Satellite Retrieval of Surface Evapotranspiration with Nonparametric Approach: Accuracy Assessment over a Semiarid Region

Surface evapotranspiration (ET) is one of the key surface processes. Reliable estimation of regional ET solely from satellite data remains a challenge. This study applies recently proposed nonparametric (NP) approach to retrieve surface ET, in terms of latent heat flux (LE), over a semiarid region. The involved input parameters are surface net radiation, land surface temperature, near-surface air temperature, and soil heat flux, all of which are retrievals or products of the Moderate-Resolution Imaging Spectroradiometer (MODIS). Field observations are used as ground references, which were obtained from six eddy covariance (EC) sites with different land covers including desert, Gobi, village, orchard, vegetable field, and wetland. Our results show that the accuracy of LE retrievals varies with EC sites with a determination of coefficient from 0.02 to 0.76, a bias from −221.56 W/m2 to 143.77 W/m2, a relative error from 8.82% to 48.35%, and a root mean square error from 67.97 W/m2 to 239.55 W/m2. The error mainly resulted from the uncertainties from MODIS products or the retrieval of net radiation and soil heat flux in nonvegetated region. It highlights the importance of accurate retrieval of the input parameters from satellite data, which are the ongoing tasks of remote sensing community

Link Selection in Buffer-Aided Cooperative Networks for Green IoT

In this paper, the link selection algorithm in buffer-aided cooperative networks with multiple co-channel interferers for green internet of things (IoT) is investigated. In the considered system, the relay node selects the better link from the first and the second hop with the help of buffers. We evaluate the effects of the system parameters by deriving the analytical analysis as well as the asymptotic results on the exact expressions of the outage probability. The impacts of system parameters, such as the predefined target rate, the priority-outage tradeoff parameter, the buffer length and the number of interferers are evaluated in different setup scenarios. Considerable performance gains can be obtained compared with the classical decode-and-forward relaying networks. Simulation results are provided to verify the theoretical analysis

Determination of cinnamic acid in human urine by flow injection chemiluminescence

It was found that cinnamic acid can react with potassium permanganate in the acidic medium and produce chemiluminescence, which was greatly enhanced by glyoxal. Under the optimum conditions, the linear range for the determination of cinnamic acid was 1.0×10-8 to 1.0×10-4 mol L-1 with a detection limit of 8.0×10-9 mol L-1, the relative standard deviation was 1.7% for 2.0×10-6 mol L-1 cinnamic acid solution in nine repeated measurements. This method was found to be novel0simple0fast and sensitive, it was successfully applied to the determination of cinnamic acid in human urine. Furthermore, the possible reaction mechanism was also discussed

Using a MODIS Index to Quantify MODIS-AVHRRs Spectral Differences in the Visible Band

Spectral band differences are a critical issue for progressing into an integrated earth observation framework and in particular, in sensor intercalibration. The differences are currently normalized using spectral band adjustment factor (SBAF) that is generated from hyperspectral data. In this context, the current study proposes a method for calculating moderate-resolution imaging spectroradiometer (MODIS)-advanced very high resolution radiometers (AVHRRs) SBAF in the visible band, using the MODIS surface reflectance data. The method involves a uniform ratio index calculated using the MODIS 552-nm and 645-nm bands, and a sensor-specific quadratic equation, producing SBAF data at 500-m spatial resolution. The calculated SBAFs are in good agreement at site scale with literature reported data (relative error < 1.0%), and at local scale with Hyperion-derived data (total uncertainty ≈ 0.001), and significantly improve MODIS-AVHRR surface reflectance data consistency in the visible band (better than 1.0% reflectance units). The calculation is more sensitive to atmospheric effects over the vegetated areas. At global scale, MODIS-AVHRRs SBAFs are generally large (>1.0) over densely vegetated areas and extremely low over deserts and barren lands (0.96–0.98), indicative of large MODIS-AVHRRs differences. Deserts show temporally stable SBAF values, while still suffer from intra-annual BRDF effects and short-term cloud contamination. By means of daily MODIS data, the proposed method can produce ongoing SBAF data at a spatial scale that is comparable to AVHRRs. It increases the sampling of MODIS-AVHRRs image pairs for intercalibration, and offers insight into spectral band conversion, finally contributing to an integrated earth observation at moderate spatial resolutions

Validation of Multiple Soil Moisture Products over an Intensive Agricultural Region: Overall Accuracy and Diverse Responses to Precipitation and Irrigation Events

Remote sensing and land surface models promote the understanding of soil moisture dynamics by means of multiple products. These products differ in data sources, algorithms, model structures and forcing datasets, complicating the selection of optimal products, especially in regions with complex land covers. This study compared different products, algorithms and flagging strategies based on in situ observations in Anhui province, China, an intensive agricultural region with diverse landscapes. In general, models outperform remote sensing in terms of valid data coverage, metrics against observations or based on triple collocation analysis, and responsiveness to precipitation. Remote sensing performs poorly in hilly and densely vegetated areas and areas with developed water systems, where the low data volume and poor performance of satellite products (e.g., Soil Moisture Active Passive, SMAP) might constrain the accuracy of data assimilation (e.g., SMAP L4) and downstream products (e.g., Cyclone Global Navigation Satellite System, CYGNSS). Remote sensing has the potential to detect irrigation signals depending on algorithms and products. The single-channel algorithm (SCA) shows a better ability to detect irrigation signals than the Land Parameter Retrieval Model (LPRM). SMAP SCA-H and SCA-V products are the most sensitive to irrigation, whereas the LPRM-based Advanced Microwave Scanning Radiometer 2 (AMSR2) and European Space Agency (ESA) Climate Change Initiative (CCI) passive products cannot reflect irrigation signals. The results offer insight into optimal product selection and algorithm improvement

Exploiting TERRA-AQUA MODIS Relationship in the Reflective Solar Bands for Aerosol Retrieval

Satellite remote sensing has been providing aerosol data with ever-increasing accuracy, representative of the MODerate-resolution Imaging Spectroradiometer (MODIS) Dark Target (DT) and Deep Blue (DB) aerosol retrievals. These retrievals are generally performed over spectrally dark objects and therefore may struggle over bright surfaces. This study proposed an analytical TERRA-AQUA MODIS relationship in the reflective solar bands for aerosol retrieval. For the relationship development, the bidirectional reflectance distribution function (BRDF) effects were adjusted using reflectance ratios in the MODIS 2.13 μm band and the path radiance was approximated as an analytical function of aerosol optical thickness (AOT) and scattering phase function. Comparisons with MODIS observation data, MODIS AOT data, and sun photometer measurements demonstrate the validity of the proposed relationship for aerosol retrieval. The synergetic TERRA-AQUA MODIS retrievals are highly correlated with the ground measured AOT at TERRA MODIS overpass time (R2 = 0.617; RMSE = 0.043) and AQUA overpass time (R2 = 0.737; RMSE = 0.036). Compared to our retrievals, both the MODIS DT and DB retrievals are subject to severe underestimation. Sensitivity analyses reveal that the proposed method may perform better over non-vegetated than vegetated surfaces, which can offer a complement to MODIS operational algorithms. In an analytical form, the proposed method also has advantages in computational efficiency, and therefore can be employed for fine-scale (relative to operational 10 km MODIS product) MODIS aerosol retrieval. Overall, this study provides insight into aerosol retrievals and other applications regarding TERRA-AQUA MODIS data