IGARSS 2011, Jul. 27, Vancouver 1 Monitoring Vegetation Water Content by Using Optical Vegetation Index and Microwave Vegetation Index: Field Experiment and Application Hui Lu ( Tsinghua University, China) Toshio Koike & Hiroyuki Tsutsui (The University of Tokyo) Hedeyuki Fujii (JAXA)

IGARSS 2011, Jul. 27, Vancouver 2 Outline Background and motivation Microwave vegetation index Field Experiment –Setting and instruments –Observed Results Application –Mongolia site Remark

IGARSS 2011, Jul. 27, Vancouver 3 Background Global vegetation information is closely related to –Food productivity, famine, …… –Environment, ecological system, …. In land surface modeling and remote sensing retrieval, vegetation is –A key variable of land surface remote sensing Soil moisture, soil temperature, vegetation water content –A key parameter in GCM, hydrology and land surface scheme LAI, fPAR, ET, precipitation interception –A key parameter in terrestrial ecosystem model Carbon cycle

IGARSS 2011, Jul. 27, Vancouver 4 Motivation Vegetation parameters observed by satellites: –VIS/IR: fractional coverage, NDVI, LAI, NDWI, EVI –MW: Vegetation water content (VWC), Microwave vegetation index (MVI) MW RS has daily global coverage and deeper penetration depth –Complement vegetation information to VIS/IR What the relationship between these parameters? Accurate VWC is useful in –Improving soil moisture retrieval algorithm –Improving LDAS

IGARSS 2011, Jul. 27, Vancouver 5 VWC, MVI, NDVI, NDWI Microwave vegetation index by Shi NDVI: VIS ( nm) & NIR ( nm) NDWI:SWIR in band 5 ( nm) or band 6 ( nm)

IGARSS 2011, Jul. 27, Vancouver 6 Field Experiment --Instruments and setting Brightness temperature observed by Ground Based Microwave Radiometer, at 6.925, 10.65, 18.7, 23.8, 36.5, 89 GHz VIS/IR reflectance measured by ASD FieldSpec Pro in a spectral range of 350nm – 2500nm

IGARSS 2011, Jul. 27, Vancouver 7 Experiment design 12 3

IGARSS 2011, Jul. 27, Vancouver 8 Experiment design Observing winter wheat –One kind of main crops –VWC is not so big, C-band could penetrate. Winter wheat developmentVWC was measured by sampling

IGARSS 2011, Jul. 27, Vancouver 9 Vegetation

IGARSS 2011, Jul. 27, Vancouver 10 Observed Results VWC ~ NDVI NDVI shows a poor correlation to the VWC, with an R-square less than 0.2. It is not good to estimate VWC from NDVI observation!

IGARSS 2011, Jul. 27, Vancouver 11 Observed Results VWC ~ NDWI NDWI has a good correlation to VWC, while band 5 has bigger R value VWC information maybe can be estimated by NDWI 5, for vwc in [0,4]

IGARSS 2011, Jul. 27, Vancouver 12 Observed Results VWC ~ MWI VWC = linear regression function of MVI High R for X-C band

IGARSS 2011, Jul. 27, Vancouver 13 Application Domain AMPEX –Mongolia; –Relative homogenous –VWC survey at 2003 Jul and Aug; –160*120km;

IGARSS 2011, Jul. 27, Vancouver 14 Application: VWC retrieved from JAXA algorithm Vs. in situ VWC provided by JAXA algorithm is comparable to the in situ observed VWC Using as reference data to check the performance of MVI-based method

IGARSS 2011, Jul. 27, Vancouver 15 Results: VWC from MVI-based method A3 H7 MVI(10,6)MVI (18,10) High R for X-C band

IGARSS 2011, Jul. 27, Vancouver 16 Remark Field experiment which observing winter wheat development by using microwave radiometer and VIS/IF spectroradiometer simultaneously. Comparing to in situ observed VWC –NDVI show poor correlation –NDWI show good correlation –MVI show strong correlation MVI-based linear equation could provide VWC information, but the absolute values should be scaled –Can be used to monitor the vegetation temporal variation –The coefficient of linear equation should be related to (vfc, vegetation type) Future work: –Quantify the coefficient by each vegetation type (LSM classification, or real type) –Test for more observation sites (US site, MDB site, China)

IGARSS 2011, Jul. 27, Vancouver 17 Thank you for your attention!