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Human life and the entire ecosystem of South East Asia depend upon the monsoon climate and its predictability. More than 40% of the earths population lives in this region. Droughts and floods associated with the variability of rainfall frequently cause serious damage to ecosystems in these regions and, more importantly, injury and loss of human life. The headwater areas of seven major rivers in SE Asia, i.e. Yellow River, Yangtze, Mekong, Salween, Irrawaddy, Brahmaputra and Ganges, are located in the Tibetan Plateau. Estimates of the Plateau water balance rely on sparse and scarce observations that cannot provide the required accuracy, spatial density and temporal frequency. Fully integrated use of satellite and ground observations is necessary to support water resources management in SE Asia and to clarify the roles of the interactions between the land surface and the atmosphere over the Tibetan Plateau in the Asian monsoon system. The goal of this project is to: 1. Construct out of existing ground measurements and current / future satellites an observing system to determine and monitor the water yield of the Plateau, i.e. how much water is finally going into the seven major rivers of SE Asia; this requires estimating snowfall, rainfall, evapotranspiration and changes in soil moisture; 2. Monitor the evolution of snow, vegetation cover, surface wetness and surface fluxes and analyze the linkage with convective activity, (extreme) precipitation events and the Asian Monsoon; this aims at using monitoring of snow, vegetation and surface fluxes as a precursor of intense precipitation towards improving forecasts of (extreme) precipitations in SE Asia. A series of international efforts initiated in 1996 with the GAME-Tibet project. The effort described in this proposal builds upon 10 years of experimental and modeling research and the consortium includes many key-players and pioneers of this long term research initiative.

Agency: European Commission | Branch: H2020 | Program: RIA | Phase: BG-09-2016 | Award Amount: 15.49M | Year: 2016

The overall objective of INTAROS is to develop an integrated Arctic Observation System (iAOS) by extending, improving and unifying existing systems in the different regions of the Arctic. INTAROS will have a strong multidisciplinary focus, with tools for integration of data from atmosphere, ocean, cryosphere and terrestrial sciences, provided by institutions in Europe, North America and Asia. Satellite earth observation data plays an increasingly important role in such observing systems, because the amount of EO data for observing the global climate and environment grows year by year. In situ observing systems are much more limited due to logistical constraints and cost limitations. The sparseness of in situ data is therefore the largest gap in the overall observing system. INTAROS will assess strengths and weaknesses of existing observing systems and contribute with innovative solutions to fill some of the critical gaps in the in situ observing network. INTAROS will develop a platform, iAOS, to search for and access data from distributed databases. The evolution into a sustainable Arctic observing system requires coordination, mobilization and cooperation between the existing European and international infrastructures (in-situ and remote including space-based), the modeling communities and relevant stakeholder groups. INTAROS will include development of community-based observing systems, where local knowledge is merged with scientific data. An integrated Arctic Observation System will enable better-informed decisions and better-documented processes within key sectors (e.g. local communities, shipping, tourism, fisheries), in order to strengthen the societal and economic role of the Arctic region and support the EU strategy for the Arctic and related maritime and environmental policies.

Agency: European Commission | Branch: FP7 | Program: CP | Phase: ENV.2013.6.2-6 | Award Amount: 11.20M | Year: 2013

By 2050, global agricultural productivity will need to increase with at least 70%. In order to guarantee food production for future generations, agricultural production will need to be based on sustainable land management practises. At present, earth observation based (global) crop monitoring systems focus mostly on short-term agricultural forecasts, thereby neglecting longer term environmental effects. However, it is well known that unsustainable cultivation practises may lead to a degradation of the (broader) environment resulting in lower agricultural productivity. As such, agricultural monitoring systems need to be complemented with methods to also assess environmental impacts of change in crop land and shifting cultivation practises. It is thereby important that this is addressed at the global level. SIGMA presents a global partnership of expert institutes in agricultural monitoring, with a strong involvement in GEO and the Global Agricultural Geo-Monitoring (GEO-GLAM) initiative. SIGMA aims to develop innovative methods, based upon the integration of in-situ and earth observation data, to enable the prediction of the impact of crop production on ecosystems and natural resources. The proposed project will address methods to: i. enable sharing and integration of satellite and in situ observations according to GEOSS Data CORE principles; ii. assess the impact of cropland areas and crop land change on other ecosystems; iii. understand and assess shifts in cultivation practises and cropping systems to evaluate impacts on biodiversity and the environment. Furthermore, dedicated capacity building activities are planned to increase national and international capacity to enable sustainable management of agriculture. Lastly, a strong coordinating mechanism will be put in place, through the project partners, between SIGMA and the G20 Global Agricultural Geo-Monitoring Initiative (GEOGLAM), in order to assure transparency and alignment of the SIGMA activities.

Tong Q.,CAS Institute of Remote Sensing | Xue Y.,CAS Shanghai Institute of Technical Physics | Zhang L.,CAS Institute of Remote Sensing
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing | Year: 2014

This paper reviews progress in hyperspectral remote sensing (HRS) in China, focusing on the past three decades. China has made great achievements since starting in this promising field in the early 1980s. A series of advanced hyperspectral imaging systems ranging from ground to airborne and satellite platforms have been designed, built, and operated. These include the field imaging spectrometer system (FISS), the Modular Airborne Imaging Spectrometer (MAIS), and the Chang'E-I Interferometer Spectrometer (IIM). In addition to developing sensors, Chinese scientists have proposed various novel image processing techniques. Applications of hyperspectral imaging in China have been also performed including mineral exploration in the Qilian Mountains and oil exploration in Xinjiang province. To promote the development of HRS, many generic and professional software tools have been developed. These tools such as the Hyperspectral Image Processing and Analysis System (HIPAS) incorporate a number of special algorithms and features designed to take advantage of the wealth of information contained in HRS data, allowing them to meet the demands of both common users and researchers in the scientific community. © 2013 IEEE.

Cheng T.,CAS Institute of Remote Sensing | Wu Y.,CAS Institute of Remote Sensing | Chen H.,CAS Institute of Remote Sensing
Optics Express | Year: 2014

Light absorbing carbon aerosols play a substantial role in climate change through radiative forcing, which is the dominant absorber of solar radiation. Radiative properties of light absorbing carbon aerosols are strongly dependent on the morphological factors and the mixing mechanism of black carbon with other aerosol components. This study focuses on the morphological effects on the optical properties of internally mixed light absorbing carbon aerosols using the numerically exact superposition Tmatrix method. Three types aerosols with different aging status such as freshly emitted BC particles, thinly coated light absorbing carbon aerosols, heavily coated light absorbing carbon aerosols are studied. Our study showed that morphological factors change with the aging of internally mixed light absorbing carbon aerosols to result in a dramatic change in their optical properties. The absorption properties of light absorbing carbon aerosols can be enhanced approximately a factor of 2 at 0.67 um, and these enhancements depend on the morphological factors. A larger shell/core diameter ratio of volume-equivalent shell-core spheres (S/C), which indicates the degree of coating, leads to stronger absorption. The enhancement of absorption properties accompanies a greater enhancement of scattering properties, which is reflected in an increase in single scattering albedo (SSA). The enhancement of single scattering albedo due to the morphological effects can reach a factor of 3.75 at 0.67 μm. The asymmetry parameter has a similar yet smaller enhancement. Moreover, the corresponding optical properties of shell-and-core model determined by using Lorenz-Mie solutions are presented for comparison. We found that the optical properties of internally mixed light absorbing carbon aerosol can differ fundamentally from those calculated for the Mie theory shell-andcore model, particularly for thinly coated light absorbing carbon aerosols. Our studies indicate that the complex morphology of internally mixed light absorbing carbon aerosols must be explicitly considered in climate radiation balance © 2014 Optical Society of America.

Huo L.-Z.,CAS Institute of Remote Sensing | Tang P.,CAS Institute of Remote Sensing
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing | Year: 2014

In this paper, a region-partitioning active learning (AL) technique is proposed for classification of remote sensing (RS) images based on the support vector machines (SVM) classifier. In the batch-mode AL process, diversity information is required to select a batch of informative samples. A new AL technique that aims to introduce diversity information is proposed based on relative positions of candidate samples in the feature space. The proposed technique selects informative samples according to an uncertainty criterion at each iteration. These samples are selected with an extra constraint to guarantee that they are not located in the same region of the feature space. The proposed technique is compared with state-of-the-art methods adopted in the RS community. Experimental tests were performed on three data sets, including one very high spatial resolution multispectral data set and two hyperspectral data sets. The proposed algorithm displays a classification performance that is similar to or even better than the state-of-the-art methods. In addition, the proposed algorithm performs efficiently in terms of computational time. © 2008-2012 IEEE.

Huang N.,CAS Institute of Remote Sensing | Niu Z.,CAS Institute of Remote Sensing
Plant and Soil | Year: 2013

Aims: Our aims were to identify the primary factors involved in soil respiration (Rs) variability and the role that spectral vegetation indices played in Rs estimation in irrigated and rainfed agroecosystems during the growing season. Methods: We employed three vegetation indices [i.e., normalized difference vegetation index (NDVI), green edge chlorophyll index (CIgreen edge) and enhanced vegetation index (EVI)] derived from the Moderate-resolution Imaging Spectroradiometer (MODIS) surface reflectance product as approximations of crop gross primary production (GPP) for Rs estimation. Different statistical models were used to analyze the dependencies of Rs on soil temperature, soil water content and plant photosynthesis, and accuracy of these models were compared in the irrigated and rainfed agroecosystems. Results: The results demonstrated that a model based only on abiotic factors (e.g., soil temperature and soil water content) failed to describe part of the growing-season variability in Rs. Residual analysis indicated that Rs was influenced by a short-term gross primary production (GPP) and a longer-term (≥3 days) accumulated GPP in the irrigated and rainfed agroecosystems. Therefore, photosynthesis dependency of Rs should be included in the Rs model to describe the growing-season dynamics of Rs. Among the three VIs, CIgreen edge showed generally better correlations with GPP at different cumulative times and canopy green leaf area index than EVI and NDVI. Adding the CIgreen edge into the model considering only soil temperature and soil water content significantly improved the simulation accuracy of Rs. Conclusions: Our results suggest that spectral vegetation index from remote sensing could be used to estimate Rs, which will be helpful for the development of a future Rs model over a large spatial scale. © 2012 Springer Science+Business Media Dordrecht.

Li X.-M.,CAS Institute of Remote Sensing
Geophysical Research Letters | Year: 2016

A half-century ago, it was recorded that ocean swells can propagate up to halfway around the globe. However, from a global perspective, how ocean swells propagate in the global oceans has yet to be depicted. To date, synthetic aperture radar (SAR) is the only available remote sensing instrument to measure the two-dimensional information of ocean surface waves. Here a 10 year (2002–2012) global wave data set of the spaceborne advanced SAR on board the European Space Agency's satellite Envisat and the global wind data set of the WindSat were used to (1) depict the propagation routes of ocean swells in the global oceans, (2) identify four distinguished crossing swell “pools,” and (3) interpret how these pools are formed. Together, these findings yield a new insight into ocean swells propagation and the consequent occurrence of crossing swells on a global ocean scale from space, which will further deepen our understanding of nature of ocean. ©2016. American Geophysical Union. All Rights Reserved.

Yan N.,CAS Institute of Remote Sensing | Wu B.,CAS Institute of Remote Sensing
Agricultural Water Management | Year: 2014

Achieving higher yield per unit of water is one of the most important challenges in water-limited agriculture. In this paper, crop water productivity (CWP) of winter wheat was calculated and analyzed in the plain of Hai Basin in northeastern China. The average CWP of winter wheat (Triticumaestivum L.) in the basin for 2003-2009 was 1.049kgm-3, with CWP values across the basin ranging between 0.7 and 1.4kgm-3. The spatial analysis of the relationships among CWP, yield, and evapotranspiration (ET) across the basin showed a strongly linear relationship between ET and yield (R2=0.86). The temporal analysis showed increases in yield of between 100.4-211.4kgha-1year-1 between 1984 and 2002 at eight agro-meteorological research stations across the basin without a corresponding increase in ET, corresponding to an increase in CWP of 0.02-0.1kgm-3per year. It was concluded that the improvements in CWP have resulted from improvements in crop varieties and crop husbandry rather than reductions in water consumption. © 2013 Elsevier B.V.

Zhao Y.,CAS Institute of Remote Sensing | Wu B.F.,CAS Institute of Remote Sensing | Zeng Y.,CAS Institute of Remote Sensing
Biogeosciences | Year: 2013

Anthropogenic activity has led to significant emissions of greenhouse gas (GHG), which is thought to play important roles in global climate changes. It remains unclear about the kinetics of GHG emissions, including carbon dioxide (CO2), methane (CH4) and nitrous Oxide (N2O) from the Three Gorges Reservoir (TGR) of China, which was formed after the construction of the famous Three Gorges Dam. Here we report monthly measurements for one year of the fluxes of these gases at multiple sites within the TGR region, including three major tributaries, six mainstream sites, two downstream sites and one upstream site. The tributary areas have lower CO2 fluxes than the main storage; CH4 fluxes in the tributaries and upper reach mainstream sites are relative higher. Overall, TGR showed significantly lower CH4 emission rates than most new reservoirs in temperate and tropical regions. We attribute this to the well-oxygenated deep water and high water velocities that may facilitate the consumption of CH4. TGR's CO2 fluxes were lower than most tropical reservoirs and higher than most temperate systems. This could be explained by the high load of labile soil carbon delivered through erosion to the Yangtze River. Compared to fossil-fuelled power plants of equivalent power output, TGR is a very small GHG emitter-annual CO2-equivalent emissions are approximately 1.7% of that of a coal-fired generating plant of comparable power output. © 2012 Author(s).

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