Lingling Liu

Characteristics and drivers of global NDVI‐based FPAR from 1982 to 2006

Fraction of Absorbed Photosynthetically Active Radiation (FPAR) is a state parameter in most ecosystem productivity models and is also the key terrestrial product. In this study, Normalized Difference Vegetation Index (NDVI) from Advanced Very High Resolution Radiometer (AVHRR) Global Inventory Modeling and Mapping Studies (GIMMS) was used to estimate FPAR from 1982 to 2006, using an intermediate model. Our research focused on the analysis of long-term global FPAR interannual trend patterns and driving forces involving climate and land cover changes. Results showed that interannual trend and spatial distribution patterns of global FPAR were independent of the changes in AVHRR instruments, and differed by season dynamics and vegetation types. Compared with other seasons, the period during JJA (June-August) exhibited more areas with decreasing FPAR and greater reduction range. For FPAR interannual trend, a wholly different correlation pattern was observed between temperature and precipitation, especially for arid and semi-arid regions. A significant influence of extreme droughts such as those associated with El Nino/Southern Oscillation (ENSO) on FPAR variability was found. The result also revealed the increasing and decreasing interannual trend of FPAR corresponding to the afforestation in the Three North Shelterbelts Program in China and deforestation in tropical forests in Southeast Asia. Driving factor analysis indicated that the climate and land cover changes had an interactive effect on the FPAR annual anomalous variation.

Monitoring the distribution of C3 and C4 grasses in a temperate grassland in northern China using moderate resolution imaging spectroradiometer normalized difference vegetation index trajectories

Using remote-sensing technologies, this study sought to provide an up-to-date map of C3/C4 distribution representative of temperate grassland in northern China. Several studies focused on the central grasslands of North America have demonstrated that C4 species coverage can be discriminated from C3 species by using time-series vegetation index data based on their phenological differences. Considering that the hydrothermal patterns and C4 percentage of grass flora in the study area and North America are different, we first examined temporal features of C3/C4 communities by using multitemporal moderate resolution imaging spectroradiometer normalized difference vegetation index data throughout the 2010 growing season. It was found that the asynchronous seasonality exhibited by communities with varied C3/C4 compositions also existed in our study region. Based on this asynchrony and separation rate, a hierarchical decision tree was developed to classify four grassland types with varied C3/C4 compositions. As a result, a classification map of the mixed C3/C4 grassland was generated with an overall accuracy of 87.3% and a kappa coefficient of 0.83. The geographic distribution of C3 and C4 species showed that the study area was dominated by C3 grasses, but C4-dominated grasslands accounted for 39% of the land cover. Thus C4 species also made an important contribution to grassland biomass, especially in dry and low-lying saline-alkaline habitats. The results also indicated that the trajectory-based methods for C3/C4 mapping rooted in asynchronous seasonality worked effectively in the climate regimes of both northern China and North America.

A Landsat-5 Atmospheric Correction Based on MODIS Atmosphere Products and 6S Model

The Landsat satellite series represents the longest record of global-scale medium spatial resolution earth observations, and the utility of Landsat data for long-term and/or large-area monitoring depends on accurate and quantitative atmospheric correction to produce a consistently corrected surface reflectance (SR) dataset. In this study, we developed a rapid, automated atmospheric correction procedure based on Moderate Resolution Imaging Spectrometer (MODIS) atmospheric characterization products and the 6S (Second Simulation of a Satellite Signal in the Solar Spectrum) radiative transfer code for Landsat-5 Thematic Mapper (TM) imagery. Three MODIS atmosphere products at the resolution of 0.05°, MOD04, MOD05, and MOD07 were used as input to the 6S radiative transfer model in order to compute the parameters required for atmospheric correction, which were then used to correct TM imagery per-pixel automatically. This method was tested using five multi-date Landsat TM images in Beijing, China, and the atmospheric correction precision was assessed using ground-measured reflectance. The result showed that the SR retrieved from Landsat TM is consistent with the ground measurements, with a mean R2 of 0.773 and a mean root mean square error value of 0.045.

Comparison of absolute and relative radiometric normalization use Landsat time series images

For most remote sense image applications, variations in solar illumination conditions, atmospheric scattering and absorption, and detector performance need to be normalized, especially in time series analysis such as change detection. For the purpose of radiometric correction, two levels of radiometric correction, absolute and relative, have been developed for remote sense imagery. In this paper, we select the Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes (FLAASH) algorithm as the Atmospheric correction method, and compare it with an automatic method for relative radiometric normalization based on a linear scale invariance of the multivariate alteration detection (MAD) transformation. The performances of both methods are compared using a landsat TM image pairs, the results from the two techniques have been compared both visually and using a measure of the fit based on standard error statistic.

Response of Spring Phenology to Climate Change across Tibetan Plateau

To investigate the climate change effects on spring phenology at a spatial-temporal scale in Tibetan Plateau, an algorithm of dynamic threshold was applied to extract start of season (SOS) based on MODIS 250 m 16-days NDVI products. Then, the phenological responses to climate change were assessed by the linear regression of phenological dates against the current monthly temperature from 2000 to 2009. The vegetation ecosystems are sensitive to climate warming in Tibetan Plateau (2.72-9.72 days°C-1). The SOS of meadow and steppe were advanced by up to 8.17 days°C-1 and 5.69 days °C-1 under climate warming. Finally, we investigated the impact of altitude on the spring phenology dates for steppe and meadow in 2000 and 2009, respectively. For each degree warming along with the altitude, the spring phenology was advanced by about 2.04 days and 1.8 days for steppe and meadow, respectively. Compared to the spring phenology variation adapted to local climate gradient (along with altitude gradient), the spring phenology on the Tibetan Plateau under climate warming is more sensitive, with about 3 times larger response amplitude.

Real-Time Monitoring of Crop Phenology in the Midwestern United States Using VIIRS Observations

Real-time monitoring of crop phenology is critical for assisting farmers managing crop growth and yield estimation. In this study, we presented an approach to monitor in real time crop phenology using timely available daily Visible Infrared Imaging Radiometer Suite (VIIRS) observations and historical Moderate Resolution Imaging Spectroradiometer (MODIS) datasets in the Midwestern United States. MODIS data at a spatial resolution of 500 m from 2003 to 2012 were used to generate the climatology of vegetation phenology. By integrating climatological phenology and timely available VIIRS observations in 2014 and 2015, a set of temporal trajectories of crop growth development at a given time for each pixel were then simulated using a logistic model. The simulated temporal trajectories were used to identify spring green leaf development and predict the occurrences of greenup onset, mid-greenup phase, and maximum greenness onset using curvature change rate. Finally, the accuracy of real-time monitoring from VIIRS observations was evaluated by comparing with summary crop progress (CP) reports of ground observations from the National Agricultural Statistics Service (NASS) of the United States Department of Agriculture (USDA). The results suggest that real-time monitoring of crop phenology from VIIRS observations is a robust tool in tracing the crop progress across regional areas. In particular, the date of mid-greenup phase from VIIRS was significantly correlated to the planting dates reported in NASS CP for both corn and soybean with a consistent lag of 37 days and 27 days on average (p < 0.01), as well as the emergence dates in CP with a lag of 24 days and 16 days on average (p < 0.01), respectively. The real-time monitoring of maximum greenness onset from VIIRS was able to predict the corn silking dates with an advance of 9 days (p < 0.01) and the soybean blooming dates with a lag of 7 days on average (p < 0.01). These findings demonstrate the capability of VIIRS observations to effectively monitor temporal dynamics of crop progress in real time at a regional scale.

Ventilation of a Monsoon‐Dominated Ocean: Subduction and Obduction in the North Indian Ocean

Based on the characteristics of oceanic circulation in a monsoon-dominated ocean, a new framework of annual ventilation, including subduction and obduction, is postulated and applied to the North Indian Ocean based on both SODA and GODAS. It is revealed that besides the winter season, ventilation can also occur in summer. Considering the horizontal resolution, SODA results are mainly discussed, with GODAS results given for validity of key conclusions. The annual subduction/obduction rate in the North Indian Ocean based on SODA is estimated at 10.2 Sv/11 Sv averaged from 1960 to 2009, with 4.2 Sv/6.2 Sv occurring during winter monsoon period and 6 Sv/4.8 Sv during summer monsoon period, respectively. Both subduction and obduction feature great interannual variability, with the vertical pumping term of decisive importance. Furthermore, the concepts of the penetration depth through subduction and the origin depth through obduction are postulated. The penetration depth in the Arabian Sea is on the order of 50 to 200 m; the origin depth through obduction in the Arabian Sea is deeper than that in the Bay of Bengal, with the deepest on the order of 200 to 250 m along the western boundary.

Autumn leaf phenology: discrepancies between in situ observations and satellite data at urban and rural sites

ABSTRACT Autumn phenophases, such as leaf colouration (LC) and leaf fall (LF), have received considerably less attention than their spring counterparts (budburst and leaf unfolding) but are equally important determinants of the duration of the growing season and thus have a controlling influence on the carbon-uptake period. Here, we examined THE trends (1968–2016) in in situ observations of the timing of LC and LF from a suite of deciduous trees at three rural sites and one urban site in Ireland. Satellite-derived autumn phenological metrics including mid-senescence (MS) and end of senescence (ES) based on two-band enhanced vegetation index (EVI2) from Advanced Very High Resolution Radiometer (AVHRR) and Moderate Resolution Imaging Spectroradiometer (MODIS) from 1982 to 2016 at a spatial resolution of 5km2 were also examined. The aim of this study was to assess the effectiveness of satellite remote sensing in capturing autumn phenology as determined by in situ observations . Analysis of in situ data (1968–2016) revealed the urban site to be significantly different from the rural sites as LC and LF occurred later in the season and the duration of the autumn season (LF-LC) became shorter over time. These trends may be partly driven by the presence of artificial light in the city. On average (1982–2016), there was a 6-day delay in the timing of MS compared to LC and a much larger difference (21 days) between ES and LF. This resulted in a 31-day autumn duration as defined by satellite data compared to 16 days from in situ observations. Furthermore, there was little overlap in timing between LC and MS, and LF and ES at the rural sites only. Discrepancies between in situ and satellite data may be attributed to the satellite data integrating a much broader vegetation signal across a heterogeneous landscape than in situ observations of individual trees. Therefore, at present, satellite-derived autumn phenology may be more successful in capturing in situ observations across large homogeneous landscapes of similar vegetation types (e.g. forested areas) than in heterogeneous landscapes (e.g. small mixed farms, urban areas, etc.) as is the case in Ireland where the in situ observations of trees may not be reflective of the overall vegetation. Matching the scale of satellite data with in situ observations remains a challenging task but may, at least in part, be overcome by increasing the extent of observations to include a wider range of species and in future as satellite data become available at higher spatial and temporal resolutions.

Mapping land cover of the Yellow River source using multi-temporal Landsat images

Land cover is a crucial product required to be calibrated, validated and used in various land surface models that provide the boundary conditions for the simulation of climate, carbon cycle and ecosystem change. This paper presented a method to map land cover from multitemporal landsat images using Dempster-Shafer theory of evidence. The method firstly resolved in Gaussian probability density function calculate the basic probability assignment of each single satellite image, then multitemporal landsat images were combined using Dempsters Rule of combination. Finally, a decision rule based on ancillary information is used to make classification decisions. This method had 87.91% overall accuracy for the land cover types compared with the result of the Aerial hyperspectral image classification. The results of this study showed that Dempster-Shafer theory of evidence is an effective tool to map land cover using multitemporal landsat image.