AGU poster

Views:
 
Category: Education
     
 

Presentation Description

No description available.

Comments

Presentation Transcript

Slide1: 

FIGURE 6. Multidate smoothed composite of soil moisture product on 07/06/02. Red = pre-dawn mode (1:30 a.m.), Green = afternoon mode (1:30 p.m.), Blue = pre-dawn mode. Significant correspondence of temporal evolution of daily vegetation water content retrievals and the land surface phenology as captured by 16 day composites of MODIS NDVI was found for areas dominated by herbaceous vegetation. The sensitivity of the vegetation water content is limited for MODIS NDVI > 0.5. Predawn and afternoon VWC retrievals provided important information about diel changes in the canopy water content. A high correspondence between vegetation water content and soil moisture was evident following an extreme precipitation event. The rapidity of the exponential drydown was modulated by land cover type. Vegetation Water Content & Land Surface Phenology 3. RESULTS I 7. CONTACT INFO SYNERGISTIC USE OF AMSR-E AND MODIS DATA FOR UNDERSTANDING LAND SURFACE PHENOLOGY: A CASE STUDY FROM GRASSLANDS OF GREAT PLAINS Marcela Doubková, Center for Advanced Land Management Information Technologies (CALMIT) Graduate Program in Geography, University of Nebraska-Lincoln Geoffrey M. Henebry, Ph.D., Geographic Information Science Center of Excellence (GIScCE) South Dakota State University A positive correspondence of canopy water content (represented by Normalized Differential Water Index, NDWI) and vegetation biomass (represented by Normalized Differential Vegetation Index, NDVI) has been amply demonstrated (Jackson et al., 2003). Here we used AMSR-E and MODIS NDVI data to study similar relationships. Data from 24 AMSR-E pixels were investigated. Three of these located within approximately 160 km from each other are displayed in Figure 2. These are: Tryon (98% grassland), Oconto (58% cropland and 42% cropland/natural vegetation mosaic), and Holdrege (98% cropland). Even though the precipitation patterns at three studied weather stations provided similar patterns, the VWC response to precipitation varied rapidly Strong correspondence was found only in between VWC and NDVI values in Tryon (grassland). High NDVI values were associated with lower VWC values. The difference in peak occurrences of VWC (predawn) and NDVI increase rapidly once maximum NDVI values exceeded value of 0.5. At the beginning of the growing season when soil moisture does not limit evapotranspiration, the afternoon acquisitions exhibit higher VWC than predawn. Once soil moisture becomes limiting to a mature canopy later in the season, predawn VWC is higher than afternoon. All 24 AMSR-E pixel located within Sand Hills and five adjacent ecoregions were studied as demonstrated in Figure 2. The peak occurrences in Accumulated Growing Degree-Days (AGDD) are shown in Figure 4. Ogallala Flood event Here we concentrate on the soil moisture and vegetation water content response to the rapid precipitation event from Ogallala on 7/6/02. Rainfall data from nine weather stations on the storm path (Figure 5) and vegetation water content and soil moisture from the corresponding AMSR_E pixels were acquired and plotted in Figure 7. FIGURE 5. Ogallala flood as recorded by NEXRAD. These data were obtained from the Doppler radar located in North Platte, Nebraska. The range of the radar reaches only to the border of Nebraska and South Dakota, hence precipitation beyond this limit is not displayed above. On Saturday, July 6, 2002, a large storm front, moving east through the state of Colorado and into Nebraska dumped a large amount of rain in the vicinity of Ogallala, Nebraska. The storm front resulted in as much as 28 cm of rain in 10 hours. The increase of total NDVI values was followed by the shift of the peak occurrence of VWC and NDVI towards higher AGDD. Simultaneously, high NDVI values cause the peak in predawn water content to shift to significantly higher AGDD than NDVI peak. FIGURE 4. NDVI and peak occurrences of VWC and NDVI in 2003. (Stations arranged from North to South.) National Climatic Data Center, extreme weather and climate events: “Highway 61 was closed due to water over the road. Water runoff occurred quickly as heavy rain from thunderstorms fell on already saturated ground from flash flooding early that morning.” Recently, retrievals for surficial (<5 cm) soil moisture and vegetation water content (VWC) at 25 km spatial resolution became available as standard data products from the Advanced Microwave Scanning Radiometer (AMSR-E) on the Aqua orbital platform (Njoku, 2004). AMSR-E, like other passive microwave radiometers, offers synoptic views of “cool” earthlight, the terrestrial radiation emitted at longer wavelengths (0.34 - 4.3 cm). The sensitivity of the microwaves bands on AMSR-E to the moisture content of vegetation and soil has been amply demonstrated (Wang, 1992). Our study area covers 700 000 km2 and spans from southeastern Kansas to eastern Montana. Meteorological data were acquired from 24 representative stations which were distributed across 24 AMSR-E pixels served as the focal site for study (Figure 3). FIGURE 1. Multidate composite of vegetation water content product (ascending mode) in 2004. Red= 01JUN Green= 15JUL Blue= 01SEP Overlays: Omernik’s level III ecoregions with selected 24 representative stations. Data Processing Within the study area 24 regularly distributed weather stations were overlaid with corresponding AMSR-E pixels. Land cover composition and NDVI values within the AMSR-E pixel extent were retrieved and plotted together with the vegetation water content product. Quadratic models were fit separately to explain VWC (am and pm retrievals) and NDVI by Accumulated Growing Degree-Days (AGDD; base 0 oC). Timing of the peak occurrences of NDVI and VWC were calculated from model parameter coefficients. This method has been successfully used to model land surface phenology in other grassland biomes (de Beurs and Henebry, 2005). Weather data from nine weather stations within a region influenced by the Nebraska rainfall event from 7/6/02 were obtained. Moreover, vegetation water content and soil moisture daily data were acquired and its sensitivity to the precipitation was examined. Data Source The AMSR-E L3 land products are distributed through the National Snow and Ice Data Center (NSIDC; http://www.nsidc.org/data/ae_land3.html) We restrict our attention to the vegetation water content and soil moisture products. MODIS vegetation index products (MOD13A2, MYD13A2) in the form of 16-day maximum-value composites and MODIS Land Cover Type (MOD12Q1) product were acquired through the Land Processes Distributed Active Archive Center (LPDAAC; http://edcdaac.usgs.gov/main.asp). Meteorological data were gathered through the National Weather Service Cooperative Observer Network and provided by the High Plains Regional Climate Center (http://www.hprcc.unl.edu/coop/home.html). NEXRAD data were acquired from National Climatic Data Inventory archive (http://www.ncdc.noaa.gov/nexradinv/index.jsp). FIGURE 3. MODIS Land Cover Type product overlaid with Omernik’s ecoregions (Chapman et.al. 2001) and 24 weather stations with AMSR-E pixels . FIGURE 2. AMSR-E VWC retrievals from ascending (afternoon) and descending (pre-dawn) orbits averaged over 16 days, MODIS NDVI time series, and total precipitation in 2003 and 2004. Marcela Doubková , M.A. student, (mdoubkova@calmit.unl.edu) Geoffrey M. Henebry, Ph.D., (Geoffrey.Henebry@sdstate.edu) This research was supported in part by the NSF Sand Hills Biocomplexity Project and the USDA RMA Grasslands Ecological Monitoring System project. AMSR-E data were acquired from the National Snow and Ice Data Center. 6. ACKNOWLEDGMENTS 1. INTRODUCTION In recent investigations into the response of native grasslands to global environmental changes, rainfall variability was offered as a key factor to explain ecosystem structure and function. In particular, changes in temporal patterns of precipitation was shown to alter the key carbon cycling processes, such as net photosynthesis and above ground productivity, and ecological patterns like community composition. To understand the impact of rainfall variability in grasslands, an understanding of soil moisture dynamics is critical. Here the spatio-temporal trends of two Advanced Microwave Scanning Radiometer (AMSR-E) standard data products (vegetation water content and soil moisture) and high sensitivity to the precipitation event and land cover composition are demonstrated. The sensitivities of vegetation water content and soil moisture retrievals were found to be dependent on the NDVI value, with an apparent loss of sensitivity at high NDVI values. ABSTRACT 2. METHODS Strong response to the extreme precipitation event was encountered in both soil moisture (Figure 6) and VWC products in all 9 stations. Results from Ogallala, Hyannis, and Gettysburg are displayed in Figure 7. We were able to fit negative exponential curves to data from all nine stations. The behavior of these curves was influenced by land cover composition. Both soil moisture and VWC rapidly decreased after the rainfall in those pixels with mostly grassland. A slower decrease was found in regions covered with mixture of croplands and grasslands. FIGURE 7. Soil moisture and vegetation water content products (afternoon acquisitions) responding to an extreme precipitation event. 15Jul 4. RESULTS II 5. CONCLUSIONS Chapman, S. S., Omernik, J.M., Freeouf, J.A., Huggins, D.A., McCauley, J.R., Freeman, C. C., Steinauer, G., Angelo R.T. and Schlepp, R.L. (2001). Ecoregions of Nebraska and Kansas (2 sided color poster with map, descriptive text, summary tables, and photographs). 1:1,800,000. U.S. Geological Survey, Reston, VA.. de Beurs, KM, and Henebry, GM. (2005). A statistical framework for the analysis of long image time series. International Journal of Remote Sensing, 26(8): 1551-1573. Jackson, T., Chen, D., Cosh, M., Li, F., Anderson, M., Walthall, C., Doraiswamy, P., Hunt, E.R. (2004). Vegetation Water Content Mapping Using Landsat Data Normalized Difference Water Index (NDWI) For Corn And Soybean. Remote Sensing Of Environment. 92(4):475-482. Njoku, E. (2004, updated daily), AMSR-E/Aqua Daily L3 Surface Soil Moisture, Interpretive Parms, & QC EASE-Grids V001, March to June 2004. Boulder, CO, USA: National Snow and Ice Data Center. Digital media. Wang, J. R. (1992). An overview of the measurements of soil moisture and modeling of modeling of moisture flux in FIFE. Journal of Geophysical Research, 97(D17):18,955-18,959. 8. REFERENCES

authorStream Live Help