scholarly journals Effects of Patchiness on Surface Soil Moisture of Alpine Meadow on the Northeastern Qinghai-Tibetan Plateau: Implications for Grassland Restoration

2020 ◽  
Vol 12 (24) ◽  
pp. 4121
Author(s):  
Wei Zhang ◽  
Shuhua Yi ◽  
Yu Qin ◽  
Yi Sun ◽  
Donghui Shangguan ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Tibetan Plateau ◽  
Surface Soil ◽  
Alpine Meadow ◽  
Alpine Grassland ◽  
Grassland Restoration ◽  
Landscape Indices ◽  
Surface Soil Moisture ◽  
Bare Patch

Surface soil moisture (SSM) is a key limiting factor for vegetation growth in alpine meadow on the Qinghai-Tibetan Plateau (QTP). Patches with various sizes and types may cause the redistribution of SSM by changing soil hydrological processes, and then trigger or accelerate alpine grassland degradation. Therefore, it is vital to understand the effects of patchiness on SSM at multi-scales to provide a reference for alpine grassland restoration. However, there is a lack of direct observational evidence concerning the role of the size and type of patches on SSM, and little is known about the effects of patches pattern on SSM at plot scale. Here, we first measured SSM of typical patches with different sizes and types at patch scale and investigated their patterns and SSM spatial distribution through unmanned aerial vehicle (UAV)-mounted multi-type cameras at plot scale. We then analyzed the role of the size and type of patchiness on SSM at both patch and plot scales. Results showed that: (1) in situ measured SSM of typical patches was significantly different (P < 0.01), original vegetation patch (OV) had the highest SSM, followed by isolate vegetation patch (IV), small bare patch (SP), medium bare patch (MP) and large bare patch (LP); (2) the proposed method based on UAV images was able to estimate SSM (0–40 cm) with a satisfactory accuracy (R2 = 0.89, P < 0.001); (3) all landscape indices of OV, with the exception of patch density, were positively correlated with SSM at plot scale, while most of the landscape indices of LP and IV showed negative correlations (P < 0.05). Our results indicated that patchiness intensified the spatial heterogeneity of SSM and potentially accelerated the alpine meadow degradation. Preventing the development of OV into IV and the expansion of LP is a critical task for alpine meadow management and restoration.

scholarly journals Monitoring Surface Soil Moisture Content over the Vegetated Area by Integrating Optical and SAR Satellite Observations in the Permafrost Region of Tibetan Plateau

2020 ◽  
Vol 12 (1) ◽  
pp. 183 ◽  
Author(s):  
Chenyang Xu ◽  
John J. Qu ◽  
Xianjun Hao ◽  
Di Wu
Keyword(s):  
Soil Moisture ◽  
Tibetan Plateau ◽  
Water Content ◽  
Vegetation Index ◽  
Surface Soil ◽  
Cloud Model ◽  
Incidence Angle ◽  
Permafrost Region ◽  
Surface Soil Moisture ◽  
The Impact

Surface soil moisture (SSM), the average water content of surface soil (up to 5 cm depth), plays a key role in the energy exchange within the ecosystem. We estimated SSM in areas with vegetation cover (grassland) by combining microwave and optical satellite measurements in the central Tibetan Plateau (TP) in 2015. We exploited TERRA moderate resolution imaging spectroradiometer (MODIS) and Sentinel-1A synthetic aperture radar (SAR) observations to estimate SSM through a simplified water-cloud model (sWCM). This model considers the impact of vegetation water content (VWC) to SSM retrieval by integrating the vegetation index (VI), the normalized difference water index (NDWI), or the normalized difference infrared index (NDII). Sentinel-1 SAR C-band backscattering coefficients, incidence angle, and NDWI/NDII were assimilated in the sWCM to monitor SSM. The soil moisture and temperature monitoring network on the central TP (CTP-SMTMN) measures SSM within the study area, and ground measurements were applied to train and validate the model. Via the proposed methods, we estimated the SSM in vegetated area with an R2 of 0.43 and a ubRMSE of 0.06 m3/m3 when integrating the NDWI and with an R2 of 0.45 and a ubRMSE of 0.06 m3/m3 when integrating the NDII.

Remote Sensing of Surface Soil Moisture from FengYun MicroWave Radiation Imager (MWRI) Data Using a Machine Learning Technique

2020 ◽  
Author(s):  
Sibo Zhang ◽  
Wei Yao
Keyword(s):  
Machine Learning ◽  
Soil Moisture ◽  
Tibetan Plateau ◽  
Microwave Radiation ◽  
Missing Values ◽  
Surface Soil ◽  
Low Frequency ◽  
Global Scale ◽  
Western China ◽  
Surface Soil Moisture

&lt;p&gt;In the past, soil moisture can be retrieved from microwave imager over most of land conditions. However, the algorithm performances over Tibetan Plateau and the Northwest China vary greatly from one to another due to frozen soils and surface volumetric scattering. The majority of western Chinese region is often filled with invalid retrievals. In this study, Soil Moisture Operational Products System (SMOPS) products from NOAA are used as the learning objectives to train a&amp;#160; machine learning (random forest) model for FY-3C microwave radiation imager (MWRI) data with multivariable inputs: brightness temperatures from all 10 MWRI channels from 10 to 89 GHz, brightness temperature polarization ratios at 10.65, 18.7 and 23.8 GHz, height in DEM (digital elevation model) and statistical soil porosity map data. Since the vegetation penetration of MWRI observations is limited, we exclude forest, urban and snow/ice surfaces in this work. It is shown that our new method performs very well and derives the surface soil moisture over Tibetan Plateau without major missing values. Comparing to other soil moisture data, the volumetric soil moisture (VSM) from this study correlates with SMOPS products much better than the MWRI operational L2 VSM products. R&lt;sup&gt;2&lt;/sup&gt; score increases from 0.3 to 0.6 and ubRMSE score improves significantly from 0.11 m&lt;sup&gt;3&lt;/sup&gt; m&lt;sup&gt;-3&lt;/sup&gt; to 0.04 m&lt;sup&gt;3&lt;/sup&gt; m&lt;sup&gt;-3&lt;/sup&gt; during the time period from 1 August 2017 to 31 May 2019. The spatial distribution of our MWRI VSM estimates is also much improved in western China. Moreover, our MWRI VSM estimates are in good agreement with the top 7 cm soil moisture of ECMWF ERA5 reanalysis data: R&lt;sup&gt;2&lt;/sup&gt; = 0.62, ubRMSD = 0.114 m&lt;sup&gt;3&lt;/sup&gt; m&lt;sup&gt;-3&lt;/sup&gt; and mean bias = -0.014 m&lt;sup&gt;3&lt;/sup&gt; m&lt;sup&gt;-3&lt;/sup&gt; for a global scale. We note that there is a risk of data gap of AMSR2 from the present to 2025. Obviously, for satellite low frequency microwave observations, MWRI observations from FY-3 series satellites can be a benefit supplement to keep the data integrity and increase the data density, since FY-3B\-3C\-3D satellites launched in November 2010\September 2013\November 2017 are still working today, and FY-3D is designed to work until November 2022.&lt;/p&gt;

Interannual Variations and Trends in Remotely Sensed and Modeled Soil Moisture in China

2018 ◽  
Vol 19 (5) ◽  
pp. 831-847 ◽  
Author(s):  
Binghao Jia ◽  
Jianguo Liu ◽  
Zhenghui Xie ◽  
Chunxiang Shi
Keyword(s):  
Soil Moisture ◽  
Tibetan Plateau ◽  
Surface Soil ◽  
Central China ◽  
The Tibetan Plateau ◽  
Interannual Variations ◽  
Surface Soil Moisture ◽  
Community Land Model ◽  
In Situ Observations

Abstract In this study, a microwave-based multisatellite merged product released from the European Space Agency’s Climate Change Initiative (ESA CCI) and two model-based simulations from the Community Land Model 4.5 (CLM4.5) and Global Land Data Assimilation System (GLDAS) were used to investigate interannual variations and trends of soil moisture in China between 1979 and 2010. They were also evaluated using in situ observations from the nationwide agrometeorological network. These three datasets show consistent drying trends for surface soil moisture in northeastern and central China, as well the eastern portion of Inner Mongolia, and wetting trends in the Tibetan Plateau, which are also identified by in situ observations. Trends in the root-zone soil moisture are in line with those of surface soil moisture seen in the CLM4.5 and GLDAS simulations obtained from most areas in China (78%–88%), except for northwestern China and southwest of the Tibetan Plateau. Moreover, the drying trend intensifies with increasing soil depth. Taking the in situ measurements as reference, it is found that ESA CCI has better accuracy in identifying the significant drying trends while CLM4.5 and GLDAS capture wetting trends better. Compared to temperature, precipitation is the primary factor responsible for these trends, which controls the direction of soil moisture changes, while increasing temperatures can also enhance soil drying during periods of decreased precipitation.

scholarly journals The critical factors that affected the distribution of aboveground biomass in the alpine steppe and meadow, Tibetan Plateau

2012 ◽  
Vol 9 (10) ◽  
pp. 14559-14588
Author(s):  
J. Sun ◽  
G. W. Cheng ◽  
W. P. Li
Keyword(s):  
Soil Moisture ◽  
Tibetan Plateau ◽  
Environmental Factors ◽  
Spatial Pattern ◽  
Aboveground Biomass ◽  
Alpine Meadow ◽  
Alpine Grassland ◽  
Critical Factors ◽  
Alpine Steppe ◽  
Habitat Factors

Abstract. Tibetan Plateau – the third pole of the world, with its extremly harsh and fragile ecological environment, is so sensitive to global change that it attracts many scientists' attention. Alpine grassland here is an important component of the global carbon cycle. Many studies have examined links between environmental factors and distribution of biomass, but little showed the critical environmental factors affecting the distribution of biomass. To document the general relationships between the habitat factors and aboveground biomass (AGB) in Tibetan Plateau, and to identify the critical factors for the distribution of AGB in the alpine steppe and meadow, the data of AGB and habitat factors from 110 field sites across the widely distributed alpine steppe and meadow of the plateau were compiled and analyzed with the classification and regression tree (CART) model, and the generalized additive model (GAM). The results showed that (1) the spatial pattern of AGB in alpine steppe was determined by six major environmental factors: soil organic carbon density of soil 0–30 cm depth (SOC1), longitude, mean annual precipitation (MAP), latitude, clay and soil moisture. As to the alpine meadow, the major factors were altitude, soil moisture, nitrogen, MAP and mean annual temperature (MAT). (2) As to the alpine steppe, increased SOC1, MAP and latitude were associated with increased AGB abundance, but increased longitude resulted in lower abundance of AGB. As to the alpine meadow, the distribution of AGB had strong negative relationships with altitude and soil moisture, but a positive correlation with soil nitrogen content across sites. The results suggested that the combined effects of meteorological factors, topographic factors, and soil factors were more significant for the spatial pattern of AGB in Tibetan Plateau. In addition, our work highlights the importance of further studies to seek effects of slope and aspect in alpine grassland.

scholarly journals Is Himalayan-Tibetan Plateau “drying”? Historical estimations and future trends of surface soil moisture

2019 ◽  
Vol 658 ◽  
pp. 374-384 ◽  
Author(s):  
Qiang Zhang ◽  
Keke Fan ◽  
Vijay P. Singh ◽  
Changqing Song ◽  
Chong-Yu Xu ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Tibetan Plateau ◽  
Surface Soil ◽  
Future Trends ◽  
Surface Soil Moisture

scholarly journals Meta-analysis of relationships between environmental factors and aboveground biomass in the alpine grassland on the Tibetan Plateau

2013 ◽  
Vol 10 (3) ◽  
pp. 1707-1715 ◽  
Author(s):  
J. Sun ◽  
G. W. Cheng ◽  
W. P. Li
Keyword(s):  
Soil Moisture ◽  
Tibetan Plateau ◽  
Environmental Factors ◽  
Aboveground Biomass ◽  
Alpine Meadow ◽  
Meta Analysis ◽  
Alpine Grassland ◽  
Mean Annual Precipitation ◽  
The Tibetan Plateau ◽  
Alpine Steppe

Abstract. The Tibetan Plateau, known as the "world's third pole" for its extremely harsh and fragile ecological environment, has attracted great attention because of its sensitivity to global changes. Alpine grassland on the Tibetan Plateau has an important function in the global carbon cycle. Many studies have examined the effects of various environmental factors on biomass distribution. In this study, the relationships between the habitat parameters and the aboveground biomass (AGB) abundance on the Tibetan Plateau were examined through a meta-analysis of 110 field sites across the widely distributed alpine steppe and meadow. The obtained data were then analysed using the classification and regression tree model and the generalized additive model. The results showed that the AGB abundance in alpine steppe was positively correlated with six environmental factors, namely, soil organic carbon density of the top soil layer from 0 cm to 30 cm (SOC30 cm), longitude, mean annual precipitation (MAP), latitude, clay, and soil moisture. For the alpine meadow, five main factors were detected, namely, altitude, soil moisture, nitrogen, MAP, and mean annual temperature. The increased AGB abundance in the alpine steppe was associated with the increased SOC30 cm, MAP, and latitude, and the increased longitude resulted in decreased AGB abundance. For the alpine meadow, altitude and soil moisture showed strongly negative effects on AGB abundance, and soil nitrogen content was positively related to the AGB distribution across all examined sites. Our results suggest the combined effects of meteorological, topographic, and soil factors on the spatial patterns of AGB on the Tibetan Plateau.

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