Daily daytime Advanced Very High Resolution Radiometer (AVHRR) 4-km global area coverage data have been processed to produce a Normalized Difference Vegetation Index (NDVI) 8-km equal-area dataset ...from July 1981 through December 2004 for all continents except Antarctica. New features of this dataset include bimonthly composites, NOAA-9 descending node data from August 1994 to January 1995, volcanic stratospheric aerosol correction for 1982-1984 and 1991-1993, NDVI normalization using empirical mode decomposition/reconstruction to minimize varying solar zenith angle effects introduced by orbital drift, inclusion of data from NOAA-16 for 2000-2003 and NOAA-17 for 2003-2004, and a similar dynamic range with the MODIS NDVI. Two NDVI compositing intervals have been produced: a bimonthly global dataset and a 10-day Africa-only dataset. Post-processing review corrected the majority of dropped scan lines, navigation errors, data drop outs, edge-of-orbit composite discontinuities, and other artefacts in the composite NDVI data. All data are available from the University of Maryland Global Land Cover Facility (
http://glcf.umiacs.umd.edu/data/gimms/
).
Differences in the relative spectral response functions of sensors lead to data inconsistencies that should be harmonized before multisensor exploitation. In this paper, we use spectral libraries to ...simulate satellite data and build models to correct them. We then explore and compare different models for coarse and medium spatial resolution optical sensors, including moderate resolution imaging spectroradiometer, advanced very high resolution radiometer (AVHRR), visible infrared imaging radiometer suite, multispectral instrument aboard Sentinel-2, and Operational Land Imager aboard Landsat 8. We found that optimal correction of different bands depends on the model used. For the green and near infrared bands, a multilinear land cover dependent regression improves the accuracy by up to 80.9%. For the red band, a novel exponential dependence of the spectral band adjustment factor with the normalized difference vegetation index (NDVI) provides an accuracy improvement of up to 72.8%. The best way to correct the NDVI value is to use the corrected NIR and red bands using these models. We apply the proposed methods to 445 BELMANIP2 sites using AVHRR data from the long-term data record from 1982-2017. High NDVI pixels result in 30-year trends varying up to 0.06 when comparing uncorrected to spectrally adjusted NDVI. Further application of these methods to NASA's Harmonized Landsat and Sentinel 2 product shows that for the red band and NDVI, our proposed method provides improved accuracy (54.6% and 62.5%) over the linear spectral adjustment currently used.
An illumination correction algorithm has been developed to improve the accuracy of forest change detection from Landsat-derived reflectance data. This algorithm is based on an empirical rotation ...model and was tested on Landsat image pairs over the Cherokee National Forest, Tennessee; Uinta–Wasatch–Cache National Forest, Utah; San Juan National Forest, Colorado; and Sinkyone Wilderness State Park, California. The illumination correction process successfully eliminated correlation between Landsat reflectance and illumination condition. Comparison to forest-change maps derived from uncorrected images showed significant disagreement, ranging from 23% to 45%. Validated against high-resolution (1m or less) time-serial images, the illumination correction decreased overestimation of forest gains and losses and improved specificity in detection of major forest changes. The overall accuracy increases 34% at the Cherokee Forest site and about 10% at the other three sites. The disagreement rate between change maps from the original and corrected Landsat images increased with increasing terrain inclination angle, with the relationship between illumination condition and the disagreement rate following a V-shaped curve that varied among sites. The lowest disagreement rate occurred when illumination condition was slightly smaller than that of a horizontal field. The correction for topographic illumination should be considered as a standard pre-processing step for land cover classification and land use change detection, especially for mountainous areas.
•An illumination correction algorithm has been developed.•The illumination correction greatly improves the forest change detection accuracy.•More accuracy improvement achieved at regions with larger inclination angle.•The relationship between IC and accuracy improvement is site-specific.
Surface reflectance adjusted for atmospheric effects is a primary input for land cover change detection and for developing many higher level surface geophysical parameters. With the development of ...automated atmospheric correction algorithms, it is now feasible to produce large quantities of surface reflectance products using Landsat images. Validation of these products requires in situ measurements, which either do not exist or are difficult to obtain for most Landsat images. The surface reflectance products derived using data acquired by the Moderate Resolution Imaging Spectroradiometer (MODIS), however, have been validated more comprehensively. Because the MODIS on the Terra platform and the Landsat 7 are only half an hour apart following the same orbit, and each of the 6 Landsat spectral bands overlaps with a MODIS band, good agreements between MODIS and Landsat surface reflectance values can be considered indicators of the reliability of the Landsat products, while disagreements may suggest potential quality problems that need to be further investigated. Here we develop a system called Landsat–MODIS Consistency Checking System (LMCCS). This system automatically matches Landsat data with MODIS observations acquired on the same date over the same locations and uses them to calculate a set of agreement metrics. To maximize its portability, Java and open-source libraries were used in developing this system, and object-oriented programming (OOP) principles were followed to make it more flexible for future expansion. As a highly automated system designed to run as a stand-alone package or as a component of other Landsat data processing systems, this system can be used to assess the quality of essentially every Landsat surface reflectance image where spatially and temporally matching MODIS data are available. The effectiveness of this system was demonstrated using it to assess preliminary surface reflectance products derived using the Global Land Survey (GLS) Landsat images for the 2000 epoch. As surface reflectance likely will be a standard product for future Landsat missions, the approach developed in this study can be adapted as an operational quality assessment system for those missions.
► We developed a system to assess the quality of global Landsat surface reflectance. ► This system can be used on every Landsat image where MODIS data is available. ► Open-source libraries and OOP principles make the system flexible for expansion. ► The system was demonstrated using preliminary Landsat surface reflectance product.
The Long Term Data Record (LTDR) project has the goal of developing a quality and consistent surface reflectance product from coarse resolution optical sensors. This paper focuses on the Advanced ...Very High Resolution Radiometer (AVHRR) part of the record, using the Moderate Resolution Imaging Spectrometer (MODIS) instrument as a reference. When a surface reflectance time series is acquired from satellites with variable observation geometry, the directional variation generates an apparent noise which can be corrected by modeling the bidirectional reflectance distribution function (BRDF). The VJB (Vermote, Justice and Bréon, 2009) method estimates a target’s BRDF shape using 5 years of observation and corrects for directional effects maintaining the high temporal resolution of the measurement using the instantaneous Normalized Difference Vegetation Index (NDVI). The method was originally established on MODIS data but its viability and optimization for AVHRR data have not been fully explored. In this study we analyze different approaches to find the most robust way of applying the VJB correction to AVHRR data, considering that high noise in the red band (B1) caused by atmospheric effect makes the VJB method unstable. Firstly, our results show that for coarse spatial resolution, where the vegetation dynamics of the target don’t change significantly, deriving BRDF parameters from 15+ years of observations reduces the average noise by up to 7% in the Near Infrared (NIR) band and 6% in the NDVI, in comparison to using 3-year windows. Secondly, we find that the VJB method can be modified for AVHRR data to improve the robustness of the correction parameters and decrease the noise by an extra 8% and 9% in the red and NIR bands with respect to using the classical VJB inversion. We do this by using the Stable method, which obtains the volumetric BRDF parameter (V) based on its NDVI dependency, and then obtains the geometric BRDF parameter (R) through the inversion of just one parameter.
Timely and accurate information on crop yield is critical to many applications within agriculture monitoring. Thanks to its coverage and temporal resolution, coarse spatial resolution satellite ...imagery has always been a source of valuable information for yield forecasting and assessment at national and regional scales. With availability of free images acquired by Landsat-8 and Sentinel-2 remote sensing satellites, it becomes possible to enable temporal resolution of an image every 3-5 days, and therefore, to develop next generation agriculture products at higher spatial resolution (30 m). This paper explores the combined use of Landsat-8 and Sentinel-2A for winter crop mapping and winter wheat assessment at regional scale. For the former, we adapt a previously developed approach for Moderate Resolution Imaging Spectroradiometer (MODIS) at 250 m resolution that allows automatic mapping of winter crops taking into account knowledge on crop calendar and without ground truth data. For the latter, we use a generalized winter wheat yield model that is based on NDVI-peak estimation and MODIS data, and further downscaled to be applicable at 30 m resolution. We show that integration of Landsat-8 and Sentinel-2A has a positive impact both for winter crop mapping and winter wheat yield assessment. In particular, the error of winter wheat yield estimates can be reduced up to 1.8 times comparing to the single satellite usage.
Biomass burning is an important global phenomenon impacting atmospheric composition. Application of satellite based measures of fire radiative energy (FRE) has been shown to be effective for ...estimating biomass consumed, which can then be used to estimate gas and aerosol emissions. However, application of FRE has been limited in both temporal and spatial scale. In this paper we offer a methodology to estimate FRE globally for 2001–2007 at monthly time steps using MODIS. Accuracy assessment shows that our FRE estimates are precise (R2 = 0.85), but may be underestimated. Global estimates of FRE show that Africa and South America dominate biomass burning, accounting for nearly 70% of the annual FRE generated. Applying FRE‐based combustion factors to Africa yields an annual average biomass burned of 716–881 Tg of dry matter (DM). Comparison with the GFEDv2 biomass burned estimates shows large annual differences suggesting significant uncertainty remains in emission estimates.
Despite early speculation to the contrary, all tropical forests studied to date display seasonal variations in the presence of new leaves, flowers, and fruits. Past studies were focused on the timing ...of phenological events and their cues but not on the accompanying changes in leaf area that regulate vegetation-atmosphere exchanges of energy, momentum, and mass. Here we report, from analysis of 5 years of recent satellite data, seasonal swings in green leaf area of ≈25% in a majority of the Amazon rainforests. This seasonal cycle is timed to the seasonality of solar radiation in a manner that is suggestive of anticipatory and opportunistic patterns of net leaf flushing during the early to mid part of the light-rich dry season and net leaf abscission during the cloudy wet season. These seasonal swings in leaf area may be critical to initiation of the transition from dry to wet season, seasonal carbon balance between photosynthetic gains and respiratory losses, and litterfall nutrient cycling in moist tropical forests.
Biomass burning is the main global source of fine primary carbonaceous aerosols in the form of organic carbon (OC) and black carbon (BC). We present an approach to estimate biomass burning aerosol ...emissions based on the measurement of radiative energy released during combustion. We make use of both Aqua and Terra MODIS observations to estimate the fire radiative energy using a simple model to parameterize the fire diurnal cycle based on the long‐term ratio between Terra and Aqua MODIS FRP. The parameterization is developed using cases of frequent (up to 12 times daily) MODIS observations, geostationary data from SEVIRI, and precessing observations from TRMM VIRS. FRE‐based emission coefficients for the organic and black carbon (OCBC) component of fine mode aerosols are computed from multiple regions encompassing grassland/savanna, tropical forest, and extratropical forest biomes using OCBC emission estimates derived from the MODIS fine mode aerosol product and an inverse aerosol transport model. The values of emission coefficients for OCBC retrieved were 2.7 ± 0.3 g/MJ for grassland/savanna, 8.6 ± 0.8 g/MJ for tropical forest, and 14.4 ± 0.8 g/MJ for extratropical forest. The FRE monthly data are then used to estimate OCBC emissions from biomass burning on a global basis. For 2001 to 2007, our annual estimates are comparable to previously published values. According to our estimate, the OCBC emissions are the largest for 2003 (18.8 Tg), roughly 20% above average and primarily driven by wildland fires in the Lake Baikal region (Russia).
Global, long-term monitoring of changes in Earth's land surface requires quantitative comparisons of satellite images acquired under widely varying atmospheric conditions. Although physically based ...estimates of surface reflectance (SR) ultimately provide the most accurate representation of Earth's surface properties, there has never been a globally consistent SR dataset at the spatial resolution (<1ha) or temporal extent (~40years) of the Landsat mission. To increase the consistency and robustness of Landsat-based land cover monitoring, we atmospherically corrected the Global Land Survey (GLS) Landsat dataset using the Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS) implementation of the Second Simulation of the Satellite Signal in the Solar Spectrum (6S) radiative transfer model. The GLS provides synoptic, orthorectified, cloud-free Landsat coverage of Earth's land area in four nominal epochs (1975, 1990, 2000, and 2005). This paper presents the resulting GLS surface reflectance dataset and a global assessment of the 2000- and 2005-epoch data against coincident Moderate Resolution Imaging Spectroradiometer (MODIS) daily SR and Normalized Bidirectional Distribution Function-Adjusted Reflectance (NBAR) measurements. Agreement with respect to MODIS SR and NBAR data is very high, with overall discrepancies (Root-Mean-Squared Deviation (RMSD)) between 1.3 and 2.8percent reflectance for Landsat-7 Enhanced Thematic Mapper Plus (ETM+) and between 2.2 and 3.5percent reflectance for Landsat-5 Thematic Mapper (TM). The resulting Landsat surface reflectance dataset and the associated quality metrics for each image are hosted on the Global Land Cover Facility web site for free download (http://www.landcover.org/data/gls_SR). This new repository will provide consistent, calibrated, multi-decadal image data for robust land cover change detection and monitoring across the Earth sciences.
•We atmospherically corrected the GLS Landsat dataset for ca. 2000 and 2005.•First global multi-temporal Landsat surface reflectance (SR) dataset.•Assessment of the Landsat SR against coincident MODIS measurements.•Agreement with respect to MODIS SR and NBAR data is very high.•The dataset is hosted on the GLCF web site for free download.
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