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Monitoring Vegetation Water Content with Optical and Microwave Vegetation Index

This study explores the relationship between vegetation parameters observed by satellites and presents a field experiment using optical and microwave vegetation indices to monitor vegetation water content. The experiment design, observed results, and application in Mongolia are discussed.

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Monitoring Vegetation Water Content with Optical and Microwave Vegetation Index

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  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 (620 - 670nm) & NIR (841 - 876 nm) • NDWI:SWIR in band 5 (1230-1250 nm) or band 6 (1628-1652 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 2 1 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 development VWC was measured by sampling IGARSS 2011, Jul. 27, Vancouver

  9. Vegetation 01-16 01-19 01-24 02-07 11-29 12-08 12-13 12-20 IGARSS 2011, Jul. 27, Vancouver

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

  13. ApplicationDomain • 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 MVI(10,6) MVI (18,10) A3 H7 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! IGARSS 2011, Jul. 27, Vancouver

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