L3TBsfrom NRT Tbs ๏ L1C No angle binning XY polarization reference frame ISEA grid๏ L3TB๏ angle bins of 5º๏ HV polarization๏ EASE grid18
SMOS Brightness temperaturesMedian TB at 42.5 during summer2010-2014Amazoniandense forest19Congodenseforest
SMOS Brightness temperaturesMedian TB at 42.5 during summer2010-2014Sahel20
SMOS Brightness temperaturesMedian TB at 42.5 during summer2010-2014Ice dynamics21
SMOS Brightness temperaturesMedian TB at 42.5 during summer2010-2014Freezing andThawing22
SMOS Brightness temperaturesMedian TB at 42.5 during summer2010-2014RFI impact23RFI impactRFI impact
SMOS Brightness temperaturesMedian TB at 42.5 during summer2010-201424
Higher level comparison Global maps of brightness temperatures are averaged over 3 months periods and comparedNeed for careful selection of acquisition to remove potential contaminationCompared at top of atmosphereOverall consistent with previous results(Al Bitar et al. ESSD 2017)
Microwave Radiometry for the hydrological CycleBascis ns
Physical model inversion approachL2SM retrieval algorithmRef: (Kerr Y. et al.,ieee-tgrs 2012) and ATBD L2 SMTB model atantenna levelTB observed atantenna levelInversionalgorithmAnciliary dataSoil moisture,Vegetationoptical depth
TB modeling at Top of atmosphereTB,tot(P,θ) TotalesTs γ (1 – ω) (1 - γ) Tv Soil(1)Vegetation(2)(1 – ω) (1 – γ )Tv (1 - es)γ Soil Vegetation(3)esTsTvωγTBskysoil emissivity; linked to soil moisture through dielectric constantphysical temperature of soilphysical temperature of vegetationsingle scattering albedo of vegetation (omega)canopy transmissivity; vegetation optical depth τ (tau)sky brightness temperaturePθpolarisation (H or V)incidence angleTB,skyγ2 (1 - es)Sky(4)Jennifer Gant SMOS Training Course 2017for a recent review see (Wigneron et al. 2017, RSE)
Reflectivity/ dielectric constant /Soil moisturee(λ) 1 - r(λ) ‘Fresnel equations’: Dielectric constant (ε ε' i ε") determinessmooth surface reflectivity R, depending on incidence angle θ:RHRV cos θ cos θ (ε sin θ ) (ε sin 2 θ ) ε cos θ ε cos θ (ε sin θ ) (ε sin 2 θ ) 2222(Jennifer Grant, Wigneron et al. RSE L-MEB)
L-Band – 1.4 GHzL-Band – 1.4 GHzSMOSAQUARIUSSMAP(2015)C-Band – 6 GHzAMSR-EASCAT (Active)C-Band – 6 GHzWhat frequency for soil moisture ?21.3 cm – 1.4GHzMicrowavevisibleSource CESBIOSource NASA
Soil moisture products
SMOSSoilmoistureretrievalsRetrieval methodology- Physically based retrieval (Wigneron et al. RSE, Kerr et al. 2012 IEEE TGRS, Lievens et al. 2014)- Multi orbit retrieval (L3) (Al Bitar et al., ESSD 2017)- Single channel algorithm (Jackson et al., Maciej et al. 2014, Delannoy et al. 2012)- Neural Network retrievals (Rodriguez et al., 2017) Validation- Comparison with global data(Alyaari et al. 2014a, Alyaari et al. 2014b, )- Validation with in-situ networks (Wigneron et al. 2012, Bircher et al. 2012, Leroux et al.2014, Al Bitar et al. 2012, Albergel et al. 2012 )(Merlin et al. 2010,2012, Piles et al.)- Downscaling and validatingEnhancingretrievals- - Impact of Roughness(Mialon et al. 2012, Parrens et al. in review)- Enhancing vegetation parametrisation (Rahmoune et al., 2014, Wigneron et al. 2012)- Enhancing snow and ice representation (Mike Schwank, Gamma RS)- Enhancing retrievals over organic soil(SMOS HiLat – Bircher et al.)
An “aparté” on Soil moisture from SAR
About Radar backscattered signalModeling the vegetated soil backscatter0000 σ veg γ 2σ sol σ vegσ total solmilieu 1milieu 2εrModeling of the soil backscatterσ pp k22n exp[ 2k z s 2 ] I pp2n 12w n ( 2k x ,0)n!Réunion CESBIO, 9 mars 2016From M. Zribi 2015, CESBIO
Inversion algorithms to estimate surface soil moistureReferencesFrequency& polParameters retievalAncillary dataSurface typeAlgorithm baseDubois et al, 95, Oh et al., 92,Zribi et al., 03, Zribi et al., 08,Baghdadi et al., 11, 2012,Balenzo et al., 09L, C band/ HH,VV, HVMv, R/ Mv/ R-Bare soil/ sparse vegRegression modelWagner et al, 99, Wagner etal., 08, Kim&VanZyl, 09, VanDoninck et al., 12, Zribi et al.,14, Kumar et al., 15, Gorrabet al., 15C band/ HH,VVMvOptical dataBare soil/ veg surfacesChange detectionPaloscia et al., 08, Baghdadiet al., 10, El-Hajj et al. 15C band/ HH,VV, HVMv/ Mv, VWCOptical dataBare soil/ veg surfacesNeural NetworksKim et al., 12, Kim et al., 14L band/ HH,VVMv, R/ Mv, R, VWC-Bare soil/ sparse vegNumerical scatteringmodelShi et al., 97, Joseph et al.,08, Pierdicca et al., 2010,L, C band/HH, VV, HVMv/ Mv, R-Bare soilsPhysical modelingIEMFrom M. Zribi 2015, CESBIO
Microwave Radiometry for the hydrological CycleBascis ns
WaterRainfallCarbonHydrolosurface ge ingn BiomassLandpredictionApplicationsfromWeatherFreeze PermafrostmonitoringForecast Root zone htthicknessClimatemonitoringFire riskETPchange
38Source: National Drought Mitigation Centre, and G. Rossi, B. Bonaccorso, A. Cancelliere, (2003)
Is it a priority to observe the hydrological cycle ?The Global Risks Perception Survey from the World Economic Forum?
PrecipitationdeficitIrrigationdeficitProcessesSoil moistureshortageVegetationWater stressDecreasedphotosyntheticactivitySensing frequencyMicrowave(K-Band)Only forprecipitationGPM(MT, GPMcore )Microwave(L-Band / C-Band)Thermal IRFluorescenceDryingtimeNDVI / NDWI /SLA / LAISensors(examples)SMOS, SMAP,Sentinel-1Biomass ?LandSat-8,Sentinel-3Flex ?Sentinel-2
Root zone soil moisture is a very usefullinformation to access agricultural drought in anearly warning systemSMOS measures surface soil moisture, rootzone soil moisture need to be modeledAt CESBIO SMOS surface soil moisture and MODISLAI are assimilated into a double bucket model tocompute root zone soil moisture.(Al Bitar et al. 2013, Kerr et al. 2016)Surface SM 0-5 cmRoot zone SM 0 - 1 m
SMOS Global root zone soil moisture mapsMay 2016Al Bitar et al., 2013, Kerr et al. 2016available on www.catds.fr
SMOS dailySurface soil moistureLayer 1 model soil modelClimatedataFirst Layer(5-18cm)EO LAILayer 2 model soil modelSecond layer(20 -120 cm)Root zoneprobabilities Drought probabilitiesDrought indexweb application Netcdf products :EASEgridAl Bitaret25kmal., 2013
First soil layer modelSMOS dailySurface soil moistureLayer 1 model soil modelFirst Layer(5-18cm)(Al Bitar et al. 2017 ESSD)from surface to 20 cmsequential formulation of the exponential filterBased on Albergel et al. (2006)But doesn’t take into account the capillary effect (interaction between thedifferent layers) and vegetation transpirationAl Bitar et al., 2013
Second layer model: 20cm – 1.5 mTheta based Richards Equation θ (h, x ) T .[K h ] .[K g B ] th : capilary pressure in (m)Θ : water content ( m3/m-3K : hydraulique conductivity (m/s)g : unit gravity vertorT : vegetation transpiration (m3/m3/s)A linearized (force restore) formulation is used
Vegetation transpiration model (T)T : Transpiration of the vegetation (m3/j)computed using FAO-56 method forced by NDVIand air temperatureKcb a exp (b. NDVI)adapted from Er-Raki et al. (2010)
Why are we using the remote sensing drivenFAO approach ?Radiation and storage stationsAuradéLocal scaleEddycovariance set up(Gill HS50 LI-7200)LamasquèreLamasquèreIntermediate scaleSmall agricultural region scalegional scale(Battude et al. RSE 2016, AWM 2017)
Validation over AMMA sites (Benin and Niger)Pellarin, T., de Rosnay, P., Albergel, C., Abdalla, S., & Al Bitar, A. H-SAF Visiting Scientist Program HSAF CDOP2 VS12 02, 2013.
Comparision to root zone productsPellarin, T., de Rosnay, P., Albergel, C., Abdalla, S., & Al Bitar, A. H-SAF Visiting Scientist Program HSAF CDOP2 VS12 02, 2013.
RZ soil moisture vs NOAA NCEP Bucket modelMay 2011June 2011July 2011August2011September 2011
Drought in the horn of Affrica(Al Bitar et al. , in revue sécheresse 2016)51Al Bitar, R. Escadafald, Kerr Y. Revue Sécheresse , 2014
We found the missing link betweenSMOS sandwich & SMOS sat !
CA, USASept. 2015IndiaOct. 2015BrazilSouthAfricaMay 2015April 2015JanFeb Mar AprDrought indexRoot zone soil moisture0.10.20.30.40.5May Jun0.60.70.8 (m3/m3)moderatmildJulAugSep OctNov DecAustraliaJune 2015extremahmad.albitar@cesbio.cnrs.fr
Root zone soil moisture in 2016Feb. / May / Aug. / Nov/ 2016
Droughts from Root zone soil moisture anomalies 2016Feb. / May / Aug. / Nov/ 2016What is looming a world food crises because of prolonged drought conditions,that can be driven from socio-climatique situations.
Proof of the adequacy of the SMOS rootzone soil moisture as a index into an earlydrought and fire risk warning system.Root zone soil moisture1st may 2016Communication over ESA web portalDrought index1st may 2016moderatmildextrem
WaterHydroloRainfallCarbonsurface ge ingn re riskfrom SMOSFreeze PermafrostmonitoringRoot zonemissionandsoilLong time ringETPchange
Apport de l’humidité du sol SMOS dans laprévision du débit.PI Murray Darling BasinUpper Misssissippi Basin
Land Data Assimilation System - LDASInputs(Land cover )Eco-hydro modelLAIRadiativemodelNOAASM / TB 25kmSMOSEqvSMAPDownscalingAnalysis stepRouting model9kmEnFK: Filtre de Kalman d’ensembleDischarge
Surface soil moisture - Murray Darling BasinSM recordRMSE m³/m³R (-)SMOS0.0450.726VIC open loop0.0580.549DA SM coarse0.0450.713DA SM downscaled0.0470.727DA TB SMOS0.0500.661DA TB SMOS(SMAP)0.0460.700(Lievens et al. 2015 RSE)(Lievens et al. 2016 RSE)(Lievens, Al Bitar et al. 2015 JHM)(Verhoest et al. 2014)
Discharge – Murray Darling BasinComparison of DA experimentsSM coarseR 0.653nRMSE 0.784SM downscaledR 0.617nRMSE 0.810Open loop: R 0.608 / nRMSE 0.812TB SMOSR 0.623nRMSE 0.808TB SMAPR 0.602nRMSE 0.816Lievens et al. 2015 RSE)61
Débit sous bassin – Upper Mississippi BassinPearson's rBias (VIC - SMOS)
Débit sous bassin – Upper Mississippi BassinTBFine SMCoarse SMwithout bias correctionCoarse SMSize of the dot represents the relative area of the basin.TB is for May-Nov, 2011, others are for Jan, 2010 – Dec, 2011S. K. Tomer et al. 2014
WaterHydroloRainfallCarbonsurface ge ingn re riskfrom SMOSFreeze PermafrostmonitoringRoot zonemissionandsoilLong time ringETPchange
SMOSL4 – Flood risk ForecastAl Bitar A., Chone A., Tomer S. K., Joyeux J., Villard P. ,Bodnar R., Kerr Y.65
Flood Risk ForecastStartPrediction / Early alertEndMonitoringPost / damage analysisCyclone YASI, 2011SMOS Blog, Gruhier C., Kerr Y., BoM66
Flood Risk ForecastPrediction / Early alertIn this studyMonitoringPost / damage analysis Flood can be classified into several types : Hurricanes, storm surge, heavyrainfall Soil moisture is expected to play a role for heavy rainfall driven floods, butthere are still many ways of implementing this information in hydrologicalmodeling. Here we consider that soil moisture conditions prior to the flooding willinfluence the projected flood risk in a 1-5 days for the following reasons : saturated soils increase risks of flooding Soil moisture is a proxy for rainfall Land surface / atmospheric coupling (Koster et al. 2010)67
SMOS Flood Risk ForecastMethodologyLeveraging inundation risk based on SMOS soil moisture prior ipitationFlood RiskSMOS SoilmoistureprobabilitiesPrecipitationInundation riskSMOSSM L3productsFlood Risk(Precip SMOS)SMOSInnundationRiskSM vs SM percNone /0Low /1Moderate /2High /30.8None /0None /0Low / 1Moderate /20.8 - 0.9None /0Low / 1Moderate /2High / 30.9 None /0Moderate /2High / 3Ext High / 4
Operational implementationby CapGemini and CESBIOSMOS flood risk on 07 Oct. 2014 at 12h45 for thenext 5 daysStorm risk by NOAA on 07 Oct. 2014 at 12h4569
WaterHydroloRainfallCarbonsurface ge ingn re riskfrom SMOSFreeze PermafrostmonitoringRoot zonemissionandsoilLong time ringETPchange
Hydrology in the context of Earth Systemobservation approachSMOS, SMAP,AMSR-E, SSMIJason, S3Saral AltikaSWOT, S1GroundwaterdS SMdS SWdSGW P E QdtdtdtGRACE,GRACE-FOTRMM, GPM,MeghaTropiques,IASICERES, S2AIRS, MODISJason, S3Saral AltikaSWOTSPOT, PléiadesVis, NIR, TIRNot all components are observed at the desired resolution and accuracy
SWAF - Water fraction using SMOS dataAl Bitar et al., in reviewMedian TB H @ 42.5TB mixteForestTB landwetlandsTB WaterlandSWaFPermanent waterAl Bitar et al. - AGU Fall meeting - H51P-02 - 12-15 Dec. 2016 –San Francisco, CA, USA
Monitoring of water surfaces from space- VisibleSentinel-2MODISLANDSATOptical- NIRLANDSATRadar- C-bandSentinel 1RadarSAT- L-bandPALSAR-C-bandMicrowave- X-bandAMSR-ETERRASAR- K - ka GHz- SSMIPecklet al. 2017, Aires et al. 2017, Ferrant et al. 2017, Parrens et al. 2017
Impact of polarisation and incidence angleMean SWAF for 2010 - 2016Parrens et al. Waters 2017
Validation of the SMOS Water fractionIGBPAgainst static mapsGlobCoverSWAFGIEMSESA CCIAl Bitar et al. - AGU Fall meeting - H51P-02 - 12-15 Dec. 2016 –San Francisco, CA, USA
Droughts of 2010Clim. Water. IndexAnomaly of water fractionwater deficitanomaly of SMOS water fraction(Lewis et al., Science 2011)Jul. – Sept. 2010abnormaly dryabnormaly wetDrought depicted forthe South amazonebut also for theinnundation plains,which can not bedetected using theClim. Water Indexwhich is based onoptical data.Reuters
Droughts of 2010 vs 2015Clim. Water. IndexAnomaly of water fractionwater deficitanomaly of SMOS water fraction(Lewis et al., Science 2011)Jul. – Sept. 2010abnormaly dryabnormaly wetAnomaly of water fractionOct. – Dec. 2015anomaly of SMOS water fractionabnormaly dryabnormaly wet
Nitrogen and Carbon fluxes of inland water surfacesDenitrification rate was estimated as following(Peyrard et al. 2011): 𝑹𝑹𝑵𝑵𝑵𝑵𝟑𝟑 𝟎𝟎. 𝟖𝟖 𝝆𝝆. 𝟏𝟏 𝝋𝝋 𝒌𝒌𝑷𝑷𝑷𝑷𝑷𝑷 𝑷𝑷𝑷𝑷𝑷𝑷𝝋𝝋 𝟏𝟏𝟏𝟏𝟔𝟔𝑴𝑴𝑪𝑪
L4 Water Surfaces at High resolution (New)MNT MERIT GSWO(Peckel et al. 2016)
WaterHydroloRainfallCarbonsurface ge ingn re riskfrom SMOSFreeze PermafrostmonitoringRoot zonemissionandsoilLong time ringETPchange
Yield and soil moisture availabilityare highly correlated.Irrigation accounts for about70% of water ressources uses.Gravitary irrigation in South India(Battude, Al Bitar et al. RSE 2016, AWM 2017) But Irrigation and yield applications will need high resolution (subkilometric products while conserving revisit).
Rationale for evaporation-based SM downscalingGeneric schemeHR LSTAncillary HR data:NDVI, DEMLR SMSM proxyat HRDownscalingrelationshipat HR and LRHR SMMerlin et al., TGRS 2012, Molero et al. 2016
C4DIS - L4 high resolution soil moistureNow runing operationaly !Dispatch is a disaggregation algorithm using microwave optical (visible &
MAPSM: Active-Passive fusionChange in low res. SMIn which direction ?(wetting drying)Change in high res. SMAt which ammount ? Tomer, S. K., Al Bitar, A., Sekhar, M., Zribi, M., Bandyopadhyay, S., & Kerr, Y. (2016). MAPSM: A spatio-temporal algorithm formerging soil moisture from active and passive microwave remote sensing. Remote Sensing, 8(12), 990. Tomer, S. K., Al Bitar, A., Sekhar, M., Zribi, M., Bandyopadhyay, S., Sreelash, K., . & Kerr, Y. (2015). Retrieval and multi-scalevalidation of soil moisture from multi-temporal SAR data in a semi-arid tropical region. Remote Sensing, 7(6), 8128-8153.
Validation MAPSM: SMOS Radarsat2Existing Enviroscan sitesProposed Hydra probe sites
MAPSM SMOS S1 (500m)www.aapahinnovations.com
Remote Sensed high resolution relative soil moisture for KarnatakaSpatial resolution: 500 m; Temporal resolution: 1 day06 Apr 201608 May 201609 Jun 2016Soilmoisturewww.aapahinnovations.com
Remote Sensed high resolution relative soil moisture for KarnatakaSpatial resolution: 500 m; Temporal resolution: 1 day11 Jul 201612 Aug 201613 Sep 2016Soilmoisturewww.aapahinnovations.com
Remote Sensed high resolution relative soil moisture for KarnatakaSpatial resolution: 500 m; Temporal resolution: 1 day15 Oct 201616 Nov 201618 Dec 2016Soilmoisturewww.aapahinnovations.com
Determining optimal Cloud seeding pogramatic15 Oct 2016www.aapahinnovations.comCloud seeding aircraft -Blomberg
Interstate water management –The case of Cauvery Basin south roughtSugarcanegrownarea:Underdrought
Cauvery river basinwww.aapahinnovations.com
Cauvery river basinRelative soil moisture aduKarnataka
WaterHydroloRainfallCarbonsurface ge ingn re riskfrom SMOSFreeze PermafrostmonitoringRoot zonemissionandsoilLong time ringETPchange
WaterWigneron et aRainfallCarbonHydrolosurface correctioncyclegicalBrocca et al.monitorPellarin et al. Vegetatio et al.droughtDischarge ingn Biomass Wigneron et al.LandpredictionApplicationsMiralles et al.Many other land applications Brocca et al.WeatherForecastFire riskfrom SMOSFreeze PermafrostmonitoringRoot zonemissionandsoilLong time ralles et al.ClimateMiralles et al.monitoringETPchange
Lessons learnt from SMOS1 - L-Band is a low energy signal.but very rich ininformation.2- Soil Moisture monitoring is key to many processesbut we didn’t grasp yet it’s full potential.3 – Validation of soil moisture at low resolution remainsa challenge
General Lessons learnt, beyond SMOS- One should leave space for imagination and innovation don’t limit yourapplications to mission objectives.- Synergie is the key to advancing knowledge and reducing equifinality.- Information is in the data awainting even when at low resolution
What next an operational L-Band mission ? maybe our practical session on a shorter timescale
Not all components are observed at the desired resolution and accuracy. Bascis of microwave . L-Band. Microwave missions for global soil moisture mapping. Sentinel 1. L-Band missions. 10 2008. 2010 2012. 2014. 2016. 2018. 2020. 2022. 2024. 2026. 2028. 2030. 2032. SMOS (ESA CNES) (40 km / 3days / global ) . Directed antenna with high .
May 02, 2018 · D. Program Evaluation ͟The organization has provided a description of the framework for how each program will be evaluated. The framework should include all the elements below: ͟The evaluation methods are cost-effective for the organization ͟Quantitative and qualitative data is being collected (at Basics tier, data collection must have begun)
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The hydrological cycle is usually called a recurring consequence of different forms of movement of water and changes of its physical state on a given area of the Earth. The role of different processes in the hydrological cycle and their description depends on the
