Zhongli Zhu

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...

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.

Estimating the spatial distribution of soil moisture based on Bayesian maximum entropy method with auxiliary data from remote sensing

Abstract Soil moisture (SM) plays a fundamental role in the land–atmosphere exchange process. Spatial estimation based on multi in situ (network) data is a critical way to understand the spatial structure and variation of land surface soil moisture. Theoretically, integrating densely sampled auxiliary data spatially correlated with soil moisture into the procedure of spatial estimation can improve its accuracy. In this study, we present a novel approach to estimate the spatial pattern of soil moisture by using the BME method based on wireless sensor network data and auxiliary information from ASTER (Terra) land surface temperature measurements. For comparison, three traditional geostatistic methods were also applied: ordinary kriging (OK), which used the wireless sensor network data only, regression kriging (RK) and ordinary co-kriging (Co-OK) which both integrated the ASTER land surface temperature as a covariate. In Co-OK, LST was linearly contained in the estimator, in RK, estimator is expressed as the sum of the regression estimate and the kriged estimate of the spatially correlated residual, but in BME, the ASTER land surface temperature was first retrieved as soil moisture based on the linear regression, then, the t-distributed prediction interval (PI) of soil moisture was estimated and used as soft data in probability form. The results indicate that all three methods provide reasonable estimations. Co-OK, RK and BME can provide a more accurate spatial estimation by integrating the auxiliary information Compared to OK. RK and BME shows more obvious improvement compared to Co-OK, and even BME can perform slightly better than RK. The inherent issue of spatial estimation (overestimation in the range of low values and underestimation in the range of high values) can also be further improved in both RK and BME. We can conclude that integrating auxiliary data into spatial estimation can indeed improve the accuracy, BME and RK take better advantage of the auxiliary information compared to Co-OK, and BME outperforms RK by integrating the auxiliary data in a probability form.

Observation on Soil Moisture of Irrigation Cropland by Cosmic-Ray Probe

The newly developed cosmic-ray probe (CRP) method for measuring area-average soil moisture at the hectometer horizontal scale was tested in the 2012 observation campaign of the Heihe Watershed Allied Telemetry Experimental Research (HiWATER) in the irrigated cropland area of Zhangye Oasis. As compared with the traditional point observation, data analysis shows that the CRP could measure the real areal soil moisture, except when the water is frozen and during thaw. During the irrigation period, the presence of surface water can lead to an overestimation of the soil moisture. During non-irrigated periods, the CRP has a very strong correlation with the averaged soil moisture of 19 SoilNET probes in its footprint, whose R2 is 0.73 and root-mean-square error is 0.0275 m3/m3. In comparison with the Polarimetric L-band Multi-beam Radiometer (PLMR) retrieved soil moisture, which has a pixel resolution of 700 m, the CRP provides much better results, with a coefficient of determination of 0.96 and 0.64, respectively. The results show that the CRP is a robust area-averaged soil moisture observation method, which can be used to obtain “true” values of field-scale soil moisture for remote sensing validation.

Spatial Upscaling of Sparse Soil Moisture Observations Based on Ridge Regression

Available ground-based observation networks for the validation of soil moisture remote sensing products are commonly sparse; thus, ground truth determinations are difficult at the validated remote sensing pixel scale. Based on the consistency of temporal trends between ground truth and in situ measurements, it is feasible to estimate ground truth by building a linear relationship between temporal sparse ground observations and truth samples. Herein, auxiliary remote sensing data with a moderate spatial resolution can be transformed into truth samples depending on the stronger representation of remote sensing data to spatial heterogeneity in the validated pixel relative to limited sites. When solving weighting coefficients for the relationship model, the underlying correlations among the in situ measurements cause the multicollinearity problem, leading to failed predictions. An upscaling algorithm called ridge regression (RR) addresses this by introducing a regularization parameter. With sparse sites, the RR method is tested in two cases employing six and nine sites, and compared with the ordinary least squares and the arithmetic mean. The upscaling results of the RR method show higher prediction accuracies compared to the other two methods. When the RR method is used, the six-site case has the same estimation accuracy as the nine-site case due to maintaining the diversity of in situ measurements through the analysis of the ridge trace and variance inflation factor (VIF). Thus, the ridge trace and VIF analysis is considered as the optimal selection method for the existing observation networks if the RR method will be used in future validation work. With a different number of sites, the RR method always displays the best estimation accuracy and is not sensitive to the number of sites, which indicates that the RR method can potentially upscale sparse sites. However, if the sites are too few, e.g., one to four, it is difficult to perform the upscaling method.

A framework for validating remotely sensed evapotranspiration

Remotely sensed evapotranspiration (RS_ET) products have been applied from regional to global. However, the validation of remote sensing products over heterogeneous land surfaces has been hindered due to the challenges in the theory and methods in recent decades, especially in estimation of “ground-truth” at the satellite pixel scale. In this study, an innovative validation framework including quantification of the spatial heterogeneity, optimization of the ground sampling strategy, multi-scale measurement, upscaling theory, uncertainty analyses, and validation method (direct validation, indirect validation and cross validation), was proposed to validate RS_ET products at different scales. Here, the framework was applied in Haihe and Heihe basin in China. The results showed the proposed validation framework of RS_ET product was reasonable and feasible.