Merging Satellite Research Articles

A global water cycle reanalysis (2003–2012) merging satellite gravimetry and altimetry observations with a hydrological multi-model ensemble

Abstract. We present a global water cycle reanalysis that merges water balance estimates derived from the Gravity Recovery And Climate Experiment (GRACE) satellite mission, satellite water level altimetry and off-line estimates from several hydrological models. Error estimates for the sequential data assimilation scheme were derived from available uncertainty information and the triple collocation technique. Errors in four GRACE storage products were estimated to be 11–12 mm over land areas, while errors in monthly storage changes derived from five global hydrological models were estimated to be 17–28 mm. Prior and posterior water storage estimates were evaluated against independent observations of river water level and discharge, snow water storage and glacier mass loss. Data assimilation improved or maintained agreement overall, although results varied regionally. Uncertainties were greatest in regions where glacier mass loss and subsurface storage decline are both plausible but poorly constrained. We calculated a global water budget for 2003–2012. The main changes were a net loss of polar ice caps (−342 Gt yr−1) and mountain glaciers (−230 Gt yr−1), with an additional decrease in seasonal snowpack (−18 Gt yr−1). Storage increased due to new impoundments (+16 Gt yr−1), but this was compensated by decreases in other surface water bodies (−10 Gt yr−1). If the effect of groundwater depletion (−92 Gt yr−1) is considered separately, subsurface water storage increased by +202 Gt yr−1 due particularly to increased wetness in northern temperate regions and in the seasonally wet tropics of South America and southern Africa. The reanalysis results are publicly available via www.wenfo.org/wald/.

Regional-scale application of lidar: Variation in forest canopy structure across the southeastern US

Canopy structure is a fundamental property of forest ecosystems that influences microclimate, runoff, decomposition, nutrient cycling, forest disturbance, carbon storage, and biodiversity. Unlike ecosystem properties such as vegetation production, canopy structure mapping is limited by measurement constraints and is primarily measured for small areas. Consequently, few large scale studies of carbon budgets, nutrient cycling, and biodiversity use quantitative data on canopy structure. Progress in broad scale mapping of canopy structure has recently been made by merging field, airborne lidar, and satellite data. As a step towards regional mapping of canopy structure with lidar and satellite data, we examine patterns of lidar-derived canopy structure across five ecoregions from Maryland to Mississippi and evaluate relationships with climate, topography, and soils. We used NASA’s Laser Vegetation Imaging Sensor (LVIS) to quantify canopy height, canopy cover, diversity of cover, and upper and lower canopy ratio metric along a 4000-km transect. Controlling for stand age, we found that canopy structure varied among undisturbed, closed-canopy stands across the study area. Compared with the Southeast Plains Ecoregion, the Blue Ridge and Central Appalachians ecoregions were greater in canopy height (25%), canopy cover (18%), and cover in the upper third of the canopy (212%). Values in the Piedmont were similar to those in the Southeast Plains. Locations highest in canopy structure were intermediate in temperature, growing season precipitation, topographic complexity and were located on sandy soils. The strength of biophysical models differed among ecoregions, explaining 13% of the variation in canopy height in the Southeastern Plain to 60% in the Ridge and Valley Ecoregion. Canopy structure also differed among disturbance classes. Undisturbed forests were 30% higher in canopy height, 15% higher in canopy cover, and 18% higher in cover of the upper third of the canopy than disturbed forests. Managed pine plantations were intermediate in canopy structure between disturbed and undisturbed forests. This study demonstrates that airborne lidar data can be used to distinguish differences in canopy structure among undisturbed forests in varying biophysical settings and between undisturbed and disturbed forests across sub-continental transects. The results suggest that airborne lidar data in conjunction with data on biophysical gradients can be used as a basis for extrapolating canopy structure at fine spatial scales across regional extents. This would allow for fine-scale characterization of forest structure continuously across large regions. Such methods should allow breakthroughs in the use of canopy structure in ecosystem management and global change studies.

Implementation of Image Registration for Satellite Images using Mutual Information and Particle Swarm Optimization Techniques

aim of this research is to register satellite images on the DSP processor using probabilistic optimization method named as particle swarm optimization. Satellite image registration is necessary in order to find change detection, to eliminate influence of camera distortion (roll, pitch and yaw), merge satellite imagery and in urban planning. Particle Swarm Optimization is a stochastic search technique with less computation and still very effective as compared to other optimization techniques. It is based on bird flocking, fish schooling and swarm theory. Each particle changes its position and velocity based on its corresponding fitness value. Fitness value can be calculated using joint entropy and mutual information. The algorithm can be used in object recognition, image segmentation, matching and registration. The performance of this algorithm is measured and results are shown using DSK 6713 hardware along with VM32242.

Diurnal warming in shallow coastal seas: Observations from the Caribbean and Great Barrier Reef regions

A good understanding of diurnal warming in the upper ocean is important for the validation of satellite-derived sea surface temperature (SST) against in-situ buoy data and for merging satellite SSTs taken at different times of the same day. For shallow coastal regions, better understanding of diurnal heating could also help improve monitoring and prediction of ecosystem health, such as coral reef bleaching. Compared to its open ocean counterpart which has been studied extensively and modeled with good success, coastal diurnal warming has complicating localized characteristics, including coastline geometry, bathymetry, water types, tidal and wave mixing. Our goal is to characterize coastal diurnal warming using two extensive in-situ temperature and weather datasets from the Caribbean and Great Barrier Reef (GBR), Australia. Results showed clear daily warming patterns in most stations from both datasets. For the three Caribbean stations where solar radiation is the main cause of daily warming, the mean diurnal warming amplitudes were about 0.4K at depths of 4–7m and 0.6–0.7K at shallower depths of 1–2m; the largest warming value was 2.1K. For coral top temperatures of the GBR, 20% of days had warming amplitudes >1K, with the largest >4K. The bottom warming at shallower sites has higher daily maximum temperatures and lower daily minimum temperatures than deeper sites nearby. The averaged daily warming amplitudes were shown to be closely related to daily average wind speed and maximum insolation, as found in the open ocean. Diurnal heating also depends on local features including water depth, location on different sections of the reef (reef flat vs. reef slope), the relative distance from the barrier reef chain (coast vs. lagoon stations vs. inner barrier reef sites vs. outer rim sites); and the proximity to the tidal inlets. In addition, the influence of tides on daily temperature changes and its relative importance compared to solar radiation was quantified by calculating the ratio of power spectrum densities at the principal lunar semidiurnal M2 tide versus 24-hour cycle frequency representing mainly solar radiation forcing, i.e., (PSDM2/PSD24). Despite the fact that GBR stations are generally located at regions with large tidal changes, the tidal effects were modest: 80% of stations showed value of (PSDM2/PSD24) of less than 10%.

Randomness, Symmetry, and Scaling of Mesoscale Eddy Life Cycles

Abstract It is shown that the life cycles of nonlinear mesoscale eddies, a major component of low-frequency ocean physical variability, have a characteristic structure that differs fundamentally from that which would be expected on the basis of classical interpretations of ocean eddy evolution in terms of mean flow instability and equilibration followed by frictional, radiative, or barotropic decay, or of vortex merger dynamics in quasigeostrophic turbulent cascades. Further, it is found that these life cycles can be accurately modeled in terms of the large-amplitude excursions of a stochastic process. These conclusions, which apply in the corresponding global-mean context, follow from the examination of ensemble-mean and standard deviation time series of normalized eddy amplitude from an automated eddy identification and tracking analysis of a nearly two decade–merged satellite altimeter record of global sea surface height (SSH). The resulting series are found to have several striking and unexpected characteristics, including time-reversal symmetry and approximate self-similarity. Consistent results are obtained from a similar analysis of a 7-yr record of global SSH from a numerical ocean circulation model. The basic qualitative and quantitative statistical properties of these series can be remarkably well reproduced with an extremely simple stochastic model, in which the SSH increments between successive time points are random numbers, and the eddy life cycles are represented by excursions exceeding a given threshold. The stochastic model is found also to predict accurately the empirical autocorrelation structure of the underlying observed SSH field itself, when the autocorrelations are computed along long planetary (Rossby) wave characteristics.

Stellar substructures in the solar neighbourhood

From correlations between orbital parameters, several new coherent groups of stars were recently identified in the Galactic disc and suggested to correspond to remnants of disrupted satellites. To reconstruct their origin at least three main observational parameters - kinematics, chemical composition and age - must be known. We determine detailed elemental abundances in stars belonging to the so-called Group 2 of the Geneva-Copenhagen Survey and compare the chemical composition with Galactic thin- and thick-disc stars, as well as with the Arcturus and AF06 streams. The aim is to search for chemical signatures that might give information about the formation history of this kinematic group of stars. High-resolution spectra were obtained with the FIES spectrograph at the Nordic Optical Telescope, La Palma, and were analysed with a differential model atmosphere method. Comparison stars were observed and analysed with the same method. The average value of [Fe/H] for the 32 stars of Group 2 is -0.42 +- 0.10 dex. The investigated group consists mainly of two 8- and 12-Gyr-old stellar populations. Abundances of oxygen, alpha-elements, and r-process-dominated elements are higher than in Galactic thin-disc dwarfs. This elemental abundance pattern has similar characteristics as that of the Galactic thick-disc. The similarity in chemical composition of stars in Group 2 with that in stars of the thick-disc might suggest that their formation histories are linked. The chemical composition together with the kinematic properties and ages of stars in the investigated stars provides evidence of their common origin and possible relation to an ancient merging event. A gas-rich satellite merger scenario is proposed as the most likely origin. Groups 2 and 3 of the Geneva-Copenhagen Survey might have originated in the same merging event.

Multivariate Analysis of the Senegalo-Mauritanian Area by Merging Satellite Remote Sensing Ocean Color and SST Observations

The Senegalo-Mauritanian upwelling is a very productive upwelling occurring along the West coast of Africa. The seasonal and inter-annual variability of the upwelling region between 9° and 22°N and 14° and 25°W was studied by merging monthly ocean color data and sea surface temperature provided by satellite sensors during twelve years from 1998 up to 2010. We combined these two parameters to obtain a unique index describing the spatio-temporal variability of the upwelling. We used a classification methodology consisting in a neural network topological map and a hierarchical ascendant classification. Six classes can explain most of the variability of this region, one of them (class 6) being dedicated to the coastal upwelling water, another being the signature of the Gulf of Guinea dome water (class 2), a third one to case 2 water (class 5). The classes can be considered as multi-factorial statistical indices allowing us to characterize the different water types of this region and to investigate their variability. It is shown that the upwelling extent is maximum in February-March, minimum in August-September. Its variability is linked to that of the wind and to the ITCZ position. The Gulf of Guinea waters moves northward in June and relaxes to their southward position in December. During the twelve years of observation, we were not able to evidence climatic trends of the SST and Chl-a concentration. The methodology we have developed can be used in a large variety of problems implying multi sensor measurements.

Impacts of disturbance history on forest carbon stocks and fluxes: Merging satellite disturbance mapping with forest inventory data in a carbon cycle model framework

Forest carbon stocks and fluxes are highly dynamic following stand-clearing disturbances from severe fire and harvest and this presents a significant challenge for continental carbon budget assessments. In this work we use forest inventory data to parameterize a carbon cycle model to represent post-disturbance carbon trajectories of carbon pools and fluxes for specific forest types growing in high and low site productivity class settings. We then apply these trajectories to landscapes and regions based on forest age distributions derived from either the FIA data or from Landsat time series stacks (1985–2006) for 54 representative scenes throughout most of the conterminous United States. We estimate the net carbon uptake in forests caused by post-disturbance growth and decomposition (“regrowth sink”) for forested regions across the country. At the landscape scale, the prevailing condition of positive net ecosystem productivity (NEP) is in stark contrast to local patches with large sources, particularly in the west where fires and clear cuts create contiguous disturbed patches. At the continental scale, regional differences in disturbance rates reflect management patterns of high disturbance rates in the Southeastern and South Central states, and lower disturbance rates in the Northeast and Northern Lakes States. Despite low contemporary disturbance rates in the Northeast and Northern Lakes States (0.61 and 0.74%y−1), the regrowth sink there remains of moderate to large strength (88 and 57gCm−2y−1) owing to the continued legacy from historical clearing. Large regrowth sinks are also found in the Southeast, South Central, and Pacific Southwest regions (85, 86, and 95gCm−2 y−1) where disturbance rates also tend to be higher (1.59, 1.38, and 0.93%y−1). Overall, the Landsat-derived disturbance rates are elevated relative to FIA-derived rates (1.19 versus 0.93%y−1) particularly for western regions. The differences only modestly adjust regional- and continental-scale carbon budgets, reducing NEP from forest regrowth by about 8%.

MaGICC-WDM: the effects of warm dark matter in hydrodynamical simulations of disc galaxy formation

We study the effect of warm dark matter (WDM) on hydrodynamic simulations of galaxy formation as part of the Making Galaxies in a Cosmological Context (MaGICC) project. We simulate three different galaxies using three WDM candidates of 1, 2 and 5 keV and compare results with pure cold dark matter simulations. WDM slightly reduces star formation and produces less centrally concentrated stellar profiles. These effects are most evident for the 1 keV candidate but almost disappear for |$m_ {\rm \small {wdm}} >2$| keV. All simulations form similar stellar discs independent of WDM particle mass. In particular, the disc scalelength does not change when WDM is considered. The reduced amount of star formation in the case of 1 keV particles is due to the effects of WDM on merging satellites which are on average less concentrated and less gas rich. The altered satellites cause a reduced starburst during mergers because they trigger weaker disc instabilities in the main galaxy. Nevertheless we show that disc galaxy evolution is much more sensitive to stellar feedback than it is to WDM candidate mass. Overall, we find that WDM, especially when restricted to current observational constraints (⁠|$m_ {\rm \small {wdm}} >2$| keV), has a minor impact on disc galaxy formation.

Gridded daily Indian monsoon rainfall for 14 seasons: Merged TRMM and IMD gauge analyzed values

Indian monsoon is an important component of earth’s climate system. Daily rainfall data for longer period is vital to study components and processes related to Indian monsoon. Daily observed gridded rainfall data covering both land and adjoining oceanic regions are required for numerical model validation and model development for monsoon. In this study, a new gridded daily Indian rainfall dataset at 1°×1° latitude/longitude resolution covering 14 monsoon seasons (1998–2011) are described. This merged satellite gauge rainfall dataset (NMSG) combines TRMM TMPA rainfall estimates with gauge information from IMD gridded data. Compared to TRMM and GPCP daily rainfall data, the current NMSG daily data has more information due to inclusion of local gauge analysed values. In terms of bias and skill scores this dataset is superior to other daily rainfall datasets. In a mean climatological sense and also for anomalous monsoon seasons, this merged satellite gauge data brings out more detailed features of monsoon rainfall. The difference of NMSG and GPCP looks significant. This dataset will be useful to researchers for monsoon intraseasonal studies and monsoon model development research.

Skin and bulk temperature difference at Lake Tahoe: A case study on lake skin effect

AbstractOver water, infrared radiometers on satellites measure radiation leaving from the surface skin layer and therefore the retrieved temperature is representative of the skin layer. This is slightly different from the bulk layer deeper in the water where various floating thermometers take temperature measurements to validate satellite measurements. The difference between the bulk and skin temperature (skin effect) must be understood to properly validate schemes that use surface skin temperature to infer bulk temperatures. Further skin temperatures retrieved over inland waters may show different patterns to those retrieved over oceans due to differences in conditions such as wind speed, aerosols, and elevation. We have analyzed the differences between the skin and bulk temperatures at four permanent monitoring stations (buoys) located on Lake Tahoe since 1999 and compared the results with similar studies over the ocean typically obtained from boat cruises. Skin effect distributions were found to be consistent across the buoys; however, the diurnal behavior of the skin effect was slightly different and shown to be related to wind speed measured at an individual buoy. When wind speed was less than 2 m s−1, the skin temperature osclillated and greatly increased the uncertainty in the skin effect reported over Lake Tahoe. When downwelling sky radiation was increased from clouds or high humidity, this led to nighttime skin temperatures that were warmer than bulk temperatures by as much as 0.5 K. The size of the warm skin effect is larger than other ocean studies that observed warm nighttime skin values around 0.1 K. The nighttime skin effect was seen to be more consistent with a smaller standard deviation compared to the daytime skin effect. The nighttime skin behavior had a mean and standard deviation that ranged between 0.3 and 0.5 K and between 0.3 and 0.4 K, respectively. In contrast, daytime skin effect was strongly influenced by direct solar illumination and typically had a mean of 0.5 K in the morning that decreased to 0.1 K by midday. The standard deviation of the daytime skin effect ranged from 0.3 in the morning to 0.8 by midday. As the solar heating reduces later in the day the skin effect increases to a 0.3 K mean with a standard deviation of 0.4 K. The results for Lake Tahoe clearly demonstrate that validating satellite‐derived skin measurements or merging multiple satellites data sets together would be most successful when using nighttime data at wind speeds greater than 2 m s−1 with greater uncertainties expected when using daytime measurements. Further, the assumptions used for the skin effect behavior over oceans may not be appropriate over lakes because of the greater range of environmental conditions that affect lakes.

Merging gauge and satellite rainfall with specification of associated uncertainty across Australia

Accurate estimation of spatial rainfall is crucial for modelling hydrological systems and planning and management of water resources. While spatial rainfall can be estimated either using rain gauge-based measurements or using satellite-based measurements, such estimates are subject to uncertainties due to various sources of errors in either case, including interpolation and retrieval errors. The purpose of the present study is twofold: (1) to investigate the benefit of merging rain gauge measurements and satellite rainfall data for Australian conditions and (2) to produce a database of retrospective rainfall along with a new uncertainty metric for each grid location at any timestep. The analysis involves four steps: First, a comparison of rain gauge measurements and the Tropical Rainfall Measuring Mission (TRMM) 3B42 data at such rain gauge locations is carried out. Second, gridded monthly rain gauge rainfall is determined using thin plate smoothing splines (TPSS) and modified inverse distance weight (MIDW) method. Third, the gridded rain gauge rainfall is merged with the monthly accumulated TRMM 3B42 using a linearised weighting procedure, the weights at each grid being calculated based on the error variances of each dataset. Finally, cross validation (CV) errors at rain gauge locations and standard errors at gridded locations for each timestep are estimated. The CV error statistics indicate that merging of the two datasets improves the estimation of spatial rainfall, and more so where the rain gauge network is sparse. The provision of spatio-temporal standard errors with the retrospective dataset is particularly useful for subsequent modelling applications where input error knowledge can help reduce the uncertainty associated with modelling outcomes.

Stellar substructures in the solar neighbourhood

The evolution of chemical elements in a galaxy is linked to its star formation history. Variations in star formation history are imprinted in the relative abundances of chemical elements produced in different supernova events and asymptotic giant branch stars. We determine detailed elemental abundances of s- and r-process elements in stars belonging to Group3 of the Geneva-Copenhagen survey and compare their chemical composition with Galactic disc stars. The aim is to look for possible chemical signatures that might give information about the formation history of this kinematic group of stars, which is suggested to correspond to remnants of disrupted satellites. High-resolution spectra were obtained with the FIES spectrograph at the Nordic Optical Telescope, La Palma, and were analysed with a differential model atmosphere method. Comparison stars were observed and analysed with the same method. Abundances of chemical elements produced mainly by the s-process are similar to those in the Galactic thin-disc dwarfs of the same metallicity, while abundances of chemical elements produced predominantly by the r-process are overabundant. The similar elemental abundances are observed in Galactic thick-disc stars. The chemical composition together with the kinematic properties and ages of stars in Group3 of the Geneva-Copenhagen survey support a gas-rich satellite merger scenario as the most likely explanation for the origin. The similar chemical composition of stars in Group3 and the thick-disc stars might suggest that their formation histories are linked.

Connecting Satellite Observations with Water Cycle Variables Through Land Data Assimilation: Examples Using the NASA GEOS-5 LDAS

A land data assimilation system (LDAS) can merge satellite observations (or retrievals) of land surface hydrological conditions, including soil moisture, snow, and terrestrial water storage (TWS), into a numerical model of land surface processes. In theory, the output from such a system is superior to estimates based on the observations or the model alone, thereby enhancing our ability to understand, monitor, and predict key elements of the terrestrial water cycle. In practice, however, satellite observations do not correspond directly to the water cycle variables of interest. The present paper addresses various aspects of this seeming mismatch using examples drawn from recent research with the ensemble-based NASA GEOS-5 LDAS. These aspects include (1) the assimilation of coarse-scale observations into higher-resolution land surface models, (2) the partitioning of satellite observations (such as TWS retrievals) into their constituent water cycle components, (3) the forward modeling of microwave brightness temperatures over land for radiance-based soil moisture and snow assimilation, and (4) the selection of the most relevant types of observations for the analysis of a specific water cycle variable that is not observed (such as root zone soil moisture). The solution to these challenges involves the careful construction of an observation operator that maps from the land surface model variables of interest to the space of the assimilated observations.

Mapping sub-surface geostrophic currents from altimetry and a fleet of gliders

Integrating the observations gathered by different platforms into a unique physical picture of the environment is a fundamental aspect of networked ocean observing systems. These are constituted by a spatially distributed set of sensors and platforms that simultaneously monitor a given ocean region. Remote sensing from satellites is an integral part of present ocean observing systems. Due to their autonomy, mobility and controllability, underwater gliders are envisioned to play a significant role in the development of networked ocean observatories. Exploiting synergism between remote sensing and underwater gliders is expected to result on a better characterization of the marine environment than using these observational sources individually.This study investigates a methodology to estimate the three dimensional distribution of geostrophic currents resulting from merging satellite altimetry and in situ samples gathered by a fleet of Slocum gliders. Specifically, the approach computes the volumetric or three dimensional distribution of absolute dynamic height (ADH) that minimizes the total energy of the system while being close to in situ observations and matching the absolute dynamic topography (ADT) observed from satellite at the sea surface. A three dimensional finite element technique is employed to solve the minimization problem. The methodology is validated making use of the dataset collected during the field experiment called Rapid Environmental Picture-2010 (REP-10) carried out by the NATO Undersea Research Center-NURC during August 2010. A marine region off-shore La Spezia (northwest coast of Italy) was sampled by a fleet of three coastal Slocum gliders. Results indicate that the geostrophic current field estimated from gliders and altimetry significantly improves the estimates obtained using only the data gathered by the glider fleet.

An objective methodology for merging satellite‐ and model‐based soil moisture products

An objective methodology that does not require any user‐defined parameter assumptions is introduced to obtain an improved soil moisture product along with associated uncertainty estimates. This new product is obtained by merging model‐, thermal infrared remote sensing‐, and microwave remote sensing‐based soil moisture estimates in a least squares framework where uncertainty estimates for each product are obtained using triple collocation. The merged anomaly product is validated against in situ based soil moisture data and shows higher correlations with observations than individual input products; however, it is not superior to a naively merged product acquired by averaging the products using equal weighting. The resulting combined soil moisture estimate is an improvement over currently available soil moisture products due to its reduced uncertainty and can be used as a standalone soil moisture product with available uncertainty estimates.

ASSESSMENT OF COASTAL VULNERABILITY TO SEA LEVEL RISE OF BOLINAO, PANGASINAN USING REMOTE SENSING AND GEOGRAPHIC INFORMATION SYSTEMS

Abstract. A number of studies assessing the vulnerability of Southeast Asia to climate change have classified the Philippines as one of the vulnerable countries in the region. Bolinao, Pangasinan is a municipality located in northwestern Luzon, situated in the western part of the Lingayen Gulf and is bounded on the north and west by the South China Sea (West Philippine Sea). Recent studies have verified the varying trends in sea level across the South China Sea, which is considered as one of the largest, semi-enclosed marginal seas in the northwest Pacific Ocean. Three barangays (villages) were included in the study: (1) Luciente 1.0, (2) Concordia and (3) Germinal. The Socioeconomic Vulnerability Index (SVI) was computed based on population, age, gender, employment, source of income and household size, which were gathered through a qualitative survey in the selected barangays. The Coastal Vulnerability Index (CVI) described the physical vulnerability of these coastal communities based on recorded sea level anomalies and significant wave heights of multiple satellite altimetry missions, coastal topography derived from the 25-m SRTM digital elevation model (DEM), bathymetry from WorldView-2 and additional elevation data from terrestrial laser scanning surveys. The research utilized merged satellite altimetry data downloaded from the Radar Altimetry Database System (RADS), which covered the period from 1991–2010. The SVI and CVI were calculated and evaluated in ArcGIS. The SVI and CVI were integrated to determine the Total Vulnerability Index (TVI), which characterized the vulnerability of the three barangays in five classes, from very low to very high vulnerability.

Historical land use change and associated carbon emissions in Brazil from 1940 to 1995

The evaluation of impacts of land use change is in general limited by the knowledge of past land use conditions. Most publications on the field present only a vague description of the earlier patterns of land use, which is usually insufficient for more comprehensive studies. Here we present the first spatially explicit reconstruction of historical land use patterns in Brazil, including both croplands and pasturelands, for the period between 1940 and 1995. This reconstruction was obtained by merging satellite imagery with census data, and provides a 5′ × 5′ yearly data set of land use for three different categories (cropland, natural pastureland and planted pastureland) for Brazil. The results show that important land use changes occurred in Brazil. Natural pasture dominated in the 1950s and 1960s, but since the beginning of 1970s it has been gradually replaced by planted pasture, especially in southeast and center west of Brazil. The croplands began its expansion in the 1960s reaching extensive areas in almost all states in 1980. Carbon emissions from historical land use changes were calculated by superimposing a composite biomass map on grids of a weighted average of the fractions of the vegetation types and the replacement land uses. Net emissions from land use changes between 1940 and 1995 totaled 17.2 ± 9.0 Pg‐C (90% confidence range), averaging 0.31 ± 0.16 Pg‐C yr−1, but reaching up to 0.47 ± 0.25 Pg‐C yr−1 during the 1960s and through 1986–1995. Despite international concerns about Amazon deforestation emissions, 72% of Brazil's carbon emissions during the period actually came from deforestation in the Atlantic Forest and Cerrado biomes. Brazil's carbon emissions from land use change are about 11 times larger than its emissions from fossil fuel burning, although only about 18.1% of the native biomass has been lost due to agricultural expansion, which is similar to the global mean (17.7%).

Multi-sensor data merging with stacked neural networks for the creation of satellite long-term climate data records

This article presents a novel artificial neural network technique for merging multi-sensor satellite data. Stacked neural networks (NNs) are used to learn the temporal and spatial drifts between data from different satellite sensors. The resulting NNs are then used to sequentially adjust the satellite data for the creation of a global homogeneous long-term climate data record. The proposed technique has successfully been applied to the merging of ozone data from three European satellite sensors covering together a time period of more than 16 years. The resulting long-term ozone data record has an excellent long-term stability of 0.2 ± 0.2% per decade and can therefore be used for ozone and climate studies.

A conceptual model for constructing high-resolution gauge-satellite merged precipitation analyses

[1] A conceptual model has been developed to create high-resolution precipitation analyses over land by merging gauge-based analysis and CMORPH satellite estimates using data over China for a 5 month period from April to September 2007. A two-step strategy is adopted to remove the bias inherent in the CMORPH satellite precipitation estimates and to combine the bias-corrected satellite estimates with the gauge analysis. First, bias correction is performed for the CMORPH estimates by matching the probability density function (PDF) of the satellite data with that of the gauge analysis using colocated data pairs over a spatial domain of 5°lat/lon centering at the target grid box and over a time period of 30 days, ending at the target date. The spatial domain is expanded wherever necessary over gauge-sparse regions to ensure the collection of a sufficient number of gauge-satellite data pairs. The bias-corrected CMORPH precipitation estimates are then combined with the gauge analysis through the optimal interpolation (OI) technique, in which the bias-corrected CMORPH is used as the first guess while the gauge data are used as the observations to modify the first guess over regions with station coverage. Error statistics are computed for the input gauge and satellite data to maximize the performance of the high-resolution merged analysis of daily precipitation. Cross-validation tests and comparisons against independent gauge observations demonstrate feasibility and effectiveness of the conceptual algorithm in constructing merged precipitation analysis with substantially removed bias and significantly improved pattern agreements compared with those of the input gauge and satellite data.