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China's first dynamic environment satellite constellation includes the HY-2B, HY-2C, and HY-2D satellites. In this study, the along track SLA, SWH, and SSWS of this satellite constellation were evaluated. SLA parameters are evaluated using self-crossing and dual-crossing methods. The SSWS and SWH data were evaluated by comparing with NDBC buoy and other available satellites' data. The evaluation revealed that the standard deviation of the SLA from the HY-2B/C/D satellites' single mission crossovers was 3.29 cm, 3.51 cm, and 3.72 cm, respectively. In addition, at the dual-crossovers of the Jason-3 satellite and the HY-2B satellite, the HY-2B satellite, and the HY-2C/D satellites, the standard deviation was determined to be 3.40 cm, 3.48 cm, and 4.25 cm, respectively. The accuracy of the SWH products of the HY-2B/C/D satellite radar altimeters was observed to be 0.23 m, 0.25 m, and 0.26 m, respectively. The accuracy of the SSWS data of the HY-2B/C/D satellite radar altimeters was observed to be 1.48 m/s, 1.59 m/s, and 1.35 m/s, respectively. In addition, this study also analyzed and compared the observation efficiency of the dynamic environment satellite constellation with the following six satellites: Sentinel-3(A, B), Jason-3, Sentinel-6A, Saral, and Cryosat-2. Observation efficiency refers to selection of any point on the globe to find a minimum radius of at least one observation point within a circle in a 14-day period. The analysis results demonstrated that observation efficiency of China's first dynamic environment satellite constellation was comparable to that of the six satellites. [ABSTRACT FROM AUTHOR]

• ET and T from several models are evaluated by eddy covariance and sap flow data. • Comparison of remote sensing ET models with process based hydrological model. • Remote sensing models tend to underestimate ET in dry season but overestimate in the rainy season. Evapotranspiration (ET) and its components, transpiration (T) and soil evaporation (Es) are crucial to understand hydrological cycle and effective water loss in water resource management. Remote sensing models and process based hydrological models are two kinds of effective methods for evaluating ET and its partitions. Although the ability of remote sensing models to simulate ET has been widely verified, there is limited verification of ET components, especially T, which is the major component of ET. This research used ET obtained from eddy covariance system and T obtained from sap flow observation in an apricot orchard of north China to evaluate the accuracy of ET and its components from three remote sensing models (PT-JPL, PML, and SWH) and a process-based hydrological model (SHAW). The results showed that after calibrations, all the four models satisfactorily simulated total ET particularly at annual level. Among them, the PML model had the best fit efficiency, with the most satisfied R 2 and NSE values. The PT-JPL estimated average ET was the most accurate, with the slope nearest to 1.0. While for the simulation of T, SHAW model had a better performance owing to the fact that SHAW model simulate plant transpiration using changes in soil water content. The average T/ET of the models for the growing season was close to the observed value of 0.70, except that the T/ET of SWH is high due to the lack of Ei calculation. Furthermore, nearly all the three remote sensing models underestimated ET in the dry spring and overestimated ET in the rainy summer. In comparison with the process-based SHAW model, it is suggested that remote sensing ET relies more heavily on the seasonal change of meteorological factors such as precipitation but less capable in reflecting the gradually delaying process of soil moisture in the dry season. Therefore, it is indicated that for future improvement of remote sensing ET models, adjustment of soil water availability in a much longer term is suggested at least for semi-humid and semi-arid ecosystems. [ABSTRACT FROM AUTHOR]

Consistency between national wave buoy networks is extremely important for wave climate studies and verification of global operational wave forecasting systems; however, it is insufficiently investigated. The validation of altimeter significant wave heights (SWHs) with the wave buoy networks of China, Europe and the National Data Buoy Center (NDBC) show significant divergence in assessments. This reveals a negative bias and larger root mean square error and scatter index from the Chinese buoy network than from the European and NDBC buoy networks. A remote cross-calibration method is presented using the collocations between altimeters and buoys to match the buoy observations from different networks. The Chinese buoys are found to yield a negative bias of −0.127 m compared to European/NDBC buoy networks. The cross-calibration equation is achieved by regression of the SWHs between the Chinese and European/NDBC buoy networks. The use of this remote cross-calibration significantly reduces the inconsistency between the Chinese and European/NDBC buoys in the validation of SWH from altimeter HY2B. [ABSTRACT FROM AUTHOR]