David L. Toll

The Global Land Data Assimilation System

A Global Land Data Assimilation System (GLDAS) has been developed. Its purpose is to ingest satellite- and ground-based observational data products, using advanced land surface modeling and data assimilation techniques, in order to generate optimal fields of land surface states and fluxes. GLDAS is unique in that it is an uncoupled land surface modeling system that drives multiple models, integrates a huge quantity of observation-based data, runs globally at high resolution (0.25°), and produces results in near–real time (typically within 48 h of the present). GLDAS is also a test bed for innovative modeling and assimilation capabilities. A vegetation-based “tiling” approach is used to simulate subgrid-scale variability, with a 1-km global vegetation dataset as its basis. Soil and elevation parameters are based on high-resolution global datasets. Observation-based precipitation and downward radiation and output fields from the best available global coupled atmospheric data assimilation systems are employe...

Simulating the Effects of Irrigation over the United States in a Land Surface Model Based on Satellite-Derived Agricultural Data

A novel method is introduced for integrating satellite-derived irrigation data and high-resolution crop-type information into a land surface model (LSM). The objective is to improve the simulation of land surface states and fluxes through better representation of agricultural land use. Ultimately, this scheme could enable numerical weather prediction (NWP) models to capture land‐atmosphere feedbacks in managed lands more accurately and thus improve forecast skill. Here, it is shown that the application of the new irrigation scheme over the continental United States significantly influences the surface water and energy balances by modulating the partitioning of water between the surface and the atmosphere. In this experiment, irrigation caused a 12% increase in evapotranspiration (QLE) and an equivalent reduction in the sensible heat flux (QH) averaged over all irrigated areas in the continental United States during the 2003 growing season. Local effects were more extreme: irrigation shifted more than 100 W m 22 from QH to QLE in many locations in California, eastern Idaho, southern Washington, and southern Colorado during peak crop growth. In these cases, the changes in ground heat flux (QG), net radiation (RNET), evapotranspiration (ET), runoff (R), and soil moisture (SM) were more than 3 W m 22 ,2 0 Wm 22 , 5 mm day 21 , 0.3 mm day 21 , and 100 mm, respectively. These results are highly relevant to continental-to-global-scale water and energy cycle studies that, to date, have struggled to quantify the effects of agricultural management practices such as irrigation. On the basis of the results presented here, it is expected that better representation of managed lands will lead to improved weather and climate forecasting skill when the new irrigation scheme is incorporated into NWP models such as NOAA’s Global Forecast System (GFS).

Statistical Evaluation of Combined Daily Gauge Observations and Rainfall Satellite Estimates over Continental South America

Thispaperdescribesacomprehensiveassessmentofanewhigh-resolution,gauge‐satellite-basedanalysisof daily precipitation over continental South America during 2004. This methodology is based on a combination ofadditiveandmultiplicativebiascorrectionschemestogetthelowestbiaswhencomparedwiththeobserved values (rain gauges). Intercomparisons and cross-validation tests have been carried out between independent rain gauges and different merging techniques. This validation process was done for the control algorithm [TropicalRainfallMeasuringMission(TRMM)MultisatellitePrecipitationAnalysisreal-timealgorithm]and five different merging schemes: additive bias correction; ratio bias correction; TRMM Multisatellite PrecipitationAnalysis,researchversion;andthecombinedschemeproposedinthispaper.Thesemethodologieswere tested for different months belonging to different seasons and for different network densities. All compared, merging schemes produce better results than the control algorithm; however, when finer temporal (daily) and spatial scale (regional networks) gauge datasets are included in the analysis, the improvement is remarkable. The combined scheme consistently presents the best performance among the five techniques tested in this paper. This is also true when a degraded daily gauge network is used instead of a full dataset. This technique appearstobe a suitabletoolto producereal-time, high-resolution,gauge-and satellite-based analysesofdaily precipitation over land in regional domains.

The effects of spatial resolution on the classification of Thematic Mapper data

Abstract Actual and degraded LANDSAT-4 Thematic Mapper (TM) data were analysed to examine the effect of spatial resolution on the performance of a per pixel, maximum-likelihood classification algorithm. Analysis of variance (ANOVA) and a balanced, three-factor, eight-treatment, fixed-effects model were used to investigate the interactions between spatial resolution and two other TM refinements, spectral band configuration and data quantization. The goal was to evaluate quantitatively the effects of these attributes on classification accuracies obtained with all pixels (pure pixels plus mixed pixels) and on accuracies obtained with pure pixels alone. A comparison of results from these separate analyses supported previous explanations of the effects of increasing spatial resolution. First, the difficulty in classifying mixed pixels was demonstrated by an average 21 per cent decrease in percentage accuracy from the pure-pixel case to the pure-plus-mixed-pixel case for the eight ANOVA treatments. In the pure-...

Effect of Landsat thematic mapper sensor parameters on land cover classification

Abstract Selected sensor parameter differences between TM and MSS were assessed through classification performance of a suburban/regional test site. Overall classification accuracy of a seven-band Landsat TM scene in comparison to MSS yielded an improvement in accuracy from 74.8% to 83.2%. To study the possible causes for the difference in classification performance, key sensor parameter differences between MSS and TM, including 1) spatial resolution (30 m for TM versus 80 m for MSS), 2) quantization level (256 levels for TM versus 64 for MSS), and 3) spectral regions (seven bands in four major spectral regions for TM versus four bands in two regions for MSS), were evaluated. Landsat TM data were processed to simulate all possible combinations of these MSS and TM parameters, yielding a three-factor design with two levels per factor. The results indicated that the added spectral regions (TM 1, TM 5, and TM 7) and to a lesser degree the increase in quantization level to eight bits produced the improved TM classification accuracy. However, in this study, the higher 30 m spatial resolution of TM contributed to a reduced classification accuracy from increased within-field variability or class heterogeneity.

A Statistical Evaluation of the Advantages of LANDSAT Thematic Mapper Data in Comparison to Multispectral Scanner Data

A study designed on the basis of a fixed-effects, three factor, two-level analysis or variance (ANOVA) was conducted to quantify the effect of Thematic Mapper (TM) sensor improvements on classification accuracy using TM data acquired over the Washinton, DC, area on November 2, 1982. The TM data were systematically degraded spatially, spectrally, and radiometrically to simulate the effect of changing each individual sensor parameter separately, and in conjunction with other sensor characteristics, to ultimately simulate Multispectral Scanner (MSS) data characteristics. The greatest level of variance was accounted for by the spectral waveband variable, providing an average increase in classification accuracy of 5.85 percent. This increase constituted a 21-percent relative improvement from TM data with respect to Landsat MSS data [i. e., percent relative improvement = (high accuracy value-low accuracy value)/low accuracy value X 100]. The second greatest amount of variance was accounted for by the ¿radiometric¿ variable (i. e., bit quantization level). This provided a 5.25-percent increase in percent correctly classified pixels, which constitutes a 19-percent relative improvement of TM over MSS data due to quantization level. Spatial resolution accounted for the lowest source of variability in the observed classification accuracies, with an overall average decrease of 0.7 percent. This constituted a 2-percent relative degradation from TM data with respect to MSS data. Only the differences found for the spectral waveband combinations and the quantization level were statistically significant at ¿ levels of 0.01 to 0.001.

Evaluation of model‐derived and remotely sensed precipitation products for continental South America

[1] This paper investigates the reliability of some of the more important remotely sensed daily precipitation products available for South America as a precursor to the possible implementation of a South America Land Data Assimilation System. Precipitation data fields calculated as 6 hour predictions by the CPTEC Eta model and three different satellite-derived estimates of precipitation (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), National Environmental Satellite, Data and Information Service (NESDIS), and Tropical Rainfall Measuring Mission (TRMM)) are compared with the available observations of daily total rainfall across South America. To make this comparison, the threat score, fractional-covered area, and relative volumetric bias of the model-calculated and remotely sensed estimates are computed for the year 2000. The results show that the Eta model-calculated data and the NESDIS product capture the area without precipitation within the domain reasonably well, while the TRMM and PERSIANN products tend to underestimate the area without precipitation and to heavily overestimate the area with a small amount of precipitation. In terms of precipitation amount the NESDIS product significantly overestimates and the TRMM product significantly underestimates precipitation, while the Eta model-calculated data and PERSIANN product broadly match the domain average observations. However, both tend to bias the zonal location of precipitation more heavily toward the equator than the observations. In general, the Eta model-calculated data outperform the several remotely sensed data products currently available and evaluated in the present study.

Execution phase (C/D) spectral band characteristics of the EOS moderate resolution imaging spectrometer-Nadir (MODIS-N) facility instrument

Abstract The Moderate Resolution Imaging Spectrometer (MODIS) observing facility on the Earth Observing System (EOS) is composed of two instruments: MODIS-Nadir (N) and MODIS-Tilt (T). MODIS-N has 36 spectral bands between 0.4 and 14.2 micrometers with spatial resolution between 250 and 1000 meters. MODIS-T has 32 bands with 10–15 nanometer bandwidths between 0.4 and 0.9 micrometers. MODIS-T scans fore and aft ±50 degrees. Both instruments scan cross-track so as to provide daily (MODIS-N) or once every two days (MODIS-T) coverage at 705 kilometers altitude. Both instruments are entering into the execution phases of their development in 1990. The bands of the MODIS-N have been chosen so as to provide key observations of land, ocean, and atmosphere parameters that will provide key data sets assisting in gaining an improved understanding of global processes.

Analysis of digital LANDSAT MSS and SEASAT SAR data for use in discriminating land cover at the urban fringe of Denver, Colorado

Abstract Digital SEASAT synthetic aperture radar (SAR) and LANDSAT multispectral scanner (MSS) data were evaluated to determine their utility to discriminate suburban and regional cover in the eastern fringe area of the Denver, Colorado, metropolitan area. The primary emphasis of the study was land-cover discrimination performance of MSS versus SAR and SAR/MSS combined. In addition, both a median-filtering and a data-smoothing procedure were tested in an attempt to increase the spectral separability between land-use/land-cover classes for SAR data. The results indicated that analysis of LANDSAT MSS data alone provided a significantly (α = 0·05) higher overall classification accuracy or improved spectral class separation than the best SEASAT SAR classification. However, when using LANDSAT MSS and SEASAT SAR data simultaneously, a significant increase in classification accuracy was obtained. Analysis indicated that SEASAT SAR data provided a measure of surface geometry that complemented the reflective chara...

Preliminary evidence for the influence of physiography and scale upon the autocorrelation function of remotely sensed data

Abstract Previously established results of Craig (1976, 1979) and Craig and Labovitz (1980) demonstrated that Landsat data are autocorrelated and can be described by a univariate linear stochastic process known as an auto regressive integrated moving average model of degree 1, 0, 1, or ARIMA (1, 0, 1). This model has two coefficients of interest for interpretation: φandθ In a comparison of Landsat Thematic Mapper Simulator (TMS) data and Landsat MSS data several results are established:(1)The form of the relatedness as described by this model is not dependent upon system look angle or pixel size.(2)The coefficient φ increases with decreasing pixel size and increasing topographic complexity.(3)Changes in topography have a greater influence upon φ than changes in land cover class.(4)Theθ seems to vary with the amount of atmospheric haze. These patterns of variation in φandθ are potentially exploitable by the remote sensing community to yield stochastically independent sets of observations, to characterize t...