Northern Hemisphere permafrost map based on TTOP modelling for 2000-2016 at 1 km²scale

Permafrost is a key element of the cryosphere and an essential climate variable in the Global Climate Observing System. There is no remote-sensing method available to reliably monitor the permafrost thermal state. To estimate permafrost distribution at a hemispheric scale, we employ an equilibrium state model for the temperature at the top of the permafrost (TTOP model) for the 2000–2016 period, driven by remotely-sensed land surface temperatures, down-scaled ERA-Interim climate reanalysis data, tundra wetness classes and landcover map from the ESA Landcover Climate Change Initiative (CCI) project. Subgrid variability of ground temperatures due to snow and landcover variability is represented in the model using subpixel statistics. The results are validated against borehole measurements and reviewed regionally. The accuracy of the modelled mean annual ground temperature (MAGT) at the top of the permafrost is ±2 °C when compared to permafrost borehole data. The modelled permafrost area (MAGT 0) is around 21 × 106 km2 (22% of exposed land area), which is approximately 2 × 106 km2 less than estimated previously. Detailed comparisons at a regional scale show that the model performs well in sparsely vegetated tundra regions and mountains, but is less accurate in densely vegetated boreal spruce and larch forests

Satellite-based monitoring of pasture degradation on the Tibetan Plateau: A multi-scale approach

The Tibetan Plateau has been entitled Third-Pole-Environment'' because of its outstanding importance for the global climate and the hydrological system of East and Southeast Asia. Its climatological and hydrological influences are strongly affected by the local vegetation which is supposed to be subject to ongoing degradation. The degradation of the Tibetan pastures was investigated on the local scale by numerous studies. However, because methods and scales substantially differed among the previous studies, the overall pattern of degradation on the Tibetan Plateau is hitherto unknown. Consequently, the aims of this thesis are to monitor recent changes in the grassland degradation on the Tibetan Plateau and to detect the underlying driving forces of the observed changes. Therefore, a comprehensive remote sensing based approach is developed. The new approach consists of three parts and incorporates different spatial and temporal scales: (i) the development and testing of an indicator system for pasture degradation on the local scale, (ii) the development of a MODIS-based product usable for degradation monitoring from the local to the plateau scale, and (iii) the application of the new product to delineate recent changes in the degradation status of the pastures on the Tibetan Plateau. The first part of the new approach comprised the test of the suitability of a new two-indicator system and its transferability to spaceborne data. The indicators were land-cover fractions (e.g.,~green vegetation, bare soil) derived from linear spectral unmixing and chlorophyll content. The latter was incorporated as a proxy for nutrient and water availability. It was estimated combining hyperspectral vegetation indices as predictors in partial least squares regression. The indicator system was established and tested on the local scale using a transect design and textit{in situ} measured data. The promising results revealed clear spatial patterns attributed to degradation, indicating that the combination of vegetation cover and chlorophyll content is a suitable indicator system for the detection of pasture degradation on local scales on the Tibetan Plateau. To delineate patterns of degradation changes on the plateau scale, the green plant coverage of the Tibetan pastures was derived in the second part. Therefore, an upscaling approach was developed. It is based on satellite data from high spatial resolution sensors on the local scale (WorldView-type) via medium resolution data (Landsat) to low resolution data on the plateau scale (MODIS). The different spatial resolutions involved in the methodology were incorporated to enable the cross-validation of the estimations in the new product against field observations (over 600 plots across the entire Tibetan Plateau). Four methods (linear spectral unmixing, spectral angle mapper, partial least squares regression, and support vector machine regression) were tested on their predictive performance for the estimation of plant cover and the method with the highest accuracy (support vector machine regression) was applied to 14 years of MODIS data to generate a new vegetation coverage product. In the third part, the changes in vegetation cover between the years 2000 and 2013 and their driving forces were investigated by comparing the trends in the new vegetation coverage product against climate variables (precipitation from tropical rainfall measuring mission and 2 m air temperature from ERA-Interim reanalysis data) on the entire Tibetan Plateau. Large areas in southern Qinghai were identified where vegetation cover increased as a result of positive precipitation trends. Thus, degradation did not proceed in these regions. Contrasting with this, large areas in the central and western parts of the Tibetan Autonomous Region were subject to an ongoing degradation. This degradation can be attributed to the coincidence of rising temperatures and anthropogenic induced increases in livestock numbers as a consequence of local land-use change. In those areas, the ongoing degradation influenced local precipitation patterns because sensible heat fluxes were accelerated above degraded pastures. In combination with advected moist air masses at higher atmospheric levels, the accelerated heat fluxes led to an intensification of local convective rainfall. The ongoing degradation detected by the new remote sensing approach in this thesis is alarming. The affected regions encompass the river systems of the Indus and Brahmaputra Rivers, where the ongoing degradation negatively affects the water storage capacities of the soils and enhances erosion. In combination with the feed-back mechanisms between plant coverage and the changed precipitation on the Tibetan Plateau, the reduced water storage capacity will exacerbate runoff extremes in the middle and lower reaches of those important river systems

Variations and controlling factors of vegetation dynamics on the Qingzang Plateau of China over the recent 20 years

The impacts of climate change and human activities on vegetation dynamics have attracted wide attention, especially in sensitive and vulnerable areas such as the Qingzang Plateau of China. In this region, a series of ecological restoration projects have been launched while the effectiveness of these projects requires evaluation and further improvements. Remote sensing with high temporal resolution and spatial coverage is an effective way for the vegetation dynamics research in this region. In this study, the spatial and temporal distribution of climate factors and vegetation coverage as well as the influencing factors such as air temperature, precipitation, land use, slope, slope direction, soil and altitude were analyzed. The geographical detector was used to analyze the influence of climate factors on vegetation coverage and the interaction among factors in different eco-geographical regions. The results showed that: 1) the average values from the 20 years of normalized difference vegetation index (NDVI) decreased gradually from southeast (> 0.61) to northwest (0.12). The overall average of NDVI increased 0.02 per year from 1998 to 2018 and the impact factors varied among different eco-geographical regions; 2) some controlling factors showed nonlinear enhancement such as altitude and slope; 3) land use was an important factor affecting the distribution of vegetation especially in humid, semi-arid and arid areas, but the impacts of elevation and temperature were stronger than land use types in semi-humid and humid areas. The design and construction of ecological protection and restoration projects on the Qingzang Plateau required scientific and detailed demonstration as well as monitoring and evaluation. In addition, new tools and theories were also needed in the selection of ecosystem restoration strategies. Based on the findings, this study also provides suggestions for the sustainable ecological restoration on the Qingzang Plateau

The recent developments in cloud removal approaches of MODIS snow cover product

The snow cover products of optical remote sensing systems play an important role in research into global climate change, the hydrological cycle, and the energy balance. Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover products are the most popular datasets used in the community. However, for MODIS, cloud cover results in spatial and temporal discontinuity for long-term snow monitoring. In the last few decades, a large number of cloud removal methods for MODIS snow cover products have been proposed. In this paper, our goal is to make a comprehensive summarization of the existing algorithms for generating cloud-free MODIS snow cover products and to expose the development trends. The methods of generating cloud-free MODIS snow cover products are classified into spatial methods, temporal methods, spatio-temporal methods, and multi-source fusion methods. The spatial methods and temporal methods remove the cloud cover of the snow product based on the spatial patterns and temporal changing correlation of the snowpack, respectively. The spatio-temporal methods utilize the spatial and temporal features of snow jointly. The multi-source fusion methods utilize the complementary information among different sources among optical observations, microwave observations, and station observations.</p

Remote Sensing of Land Surface Phenology

Land surface phenology (LSP) uses remote sensing to monitor seasonal dynamics in vegetated land surfaces and retrieve phenological metrics (transition dates, rate of change, annual integrals, etc.). LSP has developed rapidly in the last few decades. Both regional and global LSP products have been routinely generated and play prominent roles in modeling crop yield, ecological surveillance, identifying invasive species, modeling the terrestrial biosphere, and assessing impacts on urban and natural ecosystems. Recent advances in field and spaceborne sensor technologies, as well as data fusion techniques, have enabled novel LSP retrieval algorithms that refine retrievals at even higher spatiotemporal resolutions, providing new insights into ecosystem dynamics. Meanwhile, rigorous assessment of the uncertainties in LSP retrievals is ongoing, and efforts to reduce these uncertainties represent an active research area. Open source software and hardware are in development, and have greatly facilitated the use of LSP metrics by scientists outside the remote sensing community. This reprint covers the latest developments in sensor technologies, LSP retrieval algorithms and validation strategies, and the use of LSP products in a variety of fields. It aims to summarize the ongoing diverse LSP developments and boost discussions on future research prospects

Remote Sensing of Environmental Changes in Cold Regions

This Special Issue gathers papers reporting recent advances in the remote sensing of cold regions. It includes contributions presenting improvements in modeling microwave emissions from snow, assessment of satellite-based sea ice concentration products, satellite monitoring of ice jam and glacier lake outburst floods, satellite mapping of snow depth and soil freeze/thaw states, near-nadir interferometric imaging of surface water bodies, and remote sensing-based assessment of high arctic lake environment and vegetation recovery from wildfire disturbances in Alaska. A comprehensive review is presented to summarize the achievements, challenges, and opportunities of cold land remote sensing

Vegetation Dynamics Revealed by Remote Sensing and Its Feedback to Regional and Global Climate

This book focuses on some significant progress in vegetation dynamics and their response to climate change revealed by remote sensing data. The development of satellite remote sensing and its derived products offer fantastic opportunities to investigate vegetation changes and their feedback to regional and global climate systems. Special attention is given in the book to vegetation changes and their drivers, the effects of extreme climate events on vegetation, land surface albedo associated with vegetation changes, plant fingerprints, and vegetation dynamics in climate modeling

Detection of Snow Cover from Historical and Recent AVHHR Data - A Thematic TIMELINE Processor

Global snow cover forms the largest and most transient part of the cryosphere in terms of area. On the local and regional scale, small changes can have drastic effects such as floods and droughts, and on the global scale is the planetary albedo. Daily imagery of snow cover forms the basis of long-term observation and analysis, and only optical sensors offer the necessary spatial and temporal resolution to address decadal developments and the impact of climate change on snow availability. The MODIS sensors have been providing this daily information since 2000; before that, only the Advanced Very High-Resolution Radiometer (AVHRR) from the National Oceanographic and Atmospheric Administration (NOAA) was suitable. In the TIMELINE project of the German Aerospace Center, the historic AVHRR archive in HRPT (High Resolution Picture Transmission) format is processed for the European area and, among other processors, one output is the thematic product 'snow cover' that will be made available in 1 km resolution since 1981. The snow detection is based on the Normalized Difference Snow Index (NDSI), which enables a direct comparison with the MODIS snow product. In addition to the NDSI, ERA5 re-analysis data on the skin temperature and other level 2 TIMELINE products are included in the generation of the binary snow mask. The AVHRR orbit segments are projected from the swath projection into LAEA Europe, aggregated into daily coverages, and from this, the 10-day and monthly snow covers are finally calculated. In this publication, the snow cover algorithm is presented, as well as the results of the first validations and possible applications of the final product

The impact of agricultural irrigation on land surface characteristics and near surface climate in China

It is well known that land cover and land use change can significantly influence the climate system by modulating surface-atmosphere exchanges. Land management, such as irrigation, also has a profound influence on the climate system. Irrigation can alter the water and energy flux from ground surface to the atmosphere and further influence near surface climate. Considering its dramatic expansion during the last century, the widespread use of irrigation has had an ongoing impact on our climate system. However, until now, this relationship between increased irrigation and its effect on climate system has not been well examined. The main objective of this dissertation is to quantify the irrigation impacts on land surface characteristics and near surface climate over China by using both observational (remote sensing and meteorological observation) and modeling studies with four specific questions: Where are the irrigated areas in China? What might have happened in the past? What will happen as a result of irrigation expansion in the future? And what is the relationship between the land cover land use change (LCLUC) impact and the irrigation impact on near surface climate in China? To answer these questions, I 1) developed three irrigation potential indices and produced a high resolution irrigation map of China; 2)analyzed and compared meteorological and remote sensing observations in irrigated and non-irrigated agriculture areas of China; 3) simulated both irrigation and LCLUC impact on land surface energy balance components (i.e., land surface temperature, latent flux, and sensible flux) and near surface climate (i.e., air temperature, water vapor, relative humidity) of China in the past (1978-2004) and also in two future time periods (2050 and 2100) by using the Community Land Model and compared the impact of irrigation with that of LUCC. Meteorological observations in Jilin Province show that the temperature differences between highly and lightly irrigated areas are statistically significant. The differences are highly correlated with the effective irrigation area (EIA) and sown area of crop (CSA). Results from satellite observations show that highly irrigated areas corresponded to lower albedo and daytime land surface temperature (LST), and higher normalized difference vegetation index (NDVI) and evapotranspiration (ET). The difference between highly and lightly irrigated areas is bigger in drier areas and in drier years. The modeling studies show that the irrigation impact on temperature is much less in the future than in the 20th century and that irrigation impacts more on the maximum air temperature than on the minimum air temperature. Both contemporary and future irrigation simulations show, nationally, irrigation decreases daily maximum temperature (Tmax) but increase daily minimum temperature (Tmin). Daily mean temperature (Tmean) decreases in contemporary irrigation simulations but increases in most of the cases in future irrigation simulations. In the 20th century, nationally, the spray irrigation leads to a decrease in Tmax of 0.079K and an increase in Tmin of 0.022K. Nationally, the spray irrigation leads to a decrease in Tmax between 0.022K and 0.045K and an increase in Tmin between 0.019K and 0.057K under future scenarios. This study demonstrates that the irrigation patterns (flood irrigation and spray irrigation) have statistically significant impacts on local climate. Moreover, this study suggests that, in the national respective, the impacts of changes in land management on climate are not comparable to the impacts of changes in land cover land use. This dissertation on irrigation and its impact is the first study which focuses solely on China using observational and modeling methods. The results from this dissertation contribute to a better understanding of the irrigation impact on near-surface climate which can improve our knowledge of how human activities influence climate, guide future policies aimed at mitigating or adapting to climate change, and help design a precise model to project the impact of irrigation on the climate system and irrigation requirements in the future. It can also be useful in assessing future food and water security issues

Review article: Inferring permafrost and permafrost thaw in the mountains of the Hindu Kush Himalaya region

The cryosphere reacts sensitively to climate change, as evidenced by the widespread retreat of mountain glaciers. Subsurface ice contained in permafrost is similarly affected by climate change, causing persistent impacts on natural and human systems. In contrast to glaciers, permafrost is not observable spatially and therefore its presence and possible changes are frequently overlooked. Correspondingly, little is known about permafrost in the mountains of the Hindu Kush Himalaya (HKH) region, despite permafrost area exceeding that of glaciers in nearly all countries. Based on evidence and insight gained mostly in other permafrost areas globally, this review provides a synopsis on what is known or can be inferred about permafrost in the mountains of the HKH region. Given the extreme nature of the environment concerned, it is to be expected that the diversity of conditions and phenomena encountered in permafrost exceed what has previously been described and investigated. We further argue that climate change in concert with increasing development will bring about diverse permafrost-related impacts on vegetation, water quality, geohazards, and livelihoods. To better anticipate and mitigate these effects, a deepened understanding of high-elevation permafrost in subtropical latitudes as well as the pathways interconnecting environmental changes and human livelihoods are needed