Ronggao Liu

Estimation of incident photosynthetically active radiation from Moderate Resolution Imaging Spectrometer data

[ 1] Incident photosynthetically active radiation ( PAR) is a key variable needed by almost all terrestrial ecosystem models. Unfortunately, the current incident PAR products estimated from remotely sensed data at spatial and temporal resolutions are not sufficient for carbon cycle modeling and various applications. In this study, the authors develop a new method based on the look-up table approach for estimating instantaneous incident PAR from the polar-orbiting Moderate Resolution Imaging Spectrometer (MODIS) data. Since the top-of-atmosphere (TOA) radiance depends on both surface reflectance and atmospheric properties that largely determine the incident PAR, our first step is to estimate surface reflectance. The approach assumes known aerosol properties for the observations with minimum blue reflectance from a temporal window of each pixel. Their inverted surface reflectance is then interpolated to determine the surface reflectance of other observations. The second step is to calculate PAR by matching the computed TOA reflectance from the look-up table with the TOA values of the satellite observations. Both the direct and diffuse PAR components, as well as the total shortwave radiation, are determined in exactly the same fashion. The calculation of a daily average PAR value from one or two instantaneous PAR values is also explored. Ground measurements from seven FLUXNET sites are used for validating the algorithm. The results indicate that this approach can produce reasonable PAR product at 1 km resolution and is suitable for global applications, although more quantitative validation activities are still needed.

Detection and attribution of vegetation greening trend in China over the last 30 years

The reliable detection and attribution of changes in vegetation growth is a prerequisite for the development of strategies for the sustainable management of ecosystems. This is an extraordinary challenge. To our knowledge, this study is the first to comprehensively detect and attribute a greening trend in China over the last three decades. We use three different satellite-derived Leaf Area Index (LAI) datasets for detection as well as five different process-based ecosystem models for attribution. Rising atmospheric CO2 concentration and nitrogen deposition are identified as the most likely causes of the greening trend in China, explaining 85% and 41% of the average growing-season LAI trend (LAIGS) estimated by satellite datasets (average trend of 0.0070 yr(-1), ranging from 0.0035 yr(-1) to 0.0127 yr(-1)), respectively. The contribution of nitrogen deposition is more clearly seen in southern China than in the north of the country. Models disagree about the contribution of climate change alone to the trend in LAIGS at the country scale (one model shows a significant increasing trend, whereas two others show significant decreasing trends). However, the models generally agree on the negative impacts of climate change in north China and Inner Mongolia and the positive impact in the Qinghai-Xizang plateau. Provincial forest area change tends to be significantly correlated with the trend of LAIGS (P < 0.05), and marginally significantly (P = 0.07) correlated with the residual of LAIGS trend, calculated as the trend observed by satellite minus that estimated by models through considering the effects of climate change, rising CO2 concentration and nitrogen deposition, across different provinces. This result highlights the important role of Chinas afforestation program in explaining the spatial patterns of trend in vegetation growth.

Simultaneous estimation of both soil moisture and model parameters using particle filtering method through the assimilation of microwave signal

[1] Soil moisture is a very important variable in land surface processes. Both field moisture measurements and estimates from modeling have their limitations when being used to estimate soil moisture on a large spatial scale. Remote sensing is becoming a practical method to estimate soil moisture globally; however, the quality of current soil surface moisture products needs to be improved in order to meet practical requirements. Data assimilation (DA) is a promising approach to merge model dynamics and remote sensing observations, thus having the potential to estimate soil moisture more accurately. In this study, a data assimilation algorithm, which couples the particle filter and the kernel smoothing technique, is presented to estimate soil moisture and soil parameters from microwave signals. A simple hydrological model with a daily time step is utilized to reduce the computational burden in the process of data assimilation. An observation operator based on the ratio of two microwave brightness temperatures at different frequencies is designed to link surface soil moisture with remote sensing measurements, and a sensitivity analysis of this operator is also conducted. Additionally, a variant of particle filtering method is developed for the joint estimation of soil moisture and soil parameters such as texture and porosity. This assimilation scheme is validated against field moisture measurements at the CEOP/Mongolia experiment site and is found to estimate near-surface soil moisture very well. The retrieved soil texture still contains large uncertainties as the retrieved values cannot converge to fixed points or narrow ranges when using different initial soil texture values, but the retrieved soil porosity has relatively small uncertainties.

Retrospective retrieval of long-term consistent global leaf area index (1981-2011) from combined AVHRR and MODIS data

In this paper, we present an approach for generating a consistent long-term global leaf area index (LAI) product (1981-2011) by quantitative fusion of Moderate Resolution Imaging Spectroradiometer (MODIS) and historical Advanced Very High Resolution Radiometer (AVHRR) data. First, a MODIS LAI series was generated from MODIS data based on the GLOBCARBON LAI algorithm. Then, the relationships between AVHRR observations and MODIS LAI were established pixel by pixel using two data series during overlapped period (2000-2006). Then the AVHRR LAI back to 1981 was estimated from historical AVHRR observations based on these pixel-level relationships. The long-term LAI series was made up by combination of AVHRR LAI (1981-2000) and MODIS LAI (2000-2011). The LAI derived from AVHRR was intercompared with that from MODIS during the overlapped period. The results show that the LAIs from these two different sensors are good consistency, with LAI differences are within +/- 0.6 over 99.0% vegetated pixels. The long-term LAI was also compared with field measurements, which has an error of 0.81 LAI on average. Compared with the LAI retrieved directly from the GLOBCARBON algorithm, the LAI derived by our method has a lower temporal noise, which means uncertainties from the low quality of AVHRR measurements can be reduced with the aid of high-quality MODIS data. This product is hosted on the GlobalMapping Web site (http://www.globalmapping.org/globalLAI) for free download, which will provide a long-term LAI over 30 years for modeling the carbon and water cycles.

A Weak-Constraint-Based Data Assimilation Scheme for Estimating Surface Turbulent Fluxes

Much attention has been focused on the assimilation of satellite data and products into land surface processes. In this letter, a variational data assimilation scheme is developed based on the weak-constraint concept. It assimilates surface skin temperature into a simple land surface model for the estimation of turbulent fluxes. An automatic differentiation technique is used to derive the adjoint codes to evaluate the gradient of the cost function. After the construction of this assimilation system, numerical experiments are conducted to test its performance with different model errors, and the comparison is also made with the strong constraint scheme. The results show that the land surface turbulent fluxes can be retrieved with highly satisfactory accuracy.

Estimation of daily-integrated PAR from sparse satellite observations: comparison of temporal scaling methods

Incident Photosynthetically Active Radiation (PAR) is a critical parameter for modelling ecosystem productivity. An algorithm for estimating instantaneous PAR from Moderate Resolution Imaging Spectroradiometer (MODIS) data was developed earlier; however, daily-integrated PAR is more meaningful than instantaneous PAR in many cases because many land surface models require a daily or coarser temporal resolution. This paper compares two different algorithms (adjusted sinusoidal interpolation and look-up table) for estimating daily-integrated PAR from instantaneous PAR values. Statistical analysis of the validation results indicates that the look-up table method more accurately estimates daily-integrated PAR than the use of adjusted sinusoidal interpolation. We also investigated how window size, daytime length and the number of overpass counts per day affect bias and the relative error of estimation. Validation using field measurements, and comparison with the Geostationary Operational Environmental Satellites PAR product, demonstrates that data collected by MODIS can be used to provide reliable estimates of daily-integrated PAR.

An algorithm for estimating downward shortwave radiation from GMS 5 visible imagery and its evaluation over China

This paper presents an operational scheme to estimate downward shortwave radiation (DSR) over China from the visible-band top-of-atmosphere reflectance of the Geostationary Meteorological Satellite (GMS) 5 imagery. The proposed algorithm retrieves surface reflectance and atmospheric parameters directly from GMS 5 images by searching lookup tables, which are created by the radiative transfer model SBDART and consider the effects of water vapor absorption and surface altitude variations. Experiments show that the DSR retrieval is more sensitive to the selection of aerosol type and less to that of the cloud type. Uncertainty in the reflectance of a bright surface leads to a considerable DSR retrieval error (+/-(6-9%)). The instantaneous retrieved DSR is evaluated by field measurements on the Tibetan Plateau, and the daily retrieved DSR is compared with one years ground-based measurements at 96 stations in China. The results show that the estimated DSR is in good agreement with ground measurements with a correlation coefficient of similar to 0.9 and a bias of 1.5%. Root-mean square differences in the daily DSR are 17.7% for all-sky and 13.1% for clear-sky conditions. These results suggest that the proposed method applied to the GMS 5 satellite data can accurately estimate temporally and spatially continuous instantaneous and daily DSR. These DSR data sets will be useful for a wide range of applications.

Continuous tree distribution in China: A comparison of two estimates from Moderate-Resolution Imaging Spectroradiometer and Landsat data

Received 2 April 2005; revised 27 October 2005; accepted 5 December 2005; published 18 April 2006. [1] Forest change is a major contributor to changes in carbon stocks and trace gas fluxes between terrestrial and atmospheric layers. This study compares two satellite estimates of percent tree distribution data sets over China. One estimate is from the Chinese National Land Cover Data Set (NLCD) generated by a multiyear national land cover project in China through visual interpretation of Landsat thematic mapper (TM) and the Enhanced Thematic Mapper Plus (ETM+) images primarily acquired in the year 2000. The other estimate is the Moderate-Resolution Imaging Spectroradiometer (MODIS) standard product (MOD44B) from the same year. The two products reveal some common features, but significant discrepancies exist. Detailed analyses are carried out with different land cover types and over different regions. Comparison results show that the difference of the total tree canopy area for the whole country is 159,000 km 2 . The pixel counts in the NLCD data set for dense forest are � 4 times those in the MODIS data set with the reverse holding for sparse forest. Generally, the percent tree canopy area of the NLCD data set is larger in eastern China and lower in the Tibetan plateau margin region. For different land cover types the percentage of tree canopy areas shows a good agreement for evergreen forests but a large discrepancy for deciduous forests. The largest variations are associated with grassland and nonvegetation classes. Regarding the spatial distributions of their differences, Inner Mongolia is the place where both data sets show a diverse result, but Guizhou and Fujian present the least divergence among those provinces with the tree canopy area being more than 20,000 km 2 .

Mapping global seasonal forest background reflectivity with Multi‐angle Imaging Spectroradiometer data

Forest background reflectivities with seasonal and spatial variations are critically important in the estimation of canopy biophysical parameters of the forest canopy. In this paper, seasonal background reflectivity for global forested areas was mapped at 1.1 km resolution using four-scale model and Multi-angle Imaging Spectroradiometer data of the nadir and 45 degrees forward directions. The largest seasonal variation of forest background reflectivities was observed in middle and high latitudes of Northern Hemisphere. The background reflectivity differs between deciduous broadleaf forest and coniferous forest in the near-infrared band and varies with increasing canopy leaf area index. The partial validation of forest background reflectivity with adjacent grassland in the Northern Hemisphere and the comparison of understory leaf area index on leaf appearance day for larch forest in North Asia both indicate the relative reliability of results. The nearly 70% spatial coverage of retrieval with high-quality flags makes it eligible for applications over global coniferous and deciduous broadleaf forest areas.

Nitrogen Availability Dampens the Positive Impacts of CO2 Fertilization on Terrestrial Ecosystem Carbon and Water Cycles

Author(s): He, L; Chen, JM; Croft, H; Gonsamo, A; Luo, X; Liu, J; Zheng, T; Liu, R; Liu, Y | Abstract: ©2017. American Geophysical Union. All Rights Reserved. The magnitude and variability of the terrestrial CO2 sink remain uncertain, partly due to limited global information on ecosystem nitrogen (N) and its cycle. Without N constraint in ecosystem models, the simulated benefits from CO2 fertilization and CO2-induced increases in water use efficiency (WUE) may be overestimated. In this study, satellite observations of a relative measure of chlorophyll content are used as a proxy for leaf photosynthetic N content globally for 2003–2011. Global gross primary productivity (GPP) and evapotranspiration are estimated under elevated CO2 and N-constrained model scenarios. Results suggest that the rate of global GPP increase is overestimated by 85% during 2000–2015 without N limitation. This limitation is found to occur in many tropical and boreal forests, where a negative leaf N trend indicates a reduction in photosynthetic capacity, thereby suppressing the positive vegetation response to enhanced CO2 fertilization. Based on our carbon-water coupled simulations, enhanced CO2 concentration decreased stomatal conductance and hence increased WUE by 10% globally over the 1982 to 2015 time frame. Due to increased anthropogenic N application, GPP in croplands continues to grow and offset the weak negative trend in forests due to N limitation. Our results also show that the improved WUE is unlikely to ease regional droughts in croplands because of increases in evapotranspiration, which are associated with the enhanced GPP. Although the N limitation on GPP increase is large, its associated confidence interval is still wide, suggesting an urgent need for better understanding and quantification of N limitation from satellite observations.