Plowing, seeding, and fertilizing differentially influence species diversity, functional groups and community productivity in a degraded steppe

Yang, Jun; Hou, Dongjie; Qiao, Xiaojuan; Geng, Xi-Mei; Guo, Ke; He, Wei‐Ming

doi:10.1016/j.flora.2019.05.013

Cited by 2 publications

(1 citation statement)

References 27 publications

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“…The DGS (39 • 29 N, 110 • 11 E) has an altitude of 1300 m Above Sea Level (ASL) and is located at the boundary of the Ordos Plateau and the Mu Us Desert, which belongs to the Agricultural-Pastoral Ecotone of Northern China (APENC)-one of the four largest agricultural-pastoral ecotones in the world, with an extremely fragile and sensitive ecosystem [51][52][53]. Under the influence of temperate continental climate, the annual average temperature at the DGS is 4.2 • C [54][55][56], and the annual precipitation is 351 mm, 80% of which happens from May to August [57,58]. The AGS (38 • 55 N, 100 • 30 E) is located in the Qilian Mountains, which belongs to the cold mountainous ranges of northwest China, with an altitude of 2750 m ASL, and has a semi-arid continental monsoon climate [59,60].…”

Section: Study Areasmentioning

confidence: 99%

Comparison of Data Fusion Methods in Fusing Satellite Products and Model Simulations for Estimating Soil Moisture on Semi-Arid Grasslands

Zhu,

Zhang,

et al. 2023

Remote Sensing

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In arid and semi-arid areas, soil moisture (SM) plays a crucial role in land-atmosphere interactions, hydrological processes, and ecosystem sustainability. SM data at large scales are critical for related climatic, hydrological, and ecohydrological research. Data fusion based on satellite products and model simulations is an important way to obtain SM data at large scales; however, little has been reported on the comparison of the data fusion methods in different categories. Here, we compared the performance of two widely used data fusion methods, the Ensemble Kalman Filter (EnKF) and the Back-Propagation Artificial Neural Network (BPANN), in the degraded grassland site (DGS) and the alpine grassland site (AGS). The SM data from the Community Land Model 5.0 (CLM5.0) and the Soil Moisture Active and Passive (SMAP) were fused and validated against the observations of the Cosmic-Ray Neutron Sensor (CRNS) to avoid the impacts of scale-mismatch. Results show that compared with the original data sets at both sites, the RMSE of the fused data by BPANN (FD-BPANN) and EnKF (FD-EnKF) had improved by more than 50% and 31%, respectively. Overall, the FD-BPANN performs better than the FD-EnKF because the BPANN method assigned higher weights to input data with better performance and the EnKF method is affected by the strong variabilities of both the fused CLM5.0 and SMAP data and the CRNS data. However, in terms of the percentile range, the FD-BPANN showed the worst performance, with overestimations in the low SM range of 25th percentile (<Q25), because the BPANN method tends to be trapped in a local minimum. The BPANN method performed better in humid areas, then followed by semi-humid areas, and finally arid and semi-arid areas. Moreover, compared with the previous studies in arid and semi-arid areas, the BPANN method in this study performed better.

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