Yufang Jin

First operational BRDF, albedo nadir reflectance products from MODIS

With the launch of NASA’s Terra satellite and the MODerate Resolution Imaging Spectroradiometer (MODIS), operational Bidirectional Reflectance Distribution Function (BRDF) and albedo products are now being made available to the scientific community. The MODIS BRDF/Albedo algorithm makes use of a semiempirical kernel-driven bidirectional reflectance model and multidate, multispectral data to provide global 1-km gridded and tiled products of the land surface every 16 days. These products include directional hemispherical albedo (black-sky albedo), bihemispherical albedo (white-sky albedo), Nadir BRDF-Adjusted surface Reflectances (NBAR), model parameters describing the BRDF, and extensive quality assurance information. The algorithm has been consistently producing albedo and NBAR for the public since July 2000. Initial evaluations indicate a stable BRDF/Albedo Product, where, for example, the spatial and temporal progression of phenological characteristics is easily detected in the NBAR and albedo results. These early beta and provisional products auger well for the routine production of stable MODIS-derived BRDF parameters, nadir reflectances, and albedos for use by the global observation and modeling communities.

The Impact of Boreal Forest Fire on Climate Warming

We report measurements and analysis of a boreal forest fire, integrating the effects of greenhouse gases, aerosols, black carbon deposition on snow and sea ice, and postfire changes in surface albedo. The net effect of all agents was to increase radiative forcing during the first year (34 ± 31 Watts per square meter of burned area), but to decrease radiative forcing when averaged over an 80-year fire cycle (–2.3 ± 2.2 Watts per square meter) because multidecadal increases in surface albedo had a larger impact than fire-emitted greenhouse gases. This result implies that future increases in boreal fire may not accelerate climate warming.

Forecasting Fire Season Severity in South America Using Sea Surface Temperature Anomalies

Sea surface temperature anomalies can predict annual fire season severity in South America up to 3 to 5 months in advance. Fires in South America cause forest degradation and contribute to carbon emissions associated with land use change. We investigated the relationship between year-to-year changes in fire activity in South America and sea surface temperatures. We found that the Oceanic Niño Index was correlated with interannual fire activity in the eastern Amazon, whereas the Atlantic Multidecadal Oscillation index was more closely linked with fires in the southern and southwestern Amazon. Combining these two climate indices, we developed an empirical model to forecast regional fire season severity with lead times of 3 to 5 months. Our approach may contribute to the development of an early warning system for anticipating the vulnerability of Amazon forests to fires, thus enabling more effective management with benefits for climate and air quality.

Detecting vegetation structure using a kernel-based BRDF model

The magnitude of the anisotropy of vegetation is mainly determined by its spectral and structural features. It can be described by the bidirectional reflectance distribution function (BRDF). The parameters of physical BRDF models are related to the biophysical structural information. However, for a semiempirical kernel-based BRDF model, the relationship between BRDF parameters and vegetation structure is no longer as clear as with a physical BRDF model. To reveal this relationship, a structural scattering index (SSI) and a relative structural scattering index (RSSI) are derived based on the BRDF parameters in this paper. The investigation of SSI and RSSI show that they have both theoretical and practical meaning and can be used to distinguish different land cover types or to detect structural changes.

Changes in surface albedo after fire in boreal forest ecosystems of interior Alaska assessed using MODIS satellite observations

We assessed the multidecadal effects of boreal forest fire on surface albedo using Moderate Resolution Imaging Spectroradiometer (MODIS) satellite observations within the perimeters of burn scars in interior Alaska. Fire caused albedo to increase during periods with and without snow cover. Albedo during early spring had a mean of 0.50 ± 0.03 for the first three decades after fire, substantially higher than that observed in evergreen conifer forests (0.34 ± 0.04). In older stands between 30 and 55 years, albedo showed a decreasing trend during early spring, probably from a growing spruce understory that masked surface snow and caused increases in both simple ratio (SR) and enhanced vegetation index (EVI). During summer, albedo decreased by 0.012 ± 0.005 in the year immediately after fire (from 0.112 ± 0.005 to 0.100 ± 0.010). In subsequent years, summer albedo increased rapidly at first and then more gradually, reaching a broad maximum in 20–35 year stands (0.135 ± 0.006). These measurements provide evidence for a well-developed deciduous shrub and tree phase during intermediate stages of succession. Averaged over the first 5 decades, shortwave surface forcing from fires was −6.2 W m−2 relative to an evergreen conifer control and −3.0 W m−2 relative to a control constructed from 2000 to 2003 preburn observations. These forcing estimates had a magnitude substantially smaller than previous estimates and suggest that, at a regional scale, evergreen conifer stand density may be lower than that inferred from chronosequence studies.

The influence of burn severity on postfire vegetation recovery and albedo change during early succession in North American boreal forests

Severity of burning can influence multiple aspects of forest composition, carbon cycling, and climate forcing. We quantified how burn severity affected vegetation recovery and albedo change during early succession in Canadian boreal regions by combining satellite observations from the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Canadian Large Fire Database. We used the MODIS-derived difference Normalized Burn Ratio (dNBR) and initial changes in spring albedo as measures of burn severity. We found that the most severe burns had the greatest reduction in summer MODIS Enhanced Vegetation Index (EVI) in the first year after fire, indicating greater loss of vegetation cover. By 5–8 years after fire, summer EVI for all severity classes had recovered to within 90%–108% of prefire levels. Spring and summer albedo progressively increased during the first 7 years after fire, with more severely burned areas showing considerably larger postfire albedo increases during spring and more rapid increases during summer as compared with moderate- and low-severity burns. After 5–7 years, increases in spring albedo above prefire levels were considerably larger in high-severity burns (0.20 ± 0.06; defined by dNBR percentiles greater than 75%) as compared to changes observed in moderate- (0.16 ± 0.06; for dNBR percentiles between 45% and 75%) or low-severity burns (0.13 ± 0.06; for dNBR percentiles between 20% and 45%). The sensitivity of spring albedo to dNBR was similar in all ecozones and for all vegetation types along gradients of burn severity. These results suggest carbon losses associated with increases in burn severity observed in some areas of boreal forests may be at least partly offset, in terms of climate impacts, by increases in negative forcing associated with changes in surface albedo.

Vegetation controls on northern high latitude snow‐albedo feedback: observations and CMIP5 model simulations

The snow-masking effect of vegetation exerts strong control on albedo in northern high latitude ecosystems. Large-scale changes in the distribution and stature of vegetation in this region will thus have important feedbacks to climate. The snow-albedo feedback is controlled largely by the contrast between snow-covered and snow-free albedo (Δα), which influences predictions of future warming in coupled climate models, despite being poorly constrained at seasonal and century time scales. Here, we compare satellite observations and coupled climate model representations of albedo and tree cover for the boreal and Arctic region. Our analyses reveal consistent declines in albedo with increasing tree cover, occurring south of latitudinal tree line, that are poorly represented in coupled climate models. Observed relationships between albedo and tree cover differ substantially between snow-covered and snow-free periods, and among plant functional type. Tree cover in models varies widely but surprisingly does not correlate well with model albedo. Furthermore, our results demonstrate a relationship between tree cover and snow-albedo feedback that may be used to accurately constrain high latitude albedo feedbacks in coupled climate models under current and future vegetation distributions.

Bidirectional NDVI and atmospherically resistant BRDF inversion for vegetation canopy

The normalized difference vegetation index (NDVI) has been widely applied in optical remote sensing. However, it has been demonstrated that NDVI is still partially affected by atmospheric path scattering and bidirectional (illumination and viewing geometry) effects. In this paper we present the benefit of using a bidirectional NDVI, and we discuss the problems in using the maximum NDVI composite method. Based on the assumption that a clear day has a larger NDVI value and a cloudy day has a smaller NDVI value (smaller reflectance in the near-infrared band and larger reflectance in red band due to atmospheric path scattering), the ratio of squared observed NDVI values and calculated NDVI values is used as a weight in our inversion method. The calculated NDVI values are derived from previously inverted bidirectional reflectance distribution functions (BRDFs). The inversion process will loop until all weights converge. Our research on the early Terra/MODIS data using a semiempirical kernel-driven BRDF model (the RossThick-LiTransit model) shows that this new method can improve inversion results whenever some cloudy pixels are not filtered out. As cloud detection and subpixel cloudiness are always a problem, this technique should still be very useful in improving the quality of BRDF inversion.

Improving MODIS surface BRDF/Albedo retrieval with MISR multiangle observations

We explore a synergistic approach to use the complementary angular samplings from the Multi-angle Imaging SpectroRadiometer (MISR) and Moderate Resolution Imaging Spectroradiometer (MODIS) to improve MODIS surface bidirectional reflectance distribution function (BRDF) and albedo retrieval. Preliminary case studies show that MODIS and MISR surface bidirectional reflectance factors (BRFs) are generally comparable in the green, red, and near infrared. An information index is introduced to characterize the information content of directional samplings, and it is found that MISR angular observations can bring additional information to the MODIS retrieval, especially when the MISR observations are close to the principal plane. We use the BRDF parameters derived from the MISR surface BRFs as a priori information and derive a posteriori estimates of surface BRDF parameters with the MODIS observations. Results show that adding MISR angular samplings can reduce the relative BRF prediction error by up to 10% in the red and green, compared to the retrievals from MODIS-only observations which are close to the cross-principal plane.

Contrasting controls on wildland fires in Southern California during periods with and without Santa Ana winds

Wildland fires in Southern California can be divided into two categories: fall fires, which are typically driven by strong offshore Santa Ana winds, and summer fires, which occur with comparatively weak onshore winds and hot and dry weather. Both types of fire contribute significantly to annual burned area and economic loss. An improved understanding of the relationship between Southern Californias meteorology and fire is needed to improve predictions of how fire will change in the future and to anticipate management needs. We used output from a regional climate model constrained by reanalysis observations to identify Santa Ana events and partition fires into those occurring during periods with and without Santa Ana conditions during 1959–2009. We then developed separate empirical regression models for Santa Ana and non-Santa Ana fires to quantify the effects of meteorology on fire number and size. These models explained approximately 58% of the seasonal and interannual variation in the number of Santa Ana fires and 36% of the variation in non-Santa Ana fires. The number of Santa Ana fires increased during years when relative humidity during Santa Ana events and fall precipitation were below average, indicating that fuel moisture is a key controller of ignition. Relative humidity strongly affected Santa Ana fire size. Cumulative precipitation during the previous three winters was significantly correlated with the number of non-Santa Ana fires, presumably through increased fine fuel density and connectivity between infrastructure and nearby vegetation. Both relative humidity and the preceding wet season precipitation influenced non-Santa Ana fire size. Regression models driven by meteorology explained 57% of the temporal variation in Santa Ana burned area and 22% of the variation in non-Santa Ana burned area. The area burned by non-Santa Ana fires has increased steadily by 1.7% year−1 since 1959 (p < 0.006); the occurrence of extremely large Santa Ana fires has increased abruptly since 2003. Our results underscore the need to separately consider the fuel and meteorological controls on Santa Ana and non-Santa Ana fires when projecting climate change impacts on regional fire.