Inconsistencies of interannual variability and trends in long-term satellite leaf area index products

Understanding the long-term performance of global satellite leaf area index (LAI) products is important for global change research. However, few effort has been devoted to evaluating the long-term time-series consistencies of LAI products. This study compared four long-term LAI products (GLASS, GLOBMAP, LAI3g, and TCDR) in terms of trends, interannual variabilities, and uncertainty variations from 1982 through 2011. This study also used four ancillary LAI products (GEOV1, MERIS, MODIS C5, and MODIS C6) from 2003 through 2011 to help clarify the performances of the four long-term LAI products. In general, there were marked discrepancies between the four long-term LAI products. During the pre-MODIS period (1982-1999), both linear trends and interannual variabilities of global mean LAI followed the order GLASS>LAI3g>TCDR>GLOBMAP. The GLASS linear trend and interannual variability were almost 4.5 times those of GLOBMAP. During the overlap period (2003-2011), GLASS and GLOBMAP exhibited a decreasing trend, TCDR no trend, and LAI3g an increasing trend. GEOV1, MERIS, and MODIS C6 also exhibited an increasing trend, but to a much smaller extent than that from LAI3g. During both periods, the R2 of detrended anomalies between the four long-term LAI products was smaller than 0.4 for most regions. Interannual variabilities of the four long-term LAI products were considerably different over the two periods, and the differences followed the order GLASS>LAI3g>TCDR>GLOBMAP. Uncertainty variations quantified by a collocation error model followed the same order. Our results indicate that the four long-term LAI products were neither intraconsistent over time nor interconsistent with each other. These inconsistencies may be due to NOAA satellite orbit changes and MODIS sensor degradation. Caution should be used in the interpretation of global changes derived from the four long-term LAI products

Satellite remote sensing of vegetation dynamics in the context of climate change

Vegetation is a key component of the Earth's climate system. Understanding vegetation dynamics in a changing climate requires both in situ and remote sensing data. Satellite remote sensing is especially indispensible for continuous monitoring of vegetation over large areas. This dissertation is focused on investigation of vegetation dynamics in the broader context of climate change using satellite data over two critical regions: the arctic-boreal area in the northern high latitudes and Amazonia in South America. The northern high latitudes have experienced amplified warming. We found the response of the arctic-boreal vegetation to this warming to be different between North America and Eurasia during a 30-year period since 1982: the relationship between vegetation green-up and temperature rise was stable over Eurasia, but in North America, the amount of vegetation green-up per unit amount of warming has decreased since the beginning of 21st century. This could partly be explained by the unmatched northward movements of temperature and precipitation patterns in North America. The Amazonian rainforests have highly dense canopies of green leaves. In such dense media, reflection of solar radiation tends to saturate. Thus, the satellite measurements are weakly sensitive to vegetation changes. At the same time, the data are strongly influenced by changing sun-sensor geometry. This makes it difficult to discriminate between vegetation changes and sun-sensor geometry effects. We developed a new physically based approach to detect changes in dense forests. Analyses of several years of data from three sensors on two satellites under a range of sun-sensor geometries provide robust evidence for a sunlight driven seasonal cycle in structure and greenness of Amazonian rainforests. The 2005 and 2010 dry-season droughts decreased the photosynthetic activity of Amazonian rainforests. We demonstrate that satellite data capture such decreases. Furthermore, we show that in 2004 and 2007, when there was lower wet-season water abundance compared to normal years, the photosynthetic activity of Amazonian forests also decreased. Potentially frequent water deficits over Amazon in the future, irrespective of whether they occur in the dry or wet season, will decrease the photosynthetic activity of Amazonian forests, and provide a positive feedback to global warming

Remote sensing phenology at European northern latitudes - From ground spectral towers to satellites

Plant phenology exerts major influences on carbon, water, and energy exchanges between atmosphere and ecosystems, provides feedbacks to climate, and affects ecosystem functioning and services. Great efforts have been spent in studying plant phenology over the past decades, but there are still large uncertainties and disputations in phenology estimation, trends, and its climate sensitivities. This thesis aims to reduce these uncertainties through analyzing ground spectral sampling, developing methods for in situ light sensor calibration, and exploring a new spectral index for reliable retrieval of remote sensing phenology and climate sensitivity estimation at European northern latitudes. The ground spectral towers use light sensors of either nadir or off-nadir viewing to measure reflected radiation, yet how plants in the sensor view contribute differently to the measured signals, and necessary in situ calibrations are often overlooked, leading to great uncertainties in ground spectral sampling of vegetation. It was found that the ground sampling points in the sensor view follow a Cauchy distribution, which is further modulated by the sensor directional response function. We proposed in situ light sensor calibration methods and showed that the user in situ calibration is more reliable than manufacturer’s lab calibration when our proposed calibration procedures are followed. By taking the full advantages of more reliable and standardized reflectance, we proposed a plant phenology vegetation index (PPI), which is derived from a radiative transfer equation and uses red and near infrared reflectance. PPI shows good linearity with canopy green leaf area index, and is correlated with gross primary productivity, better than other vegetation indices in our test. With suppressed snow influences, PPI shows great potentials for retrieving phenology over coniferous-dominated boreal forests. PPI was used to retrieve plant phenology from MODIS nadir BRDF-adjusted reflectance at European northern latitudes for the period 2000-2014. We estimated the trend of start of growing season (SOS), end of growing season (EOS), length of growing season (LOS), and the PPI integral for the time span, and found significant changes in most part of the region, with an average rate of -0.39 days·year-1 in SOS, 0.48 days·year-1 in EOS, 0.87 days·year-1 in LOS, and 0.79%·year-1 in the PPI integral over the past 15 years. We found that the plant phenology was significantly affected by climate in most part of the region, with an average sensitivity to temperature: SOS at -3.43 days·°C-1, EOS at 1.27 days·°C-1, LOS at 3.16 days·°C-1, and PPI integral at 2.29 %·°C-1, and to precipitation: SOS at 0.28 days∙cm-1, EOS at 0.05 days∙cm-1, LOS at 0.04 days∙cm-1, and PPI integral at -0.07%∙cm-1. These phenology variations were significantly related to decadal variations of atmospheric circulations, including the North Atlantic Oscillation and the Arctic Oscillation. The methods developed in this thesis can help to improve the reliability of long-term field spectral measurements and to reduce uncertainties in remote sensing phenology retrieval and climate sensitivity estimation

Remote sensing of snow : Factors influencing seasonal snow mapping in boreal forest region

Monitoring of snow cover in northern hemisphere is highly important for climate research and for operational activities, such as those related to hydrology and weather forecasting. The appearance and melting of seasonal snow cover dominate the hydrological and climatic patterns in the boreal and arctic regions. Spatial variability (in particular during the spring and autumn transition months) and long-term trends in global snow cover distribution are strongly interconnected to changes in Earth System (ES). Satellite data based estimates on snow cover extent are utilized e.g. in near-real-time hydrological forecasting, water resource management and to construct long-term Climate Data Records (CDRs) essential for climate research. Information on the quantitative reliability of snow cover monitoring is urgently needed by these different applications as the usefulness of satellite data based results is strongly dependent on the quality of the interpretation. This doctoral dissertation investigates the factors affecting the reliability of snow cover monitoring using optical satellite data and focuses on boreal regions (zone characterized by seasonal snow cover). Based on the analysis of different factors relevant to snow mapping performance, the work introduces a methodology to assess the uncertainty of snow cover extent estimates, focusing on the retrieval of fractional snow cover (within a pixel) during the spring melt period. The results demonstrate that optical remote sensing is well suited for determining snow extent in the melting season and that the characterizing the uncertainty in snow estimates facilitates the improvement of the snow mapping algorithms. The overall message is that using a versatile accuracy analysis it is possible to develop uncertainty estimates for the optical remote sensing of snow cover, which is a considerable advance in remote sensing. The results of this work can also be utilized in the development of other interpretation algorithms. This thesis consists of five articles predominantly dealing with quantitative data analysis, while the summary chapter synthesizes the results mainly in the algorithm accuracy point of view. The first four articles determine the reflectance characteristics essential for the forward and inverse modeling of boreal landscapes (forward model describes the observations as a function of the investigated variable). The effects of snow, snow-free ground and boreal forest canopy on the observed satellite scene reflectance are specified. The effects of all the error components are clarified in the fifth article and a novel experimental method to analyze and quantify the amount of uncertainty is presented. The five articles employ different remote sensing and ground truth data sets measured and/or analyzed for this research, covering the region of Finland and also applied to boreal forest region in northern Europe

A fractional snow cover mapping method for optical remote sensing data, applicable to continental scale

This thesis focuses on the determination of fractional snow cover (FSC) from optical data provided by satellite instruments. It describes the method development, starting from a simple regionally applicable linear interpolation method and ending at a globally applicable, semi-empirical modeling approach. The development work was motivated by the need for an easily implementable and feasible snow mapping method that could provide reliable information particularly for forested areas. The contribution of the work to the optical remote sensing of snow is mainly associated with accounting for boreal forest canopy effect to the observed reflectance, thus facilitating accurate fractional snow retrievals also for ground beneath the tree canopies. The first proposed approach was based on a linear interpolation technique, which relies on a priori known reference reflectances at a) full snow cover and b) snow-free conditions for each calculation unit-area. An important novelty in the methodology was the utilization of a forest sparseness index determined from AVHRR reflectance data acquired at full dry snow cover conditions. This index was employed to describe the similarity between different unit-areas. In practice, the index was used to determine the reference reflectances for such unit-areas for which the reflectance level could not be determined otherwise, e.g. due to frequent cloud cover. This approach was found to be feasible for Finnish drainage basins characterized by fragmented landscape with moderate canopies. Using a more physical approach instead of linear interpolation would allow the model parameterization using physical quantities (reflectances), and would therefore leave space for further model developments based on measuring and/or modeling of these quantities. The semi-empirical reflectance model-based method SCAmod originates from radiative transfer theory and describes the scene-level reflectance as a mixture of three major constituents: opaque forest canopy, snow and snow-free ground, which are interconnected through transmissivity and snow fraction. Transmissivity, in turn, can be derived from reflectance observations under conditions that highlight the presence of forest canopy, namely the presence of full snow cover on the ground. Thus, SCAmod requires a priori information on transmissivity, but given that it can be determined with the appropriate accuracy, it enables consideration of the obstructing effects of forests in fractional snow estimation. In continental-scale snow mapping, determination of the transmissivity map becomes a key issue. The preliminary demonstration of transmissivity generation using global land cover data was a part of this study. The first implementations and validations for SCAmod were presented for AVHRR data at Finnish drainage basin scale. In subsequent work, determination of the feasible reflectance constituents was addressed, followed by a sensitivity analysis targeting at selection of optimal spectral bands to be applied with SCAmod. Feasibility of the NDSI-based approach in FSC-retrievals over boreal forests is also discussed. Finally, the implementations and validations for MODIS and AATSR data are presented. The results from relative (using high-resolution Earth Observation data to represent the truth) and absolute validation (using in situ observations) indicate a good performance for both forested and non-forested regions in northern Eurasia. Accounting for the effect of forest canopy in the FSC-retrievals is the key issue in snow remote sensing over boreal regions; this study provides a new contribution to this research field and provides one solution for continental scale snow mapping

An Approach for the Long-Term 30-m Land Surface Snow-Free Albedo Retrieval from Historic Landsat Surface Reflectance and MODIS-based A Priori Anisotropy Knowledge

Land surface albedo has been recognized by the Global Terrestrial Observing System (GTOS) as an essential climate variable crucial for accurate modeling and monitoring of the Earth's radiative budget. While global climate studies can leverage albedo datasets from MODIS, VIIRS, and other coarse-resolution sensors, many applications in heterogeneous environments can benefit from higher-resolution albedo products derived from Landsat. We previously developed a "MODIS-concurrent" approach for the 30-meter albedo estimation which relied on combining post-2000 Landsat data with MODIS Bidirectional Reflectance Distribution Function (BRDF) information. Here we present a "pre-MODIS era" approach to extend 30-m surface albedo generation in time back to the 1980s, through an a priori anisotropy Look-Up Table (LUT) built up from the high quality MCD43A BRDF estimates over representative homogenous regions. Each entry in the LUT reflects a unique combination of land cover, seasonality, terrain information, disturbance age and type, and Landsat optical spectral bands. An initial conceptual LUT was created for the Pacific Northwest (PNW) of the United States and provides BRDF shapes estimated from MODIS observations for undisturbed and disturbed surface types (including recovery trajectories of burned areas and non-fire disturbances). By accepting the assumption of a generally invariant BRDF shape for similar land surface structures as a priori information, spectral white-sky and black-sky albedos are derived through albedo-to-nadir reflectance ratios as a bridge between the Landsat and MODIS scale. A further narrow-to-broadband conversion based on radiative transfer simulations is adopted to produce broadband albedos at visible, near infrared, and shortwave regimes.We evaluate the accuracy of resultant Landsat albedo using available field measurements at forested AmeriFlux stations in the PNW region, and examine the consistency of the surface albedo generated by this approach respectively with that from the "concurrent" approach and the coincident MODIS operational surface albedo products. Using the tower measurements as reference, the derived Landsat 30-m snow-free shortwave broadband albedo yields an absolute accuracy of 0.02 with a root mean square error less than 0.016 and a bias of no more than 0.007. A further cross-comparison over individual scenes shows that the retrieved white sky shortwave albedo from the "pre-MODIS era" LUT approach is highly consistent (R(exp 2) = 0.988, the scene-averaged low RMSE = 0.009 and bias = 0.005) with that generated by the earlier "concurrent" approach. The Landsat albedo also exhibits more detailed landscape texture and a wider dynamic range of albedo values than the coincident 500-m MODIS operational products (MCD43A3), especially in the heterogeneous regions. Collectively, the "pre-MODIS" LUT and "concurrent" approaches provide a practical way to retrieve long-term Landsat albedo from the historic Landsat archives as far back as the 1980s, as well as the current Landsat-8 mission, and thus support investigations into the evolution of the albedo of terrestrial biomes at fine resolution

Evaluation of high-latitude boreal forest growth using satellite-derived vegetation indices

Vegetation in northern high-latitudes plays an important role in energy exchange and carbon dynamics, thereby influencing regional and global climate. Vegetation indices derived from the space-borne Advanced Very High Resolution Radiometers (AVHRR) have suggested decreased photosynthetic activity during recent decades within some continental regions of the pan-arctic boreal forests. The purpose of this research was to determine associations between the normalized difference vegetation index (NDVI), as derived by both AVHRR and Moderate Resolution Imaging Spectroradiometers (MODIS), and inter-annual variations in radial stem growth in high-latitude coniferous forests. During 2008 and 2009, tree core samples were collected at 12 sites in northeast Russia and at 10 sites in northwest Canada. Ring-width indices (RWI; n = 27) were generated for larch, spruce, and pine genera and these were correlated with summer NDVI derived from the AVHRR sensors over the 1982 to 2008 period. The correlations between NDVI and RWI were then examined between 2000 and 2008 using both MODIS and AVHRR. The sensors showed similar abilities to proxy radial growth and NDVI-RWI correlations appeared mostly insensitive to changes in MODIS grain sizes between 250 m and 24 km. Over the 27 year period RWI and NDVI showed positive, though variable, correlations (r = 0.43 ± 0.19, n = 27). For pine and spruce, both evergreen conifers, the annual rate of radial growth was significantly correlated with growth during previous years, as was canopy development, as proxied by NDVI. Larch, however, did not show year to year persistence in either radial growth or canopy development, a finding that points to differences in growth patterns between functionally-distinct tree genera. These findings suggest that negative trends in NDVI may reflect decreased radial growth at some locations and that attempts to model tree growth and carbon uptake using NDVI need to take into account multi-year persistence in tree growth. Additionally, the work shows similarities between AVHRR and MODIS, suggesting potential to bridge the historical AVHRR record with the newer and finer resolution MODIS record