Satellite-derived land surface climate 'signal' for the Midwest U.S.A.

As a potential strategy for developing regional Land surface climatologies, a statistical method to estimate the land-cover signal from the Normalized Difference Vegetation Index (NDVl) is developed and applied to the Midwest U.S.A. summer growing season. The method evaluates the temporal correlation of NDVl for non-consecutive scenes of the National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) at Local Area Coverage (LAC) resolutions. Conventional mapped data help separate the low frequency variations related to phenology from shorter-term fluctuations involving surface moisture. The land surface signal is more stable temporally when pixel data are aggregated to spatial resolutions commensurate with the Global Area Coverage (GAC) data.     

Global data sets for land applications from the Advanced Very High Resolution Radiometer: an introduction

The Advanced Very High Resolution Radiometer (AVHRR) has become one of the most important sensors for monitoring the terrestrial environment at resolutions of 1 km to very coarse resolutions of 15 km and greater. To make these data suitable for scientific and other applications considerable effort has been devoted to the creation of global data sets. Experience has demonstrated that even for a relatively simple sensor such as the AVHRR, the task of creating global data set is fraught with difficulties and that a number of iterations have been necessary despite considerable efforts in the specification of users' requirements

Four types of data processing streams, overlapping in time, have occurred in the creation of global data sets from the AVHRR. The first three data processing streams were all based on the reduced resolution, Global Area Coverage (GAC) data set, which is collected globally every day. In the first data processing stream a much reduced data set was created in the form of the Global Vegetation Index (GVI) product: revised improved versions of the product have been produced. In the second data processing stream an improved product was created by workers at NASA's Goddard Space Flight Center with higher spatial resolution but which until recently has only been available by continent. This has resulted in the creation of a number of regional data sets. In the third data processing stream operational creation of global data sets at moderately coarse resolution (c. 8 km) is being initiated. The most notable example of this data processing stream is part of NASA's Pathfinder project and stems in large part directly from the second data processing stream: it will involved production of a reprocessed improved global data set for the period from 1982 to the present. In the fourth data processing stream the full potential of the AVHRR in terms of its spatial resolution is being realized, through the generation of a global data set at 1 1 km resolution data.     

Spatial attributes of AVHRR imagery for environmental monitoring

Abstract

Spatial analysis of Advanced Very High Resolution Radiometer (AVHRR) data at Global Area Coverage (GAC) and High Resolution Picture Transmission (HRPT) resolution shows that structural information is detectable across a range of scales and that different biomes exhibit different detectable spatial characteristics. The spatial patterns observed in GAC and HRPT data are similar at coarse resolutions. Differences between the two are observed where point phenomena occur, and where scene objects are generally linear. The undersampling in GAC data generation can cause artificial contiguity and artificial disunity to appear in the image of any scene. The spatial structure observed in GAC image data must therefore be considered unreliable, at least at the scale of the GAC AVHRR resolution-cell size. However, the use of the spatial domain in studies of surface phenomena operating at scales greater than that of the resolution-cell size are unlikely to be limited by the undersampling effects. Indeed, the spatial temporal evolution of structure in AVHRR images may provide important indicators of regional environmental change.     

Impact of orthorectification and spatial sampling on maximum NDVI composite data in mountain regions

Topography and accuracy of image geometric registration significantly affect the quality of satellite data, since pixels are displaced depending on surface elevation and viewing geometry. This effect should be corrected for through the process of accurate image navigation and orthorectification in order to meet the geolocation accuracy for systematic observations specified by the Global Climate Observing System (GCOS) requirements for satellite climate data records. We investigated the impact of orthorectification on the accuracy of maximum Normalized Difference Vegetation Index (NDVI) composite data for a mountain region in north-western Canada at various spatial resolutions (1 km, 4 km, 5 km, and 8 km). Data from AVHRR on board NOAA-11 (1989 and 1990) and NOAA-16 (2001, 2002, and 2003) processed using a system called CAPS (Canadian AVHRR Processing System) for the month of August were considered. Results demonstrate the significant impact of orthorectification on the quality of composite NDVI data in mountainous terrain. Differences between orthorectified and non-orthorectified NDVI composites (ΔNDVI) adopted both large positive and negative values, with the 1% and 99% percentiles of ΔNDVI at 1 km resolution spanning values between − 0.16 < ΔNDVI < 0.09. Differences were generally reduced to smaller numbers for coarser resolution data, but systematic positive biases for non-orthorectified composites were obtained at all spatial resolutions, ranging from 0.02 (1 km) to 0.004 (8 km). Analyzing the power spectra of maximum NDVI composites at 1 km resolution, large differences between orthorectified and non-orthorectified AVHRR data were identified at spatial scales between 4 km and 10 km. Validation of NOAA-16 AVHRR NDVI with MODIS NDVI composites revealed higher correlation coefficients (by up to 0.1) for orthorectified composites relative to the non-orthorectified case. Uncertainties due to the AVHRR Global Area Coverage (GAC) sampling scheme introduce an average positive bias of 0.02 ± 0.03 at maximum NDVI composite level that translates into an average relative bias of 10.6% ± 19.1 for sparsely vegetated mountain regions. This can at least partially explain the systematic average positive biases we observed relative to our results in AVHRR GAC-based composites from the Global Inventory Modeling and Mapping Studies (GIMMS) and Polar Pathfinder (PPF) datasets (0.19 and 0.05, respectively). With regard to the generation of AVHRR long-term climate data records, results suggest that orthorectification should be an integral part of AVHRR pre-processing, since neglecting the terrain displacement effect may lead to important biases and additional noise in time series at various spatial scales.     

AVHRR for monitoring global tropical deforestation

Abstract

Advanced Very High Resolution Radiometer (AVHRR) data have been used to assess the dynamics of forest transformations in three parts of the tropical belt. A large portion of the Amazon Basin has been systematically covered by Local Area Coverage (LAC) data in the 1985-1987 period. The analysis of the vegetation index and thermal data led to the identification and measurement of large areas of active deforestation. The Kalimantan/Borneo forest fires were monitored and their impact was evaluated using the Global Area Coverage (GAC) 4 km resolution data. Finally, High Resolution Picture Transmission (HRPT) data have provided preliminary information on current activities taking place at the boundary between the savanna and the forest in the Southern part of West Africa. The AVHRR approach is found to be a highly valuable means for carrying out deforestation assessments in regional and global perspectives.     

Identifying deforestation in Brazil using multiresolution satellite data

MSS, LAC, GAC and GOES data were used to delineate the extent of deforestation in Rondonia, Brazil, in order to identify those satellite data sources appropriate for monitoring deforestation on a continental/subcontinental scale. These data were processed to differentiate forest from non-forest (cleared, colonized areas) using two different classification procedures. The first procedure utilizes all available spectral bands of data in conjunction with a maximum likelihood classifier to discriminate cleared areas from primary forest. The technique is called probability thresholding. The second employs the red and nearinfrared spectral data to calculate a vegetation index which is subsequently thresholded from forest/non-forest delineation. Ground reference data were not available; the 80m (spatial resolution) MSS digital data products served as the reference data source. The 1·1 km LAC, 4 km GAC and 0·9 km GOES (visible band) images were compared with the MSS imagery. Areal comparisons indicated that (i) the LAC data are capable of adequately delineating colonization clearings in the Amazon; (ii) the spatial resolution of'uhe GAC data is too large to delineate linear clearings of varying length (tens to hundreds of kilometres) up to 2 km wide reliably, (iii) the visible GOES data were of little utility due to excessive data noise and (iv) probability thresholding procedures discriminated forest from non-forest more accurately than vegetation-index thresholding procedures. The results indicate that LAC data used in conjunction with probability thresholding offer the best data-source/classification-procedure combination. MSS data may be used when and where available as a ground reference data source in order to define the AVHRR threshold which most accurately discriminates cleared areas from primary forest.     

Quantifying scaling effects on satellite‐derived forest area estimates for the conterminous USA

We quantified the scaling effects on forest area estimates for the conterminous USA using regression analysis and the National Land Cover Dataset 30 m satellite‐derived maps in 2001 and 1992. The original data were aggregated to: (1) broad cover types (forest vs. non‐forest); and (2) coarser resolutions (1 km and 10 km). Standard errors of the model estimates were 2.3% and 4.9% at 1 km and 10 km resolutions, respectively. Our model improved the accuracies for 1 km by 0.6% (12 556 km²) in 2001 and 1.9% (43 198 km²) in 1992, compared to the forest estimates before the adjustments. Forest area observed from Moderate Resolution Imaging Spectroradiometer (MODIS) 2001 1 km land‐cover map for the conterminous USA might differ by 80 811 km² from what would be observed if MODIS was available at 30 m. Of this difference, 58% (46 870 km²) could be a relatively small net improvement, equivalent to 1444 Tg (or 1.5%) of total non‐soil forest CO₂ stocks. With increasing attention to accurate monitoring and evaluation of forest area changes for different regions of the globe, our results could facilitate the removal of bias from large‐scale estimates based on remote sensors with coarse resolutions.     

Evapotranspiration monitoring with Meteosat Second Generation satellites: improvement opportunities from moderate spatial resolution satellites for vegetation

The Satellite Application Facility on Land Surface Analysis proposes a land evapotranspiration (ET) product, generated in near-real time. It is produced by an energy balance model forced by radiation components derived from data of the Spinning Enhanced Visible and Infrared Imager aboard Meteosat Second Generation geostationary satellites, at a spatial resolution of approximately 3 km at the equator and covering Europe, Africa, and South America. In this article, we assess the improvement opportunities from moderate spatial resolution satellites for ET monitoring at the Meteosat Second Generation satellite scale. Four variables, namely the land cover, the leaf area index (LAI), the surface albedo, and the open water fraction, derived from moderate-resolution satellites for vegetation monitoring are considered at two spatial resolutions, 1 km and 330 m, corresponding to the imagery provided by Satellite Pour l’Observation de la Terre (SPOT)-VEGETATION and future Project for On-Board Autonomy – Vegetation (PROBA-V) space-borne sensors. The variables are incorporated into the ET model, replacing or complementing input derived from the sensor aboard the geostationary satellite, and their relative effect on the model output is analysed. The investigated processes at small scales unresolved by the geostationary satellite are better taken into account in the final ET estimates, especially over heterogeneous and transition zones. Variables derived from sensors at 250–300 m are shown to have a noticeable effect on the ET estimates compared to the 1 km resolution, demonstrating the interest of PROBA-V 330 m-derived variables for the monitoring of ET at Meteosat Second Generation resolution.     

A global 1° by 1° NDVI data set for climate studies derived from the GIMMS continental NDVI data

A nine-year (1982–1990) global normalized difference vegetation index (NDVI) data set with a spatial resolution of 1° by 1° and a temporal resolution of one month was compiled for use in climate studies. This data set was derived from higher resolution (5–8 km) monthly continental NDVI data sets that have been processed and archived by the Global Inventory Monitoring and Modelling Studies (GIMMS) group at NASA/Goddard Space Flight Center. The continental GIMMS NDVI data sets were calculated from Global Area Coverage (GAC) data collected at daily intervals by the Advanced Very High Resolution Radiometer (AVHRR) onboard the NOAA-7, -9 and -11 satellites

The global 1° by 1° NDVI data set was produced to calculate land surface parameters for use within general circulation model J of the atmosphere (GCM). In view of this quantitative application, an evaluation is given of the representation by the NDVI data of the spectral properties of vegetation at the landsurface. Errors are defined as deviations from measurements obtained under standard conditions, i.e., conditions at the-top-of-the-atmosphere with no clouds, clear atmosphere, near-nadir viewing angles, overhead Sun, and invariant soil background. The discussion includes an assessment of (1) the data collected and processed onboard the AVHRR; (2) processing of the AVHRR data into the continental GIMMS NDVI data sets; (3) resampling of the continental data sets to a 1° by 1° data set; and (4) propagation of inconsistencies and biases from (1), (2) and (3) into the 1° by 1° global NDVI data. Examples are shown of the temporal and spatial variations in spectral properties of vegetation contained in the 1° by 1° NDVI data, and these are compared with the dynamics of biophysical parameters derived from land cover classes that were used in previous climate studies.     

Spatial structure,sampling design and scale in remotely-sensed imagery of a California savanna woodland

Abstract

This article describes research related to sampling techniques for establishing linear relations between land surface parameters and remotely-sensed data. Predictive relations are estimated between percentage tree cover in a savanna environment and a normalized difference vegetation index (NDVI) derived from the Thematic Mapper sensor. Spatial autocorrelation in original measurements and regression residuals is examined using semi-variogram analysis at several spatial resolutions. Sampling schemes are then tested to examine the effects of autocorrelation on predictive linear models in cases of small sample sizes. Regression models between image and ground data are affected by the spatial resolution of analysis. Reducing the influence of spatial autocorrelation by enforcing minimum distances between samples may also improve empirical models which relate ground parameters to satellite data.     

The potential of the NASA GAC Pathfinder product for the creation of global thematic data sets: the case of biomass burning patterns

NASA's Advanced Very High Resolution Radiometer Global Area Coverage (GAC) Pathfinder data are compared with the European Commission's GAC data set, as a step towards validation of this new NASA product. Results show that the NASA data have considerable potential for describing global land surface processes, such as biomass burning patterns.     

The limitations and potential of AVHRR GAC data for continental scale fire studies

Global Area Coverage (GAC) data from the Advanced Very High Resolution Radiometer (AVHRR) are available on a daily basis, dating back to July 1981. The AVHRR's 3·55–3·93 μm channel is suitable for detection of terrestrial hot spots, such as bushfires. The long-term archives and global cover make the GAC a potentially valuable data source for large scale fire studies. However, these data are sampled spatially through a combination of line skipping and averaging. This study shows that the sampling affects the sensitivity of GAC for fire detection in relation to ecosystem and season. The GAC are found to provide a reasonable measure of fire activity in grassland and open b'ush savannah, but to perform poorly in the forest margins. Overall at least 79 per cent of fires detected with non-sampled AVHRR data are missed by the GAC. This severely limits the use of GAC data for quantitative fire studies. The GAC does appear to provide a reasonable measure of fire calendar (i.e., variations in fire activity with time) and on a continental scale successfully identifies the main regions of fire activity. The potential of these data for continental scale fire studies is illustrated through the preliminary analysis of 277 GAC mosaics of Africa for the period September 1988 to August 1989.     

A new land cover map of central Africa derived from multi-resolution,multi-temporal AVHRR data

We describe a new map of the central Africa region that was derived from National Oceanic and Atmospheric Administration Advanced Very High Resolution Radiometer (NOAA AVHRR) observations using a fusion of Local Area Coverage (LAC, 1 km), Global Area Coverage (GAC, 8 km), and ancillary information. The land cover map, produced for the Central Africa Regional Program for the Environment (CARPE), offers a synoptic view of the extent of central African dense humid forests, at relatively fine spatial resolution. Land cover types include dense humid forest, disturbed or degraded forest and various savanna classes. Ancillary information includes political and park boundaries, settlements, rivers and roads. Map validation was performed using a combination of field visits and finer resolution imagery (Landsat Multi-Spectral Scanner (MSS)). Forest cover type mapping errors were at most 20 per cent. The resulting map is useful for addressing a number of resource management issues, a few of which are examined.     

Comparative study of the tropospheric ozone derived from satellite data using different interpolation techniques

Tropospheric ozone (TO) has been derived from the Aura/Ozone Monitoring Instrument (OMI) and the Aura/Microwave Limb Sounder (MLS) over the Indian sub-continent region using a tropospheric ozone residual (TOR) technique. The TO was initially retrieved at a horizontal spatial resolution following that of the Aura/MLS (300 km), which has a lower horizontal spatial resolution than that of the Aura/OMI (25 km). To overcome the limitations imposed by data at a lower spatial resolution, we have introduced a 2D rectangular interpolation (RI) algorithm for effective resampling of data to higher horizontal spatial resolutions. The performance of this algorithm has been evaluated by comparison against existing standard techniques such as nearest neighbourhood (NN) and kriging interpolation as well as comparison against in situ ozonesonde observations. Gridded TO estimates were subsequently generated for the region of interest at 25, 50, and 100 km horizontal spatial resolutions for further study.     

Retrieval of meteorological parameters from ROSA on-board Megha Tropiques satellite: evaluation with COSMIC GPS RO,ERA-Interim and radiosonde data

The Indian Space Research Organization (ISRO), in collaboration with Centre National d’Etudes Spatiales, France, launched radio occultation sounder for atmosphere (ROSA) on-board Megha Tropiques (MT) satellite. In this article, the retrieval of atmospheric parameters from ROSA and its evaluation with a network of in situ (radiosonde), similar satellite (Constellation Observational System for Meteorology, Ionosphere and Climate (COSMIC) GPS RO), and re-analysis (ERA-Interim) data sets are presented. The refractivity is retrieved from the bending angle as a function of impact parameter, and pressure and temperature profiles are derived from the profiles of refractivity. The algorithm has been successfully applied to COSMIC data. We have applied and validated this algorithm for the first time to ROSAon board MT. The algorithm retrieves the refractivity and pressure information from surface to 40 km whereas temperature profiles are restricted between 10 and 40 km. The ROSA algorithm refractivity, pressure, and temperature profiles compare well with independent data sets mentioned above. The mean fractional difference between ROSA and COSMIC, ERA-Interim and radiosonde refractivity and the temperature is found to be 0.1% with a standard deviation of 0.5%. It is shown that the algorithm derives the atmospheric parameters with good accuracies from the ROSA instrument and provides the atmospheric community new data from GPS RO at the verge of existing COSMIC GPS RO and in between upcoming GPS RO missions.     

Effect of spatial resolution on the accuracy of satellite-based fire scar detection in the northwest of the Iberian Peninsula

In this work, an empirical study was carried out to evaluate the impact of the spatial resolution of satellite images on the accuracy and uncertainty of burned area detection using classification techniques based on neuro-fuzzy (NF) models. The study area was situated in the northwest of the Iberian Peninsula, where in the summer of 2006, a large number of fires occurred, razing a surface area of more than 100,000 ha. A set of 12 zones containing a burned area in their central part were selected. Landsat Thematic Mapper (TM), Terra Moderate Resolution Imaging Spectroradiometer (MODIS), Advanced Very High Resolution Radiometer Local Area Coverage (AVHRR-LAC), and Advanced Very High Resolution Radiometer Land Long Term Data Record (AVHRR-LTDR) images with a spatial resolution of 30, 250, 1100 m, and 0.05° (～5000 m), respectively, obtained on 20 August 2006, were used. An NF classifier at pixel level for every image was constructed, taking into account only the spectrum bands (red and near-infrared (NIR)) common to all of them. The results in the study region suggest that burned areas of ～1200 ha could be detected with a mean relative error less than 30% only in the MODIS image. In the case of the LAC and LTDR images, a minimum burned area size of >1800 ha and >3600 ha, respectively, is required to find similar errors. Burned areas greater >3600 ha can be detected in MODIS imagery with a mean relative error of ～15%. A regression model of commission and omission error intervals compared with spatial resolution is presented. The conclusion is that in regard to the conditions of the study area, both error intervals increase symmetrically and linearly with the logarithm of the pixel size. The results also suggest that red and NIR spectrum bands could be used to detect burned area in post-fire images in Iberia, but with a relative error depending on burned area size for different spatial resolutions.     

Downscaling of passive microwave soil moisture retrievals based on spectral analysis

The retrieval of soil moisture from passive microwave remote-sensing data is presently one of the most effective methods for monitoring soil moisture. However, the spatial resolution of passive microwave soil moisture products is generally low; thus, existing soil moisture products should be downscaled in order to obtain more accurate soil moisture data. In this study, we explore the theoretical feasibility of applying the spectral downscaling method to the soil moisture in order to generate high spatial resolution soil moisture based on both Moderate Resolution Imaging Spectroradiometer and Fengyun-3B (FY3B) data. We analyse the spectral characteristics of soil moisture images covering the east-central of the Tibetan Plateau which have different spatial resolutions. The spectral analysis reveals that the spectral downscaling method is reliable in theory for downscaling soil moisture. So, we developed one spectral downscaling method for deriving the high spatial resolution (1 km) soil moister data from the FY3B data (25 km). Our results were compared with the ground truth measurements from 15 selected experimental days in 16 different sites. The average coefficient of determination (R²) of the spectral downscaling increased nearly doubled than that of the original FY3B soil moisture product. The spectral downscaled soil moister data were successfully applied to examine the water exchange between the land and atmosphere in the study regions. The spectral downscaling approach could be an efficient and effective method to improve the spatial resolution of current microwave soil moisture images.     

Satellite monitoring of massive green macroalgae bloom (GMB): imaging ability comparison of multi-source data and drifting velocity estimation

A massive floating green macroalgae bloom (GMB) has occurred for several years consecutively in the Yellow Sea since 2007. In view of the rapid growth of green macroalgae, early detection of its patches at first appearance by satellite imagery is of importance, and the central issue is the selection of appropriate satellite data. As a first step towards this goal, based on quasi-synchronous satellite images of HJ-1A/B (China Small Satellite Constellation for Environment and Disaster Monitoring and Forecasting) charge-coupled devices (CCDs), Environmental Satellite (ENVISAT) Advanced Synthetic Aperture Radar (ASAR) and TERRA Moderate Resolution Imaging Spectroradiometer (MODIS), GMB monitoring abilities by these data were compared. The average percentage difference (APD) of the GMB areas derived by ASAR and CCD was less than 15%, which may be partly attributed to the inability of synthetic aperture radar (SAR) data to detect macroalgae suspended beneath the sea surface. The macroalgae area extracted by MODIS was over two times of that extracted by CCD, which was mainly explained by the difference in their spatial resolutions (250 vs 30 m). The effects of the configuration of sensor bands and the aerosol optical properties on the comparison result were found to be negligible, and the underlying reason is analysed by atmosphere radiative transfer modelling. With satellite images, the drifting velocity of macroalgae patches was estimated to be about 0.21 m s^–1, which was in agreement with the surface current field numerically simulated by the Hybrid Coordinate Ocean Model (HYCOM). It indicates that numerical modelling can aid in deduction of the situation of the patches when satellite data are not available, and on the other hand, satellite data can be used to estimate sea-surface currents through monitoring the movement of green algae. By a comprehensive comparison of available satellite data in operation, for the early detection of macroalgae patches and warning of a massive bloom, CCD data from the HJ-1A/B constellation was preferred, with 30 m spatial resolution, 700 km swath width and 2 day revisiting period. SAR data may be an effective supplement, which can avoid the effects of bad weather (cloud, fog and haze) on optical satellite monitoring.