Tiangang Liang

Development and assessment of combined Terra and Aqua snow cover products in Colorado Plateau, USA and northern Xinjiang, China

This study presents our methods to produce multi-day Terra, Aqua, and Terra-Aqua moderate resolution imaging spectroradiometer (MODIS) snow cover composite images respectively from daily Terra MODIS, Aqua MODIS, and both Terra and Aqua MODIS snow cover products, with flexible starting and ending dates and a user-defined cloud cover threshold. The methods are applied to Colorado Plateau, USA and northern Xinjiang, China. The statistical comparison gives the following results. For the 2003-2004 hydrologic year, the daily Terra-Aqua composite images exhibited ~10-15% less on annual mean cloud cover and ~1-4% more on annual mean snow cover, compared with their daily Terra or Aqua counterparts. Using 10% cloud cover as a user-defined threshold, we produced 152 (northern Xinjiang) and 162 (Colorado Plateau) multi-day Terra-Aqua composite images for the 2003-2004 hydrological year, respectively. On average, it is 2.4 and 2.2 days per composite image for northern Xinjiang and Colorado Plateau, respectively. The multi-day Terra, Aqua, and Terra-Aqua composite products result in similar annual mean snow covers (~15% for the Colorado Plateau and ~30% for the northern Xinjiang), as those from the corresponding standard NASA 8-day products, ~3 times as those from the standard NASA daily products. The lower snow cover percentage retrieved from the daily standard products is mainly due to larger cloud cover in the daily products. The Aqua products always have lower annual mean snow cover and higher annual mean cloud cover than those of the Terra products. The daily Terra-Aqua composite products have higher agreement with ground measurements than either of the standard NASA daily Terra or Aqua products. The multi-day Terra-Aqua composite products have much higher agreement with ground measurements than that of the standard daily products and have similar agreement as that of the standard 8-day products. Therefore, those new composite products generated from our methods are a significant contribution to the current MODIS snow cover product series.

Validation of MODIS snow cover products using Landsat and ground measurements during the 2001-2005 snow seasons over northern Xinjiang, China

The Moderate Resolution Imaging Spectroradiometer (MODIS)/Terra daily snow cover product MOD10A1 was compared with in situ climate station measurements and a snow map generated from Landsat Enhanced Thematic Mapper Plus (ETM+) data. Snow-covered area (SCA) dynamics were assessed using the MODIS 8-day snow cover composite product MOD10A2 for the 2001–2005 snow seasons in northern Xinjiang, China. The results indicate that the snow-mapping agreement between MODIS daily snow maps and surface observations is high at 94.6% over the four snow seasons under clear-sky conditions. The snow classification accuracy in a mountainous area was lower than that in a plain area and caused higher omission errors, probably resulting in an underestimation of the SCA. The omission errors were mainly determined by snow depth, land cover types, the terrain and mixed pixels. The cloud agreement was 95.9%, and approximately 4.1% of cloud was misclassified as snow when the sky view at the climate stations was covered by clouds. An improvement was found in suppressing clouds using the 8-day products, with MOD10A2 reducing about 88.4% of the average cloud cover compared with MOD10A1. SCA in northern Xinjiang retrieved using MOD10A2 shows a clear seasonal trend. The air temperature plays an important role in the fractional SCA, and the spatial distribution of the snow cover differs considerably in the various areas in the northern Xinjiang region.

Spatio-Temporal Change of Snow Cover and Its Response to Climate over the Tibetan Plateau Based on an Improved Daily Cloud-Free Snow Cover Product

Using new, daily cloud-free snow-cover products, this study examines snow cover dynamics and their response to climate change. The results demonstrate that the daily cloud-free snow-cover products not only posses the advantages of the AMSR-E (unaffected by weather conditions) and MODIS (relatively higher resolution) products, but are also characterized by high snow and overall classification accuracies (~85% and ~98%, respectively), substantially greater than those of the existing daily snow-cover products for all sky conditions and very similar to, or even slightly greater than, those of the daily MODIS products for clear-sky conditions. Using the snow-cover products, we analyzed the snow cover dynamics over the Tibetan Plateau and determined that the maximum number of snow-covered days (SCD) in a year followed a decreasing tendency from 2003 to 2010, with a decrease in snow-covered area (SCA) equivalent to 55.3% of the total Tibetan Plateau area. There is also a slightly increasing tendency in the maximum snow cover area (SCA), and a slightly decreasing tendency in the persistent snow cover area (i.e., pixels of SCD > 350 days) was observed for the 8-year period, which was characterized by increases in temperature (0.09 °C/year) and in precipitation (0.26 mm/year). This suggests that, on the Tibetan Plateau, changes in temperature and precipitation exert a considerable influence on the regional SCD and SCA, as well as the distribution of persistent snow cover.

Estimating vertical error of SRTM and map-based DEMs using ICESat altimetry data in the eastern Tibetan Plateau

The Geoscience Laser Altimeter System (GLAS) instrument onboard the Ice, Cloud and land Elevation Satellite (ICESat) provides elevation data with very high accuracy which can be used as ground data to evaluate the vertical accuracy of an existing Digital Elevation Model (DEM). In this article, we examine the differences between ICESat elevation data (from the 1064 nm channel) and Shuttle Radar Topography Mission (SRTM) DEM of 3 arcsec resolution (90 m) and map-based DEMs in the Qinghai-Tibet (or Tibetan) Plateau, China. Both DEMs are linearly correlated with ICESat elevation for different land covers and the SRTM DEM shows a stronger correlation with ICESat elevations than the map-based DEM on all land-cover types. The statistics indicate that land cover, surface slope and roughness influence the vertical accuracy of the two DEMs. The standard deviation of the elevation differences between the two DEMs and the ICESat elevation gradually increases as the vegetation stands, terrain slope or surface roughness increase. The SRTM DEM consistently shows a smaller vertical error than the map-based DEM. The overall means and standard deviations of the elevation differences between ICESat and SRTM DEM and between ICESat and the map-based DEM over the study area are 1.03 ± 15.20 and 4.58 ± 26.01 m, respectively. Our results suggest that the SRTM DEM has a higher accuracy than the map-based DEM of the region. It is found that ICESat elevation increases when snow is falling and decreases during snow or glacier melting, while the SRTM DEM gives a relative stable elevation of the snow/land interface or a glacier elevation where the C-band can penetrate through or reach it. Therefore, this makes the SRTM DEM a promising dataset (baseline) for monitoring glacier volume change since 2000.

Toward Improved Daily Cloud-Free Fractional Snow Cover Mapping with Multi-Source Remote Sensing Data in China

With the high resolution of optical data and the lack of weather effects of passive microwave data, we developed an algorithm to map daily cloud-free fractional snow cover (FSC) based on the Moderate Resolution Imaging Spectroradiometer (MODIS) standard daily FSC product, the Advanced Microwave Scanning Radiometer (AMSR2) snow water equivalent (SWE) product and digital elevation data. We then used the algorithm to produce a daily cloud-free FSC product with a resolution of 500 m for regions in China. In addition, we produced a high-resolution FSC map using a Landsat 8 Operational Land Imager (OLI) image as a true value to test the accuracy of the cloud-free FSC product developed in this study. The analysis results show that the daily cloud-free FSC product developed in this study can completely remove clouds and effectively improve the accuracy of snow area monitoring. Compared to the true value, the mean absolute error of our product is 0.20, and its root mean square error is 0.29. Thus, the synthesized product in this study can improve the accuracy of snow area monitoring, and the obtained snow area data can be used as reliable input parameters for hydrological and climate models. The land cover type and terrain factors are the main factors that limit the accuracy of the daily cloud-free FSC product developed in this study. These limitations can be further improved by improving the accuracy of the MODIS standard snow product for complicated underlying surfaces.

Modelling global-scale potential grassland changes in spatio-temporal patterns to global climate change

Grassland is one of the most widespread vegetation types worldwide and plays a significant role in global carbon cycling. Understanding the sensitivity of grassland to climate change and the effect of climate changes on the grassland ecosystems is a key issue in global carbon cycling. One of the goals of this study was to evaluate the three net primary productivity (NPP)–climate models, i.e. the Miami model, the Schuur model and the classification indices-based model. Results indicated that the classification indices-based model was the most effective model at estimating large-scale grassland NPP. In this research, changes in the spatial pattern of global potential grassland from recent past (1950–2000) to future (2001–2050) A2a scenario were analysed with the integrated orderly classification system of grassland (IOCSG) approach in a Geographic Information System (GIS) environment. NPP was evaluated with the classification indices-based model. Results indicate that under recent past climatic conditions, the main parts of global grassland are the savanna and tundra and alpine grassland and will be converted into the savanna, steppe and semi-desert grassland in A2a scenario. As a whole, areas of grassland will increase by 31.76 million hectares. The classification indices-based model estimated a 12.40% increase of total NPP in grassland from recent past to A2a scenario. It will impose a new issue for future grassland researches to support sustainable development and to provide action relevant knowledge to meet the challenge of climate change.

Remote sensing for snow hydrology in China: challenges and perspectives

Abstract 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.

Outburst Flooding of the Moraine-Dammed Zhuonai Lake on Tibetan Plateau: Causes and Impacts

The Kekexili region of the Tibetan Plateau has become warmer and wetter since the 1960s, resulting in a significant expansion of Zhuonai Lake (+0.46 km2/year, p <; 0.05) before an outburst flood event occurred on September 15, 2011, and mapped by the Chinese Huanjing (HJ)-A/B satellites with a two-day revisit ability and a 360-km orbit swath. The direct cause of the outburst was due to relatively heavy precipitation from May to September 2011, specifically the continuous rainfall from later August to middle September. Two nearby earthquakes that occurred two months before the outburst might have impacted the natural structure of the lakebed and moraine dam to accelerate the outburst. The outburst event of Zhuonai Lake caused large environmental impacts on the region: 1) the desertification of the exposed lakebed of Zhuonai Lake; 2) the significant expansion of the three downstream lakes Kusai, Haidingnuoer, and Salt Lakes that not only caused the grassland reduction and deteriorations but also the potential threat to the operations of the Qing-Tibet Railway and Highway; and 3) the calving relocation of Tibetan antelopes to the shore area of Kusai Lake due to the deep cutting riverbanks caused by the overflow of Zhuonai Lake. This study provides some scientific clues or alerts for local or central governments to pay some attention on this very issue so that possible future devastative disasters and environmental damages would be avoided or mitigated.

Fractional snow-cover mapping using an improved endmember extraction algorithm

Abstract We describe and validate an improved endmember extraction method to improve the fractional snow-cover mapping based on the algorithm for fast autonomous spectral endmember determination (N-FINDR) maximizing volume iteration algorithm and orthogonal subspace projection theory. A spectral library time series is first established by choosing the expected spectra information using prior knowledge, and the fractional snow cover (FSC) is then retrieved by a fully constrained least squares linear spectral mixture analysis. The retrieved fractional snow-cover products are validated by the FSC derived from Landsat imagery. Our results indicate that the improved algorithm can obtain the endmember information accurately, and the retrieved FSC has better accuracy than the MODIS standard fractional snow-cover product (MOD10A1).

Evaluation of Remote Sensing Inversion Error for the Above-Ground Biomass of Alpine Meadow Grassland Based on Multi-Source Satellite Data

It is not yet clear whether there is any difference in using remote sensing data of different spatial resolutions and filtering methods to improve the above-ground biomass (AGB) estimation accuracy of alpine meadow grassland. In this study, field measurements of AGB and spectral data at Sangke Town, Gansu Province, China, in three years (2013–2015) are combined to construct AGB estimation models of alpine meadow grassland based on these different remotely-sensed NDVI data: MODIS, HJ-1B CCD of China and Landsat 8 OLI (denoted as NDVIMOD, NDVICCD and NDVIOLI, respectively). This study aims to investigate the estimation errors of AGB from the three satellite sensors, to examine the influence of different filtering methods on MODIS NDVI for the estimation accuracy of AGB and to evaluate the feasibility of large-scale models applied to a small area. The results showed that: (1) filtering the MODIS NDVI using the Savitzky–Golay (SG), logistic and Gaussian approaches can reduce the AGB estimation error; in particular, the SG method performs the best, with the smallest errors at both the sample plot scale (250 m × 250 m) and the entire study area (33.9% and 34.9%, respectively); (2) the optimum estimation model of grassland AGB in the study area is the exponential model based on NDVIOLI, with estimation errors of 29.1% and 30.7% at the sample plot and the study area scales, respectively; and (3) the estimation errors of grassland AGB models previously constructed at different spatial scales (the Tibetan Plateau, Gannan Prefecture and Xiahe County) are higher than those directly constructed based on the small area of this study by 11.9%–36.4% and 5.3%–29.6% at the sample plot and study area scales, respectively. This study presents an improved monitoring algorithm of alpine natural grassland AGB estimation and provides a clear direction for future improvement of the grassland AGB estimation and grassland productivity from remote sensing technology.