Regional spatial variability of root-zone soil moisture in arid regions and the driving factors — a case study of Xinjiang, China

Li, Xiangdong; Shao, Mingan; Zhao, Chunlei; Liu, Tong; Jia, Xiaoxu; Ma, Changkun

doi:10.1139/cjss-2019-0006

Cited by 14 publications

(6 citation statements)

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“…This may be attributed to the soil environment variability in different regions. Li et al (2019) measured soil moisture of different depths at 142 sites in Xinjiang and found that moisture increased with soil depth, but the difference in moisture between the 0–50 cm soil layers and the 50–100 cm soil layers was not significant ( p > 0.05), which was in line with the phenomenon observed in this study at Xinjiang PM.…”

Section: Discussionsupporting

confidence: 86%

“…Geographically, Xinjiang can be divided into three regions, and the Baijiu production area in Xinjiang is mainly clustered in the northern region (which is also the sampling area of this study). The region is located between 42 • 10 and 49 • 10 latitude as well as 79 • 59 and 91 • 48 longitude, and the main regional climate is temperate continental arid and semi-arid climate with low precipitation (150-200 mm) and great evaporation potential (1,500-2,300 mm) (Li et al, 2019). These geographical and climatic factors led to the low PM moisture in Xinjiang, which has the least precipitation and the highest evaporation throughout China.…”

Section: Discussionmentioning

confidence: 99%

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The Depth-Depended Fungal Diversity and Non-depth-Depended Aroma Profiles of Pit Mud for Strong-Flavor Baijiu

et al. 2022

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Microorganisms in pit mud are the essential factor determining the style of strong flavor Baijiu. The spatial distribution characteristics of fungal communities and aroma in the pit mud for strong flavor Baijiu from Xinjiang, China, were investigated using Illumina MiSeq high-throughput sequencing and electronic nose technology. A total of 138 fungal genera affiliated with 10 fungal phyla were identified from 27 pit mud samples; of these, Saccharomycopsis, Aspergillus, and Apiotrichum were the core fungal communities, and Aspergillus and Apiotrichum were the hubs that maintain the structural stability of fungal communities in pit mud. The fungal richness and diversity, as well as aroma of pit mud, showed no significant spatial heterogeneity, but divergences in pit mud at different depths were mainly in pH, total acid, and high abundance fungi. Moisture, NH4+, and lactate were the main physicochemical factors involved in the maintenance of fungal stability and quality in pit mud, whereas pH had only a weak effect on fungi in pit mud. In addition, the fungal communities of pit mud were not significantly associated with the aroma. The results of this study provide a foundation for exploring the functional microorganisms and dissecting the brewing mechanism of strong flavor Baijiu in Xinjiang, and also contributes to the improvement of pit mud quality by bioaugmentation and controlling environmental physicochemical factors.

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Section: Discussionsupporting

confidence: 86%

Section: Discussionmentioning

confidence: 99%

The Depth-Depended Fungal Diversity and Non-depth-Depended Aroma Profiles of Pit Mud for Strong-Flavor Baijiu

et al. 2022

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“…However, both these studies often have a coarse spatial resolution, which does not apply to the field scale. Furthermore, soil hydrological processes are complexed by the diverse effects of soil, climate, vegetation, and topography [12]. Characterizing soil moisture dynamics and distribution at large spatial and temporal scales is not easy, as it is affected by various physical processes (e.g., precipitation, evapotranspiration, runoff, drainage) and environmental controlling factors (e.g., meteorological forcing, soil texture, vegetation, topography) [7].…”

Section: Introductionmentioning

confidence: 99%

Establishing an Empirical Model for Surface Soil Moisture Retrieval at the U.S. Climate Reference Network Using Sentinel-1 Backscatter and Ancillary Data

2020

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Progress in sensor technologies has allowed real-time monitoring of soil water. It is a challenge to model soil water content based on remote sensing data. Here, we retrieved and modeled surface soil moisture (SSM) at the U.S. Climate Reference Network (USCRN) stations using Sentinel-1 backscatter data from 2016 to 2018 and ancillary data. Empirical machine learning models were established between soil water content measured at the USCRN stations with Sentinel-1 data from 2016 to 2017, the National Land Cover Dataset, terrain parameters, and Polaris soil data, and were evaluated in 2018 at the same USCRN stations. The Cubist model performed better than the multiple linear regression (MLR) and Random Forest (RF) model (R2 = 0.68 and RMSE = 0.06 m3 m-3 for validation). The Cubist model performed best in Shrub/Scrub, followed by Herbaceous and Cultivated Crops but poorly in Hay/Pasture. The success of SSM retrieval was mostly attributed to soil properties, followed by Sentinel-1 backscatter data, terrain parameters, and land cover. The approach shows the potential for retrieving SSM using Sentinel-1 data in a combination of high-resolution ancillary data across the conterminous United States (CONUS). Future work is required to improve the model performance by including more SSM network measurements, assimilating Sentinel-1 data with other microwave, optical and thermal remote sensing products. There is also a need to improve the spatial resolution and accuracy of land surface parameter products (e.g., soil properties and terrain parameters) at the regional and global scales.

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“…5A ) which cautions the use of soil moisture estimates at the root zone relying solely on the ML-based method. Deeper depth soil moisture tends to vary more by soil hydraulic properties, soil texture, and LULC compared to surface layers ( Xiao et al, 2014 ; Karandish & Šimůnek, 2016 ; Das & Mohanty, 2006 ; Li et al, 2019 ). Even though our model did include LULC, LSM-derived estimates, and soil properties as covariates which were ranked with moderate to high importance for deeper soil layers ( Fig.…”

Section: Discussionmentioning

confidence: 99%

Machine learning based estimation of field-scale daily, high resolution, multi-depth soil moisture for the Western and Midwestern United States

Xia

Watts

Machmuller

et al. 2022

PeerJ

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Background High-resolution soil moisture estimates are critical for planning water management and assessing environmental quality. In-situ measurements alone are too costly to support the spatial and temporal resolutions needed for water management. Recent efforts have combined calibration data with machine learning algorithms to fill the gap where high resolution moisture estimates are lacking at the field scale. This study aimed to provide calibrated soil moisture models and methodology for generating gridded estimates of soil moisture at multiple depths, according to user-defined temporal periods, spatial resolution and extent. Methods We applied nearly one million national library soil moisture records from over 100 sites, spanning the U.S. Midwest and West, to build Quantile Random Forest (QRF) calibration models. The QRF models were built on covariates including soil moisture estimates from North American Land Data Assimilation System (NLDAS), soil properties, climate variables, digital elevation models, and remote sensing-derived indices. We also explored an alternative approach that adopted a regionalized calibration dataset for the Western U.S. The broad-scale QRF models were independently validated according to sampling depths, land cover type, and observation period. We then explored the model performance improved with local samples used for spiking. Finally, the QRF models were applied to estimate soil moisture at the field scale where evaluation was carried out to check estimated temporal and spatial patterns. Results The broad-scale QRF model showed moderate performance (R2 = 0.53, RMSE = 0.078 m3/m3) when data points from all depth layers (up to 100 cm) were considered for an independent validation. Elevation, NLDAS-derived moisture, soil properties, and sampling depth were ranked as the most important covariates. The best model performance was observed for forest and pasture sites (R2 > 0.5; RMSE < 0.09 m3/m3), followed by grassland and cropland (R2 > 0.4; RMSE < 0.11 m3/m3). Model performance decreased with sampling depths and was slightly lower during the winter months. Spiking the national QRF model with local samples improved model performance by reducing the RMSE to less than 0.05 m3/m3 for grassland sites. At the field scale, model estimates illustrated more accurate temporal trends for surface than subsurface soil layers. Model estimated spatial patterns need to be further improved and validated with management data. Conclusions The model accuracy for top 0–20 cm soil depth (R2 > 0.5, RMSE < 0.08 m3/m3) showed promise for adopting the methodology for soil moisture monitoring. The success of spiking the national model with local samples showed the need to collect multi-year high frequency (e.g., hourly) sensor-based field measurements to improve estimates of soil moisture for a longer time period. Future work should improve model performance for deeper depths with additional hydraulic properties and use of locally-selected calibration datasets.

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Regional spatial variability of root-zone soil moisture in arid regions and the driving factors — a case study of Xinjiang, China

Cited by 14 publications

References 51 publications

The Depth-Depended Fungal Diversity and Non-depth-Depended Aroma Profiles of Pit Mud for Strong-Flavor Baijiu

The Depth-Depended Fungal Diversity and Non-depth-Depended Aroma Profiles of Pit Mud for Strong-Flavor Baijiu

Establishing an Empirical Model for Surface Soil Moisture Retrieval at the U.S. Climate Reference Network Using Sentinel-1 Backscatter and Ancillary Data

Machine learning based estimation of field-scale daily, high resolution, multi-depth soil moisture for the Western and Midwestern United States

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