A Remote Sensing Approach for Estimating Regional Scale Surface Moisture Luke J. Marzen Associate Professor of Geography Auburn University Co-Director AlabamaView Research funded by Alabama Water Resources Research Institute AmericaView Membership sheet.pdfsheet.pdfsheet.pdfsheet.pdf Maintain and enhance hydrologic and meteorologic data collection capabilities and existing data sets, and develop new data sets needed to improve assessments. Automate data collection to the maximum practical extent, and collect data at the frequency and spatial scale needed to support model analyses and decision-making. Fully fund and implement the National Integrated Drought Information System (NIDIS) passed by Congress in 2006. Estimated that drought costs US $6-8 billion annually (Wilhite, D.A. and M.D. Svodoba. 2000) Estimated that drought costs US $6-8 billion annually (Wilhite, D.A. and M.D. Svodoba. 2000) Meteorological drought is usually measured by how far from normal precipitation has been over a period of time. Meteorological drought is usually measured by how far from normal precipitation has been over a period of time. Agricultural drought occurs when soil moisture is insufficient to meet crops needs to produce an average crop. It may occur in times of average precipitation depending on soil types. Agricultural drought occurs when soil moisture is insufficient to meet crops needs to produce an average crop. It may occur in times of average precipitation depending on soil types. Hydrological drought refers to deficiencies in surface and subsurface water supplies. Hydrological drought refers to deficiencies in surface and subsurface water supplies. Objective Evaluate an approach to estimate surface moisture conditions using remote sensing at the regional scale Evaluate an approach to estimate surface moisture conditions using remote sensing at the regional scale Scale methods down to field level Scale methods down to field level Vegetation and EMR small bodies in leaf that contains chlorophyll small bodies in leaf that contains chlorophyll Absorbs blue and red light, reflects green and NIR Absorbs blue and red light, reflects green and NIR Normalized Difference Vegetation Index Normalized Difference Vegetation Index (Red NIR)/(Red + NIR) (Red NIR)/(Red + NIR) Values 0-1 Values 0-1 Land Surface Temperature Land Surface Temperature Thermal RS Thermal RS Past research using AVHRR has exploited the relationship between the Normalized Vegetation Index and Land Surface Temperatures to evaluate surface moisture status (Nemani and Running, 1989) Past research using AVHRR has exploited the relationship between the Normalized Vegetation Index and Land Surface Temperatures to evaluate surface moisture status (Nemani and Running, 1989) LST and NDVI relationship During drier periods NDVI values fall and vegetation canopy temperatures increase During drier periods NDVI values fall and vegetation canopy temperatures increase LST NDVI LST NDVI Drier conditionsLess dry LST - Land Surface Temperature NDVI Normalized Difference Vegetation Index Data and Methods -Use NDVI and LST MODIS products -growing season of Evaluate ratio of NDVI/LST as an indicator of surface moisture -compare to ground-based indices Global coverage km swath 36 channels - 250m pixels, 500m, 1km various levels of processing EOS Validated products -MOD13, MOD11 The MODIS Instrument Moderate Resolution Imaging Spectroradiometer Direct to PI Websites EOS DataGateway Land Validation Home Site MOD13 NDVI composites uses best value NDVI composites uses best value Both 250m and 1km Both 250m and 1km MOD11 Land Surface Temperature Shown to be accurate within 1 degree K Shown to be accurate within 1 degree K Averaged 2 8 day composites to match NDVI Averaged 2 8 day composites to match NDVI NDVI/LST Crop Moisture Index Southeast Regional Climate Center Southeast Regional Climate Center Mean CMI was compared to the mean of NDVI/LST on a Climate division basis Mean CMI was compared to the mean of NDVI/LST on a Climate division basis NDVI NDVI LST LST Table 1. Pearson's Product Correlations for Remotely sensed variables with CMI DurationLST- CMI NDVI- CMI WSVI- CMI April-May June-July Oct April-May Oct April-May June-July Oct April-May June-July Oct Period 4 for entire southeast N=46 r = 0.79** ** significant at.001 NDVI/LST CMI Conclusions The ratio of NDVI/LST may provide an effective indicator of surface moisture conditions The ratio of NDVI/LST may provide an effective indicator of surface moisture conditions LST performed substantially better in our three year study LST performed substantially better in our three year study Future work Economic Study Economic Study local scale/field level local scale/field level Atlas thermal sensor 1m resolution High crop yield = redCool temps = red *not done by this geographer