Ecohydrologic Error Models for Improved Bayesian Inference in Remotely Sensed Catchments. Issue 6 (4th June 2019)

• Record Type: Journal Article
• Authors: Tang, Yating; Marshall, Lucy; Sharma, Ashish; Ajami, Hoori; Nott, David J.
• Is Part Of: Water resources research. Volume 55:Issue 6(2019)
• Page Start: 4533
• Page End: 4549
• Publication Date: 2019-06-04
• Subjects: ecohydrological modeling -- Bayesian inference -- observational error -- uncertainty analysis; Hydrology -- Periodicals; 333.91
• DOI: 10.1029/2019WR025055
• Languages: English
• ISSNs: 0043-1397

Abstract

Abstract: Leaf area index (LAI) is an important vegetation indicator widely used for simulating vegetation dynamics and quantifying biomass production. Spatial and temporal variability of LAI are often characterized using satellite remote sensing products. However, these types of satellite products often have relatively low quality when compared to in situ measurements. This work presents an approach for characterizing Moderate Resolution Imaging Spectroradiometer LAI observation errors in a Bayesian ecohydrological modeling framework using Moderate Resolution Imaging Spectroradiometer quality flags data. We introduce a novel ecohydrologic error model, which partitions observation and model residual error according to the estimated retrieval uncertainty of LAI and the quality flags for each pixel. We examine our approach in two study catchments in Australia with varying degrees of good and poor quality satellite LAI data. Results show improved LAI predictions and less model residual error for both catchments when accounting for satellite observational uncertainties in a Bayesian framework. Key Points: Observation error in satellite‐derived LAI is defined using MODIS data quality products Different observational errors are defined for good versus poor quality data Results show improved ecohydrologic predictions and reduced model residual errors when accounting for observational uncertainties