Satellite observations of annual variability in terrestrial carbon cycles and seasonal growing seasons at high northern latitudes

Global satellite remote sensing records show evidence of recent vegetation greening and an advance in the onset of the growing season at high latitudes. We apply a terrestrial net primary production (NPP) model driven by satellite observations of vegetation properties and daily surface meteorology from an atmospheric GCM to assess spatial patterns, annual variability, and recent trends in vegetation productivity across Alaska and northwest Canada. We compare these results with regional observations of the timing of growing season onset derived from satellite passive microwave remote sensing measurements from the Special Sensor Microwave Imager, SSM/I. Our results show substantial variability in annual NPP for the region that appears to be driven largely by variations in canopy photosynthetic leaf area and average summer air temperatures. Variability in maximum canopy leaf area and NPP also correspond closely to remote sensing observations of the timing of the primary seasonal thaw event in spring. Relatively early spring thawing appears to enhance NPP, while delays in seasonal thawing and growing season onset reduce annual vegetation productivity. Our results indicate that advances in seasonal thawing and spring and summer warming for the region associated with global change are promoting a general increase in NPP

Spring Thaw and Its Effect on Terrestrial Vegetation Productivity in the Western Arctic Observed from Satellite Microwave and Optical Remote Sensing

Global satellite remote sensing records show evidence of recent vegetation greening and an advancing growing season at high latitudes. Satellite remote sensing–derived measures of photosynthetic leaf area index (LAI) and vegetation gross and net primary productivity (GPP and NPP) from the NOAA Advanced Very High Resolution Radiometer (AVHRR) Pathfinder record are utilized to assess annual variability in vegetation productivity for Alaska and northwest Canada in association with the Western Arctic Linkage Experiment (WALE). These results are compared with satellite microwave remote sensing measurements of springtime thaw from the Special Sensor Microwave Imager (SSM/I). The SSM/I-derived timing of the primary springtime thaw event was well correlated with annual anomalies in maximum LAI in spring and summer (P ≤ 0.009; n = 13), and GPP and NPP (P ≤ 0.0002) for the region. Mean annual variability in springtime thaw was on the order of ±7 days, with corresponding impacts to annual productivity of approximately 1% day−1. Years with relatively early seasonal thawing showed generally greater LAI and annual productivity, while years with delayed seasonal thawing showed corresponding reductions in canopy cover and productivity. The apparent sensitivity of LAI and vegetation productivity to springtime thaw indicates that a recent advance in the seasonal thaw cycle and associated lengthening of the potential period of photosynthesis in spring is sufficient to account for the sign and magnitude of an estimated positive vegetation productivity trend for the western Arctic from 1982 to 2000

Variability in springtime thaw in the terrestrial high latitudes: Monitoring a major control on the biospheric assimilation of atmospheric CO2 with spaceborne microwave remote sensing

Evidence is presented from the satellite microwave remote sensing record that the timing of seasonal thawing and subsequent initiation of the growing season in early spring has advanced by approximately 8 days from 1988 to 2001 for the pan-Arctic basin and Alaska. These trends are highly variable across the region, with North America experiencing a larger advance relative to Eurasia and the entire region. Interannual variability in the timing of spring thaw as detected from the remote sensing record corresponded directly to seasonal anomalies in mean atmospheric CO2 concentrations for the region, including the timing of the seasonal draw down of atmospheric CO2 from terrestrial net primary productivity (NPP) in spring, and seasonal maximum and minimum CO2 concentrations. The timing of the seasonal thaw for a given year was also found to be a significant (P \u3c 0.01) predictor of the seasonal amplitude of atmospheric CO2 for the following year. These results imply that the timing of seasonal thawing in spring has a major impact on terrestrial NPP and net carbon exchange at high latitudes. The initiation of the growing season has also been occurring earlier, on average, over the time period addressed in this study and may be a major mechanism driving observed atmospheric CO2 seasonal cycle advances, vegetation greening, and enhanced productivity for the northern high latitudes

Linear mixing model applied to coarse resolution satellite data

A linear mixing model typically applied to high resolution data such as Airborne Visible/Infrared Imaging Spectrometer, Thematic Mapper, and Multispectral Scanner System is applied to the NOAA Advanced Very High Resolution Radiometer coarse resolution satellite data. The reflective portion extracted from the middle IR channel 3 (3.55 - 3.93 microns) is used with channels 1 (0.58 - 0.68 microns) and 2 (0.725 - 1.1 microns) to run the Constrained Least Squares model to generate fraction images for an area in the west central region of Brazil. The derived fraction images are compared with an unsupervised classification and the fraction images derived from Landsat TM data acquired in the same day. In addition, the relationship betweeen these fraction images and the well known NDVI images are presented. The results show the great potential of the unmixing techniques for applying to coarse resolution data for global studies

Use of satellite-derived heterogeneous surface soil moisture for numerical weather prediction, The

Summer 1996.Bibliography: pages [296]-320

Open access data in polar and cryospheric remote sensing

This paper aims to introduce the main types and sources of remotely sensed data that are freely available and have cryospheric applications. We describe aerial and satellite photography, satellite-borne visible, near-infrared and thermal infrared sensors, synthetic aperture radar, passive microwave imagers and active microwave scatterometers. We consider the availability and practical utility of archival data, dating back in some cases to the 1920s for aerial photography and the 1960s for satellite imagery, the data that are being collected today and the prospects for future data collection; in all cases, with a focus on data that are openly accessible. Derived data products are increasingly available, and we give examples of such products of particular value in polar and cryospheric research. We also discuss the availability and applicability of free and, where possible, open-source software tools for reading and processing remotely sensed data. The paper concludes with a discussion of open data access within polar and cryospheric sciences, considering trends in data discoverability, access, sharing and use.A. Pope would like to acknowledge support from the Earth Observation Technology Cluster, a knowledge exchange project, funded by the Natural Environment Research Council (NERC) under its Technology Clusters Programme, the U.S. National Science Foundation Graduate Research Fellowship Program, Trinity College (Cambridge) and the Dartmouth Visiting Young Scientist program sponsored by the NASA New Hampshire Space Grant.This is the final published version. It's also available from MDPI at http://www.mdpi.com/2072-4292/6/7/6183

Determination of Spring Onset and Growing Season Duration using Satellite Measurements

An integrated approach to retrieve microwave emissivity difference vegetation index (EDVI) over land regions has been developed from combined multi-platform/multi-sensor satellite measurements, including SSM/I measurements. A possible relationship of the remotely sensed EDVI and the leaf physiology of canopy is exploited at the Harvard Forest site for two growing seasons. This study finds that the EDVI is sensitive to leaf development through vegetation water content of the crown layer of the forest canopy, and has demonstrated that the spring onset and growing season duration can be determined accurately from the time series of satellite estimated EDVI within uncertainties about 3 and 7 days for spring onsets and growing season duration, respectively, compared to in-situ observations. The leaf growing stage may also be quantitatively monitored by a normalized EDVI. Since EDVI retrievals from satellite are generally possible during both daytime and nighttime under non-rain conditions, the EDVI technique studied here may provide higher temporal resolution observations for monitoring the onset of spring and the duration of growing season compared to currently operational satellite methods

Detection of dry snow using spaceborne microwave radiometer data

Snow monitoring on global scale is an important task considering the essential role of snow cover in the Earth’s climate and the scarcity of ground-based snow observations. Snow has distinctive, frequency-dependent characteristics in terms of microwave emission.This enables the use of brightness temperatures, as measured by spaceborne passive microwave sensors, not only for the estimation of snow cover extent (SCE) through (dry) snow detection, but also for the retrieval of snow depth and snow water equivalent (SWE).Approaches for SWE retrieval, such as the methodology of the GlobSnow v3.0 SWE product, frequently implement dry snow detection as one of the main processing steps. Reliable dry snow detection is thus crucial, however, common algorithms are known to generally underestimate the presence of snow due to their sensitivity to vegetation and liquid water content of the snowpack, amongst other. Although several suggestions for improvement have been proposed, an extensive, long-term comparison has not been conducted. This thesis hence investigates six current dry snow detection algorithms and their intraseasonal performance in order to identify the most appropriate one for implementation in the GlobSnow SWE product. The aim is to improve the product which is primarily affected by underestimation during the snow accumulation period from September to February. The investigated algorithms are based on the brightness temperature difference involving primarily, but not exclusively, the 18/19-GHz and 37-GHz channels which are available for the SMMR, SSM/I and SSMIS instruments covering more than 40 years of observations. In addition to conventional daily snow masks, cumulative snow masks are investigated as a means to counteract underestimation. The assessment focuses on seasonal snow above 40° North, and is conducted for the snow seasons from 1979/1980 to 2017/2018 with reference to exhaustive, in situ snow depth data from multiple sources. In addition, spatially-complete SCE maps by the Interactive Multisensor Snow and Ice Mapping System serve as reference from 2007/2008 to 2016/2017, in order to evaluate the detected snow cover extent as a whole. The results emphasise the potential of cumulative masks to counteract underestimation and increase detection accuracy, and highlight the benefit of discriminating between different scattering sources, that could otherwise be mistaken for snow. Two methods are found to be overall best-performing: the empirically-derived algorithm of the EUMETSAT H SAF H11 product (applicable to SMMR, SSM/I and SSMIS), and the decision tree published by Grody and Basist in 1996 (applicable to SSM/I and SSMIS). Promising accuracies with respect to in situ data are achieved using cumulative masks, reaching approximately 0.83 and 0.80 for the approaches of Grody and Basist and of the H SAF product, respectively. Implementing the H SAF algorithm into the GlobSnow SWE product is expected to lead to immediate improvements of the latter and is thus planned, though falls outside the scope of this thesis. Further investigation is required to adapt the approach of Grody and Basist to the whole long-term passive microwave data record including SMMR data