Mingguo Ma

Heihe Watershed Allied Telemetry Experimental Research (HiWATER): Scientific Objectives and Experimental Design

A major research plan entitled “Integrated research on the ecohydrological process of the Heihe River Basin” was launched by the National Natural Science Foundation of China in 2010. One of the key aims of this research plan is to establish a research platform that integrates observation, data management, and model simulation to foster twenty-first-century watershed science in China. Based on the diverse needs of interdisciplinary studies within this research plan, a program called the Heihe Watershed Allied Telemetry Experimental Research (HiWATER) was implemented. The overall objective of HiWATER is to improve the observability of hydrological and ecological processes, to build a world-class watershed observing system, and to enhance the applicability of remote sensing in integrated ecohydrological studies and water resource management at the basin scale. This paper introduces the background, scientific objectives, and experimental design of HiWATER. The instrumental setting and airborne mission plans a...

Change in area of Ebinur Lake during the 1998-2005 period

Ebinur Lake is located in a typical arid region in the north‐west of China. It is an area with the lowest elevation in the Junggar Basin in the Province of Xinjiang. Recent monitoring indicates that the lake surface area has increased. To obtain a continuous record of the change in lake area, a radiometric analysis of SPOT/VEGETATION (VGT) imagery was carried out based on methodology developed for regional lake area mapping. Two indices, the Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Water Index (NDWI), were selected to identify the water body of Ebinur Lake. The indices are calculated based on the spectral reflectances in the red and near infrared bands of VGT sensor. If the NDVI is less than a critical value (0) and if the NDWI is larger than a critical value (0), the pixel is flagged as a water body. Validation indicates that the methodology to identify water bodies based on multi‐spectral VGT data is applicable in our study area achieving an overall accuracy of 91.4%. Independent monitoring results elicit that the lake surface area was at its lowest in 1998. The yearly average surface area is about 503 km2. The lake area increased to 603 km2 during 1999. In the period 1999–2001 the area changes are marginal. A large area increase occurred from 2001 to 2002 till the lake area reached a surface area of 791 km2. The lake area peaks to 903 km2 in 2003 and subsequently decreased to areas of 847 km2 in 2004 and 746 km2 in 2005. Similar area change dynamics are observed when applying the remote sensing based technique. Seasonally, the typical dynamics elicit a larger surface area in spring and winter and a smaller one during summer.

A Nested Ecohydrological Wireless Sensor Network for Capturing the Surface Heterogeneity in the Midstream Areas of the Heihe River Basin, China

This letter introduces the ecohydrological wireless sensor network (EHWSN), which we have installed in the middle reach of the Heihe River Basin. The EHWSN has two primary objectives: the first objective is to capture the multiscale spatial variations and temporal dynamics of soil moisture, soil temperature, and land surface temperature in the heterogeneous farmland; and the second objective is to provide a remote-sensing ground-truth estimate with an approximate kilometer pixel scale using spatial upscaling. This ground truth can be used for validation and evaluation of remote-sensing products. The EHWSN integrates distributed observation nodes to achieve an automated, intelligent, and remote-controllable network that provides superior integrated, standardized, and automated observation capabilities for hydrological and ecological processes research at the basin scale.

Crop Leaf Area Index Observations With a Wireless Sensor Network and Its Potential for Validating Remote Sensing Products

The collection of ground measurements for validating remotely sensed crop leaf area index (LAI) is labor and time intensive. This paper presents an automatic measuring system that was designed based on a wireless sensor network (WSN). The corn LAI was continuously observed from June 25 to August 24, 2012. Approximately, 42 in situ WSN measurement nodes were used in a 4 ×4 km2 area in the Heihe watershed of northwest China. The data were analyzed in three ways: 1) a comparison with LAI-2000, 2) a daily and 5-day aggregated time series analysis, and 3) a comparison with a Moderate Resolution Imaging Spectroradiometer (MODIS) LAI using both a ground LAINet LAI and a scaled-up LAI through inversion of Advanced Spaceborne Thermal Emission and Reflection radiometer (ASTER) data. The preliminary results indicated that the measured LAI values from the LAINet were correlated with the values derived from LAI-2000 (R2 from 0.27 to 0.96 with an average of 0.42). When compared with the daily crop LAI growth trajectory, the performance of the measurement system was improved by using the data that were aggregated over a 5-day window. When compared with MODIS LAI, we found that the spatial aggregation values of the ground LAINet observations and the scaled-up ASTER LAI were identical or similar to the MODIS LAI values over time. With its low-cost and low-energy consumption, the proposed WSN observation system is a promising method for collecting ground crop LAI in flexible time and space for validating the remote sensing land products.

Comparison of Eight Techniques for Reconstructing Multi-Satellite Sensor Time-Series NDVI Data Sets in the Heihe River Basin, China

More than 20 techniques have been developed to de-noise time-series vegetation index data from different satellite sensors to reconstruct long time-series data sets. Although many studies have compared Normalized Difference Vegetation Index (NDVI) noise-reduction techniques, few studies have compared these techniques systematically and comprehensively. This study tested eight techniques for smoothing different vegetation types using different types of multi-temporal NDVI data (Advanced Very High Resolution Radiometer (AVHRR) (Global Inventory Modeling and Map Studies (GIMMS) and Pathfinder AVHRR Land (PAL), Satellite Pour l’ Observation de la Terre (SPOT) VEGETATION (VGT), and Moderate Resolution Imaging Spectroradiometer (MODIS) (Terra)) with the ultimate purpose of determining the best reconstruction technique for each type of vegetation captured with four satellite sensors. These techniques include the modified best index slope extraction (M-BISE) technique, the Savitzky-Golay (S-G) technique, the mean value iteration filter (MVI) technique, the asymmetric Gaussian (A-G) technique, the double logistic (D-L) technique, the changing-weight filter (CW) technique, the interpolation for data reconstruction (IDR) technique, and the Whittaker smoother (WS) technique. These techniques were evaluated by calculating the root mean square error (RMSE), the Akaike Information Criterion (AIC), and the Bayesian Information Criterion (BIC). The results indicate that the S-G, CW, and WS techniques perform better than the other tested techniques, while the IDR, M-BISE, and MVI techniques performed worse than the other techniques. The best de-noise technique varies with different vegetation types and NDVI data sources. The S-G performs best in most situations. In addition, the CW and WS are effective techniques that were exceeded only by the S-G technique. The assessment results are consistent in terms of the three evaluation indexes for GIMMS, PAL, and SPOT data in the study area, but not for the MODIS data. The study will be very helpful for choosing reconstruction techniques for long time-series data sets.

A LUT‐based approach to estimate surface solar irradiance by combining MODIS and MTSAT data

[1] In this paper, a new satellite‐derived approach for obtaining instantaneous surface solar irradiance (SSI) by combining Moderate Resolution Imaging Spectroradiometer (MODIS) and Multifunctional Transport Satellite (MTSAT) data is presented and validated using one year pyranometer measurements from five stations in northern China. The approach is based on look‐up tables created via comprehensive radiative transfer modeling to achieve high accuracy and high computational efficiency. The synergy of the multispectral sensor MODIS and the high‐temporal‐resolution geostationary satellite MTSAT enables the adequate use of multisource remote sensing information to determine the atmosphere and surface states, and thereby complements shortcomings of their own. Here we use MTSAT data to capture the changes of cloud fields in the atmosphere and use MODIS products to obtain the dynamic aerosol loading, water vapor content, surface reflectance, and other information. Meanwhile, on the basis of instantaneous retrieval results, the calculation of hourly average SSI is also explored. The preliminary validation demonstrates that both instantaneous and hourly SSIs can be produced accurately over northern China using this approach, and the retrieval quality of hourly SSI data is slightly better than that of instantaneous SSI data. However, in mountainous areas the results need further refinement.

Multiyear precipitation reduction strongly decreases carbon uptake over northern China

Drought has been a concern in global and regional water, carbon, and energy cycles. From 1999 to 2011, northern China experienced a multiyear precipitation reduction that significantly decreased water availability as indicated by the Palmer Drought Severity Index and soil moisture measurements. In this study, a light use efficiency model (EC-LUE) and an ecosystem physiological model (IBIS) were used to characterize the impacts of long-term drought on terrestrial carbon fluxes in northern China. EC-LUE and IBIS models showed the reduction of averaged GPP of 0.09 and 0.05 Pg C yr-1 during 1999-2011 compared with 1982-1998. Based on the IBIS model, simulated ecosystem respiration experienced an insignificant decrease from 1999 to 2011. The multiyear precipitation reduction changed the regional carbon uptake of 0.011 Pg C yr-1 from 1982 to 1998 to a net source of 0.018 Pg C yr-1 from 1999 to 2011. Moreover, a pronounced decrease in maize yield in almost all provinces in the study region was found from 1999 to 2011 versus the average of yield from1978 to 2011. The largest maize yield reduction occurred in Beijing (2499kgha-1yr-1), Jilin (2180kgha-1yr-1), Tianjing (1923kgha-1yr-1), and Heilongjiang (1791kgha-1yr-1), and the maize yield anomaly was significantly correlated with the annual precipitation over the entire study area. Our results revealed that recent climate change, especially drought-induced water stress, is the dominant cause of the reduction in the terrestrial carbon sink over northern China.

Validation of MODIS-GPP product at 10 flux sites in northern China

Gross primary production (GPP) is an important variable in studies of the carbon cycle and climate change. The Moderate Resolution Imaging Spectroradiometer (MODIS)-GPP product (MOD17) provides global GPP data for terrestrial ecosystems; however, it is not well validated in China. In this study, an eddy covariance (EC) system observed GPP at 10 sites in northern China and was used to validate MOD17. The results indicated that MOD17 presents a strong bias in the study region due to the meteorological data, MODIS FPAR (fraction of absorbed photosynthetically active radiation) (MOD15), and the model parameters in the MODIS-GPP algorithm, Biome Parameters Look Up Table (BPLUT). Maximum light-use efficiency (ϵ0) had the strongest impact on the predicted GPP of the MODIS-GPP algorithm. After using the inputs observed in situ and improving parameters in the MODIS-GPP algorithm, the model could explain 85% of the EC-observed GPP of the sites, whereas the MODIS-GPP algorithm without in situ inputs and parameters only explained 26% of EC-observed GPP.

A statistical analysis of the relationship between climatic factors and the Normalized Difference Vegetation Index in China

Climate change has a large impact on vegetation dynamics. A series of statistical analyses were employed to demonstrate the relationship between Advanced Very High Resolution Radiometer (AVHRR) Global Inventory Modelling and Mapping Studies (GIMMS) Normalized Difference Vegetation Index (NDVI) data with an 8 × 8 km resolution and meteorological data, during the period 1982–2005. Rainfall has a great impact on vegetation with varying time lags. The sensitivity of NDVI to the threshold of accumulated temperature varies regionally. To identify a ‘best factor’ for each meteorological station simple and partial correlation analyses were carried out. Multiple correlation analysis was used to validate the association between the two climatic factors and monthly maximum NDVI (MNDVI). This study led to the conclusion that good correlations between MNDVI and two climatic factors are prevalent in China. It also indicated that the ‘best factors’ for some regions identified by partial correlation analysis are better than those selected by simple correlation analysis. The partial correlation coefficients of MNDVI and each climate factor were calculated to describe the singular influence of each meteorological variable. The results indicated that the impact of other variables on vegetation should be considered in the ‘best factor’ selection for one climatic variable. Temperature has a significant positive influence on vegetation growth in China. Precipitation is the most important climatic factor that closely correlates with MNDVI, particularly in arid and semi-arid environments. However, in some wet regions, precipitation is not a limiting factor on vegetation growth. A trend analysis was carried out to study climate change and its impacts on vegetation. The annual accumulated temperature had an increasing trend in China during 1982–2005. Temperature increases had different influences on vegetation dynamics in different parts of China. The results coincided with those of the multiple and partial correlation analysis.

A revised surface resistance parameterisation for estimating latent heat flux from remotely sensed data

Abstract Estimating evapotranspiration (ET) is required for many environmental studies. Remote sensing provides the ability to spatially map latent heat flux. Many studies have developed approaches to derive spatially distributed surface energy fluxes from various satellite sensors with the help of field observations. In this study, remote-sensing-based λE mapping was conducted using a Landsat Thematic Mapper (TM) image and an Enhanced Thematic Mapper Plus (ETM+) image. The remotely sensed data and field observations employed in this study were obtained from Watershed Allied Telemetry Experimental Research (WATER). A biophysics-based surface resistance model was revised to account for water stress and temperature constraints. The precision of the results was validated using ‘ground truth’ data obtained by eddy covariance (EC) system. Scale effects play an important role, especially for parameter optimisation and validation of the latent heat flux ( λE ). After considering the footprint of EC, the λE derived from the remote sensing data was comparable to the EC measured value during the satellites passage. The results showed that the revised surface resistance parameterisation scheme was useful for estimating the latent heat flux over cropland in arid regions.