Surface albedo is an essential parameter not only for developing climate models, but also for most energy balance studies. While climate models are usually applied at coarse resolution, the energy ...balance studies, which are mainly focused on agricultural applications, require a high spatial resolution. In this context Landsat is one of the most used remote sensing sensors.
The albedo, estimated through the angular integration of the Bidirectional Reflectance Distribution Function (BRDF), requires an appropriate angular sampling of the surface. However, Landsat sampling characteristics, with nearly constant observation geometry and low illumination variation, prevent from deriving a surface albedo product.
In this paper we present an algorithm to derive a Landsat surface albedo based on the BRDF parameters estimated from the MODerate Resolution Imaging Spectroradiometer (MODIS) Climate Modeling Grid (CMG) surface reflectance product (M{O,Y}D09) using the VJB method (Vermote, Justice, & Bréon, 2009). We base our method on Landsat unsupervised classification to disaggregate the BRDF parameters to the Landsat spatial resolution. We tested the proposed algorithm over five different sites of the US Surface Radiation (SURFRAD) network and inter-compare our results with Shuai, Masek, Gao, and Schaaf (2011) method, which also provides Landsat albedo. The results show that with the proposed method we can derive the surface albedo with a Root Mean Square Error (RMSE) of 0.015 (7%). This result supposes an improvement of 5% in the RMSE compared to Shuai et al.'s (2011) method (with a RMSE of 0.024, 12%) that is mainly determined by the correction of the negative bias (lower retrieved albedo than in situ data).
•We present an algorithm to estimate Landsat albedo.•We disaggregate the MODIS BRDF parameters to Landsat resolution.•We test the results over the US Surface Radiation (SURFRAD) sites.•We can derive the albedo with a Root Mean Square Error (RMSE) of 0.015.
The Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS) at the National Aeronautics and Space Administration (NASA) Goddard Space Flight Center has processed and released 2100 Landsat ...Thematic Mapper and Enhanced Thematic Mapper Plus surface reflectance scenes, providing 30-m resolution wall-to-wall reflectance coverage for North America for epochs centered on 1990 and 2000. This dataset can support decadal assessments of environmental and land-cover change, production of reflectance-based biophysical products, and applications that merge reflectance data from multiple sensors e.g., the Advanced Spaceborne Thermal Emission and Reflection Radiometer, Multiangle Imaging Spectroradiometer, Moderate Resolution Imaging Spectroradiometer (MODIS). The raw imagery was obtained from the orthorectified Landsat GeoCover dataset, purchased by NASA from the Earth Satellite Corporation. Through the LEDAPS project, these data were calibrated, converted to top-of-atmosphere reflectance, and then atmospherically corrected using the MODIS/6S methodology. Initial comparisons with ground-based optical thickness measurements and simultaneously acquired MODIS imagery indicate comparable uncertainty in Landsat surface reflectance compared to the standard MODIS reflectance product (the greater of 0.5% absolute reflectance or 5% of the recorded reflectance value). The rapid automated nature of the processing stream also paves the way for routine high-level products from future Landsat sensors.
This paper briefly describes the land surface reflectance product (MOD09), the current Moderate Resolution Imaging Spectroradiometer (MODIS) atmospheric correction (AC) algorithm and its recent ...updates, and provides the evaluation of the algorithm performance and product quality. The accuracy of the AC algorithm has been significantly improved owing to the use of the accurate Second Simulation of a Satellite Signal in the Solar Spectrum, Vector (6SV) radiative transfer code and a better retrieval of aerosol properties by a refined internal aerosol inversion algorithm. The Collection 5 MOD09 surface reflectance product computed by the improved AC algorithm was analyzed for the year of 2003 through the comparison with a reference data set created with the help of Aerosol Robotic Network (AERONET) measurements and the 6SV code simulations. In general, the MOD09 product demonstrated satisfactory quality in all used MODIS bands except for band 3 (470 nm), which is used for aerosol inversion. The impact of uncertainties in MOD09 upon the downstream product, such as vegetation indices and albedo, was also evaluated.
Global land change from 1982 to 2016 Song, Xiao-Peng; Hansen, Matthew C; Stehman, Stephen V ...
Nature (London),
08/2018, Letnik:
560, Številka:
7720
Journal Article
Recenzirano
Odprti dostop
Land change is a cause and consequence of global environmental change
. Changes in land use and land cover considerably alter the Earth's energy balance and biogeochemical cycles, which contributes ...to climate change and-in turn-affects land surface properties and the provision of ecosystem services
. However, quantification of global land change is lacking. Here we analyse 35 years' worth of satellite data and provide a comprehensive record of global land-change dynamics during the period 1982-2016. We show that-contrary to the prevailing view that forest area has declined globally
-tree cover has increased by 2.24 million km
(+7.1% relative to the 1982 level). This overall net gain is the result of a net loss in the tropics being outweighed by a net gain in the extratropics. Global bare ground cover has decreased by 1.16 million km
(-3.1%), most notably in agricultural regions in Asia. Of all land changes, 60% are associated with direct human activities and 40% with indirect drivers such as climate change. Land-use change exhibits regional dominance, including tropical deforestation and agricultural expansion, temperate reforestation or afforestation, cropland intensification and urbanization. Consistently across all climate domains, montane systems have gained tree cover and many arid and semi-arid ecosystems have lost vegetation cover. The mapped land changes and the driver attributions reflect a human-dominated Earth system. The dataset we developed may be used to improve the modelling of land-use changes, biogeochemical cycles and vegetation-climate interactions to advance our understanding of global environmental change
.
Maintaining consistent datasets of Surface Reflectance (SR) is an important challenge to ensure long-term quality of Climate Data Records. The Landsat 5 and 7 archives offer a unique data source to ...monitor globally the land surface at high spatial resolution. The Landsat-5 TM and Landsat-7 ETM+ SR products, derived from the on-demand processing Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS), require periodic evaluation to check the data consistency. Two evaluation approaches are presented in this paper. The first approach used the Aerosol Robotic Network (AERONET) data set for the period 2000 to 2013 over 489 sites with 3600 Landsat-5 TM and Landsat-7 ETM+ scenes selected. For each scene, 10×10km subsets of LEDAPS-derived Landsat SR and AERONET-derived SR are compared. The latter are computed using Landsat top of atmosphere reflectance, AERONET measurements of atmospheric parameters, and the 6S radiative transfer model. Second, we introduce a methodology to cross-compare Landsat data and MODIS data acquired on the same day. The analysis is based on 4000 random Landsat scenes globally distributed from 2000 to 2013. This method includes: (i) a surface anisotropy adjustment, based on the VJB Bidirectional Reflectance Distribution Function (BRDF) method, to adjust Terra and Aqua MODIS data to Landsat 5 and 7 sun-view geometry, (ii) a spectral adjustment based on an artificial neural network trained with the PROSAIL vegetation radiative transfer model, to adjust MODIS data to TM and ETM+ spectral responses.
The overall results of both approaches show a good match in over 80% of the scenes, i.e. the TM and ETM+ SR uncertainty remained within the SR specification, defined as 0.05×SR+0.005. The worst results are found in the blue band used in LEDAPS to adjust the Aerosol Optical Thickness (AOT). The MODIS-Landsat SR cross-comparison confirms the utility of a BRDF adjustment method to decrease the scattering between Landsat sensors and MODIS sensors (Terra and Aqua). The spectral adjustment removes part of the biases related to spectral response differences. Global analysis is used to identify AOT retrieval issues over specific scenes, mostly over bright surfaces. From 2000 to 2013, no significant temporal variation of the performance is detected, which enhanced the consistency of LEDAPS-derived surface reflectance data set.
•We evaluate LEDAPS-derived Landsat-5 TM and −7 ETM+ surface reflectance (SR).•We evaluate the theoretical performances of LEDAPS over 489 AERONET sites.•We build a methodology to compare MODIS and Landsat SR with BRDF and spectral adjustments.•The Landsat SR uncertainty is below the specification (0.05ρ+0.005) except for blue band.•The Landsat SR uncertainty is stable through time.
Tropical forests are global epicentres of biodiversity and important modulators of climate change, and are mainly constrained by rainfall patterns. The severe short-term droughts that occurred ...recently in Amazonia have drawn attention to the vulnerability of tropical forests to climatic disturbances. The central African rainforests, the second-largest on Earth, have experienced a long-term drying trend whose impacts on vegetation dynamics remain mostly unknown because in situ observations are very limited. The Congolese forest, with its drier conditions and higher percentage of semi-evergreen trees, may be more tolerant to short-term rainfall reduction than are wetter tropical forests, but for a long-term drought there may be critical thresholds of water availability below which higher-biomass, closed-canopy forests transition to more open, lower-biomass forests. Here we present observational evidence for a widespread decline in forest greenness over the past decade based on analyses of satellite data (optical, thermal, microwave and gravity) from several independent sensors over the Congo basin. This decline in vegetation greenness, particularly in the northern Congolese forest, is generally consistent with decreases in rainfall, terrestrial water storage, water content in aboveground woody and leaf biomass, and the canopy backscatter anomaly caused by changes in structure and moisture in upper forest layers. It is also consistent with increases in photosynthetically active radiation and land surface temperature. These multiple lines of evidence indicate that this large-scale vegetation browning, or loss of photosynthetic capacity, may be partially attributable to the long-term drying trend. Our results suggest that a continued gradual decline of photosynthetic capacity and moisture content driven by the persistent drying trend could alter the composition and structure of the Congolese forest to favour the spread of drought-tolerant species.
Celotno besedilo
Dostopno za:
DOBA, IJS, IZUM, KILJ, KISLJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Remote sensing from satellite or airborne platforms of land or sea surfaces in the visible and near infrared is strongly affected by the presence of the atmosphere along the path from Sun to target ...(surface) to sensor. This paper presents 6S (Second Simulation of the Satellite Signal in the Solar Spectrum), a computer code which can accurately simulate the above problems. The 6S code is an improved version of 5S (Simulation of the Satellite Signal in the Solar Spectrum), developed by the Laboratoire d'Optique Atmospherique ten years ago. The new version now permits calculations of near-nadir (down-looking) aircraft observations, accounting for target elevation, non lambertian surface conditions, and new absorbing species (CH/sub 4/, N/sub 2/O, CO). The computational accuracy for Rayleigh and aerosol scattering effects has been improved by the use of state-of-the-art approximations and implementation of the successive order of scattering (SOS) algorithm. The step size (resolution) used for spectral integration has been improved to 2.5 nm. The goal of this paper is not to provide a complete description of the methods used as that information is detailed in the 6S manual, but rather to illustrate the impact of the improvements between 5S and 6S by examining some typical remote sensing situations. Nevertheless, the 6S code has still limitations. It cannot handle spherical atmosphere and as a result, it cannot be used for limb observations. In addition, the decoupling the authors are using for absorption and scattering effects does not allow to use the code in presence of strong absorption bands.
The seasonality of sunlight and rainfall regulates net primary production in tropical forests. Previous studies have suggested that light is more limiting than water for tropical forest productivity, ...consistent with greening of Amazon forests during the dry season in satellite data. We evaluated four potential mechanisms for the seasonal green-up phenomenon, including increases in leaf area or leaf reflectance, using a sophisticated radiative transfer model and independent satellite observations from lidar and optical sensors. Here we show that the apparent green up of Amazon forests in optical remote sensing data resulted from seasonal changes in near-infrared reflectance, an artefact of variations in sun-sensor geometry. Correcting this bidirectional reflectance effect eliminated seasonal changes in surface reflectance, consistent with independent lidar observations and model simulations with unchanging canopy properties. The stability of Amazon forest structure and reflectance over seasonal timescales challenges the paradigm of light-limited net primary production in Amazon forests and enhanced forest growth during drought conditions. Correcting optical remote sensing data for artefacts of sun-sensor geometry is essential to isolate the response of global vegetation to seasonal and interannual climate variability.
Celotno besedilo
Dostopno za:
DOBA, IJS, IZUM, KILJ, KISLJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Since first light in early 2000, operational global quantitative retrievals of aerosol properties over land have been made from Moderate Resolution Imaging Spectroradiometer (MODIS) observed spectral ...reflectance. These products have been continuously evaluated and validated, and opportunities for improvements have been noted. We have replaced the surface reflectance assumptions, the set of aerosol model optical properties, and the aerosol lookup table (LUT). This second‐generation operational algorithm performs a simultaneous inversion of two visible (0.47 and 0.66 μm) and one shortwave‐IR (2.12 μm) channel, making use of the coarse aerosol information content contained in the 2.12 μm channel. Inversion of the three channels yields three nearly independent parameters, the aerosol optical depth (τ) at 0.55 μm, the nondust or fine weighting (η), and the surface reflectance at 2.12 μm. Retrievals of small‐magnitude negative τ values (down to −0.05) are considered valid, thus balancing the statistics of τ in near zero τ conditions. Preliminary validation of this algorithm shows much improved retrievals of τ, where the MODIS/Aerosol Robotic Network τ (at 0.55 μm) regression has an equation of: y = 1.01x + 0.03, R = 0.90. Global mean τ for the test bed is reduced from ∼0.28 to ∼0.21.
In- land surface models, which are used to evaluate the role of vegetation in the context of global climate change and variability, LAI and FAPAR play a key role, specifically with respect to the ...carbon and water cycles. The AVHRR-based LAI/FAPAR dataset offers daily temporal resolution, an improvement over previous products. This climate data record is based on a carefully calibrated and corrected land surface reflectance dataset to provide a high-quality, consistent time-series suitable for climate studies. It spans from mid-1981 to the present. Further, this operational dataset is available in near real-time allowing use for monitoring purposes. The algorithm relies on artificial neural networks calibrated using the MODIS LAI/FAPAR dataset. Evaluation based on cross-comparison with MODIS products and in situ data show the dataset is consistent and reliable with overall uncertainties of 1.03 and 0.15 for LAI and FAPAR, respectively. However, a clear saturation effect is observed in the broadleaf forest biomes with high LAI (>4.5) and FAPAR (>0.8) values.
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