Yuanbo Liu

Recent declines in China?s largest freshwater lake: trend or regime shift?

Poyang Lake is China?s largest freshwater lake with a high degree of spatio-temporal variation. The lake has shrunk in size in recent years, resulting in significant hydrological, ecological and economic consequences. It remains unknown whether the shrinkage is a trend or a regime shift, which is of high importance for policymakers as it may lead to different decisions. This study constructed a four-decade record of the lake area using multi-temporal satellite images and hydrological data. The Mann?Kendall analysis revealed a decreasing trend of Poyang Lake but it was statistically insignificant. The Rodionov sequential approach disclosed an abrupt change of the lake in 2006, implying a regime shift. Basically, the lake change was a synthetic result of precipitation, evapotranspiration and outflow discharge. However, precipitation and outflow did not show any significant trend or abrupt change, and evapotranspiration had an increasing trend in addition to an abrupt change in 1998. The trigger for the recent lake declines was principally ascribed to a weakened blocking effect of the Yangtze River. The findings provide an example of hydrologic non-stationarity and are valuable for effective promotion of climate adaptation and water resource management.

Reducing the Discrepancy Between ASTER and MODIS Land Surface Temperature Products

Human-induced global warming has significantly increased the importance of satellite monitoring of land surface temperature (LST) on a global scale. The MODerate-resolution Imaging Spectroradiometer (MODIS) provides a 1-km resolution LST product with almost daily coverage of the Earth, invaluable to both local and global change studies. The Advanced Spaceborne Thermal Emission Reflection Radiometer (ASTER) provides a LST product with a high spatial resolution of 90-m and a 16-day recurrent cycle, simultaneously acquired at the same height and nadir view as MODIS. ASTER and MODIS are complementary in resolution, offering a unique opportunity for scale-related studies. ASTER and MODIS LST have been widely used but the errors in LST were mostly disregarded. Correction of ASTER-to-MODIS LST discrepancies is essential for studies reliant upon the joint use of these sensors. In this study, we compared three correction approaches: the Wan et al.s approach, the refined Wan et al.s approach, and the generalized split window (GSW) algorithm based approach. The Wan et al.s approach corrects the MODIS 1-km LST using MODIS 5-km LST. The refined approach modifies the Wan et al.s approach through incorporating ASTER emissivity and MODIS 5-km data. The GSW algorithm approach does not use MODIS 5-km but only ASTER emissivity data. We examined the case over a semi-arid terrain area for the part of the Loess Plateau of China. All the approaches reduced the ASTER-to-MODIS LST discrepancy effectively. With terrain correction, the original ASTER-to-MODIS LST difference reduced from 2.7±1.28 K to -0.1±1.87 K for the Wan et al.s approach, 0.2±1.57 K for the refined approach, and 0.1±1.33 K for the GSW algorithm based approach. Among all the approaches, the GSW algorithm based approach performed best in terms of mean, standard deviation, root mean square root, and correlation coefficient.

A physical explanation of the variation in threshold for delineating terrestrial water surfaces from multi-temporal images: effects of radiometric correction

Multi-temporal satellite images are widely used to delineate objects of interest for monitoring surface changes. Threshold value(s) are often determined from a histogram of a delineation index. However, the threshold determined may vary and be case-dependent, with images taken at different times. Although the variation is well known, its cause remains unclear, and this raises doubts about the reliability of the classification results. This study selects three widely used indices, the near-infrared (NIR) band, the normalized difference vegetation index (NDVI) and the normalized difference water index (NDWI), all of which can be used to delineate water surfaces. Our theoretical analysis reveals that sensor calibration, the Sun–target–satellite geometry and the atmospheric optical properties create synthetic effects on the satellites digital number (DN) and, subsequently, on the thresholds for delineation. The DN-based threshold has a significant dependence on the reflectance-based counterpart, which has been proved with multi-temporal Moderate Resolution Imaging Spectroradiometer (MODIS) data for the Poyang Lake region of China. Our results show that a DN-based threshold is generally higher than a reflectance-based one, and ∼90% of the difference is accounted for by temporal influences. A quantification of the temporal influences provides a physical explanation to the variation in thresholds, and the findings should be valuable for improving the reliability of long-term studies using multi-temporal images.

Soil Salinity Retrieval from Advanced Multi-Spectral Sensor with Partial Least Square Regression

Improper use of land resources may result in severe soil salinization. Timely monitoring and early warning of soil salinity is in urgent need for sustainable development. This paper addresses the possibility and potential of Advanced Land Imager (ALI) for mapping soil salinity. In situ field spectra and soil salinity data were collected in the Yellow River Delta, China. Statistical analysis demonstrated the importance of ALI blue and near infrared (NIR) bands for soil salinity. A partial least square regression (PLSR) model was established between soil salinity and ALI-convolved field spectra. The model estimated soil salinity with a R2 (coefficient of determination), RPD (ratio of prediction to deviation), bias, standard deviation (SD) and root mean square error (RMSE) of 0.749, 3.584, 0.036 g∙kg−1, 0.778 g∙kg−1 and 0.779 g∙kg−1. The model was then applied to atmospherically corrected ALI data. Soil salinity was underestimated for moderately (soil salinity within 2–4 g∙kg−1) and highly saline (soil salinity >4 g∙kg−1) soils. The underestimates increased with the degree of soil salinization, with a maximum value of ~4 g∙kg−1. The major contribution for the underestimation (>80%) may result from data inaccuracy other than model ineffectiveness. Uncertainty analysis confirmed that improper atmospheric correction contributed to a very conservative uncertainty of 1.3 g∙kg−1. Field sampling within remote sensing pixels was probably the major source responsible for the underestimation. Our study demonstrates the effectiveness of PLSR model in retrieving soil salinity from new-generation multi-spectral sensors. This is very valuable for achieving worldwide soil salinity mapping with low cost and considerable accuracy.

Comparative Assessment of Satellite-Retrieved Surface Net Radiation: An Examination on CERES and SRB Datasets in China

Surface net radiation plays an important role in land–atmosphere interactions. The net radiation can be retrieved from satellite radiative products, yet its accuracy needs comprehensive assessment. This study evaluates monthly surface net radiation generated from the Clouds and the Earth’s Radiant Energy System (CERES) and the Surface Radiation Budget project (SRB) products, respectively, with quality-controlled radiation data from 50 meteorological stations in China for the period from March 2000 to December 2007. Our results show that surface net radiation is generally overestimated for CERES (SRB), with a bias of 26.52 W/m2 (18.57 W/m2) and a root mean square error of 34.58 W/m2 (29.49 W/m2). Spatially, the satellite-retrieved monthly mean of surface net radiation has relatively small errors for both CERES and SRB at inland sites in south China. Substantial errors are found at northeastern sites for two datasets, in addition to coastal sites for CERES. Temporally, multi-year averaged monthly mean errors are large at sites in western China in spring and summer, and in northeastern China in spring and winter. The annual mean error fluctuates for SRB, but decreases for CERES between 2000 and 2007. For CERES, 56% of net radiation errors come from net shortwave (NSW) radiation and 44% from net longwave (NLW) radiation. The errors are attributable to environmental parameters including surface albedo, surface water vapor pressure, land surface temperature, normalized difference vegetation index (NDVI) of land surface proxy, and visibility for CERES. For SRB, 65% of the errors come from NSW and 35% from NLW radiation. The major influencing factors in a descending order are surface water vapor pressure, surface albedo, land surface temperature, NDVI, and visibility. Our findings offer an insight into error patterns in satellite-retrieved surface net radiation and should be valuable to improving retrieval accuracy of surface net radiation. Moreover, our study on radiation data of China provides a case example for worldwide validation.

Satellite-based detection of water surface variation in China’s largest freshwater lake in response to hydro-climatic drought

Poyang Lake, the largest freshwater lake in China, is an important water resource and iconic ecosystem in a region that has been subjected to extreme drought in recent years. The lake’s inundation area is heavily influenced by basin rainfall and also by the Yangtze River’s water flows. Exploring the lake’s inundation variation in response to drought conditions is of great importance for developing effective management planning for local water resources and for mitigating future drought. Here we demonstrate how satellites can reflect the lake’s inundation changes and processes under typical hydro-climatic droughts. Using Moderate Resolution Imaging Spectroradiometer (MODIS) medium-resolution data collected between 2000 and 2011, we documented the tempo-spatial variation characteristics of water inundation areas and two typical droughts in 2006 and 2011. 2006 was a hydrologic drought year, which occurred due to an abnormal change in the Yangtze River’s water flows. A dramatic shrinkage of the inundation area mainly occurred in autumn and winter. In contrast, 2011 was a hydro-climatic drought year, which resulted from the complicated influence of both the Poyang Lake basin and Yangtze River. The lake shrinkage appeared more severe during spring–summer, when about 70% of the inundation area disappeared before July. The results should be valuable for ecological conservation and water resource management in the Poyang Lake region.

Discrepancy Between ASTER- and MODIS- Derived Land Surface Temperatures: Terrain Effects

The MODerate resolution Imaging Spectroradiometer (MODIS) and the Advanced Spaceborne Thermal Emission Reflection Radiometer (ASTER) are onboard the same satellite platform NASA TERRA. Both MODIS and ASTER offer routine retrieval of land surface temperatures (LSTs), and the ASTER- and MODIS-retrieved LST products have been used worldwide. Because a large fraction of the earth surface consists of mountainous areas, variations in elevation, terrain slope and aspect angles can cause biases in the retrieved LSTs. However, terrain-induced effects are generally neglected in most satellite retrievals, which may generate discrepancy between ASTER and MODIS LSTs. In this paper, we reported the terrain effects on the LST discrepancy with a case examination over a relief area at the Loess Plateau of China. Results showed that the terrain-induced effects were not major, but nevertheless important for the total LST discrepancy. A large local slope did not necessarily lead to a large LST discrepancy. The angle of emitted radiance was more important than the angle of local slope in generating the LST discrepancy. Specifically, the conventional terrain correction may be unsuitable for densely vegetated areas. The distribution of ASTER-to-MODIS emissivity suggested that the terrain correction was included in the generalized split window (GSW) based approach used to rectify MODIS LSTs. Further study should include the classification-induced uncertainty in emissivity for reliable use of satellite-retrieved LSTs over relief areas.

Quantifying the Relationship Between Intersensor Images in Solar Reflective Bands: Implications for Intercalibration

Satellite instruments have acquired large volume images at different spatial, spectral, radiometric, and temporal resolutions. Reliable detection of long-term environmental change requires critical sensor intercalibration destined to minimize inconsistency in these multisensor images. However, uncertainty in intercalibration has not yet been comprehensively quantified in most existing studies. This paper developed a quantitative relationship between multisensor images in solar reflective bands by accounting for sensor difference, atmospheric condition, and Sun-target-sensor geometry. The relationship was validated with collocated and concurrent TERRA MODIS/NOAA-17 AVHRR images over the Dunhuang calibration site. Then, it was used to investigate sensitivity of intercalibration to intersensor scale factor, total ozone concentration (TOC), total precipitable water vapor content (TPW), and aerosol optical thickness (AOT). The main conclusions include: 1) error in intersensor scale factor may induce a maximum uncertainty of 2.35% for both visible (VIS) and near-infrared (NIR) bands; 2) error in TOC can produce a maximum uncertainty of 0.30% for VIS band but very minor impact on NIR band; 3) error in TPW may generate a maximum uncertainty of 8.81% for NIR band, particularly for a dry atmosphere; 4) error in AOT can result in a maximum uncertainty of 0.25% for VIS band and 0.96% for NIR band at near-nadir, and 10.16% and 8.18% for heavy aerosol loadings at a very high solar angle. The following study quantifies uncertainties in intercalibration for solar reflective bands and thus offers guidance for intercalibration.

A Comparison of Flux Variance and Surface Renewal Methods With Eddy Covariance

Sensible and latent heat fluxes are important parameters for understanding energy/mass interactions. At present, there are a number of methods available to estimate these fluxes, with varying degrees of difficulty and success. In the present study, we explore the use of the flux variance method and the surface renewal method to estimate the sensible and latent heat fluxes in two agricultural sites in China. The results of these two methods were compared to direct measurements using eddy covariance. The study sites, a wheat field in a semi-arid area and a rice paddy in a humid area provide an opportunity to examine two extreme conditions. In the arid conditions, both estimation methods provided similar sensible heat flux estimations but higher latent heat flux estimations with respect to the eddy covariance measurements. The sum of the estimated sensible and latent heat fluxes was in good agreement with measurements. In the rice paddy, sensible heat flux estimated by both methods showed similar results, whereas latent heat fluxes estimation were 1.2 times greater than eddy covariance measurements. The surface renewal method underestimated the Bowen ratio over the rice paddy, but overestimated it over the wheat field. In conclusion, both methods appear more appropriate for the estimation of sensible heat flux rather than for latent heat flux, in particular in humid environments.

Temporal influences on Landsat-5 Thematic Mapper image in visible band

The Landsat Thematic Mapper (TM) has provided fine spatial resolution data spanning two decades. These data are useful for long‐term studies of environmental change. However, temporal factors such as sensor degradation, variation in Sun–target–satellite geometry, and variable atmospheric conditions can create inconsistencies in multi‐temporal images and complicate data analysis. This study investigated temporal influences on satellite data. The methodology was developed based on a theoretically derived relationship between pixel values of pseudo‐invariant features (PIFs) across time. The relationship was validated using multi‐temporal Landsat‐5 TM images, which showed that temporal factors contribute to PIF pixel values in both multiplicative and additive ways. For Landsat‐5 TM level‐0 data, temporal influences were simulated in terms of multiplicative and additive components. The results showed that atmospheric variation is the most influential factor, followed by variation in the Sun–target–satellite geometry and TM sensor degradation.