Temporal variability corrections for Advanced Microwave Scanning Radiometer E (AMSR-E) surface soil moisture: case study in Little River Region, Georgia, U. S.

Statistical correction methods, the Cumulative Distribution Function (CDF) matching technique and Regional Statistics Method (RSM) are applied to adjust the limited temporal variability of Advanced Microwave Scanning Radiometer E (AMSR-E) data using the Common Land Model (CLM). The temporal variability adjustment between CLM and AMSR-E data was conducted for annual and seasonal periods for 2003 in the Little River region, GA. The results showed that the statistical correction techniques improved AMSR-E\u27s limited temporal variability as compared to ground-based measurements. The regression slope and intercept improved from 0.210 and 0.112 up to 0.971 and -0.005 for the non-growing season. The R-2 values also modestly improved. The Moderate Resolution Imaging Spectroradiometer (MODIS) Leaf Area Index (LAI) products were able to identify periods having an attenuated microwave brightness signal that are not likely to benefit from these statistical correction techniques

Temporal Variability Corrections for Advanced Microwave Scanning Radiometer E (AMSR-E) Surface Soil Moisture: Case Study in Little River Region, Georgia, U.S.

Statistical correction methods, the Cumulative Distribution Function (CDF) matching technique and Regional Statistics Method (RSM) are applied to adjust the limited temporal variability of Advanced Microwave Scanning Radiometer E (AMSR-E) data using the Common Land Model (CLM). The temporal variability adjustment between CLM and AMSR-E data was conducted for annual and seasonal periods for 2003 in the Little River region, GA. The results showed that the statistical correction techniques improved AMSR-E’s limited temporal variability as compared to ground-based measurements. The regression slope and intercept improved from 0.210 and 0.112 up to 0.971 and -0.005 for the non-growing season. The R2 values also modestly improved. The Moderate Resolution Imaging Spectroradiometer (MODIS) Leaf Area Index (LAI) products were able to identify periods having an attenuated microwave brightness signal that are not likely to benefit from these statistical correction techniques

Soil moisture dynamics from satellite observations, land surface modeling, and field data

Knowledge of soil moisture variability is essential to understand hydrologic processes at a range of scales. In this study, spatio-temporal variability of soil moisture and inter-comparison among different soil moisture products were analyzed. The variability patterns were well characterized by negative exponential fitting as function of observed sampling extent scale. The simple physical soil moisture dynamics model was identified as an alternative approach to characterize statistical soil moisture variability. The soil moisture variability was strongly related to physical properties including rainfall and topography. Normal and log-normal distributions were recognized as the most efficient probability density functions to capture soil moisture variability patterns for all conditions. Further, these variability patterns were well maintained for root zone profile and surface soil moisture time stable characteristics can be used to upper boundary for sub-surface time stability. Through inter-comparison analysis, average soil moisture from remotely sensed measurements, ground-based measurements, and land surface model results showed excellent agreement. However, remotely sensed soil moisture had little variation, especially during the growing season. There were complementary benefits with low random errors for the land surface model and low system errors for the remotely sensed data. The error characteristics of remotely sensed measurements can enhance the utility of satellite observations. The remote sensing measurements can provide relative soil moisture conditions to improve runoff predictions and analyze land surface-atmosphere interactions for regional climate predictions in data limited areas. However, their extremely limited variations must be refined prior to direct application in hydrological processes. Overall, the identified soil moisture variability patterns provide a new understanding of soil moisture dynamics and spatio-temporal variability patterns as related to physical variables. These organized characteristics are essential to predict land-atmosphere interactions, rainfall-runoff processes, and groundwater recharge processes. Practically, these findings can be used to calibrate land surface models and to estimate heterogeneity effects of land surface processes. Additionally, statistical information as a function of scale is critical to develop upscaling and down-scaling methodologies without significant loss of information. This dissertation\u27s findings provide critical insight to hydrologic processes related to soil moisture at a range of scales

Remote sensing observatory validation of surface soil moisture using Advanced Microwave Scanning Radiometer E, Common Land Model, and ground based data: Case study in SMEX03 Little River Region, Georgia, U.S.

Optimal soil moisture estimation may be characterized by intercomparisons among remotely sensed measurements, ground‐based measurements, and land surface models. In this study, we compared soil moisture from Advanced Microwave Scanning Radiometer E (AMSR‐E), ground‐based measurements, and a Soil‐Vegetation‐Atmosphere Transfer (SVAT) model for the Soil Moisture Experiments in 2003 (SMEX03) Little River region, Georgia. The Common Land Model (CLM) reasonably replicated soil moisture patterns in dry down and wetting after rainfall though it had modest wet biases (0.001–0.054 m3/m3) as compared to AMSR‐E and ground data. While the AMSR‐E average soil moisture agreed well with the other data sources, it had extremely low temporal variability, especially during the growing season from May to October. The comparison results showed that highest mean absolute error (MAE) and root mean squared error (RMSE) were 0.054 and 0.059 m3/m3 for short and long periods, respectively. Even if CLM and AMSR‐E had complementary strengths, low MAE (0.018–0.054 m3/m3) and RMSE (0.023–0.059 m3/m3) soil moisture errors for CLM and soil moisture low biases (0.003–0.031 m3/m3) for AMSR‐E, care should be taken prior to employing AMSR‐E retrieved soil moisture products directly for hydrological application due to its failure to replicate temporal variability. AMSR‐E error characteristics identified in this study should be used to guide enhancement of retrieval algorithms and improve satellite observations for hydrological sciences

Assessment of clear and cloudy sky parameterizations for daily downwelling longwave radiation over different land surfaces in Florida, USA

Clear sky downwelling longwave radiation (Rldc) and cloudy sky downwelling longwave radiation (Rld) formulas were tested across eleven sites in Florida. The Brunt equation, using air vapor pressure and temperature measurements, provides the best Rldc estimates with a root mean square error of less than around 12 Wm−2 across all sites. The Crawford and Duchon\u27s cloudiness factor with Brunt equation is recommended for Rld calculations. This combined approach requires no local calibration and estimates Rld with a root mean square error of less than around 13 Wm−2 and squared correlation coefficients that typically exceed 0.9

Downward Longwave Radiation Retrieved from MODIS Imagery and Possible Application on Water Resource Management at Turkey Creek Watershed in South Carolina

2010 S.C. Water Resources Conferences - Science and Policy Challenges for a Sustainable Futur

Land Surface Models Evaluation for Two Different Land-Cover Types: Cropland and Forest

Land Surface Model (LSM) is an important tool used to understand the complicated hydro-meteorological flux interaction systems between the land surface and atmosphere in hydrological cycles. Over the past few decades, LSMs have further developed to more accurately estimate weather and climate hydrological processes. Common Land Model (CLM) and Noah Land Surface Model (Noah LSM) are used in this paper to estimate the hydro-meteorological fluxes for model applicability assessment at two different flux tower sites in Korea during the summer monsoon season. The estimated fluxes such as net radiation (RN), sensible heat flux (H), latent heat flux (LE), ground heat flux (G), and soil temperature (Ts) were compared with the observed data from flux towers. The simulated RN from both models corresponded well with the in situ data. The root-mean-square error (RMSE) values were 39 - 44 W m-2 for the CLM and 45 - 50 W m-2 for the Noah LSM while the H and LE showed relatively larger discrepancies with each observation. The estimated Ts from the CLM corresponded comparatively well with the observed soil temperature. The CLM estimations generally showed better statistical results than those from the Noah LSM, even though the estimated hydro-meteorological fluxes from both models corresponded reasonably with the observations. A sensitivity test indicated that differences according to different locations between the estimations from models and observations were caused by field conditions including the land-cover type and soil texture. In addition the estimated RN, H, LE, and G were more sensitive than the estimated Ts in both models

Agricultural Drought Assessment Based on Multiple Soil Moisture Products

In this study, we evaluated three soil moisture (SM) products (Advanced Microwave Scanning Radiometer-2 [AMSR2], Advanced SCATterometer [ASCAT], and European Reanalysis Interim [ERA-interim]) across Australia in four climate zones by comparing against the Australian Water Resources Assessment-Landscape (AWRA-L) SM products from July 2012 to June 2017. The ASCAT SM indicated better performance than other SM products over Australia. To evaluate the applicability and reliability for monitoring agricultural drought, an agricultural drought index, the Soil Water Deficit Index, was estimated from three SM products and compared with three commonly-used drought indices (atmospheric water deficit [AWD], Evaporative Stress Index, and Reconnaissance Drought Index). Volumetric contingency tables were compiled to quantitatively assess the performance of agricultural drought detection using various SM products compared with the AWD. All products had reliable drought detection capability over Australia based on the results of temporal evolution and contingency tables with a mean volumetric hit index of 0.700, 0.728, and 0.787 for AMSR2, ASCAT, and ERA-interim, respectively. The slight incapability of drought detection capability of SWDI in tropical region was low due to the variation in persistence times of moisture in the atmosphere and soil. Except arid zone, in all climate zones, the reliability of SM products for drought detection followed the following order ASCAT \u3e ERA-interim \u3e AMSR2

Intercomparison of Downscaling Techniques for Satellite Soil Moisture Products

During recent decades, various downscaling methods of satellite soil moisture (SM) products, which incorporate geophysical variables such as land surface temperature and vegetation, have been studied for improving their spatial resolution. Most of these studies have used least squares regression models built from those variables and have demonstrated partial improvement in the downscaled SM. This study introduces a new downscaling method based on support vector regression (SVR) that includes the geophysical variables with locational weighting. Regarding the in situ SM, the SVR downscaling method exhibited a smaller root mean square error, from 0.09 to 0.07m(3).m(-3), and a larger average correlation coefficient increased, from 0.62 to 0.68, compared to the conventional method. In addition, the SM downscaled using the SVR method had a greater statistical resemblance to that of the original advanced scatterometer SM. A residual magnitude analysis for each model with two independent variables was performed, which indicated that only the residuals from the SVR model were not well correlated, suggesting a more effective performance than regression models with a significant contribution of independent variables to residual magnitude. The spatial variations of the downscaled SM products were affected by the seasonal patterns in temperature-vegetation relationships, and the SVR downscaling method showed more consistent performance in terms of seasonal effect. Based on these results, the suggested SVR downscaling method is an effective approach to improve the spatial resolution of satellite SM measurement