Snow is one of the most important components of the cryosphere. Remote sensing of snow focuses on the retrieval of snow parameters and monitoring of variations in snow using satellite data. These parameters are key inputs for hydrological and atmospheric models. Over the past 30 years, the field of snow remote sensing has grown dramatically in China. The 30-year achievements of research in different aspects of snow remote sensing in China, especially in (1) methods of retrieving snow cover, snow depth/snow water equivalent, and grain size and (2) applications to snowmelt runoff modeling, snow response on climate change, and remote sensing monitoring of snow-caused disasters are reviewed/summarized. The importance of the first remote sensing experiment on snow parameters at the upper reaches of the Heihe River Basin, in 2008, is also highlighted. A series of experiments, referred to as the Cooperative Observation Series for Snow (COSS), focus on some key topics on remote sensing of snow. COSS has been implemented for 3 years and will continue in different snow pattern regions of China. The snow assimilation system has been established in some regions using advanced ensemble Kalman filters. Finally, an outlook for the future of remote sensing of snow in China is given.
The complex terrain, shallow snowpack, and cloudy conditions of the Tibetan Plateau (TP) can greatly affect the reliability of different remote sensing (RS) data, and available station data are scarce for simulating and validating the snow distribution. Aiming at these problems, we design a synthesis method for simulating the snow distribution in the TP where the snow is patchy and shallow in most regions. Different RS data are assimilated into the SnowModel, using the ensemble Kalman filter method. The station observations are used for the validation of assimilated snow depth. To avoid the scale effect during validation, we design a random sampling comparison method by constructing a subjunctive region near each station. For years 2000 to 2008, the root-mean-square error of the assimilated results are in the range [0.002 m, 0.008 m], and the range of Pearson product-moment correlation coefficients between the in situ observations and the assimilated results are in the range [0.61, 0.87]. The result suggests that the snow depletion curve is the most important parameter for the simulation of the snow distribution in ungauged regions, especially in the TP where the snow is patchy and shallow.
In the eve of the Beijing Olympics Games, Qingdao in China, as the host city of OSC of Beijing 2008 Olympic Games, was surrounded by Enteromorpha prolifera, which was followed with interest by whole China and the world. The Enteromorpha often comes from other ocean, monitoring the drifting path of the Enteromorpha will become very important.The Study area is mainly the Yellow Sea. And the data sources are Terra MODIS 1B images from 2000-2010 years. The data preprocessing include BOW-TIE processing, image registration, clip, merge, and masking. And the NDVI was selected as the index of derived Enteromorpha prolifera information, to get the range of Enteromorpha prolifera, and get that of dynamic change with time, and monitor the drifting path of the Enteromorpha.
Net primary production (NPP) is the production of organic compounds from atmospheric or aquatic carbon dioxide, principally through the process of photosynthesis. Climate changes of this magnitude are expected to affect the NPP of the world’s land ecosystems. In this study, we used a light-use efficiency model and linear regression model to describe and analyze the spatial and temporal patterns of terrestrial net primary productivity (NPP) in China during 2002-2010. First, we used the reconstructed 16-day 0.05°MODIS NDVI product (MOD13C1), 0.05°gridded GLDAS (Global Land Data Assimilation System) meteorological data and land use map to estimate the NPP in China. The spatial variability of NPP was analyzed during all periods, growing seasons and different seasons, respectively. Based on regression analysis method, we quantified the trend of NPP change in China during 2002-2010.
This article utilized aerosol product of MODIS and aerosol data provided by AERONET which is real-time monitoring data about aerosol in a station to study spatial-temporal changes of aerosol in the Yellow Sea of China in 2006, and adopted some indexes such AOD (Aerosol Optical Depth) and Angstrom index (α and β), and analyzed monthly distribution and annual average of aerosol. The result suggested that AOD had significant negative correlation with Angstrom-α (r=-0.7261), and significant positive correlation with Angstrom-β (r=0.9576), and Angstrom-α had significant negative correlation with Angstrom-β (r=-0.8791). AOD and Angstrom-β came up to the maximum in Spring, then in Summer and Winter, and down to the minimum in Fall in the study area, and Angstrom-α was completely opposite. AOD and Angstrom-β had a upward trend from offshore to deep sea area, and from the north to the south of the Yellow Sea, while Angstrom-α had a downward trend. Analysis of Angstrom-α displayed that the offshore was polluted by small particles from anthropic activities, and the main content of aerosol was large particle of sea salt in the deep sea field. The main type of aerosol was consisted of small particle aerosol emitted from anthropic activities in Summer and Fall, and of sea-salt particle in Spring and Winter in the Yellow Sea. Spatially the diameter of aerosol in the north of the sea was bigger than one in the south. This study obtained the general distribution spatially and temporally in the Yellow Sea of China, and especially the fact that the main content of aerosol in the offshore was small particle from anthropic activities was paid attention to
Accurate estimation of snow properties is important for effective water resources management especially in mountainous areas. In this work, we develop a snow data assimilation scheme based on ensemble Kalman filter (EnKF), which can assimilate remotely sensed snow observations into the Common Land Model (CoLM) to produce spatially continuous and temporally consistent snow variables. The snow cover fraction (SCF) product (MOD10C1) from the moderate resolution imaging spectroradiometer (MODIS) aboard the NASA Terra satellite was used to update CoLM snow properties. The assimilation experiment is conducted during 2003-2004, in Xingjiang province, west China. The preliminary results are very promising and show that distributions of snow variables (such as SCF, snow depth, and SWE) are more reasonable and reliable after assimilating MODIS SCF data. The results also indicate that EnKF is an effective and operationally feasible solution for improve snow properties prediction.
Change detection is the process of identifying difference in the scenes of an object or a phenomenon, by observing the
same geographic region at different times. Many algorithms have been applied to monitor various environmental
changes. Examples of these algorithms are difference image, ratio image, classification comparison, and change vector
analysis. In this paper, a change detection approach for multi-temporal multi-spectral remote sensing images, based on
Independent Component Analysis (ICA), is proposed. The environmental changes can be detected in reduced second and
higher-order dependencies in multi-temporal remote sensing images by ICA algorithm. This can remove the correlation
among multi-temporal images without any prior knowledge about change areas. Different kinds of land cover changes
are obtained in these independent source images. The experimental results in synthetic and real multi-temporal
multi-spectral images show the effectiveness of this change detection approach.
The sampling protocol adopted during a field campaign at an Alpine meadow site (Shandan site), during July 2002 is
based on the so-called "Valeri" protocol (VALERI). The field campaign LAI measurements in Shandan are scaled up to
30×30 m2 raster maps based on Landsat ETM+ imagery. Regression analysis is applied to construct empirical transfer
functions for the determination of Leaf Area Index (LAI) raster imagery from ETM+ Normalized Difference Vegetation
Index (NDVI) and Simple Ratio (SR) data. Subsequently, the scaling up of the LAI raster maps is performed by the
aggregation of the 30x30 m2 data into 1×1 km2 pixels by calculating the average LAI values for the low resolution pixels.
The up-scaled data are used to validate the MODIS LAI product at the Shandan site. A power regression model
(LAI=2.3758*NDVI3.5216, R2=0.66, P<0.01), established between field measured LAI and ETM+ NDVI, elicits a high
statistical significance. A linear regression model (LAI=0.1798*SR-0.3574, R2=0.55, P<0.01) is established between
field measured LAI and ETM+ SR. The MODIS LAI product correlates best with the ETM+ LAI transfer function
obtained with NDVI data. Its R2 reaches 0.46, its slope 0.97, but the intercept is 0.7, which suggests that MODIS LAI is
systematically underestimated. The results illustrate that LAI measured with a LAI-2000 instrument at the VALERI
Shandan site leads to an underestimation of the MODIS LAI product. A plausible cause for the systematic
underestimation related with the LAI field measurements is discussed.