scholarly journals Characterizing Spatiotemporal Variations of Soil Salinization and Its Relationship with Eco-Hydrological Parameters at the Regional Scale in the Kashi Area of Xinjiang, China from 2000 to 2017

Water ◽  
2021 ◽  
Vol 13 (8) ◽  
pp. 1075
Author(s):  
Bo Wang ◽  
Xinguang Dong ◽  
Zhihui Wang ◽  
Guoqiang Qin
Keyword(s):  
Land Surface ◽  
Regional Scale ◽  
Soil Salinization ◽  
Control Measures ◽  
Western China ◽  
Spatiotemporal Variations ◽  
Decrease Rate ◽  
Hydrological Parameters ◽  
Salinized Soil ◽  
Soil Area

Soil salinization is one of the most serious issues of land degradation, especially in inland drylands, such as the Kashgar region in the Xinjiang province, western China. The investigation of the spatiotemporal variations of soil salinization and its causes is critical for regional ecological restoration and social development. In this study, salinization severity was firstly interpreted in Kashgar region for the years 2000, 2010, and 2017 using multitemporal Landsat images, and the spatiotemporal variations of salinized soil area, salinization severity index, and important index of salinization change were then analyzed using transition matrix method. Finally, the relationship between salinization and eco-hydrological parameters at the regional scale was investigated using correlation analysis and multivariate linear regression. The results show that salinized soil is mainly concentrated in irrigated oasis areas. Although the decrease rate of total salinized soil area is decreasing, the decrease rate of average salinization severity is increasing gradually. There is an increasing trend for the improved area of salinized soil, whereas an opposite trend was observed for the deteriorated area of salinized soil. The conversion from extremely severe salinized soil to the severe ones was the dominant transforming type from 2000 to 2017; meanwhile, the transformation from non-salinized soil to salinized soil for the newly reclaimed farmland was observed, indicating that some necessary irrigation control measures must be taken to avoid further soil salinizing. A significant negative correlation between salinization severity and evapotranspiration, normalized difference vegetation index (NDVI) was observed, implying that soil structure change induced by vegetation, associated with high evapotranspiration (ET) and low land surface temperature (LST), played a positive role in alleviating soil salinization in this region. It is concluded that the soil salinization had been alleviated from 2000 to 2017, mainly due to the combined effects of the farmland expansion and the reasonable irrigation system.

Revealing the spatial pattern of subsurface soil salinity over the Argentinean Dry Chaco

2021 ◽  
Author(s):  
Michiel Maertens ◽  
Veerle Vanacker ◽  
Gabriëlle De Lannoy ◽  
Frederike Vincent ◽  
Raul Giménez ◽  
...  
Keyword(s):  
Soil Salinity ◽  
Land Surface ◽  
Large Scale ◽  
Hydrological Cycle ◽  
Regional Scale ◽  
Shallow Groundwater ◽  
Soil Salinization ◽  
Dry Forest ◽  
Surface Model ◽  
Subsurface Soil

<p>The South-American Dry Chaco is a unique ecoregion as it is one of the largest sedimentary plains in the world hosting the planet’s largest dry forest. The 787.000 km² region covers parts of Argentina, Paraguay, and Bolivia and is characterized by a negative climatic water balance as a consequence of limited rainfall inputs (800 mm/year) and high temperatures (21°C). In combination with the region’s extreme flat topography (slopes < 0.1%) and shallow groundwater tables, saline soils are expected in substantial parts of the region. In addition, it is expected that large-scale deforestation processes disrupt the hydrological cycle resulting in rising groundwater tables and further increase the risk for soil salinization.</p><p>In this study, we identified the regional-scale patterns of subsurface soil salinity in the Dry Chaco.  Field data were obtained during a two-month field campaign in the dry season of 2019. A total of 492 surface- and 142 subsurface-samples were collected along East-West transects to determine soil electric conductivity, pH, bulk density and humidity. Spatial regression techniques were used to reveal the topographic and ecohydrological variables that are associated with subsurface soil salinity over the Dry Chaco. The hydrological information was obtained from a state-of-the-art land surface model with an improved set of satellite-derived vegetation and land cover parameters.</p><p>In the presentation, we will present a subsurface soil salinity map for a part of the Argentinean Dry Chaco and provide relevant insights into the driving mechanisms behind it.</p>

scholarly journals Improving the Evapotranspiration Estimation under Cloudy Condition by Extending the Ts-VI Triangle Model

2021 ◽  
Vol 13 (8) ◽  
pp. 1516
Author(s):  
Boyang Li ◽  
Yaokui Cui ◽  
Xiaozhuang Geng ◽  
Huan Li
Keyword(s):  
Remote Sensing ◽  
Surface Temperature ◽  
Land Surface ◽  
Energy Exchange ◽  
Vegetation Index ◽  
Regional Scale ◽  
Reconstruction Algorithm ◽  
Heihe River ◽  
Main Process ◽  
In Situ Data

Evapotranspiration (ET) of soil-vegetation system is the main process of the water and energy exchange between the atmosphere and the land surface. Spatio-temporal continuous ET is vitally important to agriculture and ecological applications. Surface temperature and vegetation index (Ts-VI) triangle ET model based on remote sensing land surface temperature (LST) is widely used to monitor the land surface ET. However, a large number of missing data caused by the presence of clouds always reduces the availability of the main parameter LST, thus making the remote sensing-based ET estimation unavailable. In this paper, a method to improve the availability of ET estimates from Ts-VI model is proposed. Firstly, continuous LST product of the time series is obtained using a reconstruction algorithm, and then, the reconstructed LST is applied to the estimate ET using the Ts-VI model. The validation in the Heihe River Basin from 2009 to 2011 showed that the availability of ET estimates is improved from 25 days per year (d/yr) to 141 d/yr. Compared with the in situ data, a very good performance of the estimated ET is found with RMSE 1.23 mm/day and R2 0.6257 at point scale and RMSE 0.32 mm/day and R2 0.8556 at regional scale. This will improve the understanding of the water and energy exchange between the atmosphere and the land surface, especially under cloudy conditions.

scholarly journals Evaluating Downscaling Factors of Microwave Satellite Soil Moisture Based on Machine Learning Method

2021 ◽  
Vol 13 (1) ◽  
pp. 133
Author(s):  
Hao Sun ◽  
Yajing Cui
Keyword(s):  
Machine Learning ◽  
Soil Moisture ◽  
Land Surface ◽  
Regional Scale ◽  
Support Vector ◽  
Machine Learning Method ◽  
Learning Method ◽  
Feed Forward Neural Network ◽  
Comparative Performance ◽  
Geographical Factors

Downscaling microwave remotely sensed soil moisture (SM) is an effective way to obtain spatial continuous SM with fine resolution for hydrological and agricultural applications on a regional scale. Downscaling factors and functions are two basic components of SM downscaling where the former is particularly important in the era of big data. Based on machine learning method, this study evaluated Land Surface Temperature (LST), Land surface Evaporative Efficiency (LEE), and geographical factors from Moderate Resolution Imaging Spectroradiometer (MODIS) products for downscaling SMAP (Soil Moisture Active and Passive) SM products. This study spans from 2015 to the end of 2018 and locates in the central United States. Original SMAP SM and in-situ SM at sparse networks and core validation sites were used as reference. Experiment results indicated that (1) LEE presented comparative performance with LST as downscaling factors; (2) adding geographical factors can significantly improve the performance of SM downscaling; (3) integrating LST, LEE, and geographical factors got the best performance; (4) using Z-score normalization or hyperbolic-tangent normalization methods did not change the above conclusions, neither did using support vector regression nor feed forward neural network methods. This study demonstrates the possibility of LEE as an alternative of LST for downscaling SM when there is no available LST due to cloud contamination. It also provides experimental evidence for adding geographical factors in the downscaling process.

scholarly journals Optimization of the Local Split-Window Algorithm for FY-4A Land Surface Temperature Retrieval

2019 ◽  
Vol 11 (17) ◽  
pp. 2016
Author(s):  
Lijuan Wang ◽  
Ni Guo ◽  
Wei Wang ◽  
Hongchao Zuo
Keyword(s):  
Surface Temperature ◽  
Diurnal Variation ◽  
Land Surface Temperature ◽  
Satellite Data ◽  
Land Surface ◽  
Regional Scale ◽  
Pso Algorithm ◽  
Variation Characteristics ◽  
Split Window Algorithm ◽  
Mean Square Errors

FY-4A is a second generation of geostationary orbiting meteorological satellite, and the successful launch of FY-4A satellite provides a new opportunity to obtain diurnal variation of land surface temperature (LST). In this paper, different underlying surfaces-observed data were applied to evaluate the applicability of the local split-window algorithm for FY-4A, and the local split-window algorithm parameters were optimized by the artificial intelligent particle swarm optimization (PSO) algorithm to improve the accuracy of retrieved LST. Results show that the retrieved LST can efficiently reproduce the diurnal variation characteristics of LST. However, the estimated values deviate hugely from the observed values when the local split-window algorithms are directly used to process the FY-4A satellite data, and the root mean square errors (RMSEs) are approximately 6K. The accuracy of the retrieved LST cannot be effectively improved by merely modifying the emissivity-estimated model or optimizing the algorithm. Based on the measured emissivity, the RMSE of LST retrieved by the optimized local split-window algorithm is reduced to 3.45 K. The local split-window algorithm is a simple and easy retrieval approach that can quickly retrieve LST on a regional scale and promote the application of FY-4A satellite data in related fields.

Perspective on control options forEchinococcus multiloculariswith particular reference to Japan

Parasitology ◽  
2003 ◽  
Vol 127 (S1) ◽  
pp. S159-S172 ◽  
Author(s):  
A. ITO ◽  
T. ROMIG ◽  
K. TAKAHASHI
Keyword(s):  
Close Contact ◽  
Integrated Control ◽  
Risk Behaviour ◽  
Control Measures ◽  
Domestic Dogs ◽  
Western China ◽  
Preventive Strategies ◽  
Preventive Chemotherapy ◽  
Information Campaigns ◽  
Control Options

Following a brief introduction of recent advances in molecular and immunological technology for detection of persons and animals infected withEchinococcus multilocularisand an overview of the current situation of alveolar echinococcosis (AE) in Japan, perspectives on control options are discussed with reference to different epidemiological situations. AE is considered the most serious parasitic zoonosis in temperate and arctic regions of the northern hemisphere. The number of human cases differs drastically among regions. While high numbers of patients are apparently associated with highE. multilocularisprevalence in domestic dogs, e.g. in parts of Alaska and western China, the number of cases is moderate or low in areas where the parasite is mainly transmitted by wild canid species (e.g. in central Europe or temperate North America). However, the severity of the disease, the absence of curative treatment for most cases, the high cost of long-term chemotherapy and the anxiety caused for the population in highly endemic areas call for the development of preventive strategies even in regions where human AE is rare. Furthermore, in view of (1) drastically increasing numbers and infection rates of foxes involved in transmission ofE. multilocularis, and (2) increasingly close contact between humans and foxes e.g. in Europe and Japan, there is considerable concern that AE incidences may in future increase in these regions. Control options depend on a variety of factors including the species of canid principally responsible for transmission and the socio-economic situation in the region. Where domestic dogs (stray or owned) are the principal hosts forE. multilocularis, control options can include those applicable toE. granulosus, i.e. reduction of the number of stray dogs, registration and regular preventive chemotherapy of owned dogs, and information campaigns for the population promoting low-risk behaviour for man and dogs. WhereE. multilocularisis mainly transmitted by wild canids, the situation is far more difficult with preventive strategies still being in trial stage. Integrated control measures could include prevention information campaigns, restricting access of pet animals (dogs and cats) to rodents, chemotherapy of foxes on local or regional scales, and strategies to minimize contacts between people and foxes.

scholarly journals Pan-Arctic linkages between snow accumulation and growing-season air temperature, soil moisture and vegetation

2013 ◽  
Vol 10 (11) ◽  
pp. 7575-7597 ◽  
Author(s):  
K. A. Luus ◽  
Y. Gel ◽  
J. C. Lin ◽  
R. E. J. Kelly ◽  
C. R. Duguay
Keyword(s):  
Soil Moisture ◽  
Air Temperature ◽  
Land Surface ◽  
Regional Scale ◽  
Surface Characteristics ◽  
Growing Season ◽  
Snow Accumulation ◽  
Snow Season ◽  
Air Temperatures ◽  
Land Surface Characteristics

Abstract. Arctic field studies have indicated that the air temperature, soil moisture and vegetation at a site influence the quantity of snow accumulated, and that snow accumulation can alter growing-season soil moisture and vegetation. Climate change is predicted to bring about warmer air temperatures, greater snow accumulation and northward movements of the shrub and tree lines. Understanding the responses of northern environments to changes in snow and growing-season land surface characteristics requires: (1) insights into the present-day linkages between snow and growing-season land surface characteristics; and (2) the ability to continue to monitor these associations over time across the vast pan-Arctic. The objective of this study was therefore to examine the pan-Arctic (north of 60° N) linkages between two temporally distinct data products created from AMSR-E satellite passive microwave observations: GlobSnow snow water equivalent (SWE), and NTSG growing-season AMSR-E Land Parameters (air temperature, soil moisture and vegetation transmissivity). Due to the complex and interconnected nature of processes determining snow and growing-season land surface characteristics, these associations were analyzed using the modern nonparametric technique of alternating conditional expectations (ACE), as this approach does not impose a predefined analytic form. Findings indicate that regions with lower vegetation transmissivity (more biomass) at the start and end of the growing season tend to accumulate less snow at the start and end of the snow season, possibly due to interception and sublimation. Warmer air temperatures at the start and end of the growing season were associated with diminished snow accumulation at the start and end of the snow season. High latitude sites with warmer mean annual growing-season temperatures tended to accumulate more snow, probably due to the greater availability of water vapor for snow season precipitation at warmer locations. Regions with drier soils preceding snow onset tended to accumulate greater quantities of snow, likely because drier soils freeze faster and more thoroughly than wetter soils. Understanding and continuing to monitor these linkages at the regional scale using the ACE approach can allow insights to be gained into the complex response of Arctic ecosystems to climate-driven shifts in air temperature, vegetation, soil moisture and snow accumulation.

scholarly journals A combined Terra and Aqua MODIS land surface temperature and meteorological station data product for China from 2003 to 2017

2020 ◽  
Vol 12 (4) ◽  
pp. 2555-2577
Author(s):  
Bing Zhao ◽  
Kebiao Mao ◽  
Yulin Cai ◽  
Jiancheng Shi ◽  
Zhaoliang Li ◽  
...  
Keyword(s):  
High Temperature ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Meteorological Station ◽  
Western China ◽  
Drought Monitoring ◽  
Ground Observation ◽  
Station Data ◽  
Reconstruction Model

Abstract. Land surface temperature (LST) is a key variable for high temperature and drought monitoring and climate and ecological environment research. Due to the sparse distribution of ground observation stations, thermal infrared remote sensing technology has become an important means of quickly obtaining ground temperature over large areas. However, there are many missing and low-quality values in satellite-based LST data because clouds cover more than 60 % of the global surface every day. This article presents a unique LST dataset with a monthly temporal resolution for China from 2003 to 2017 that makes full use of the advantages of MODIS data and meteorological station data to overcome the defects of cloud influence via a reconstruction model. We specifically describe the reconstruction model, which uses a combination of MODIS daily data, monthly data and meteorological station data to reconstruct the LST in areas with cloud coverage and for grid cells with elevated LST error, and the data performance is then further improved by establishing a regression analysis model. The validation indicates that the new LST dataset is highly consistent with in situ observations. For the six natural subregions with different climatic conditions in China, verification using ground observation data shows that the root mean square error (RMSE) ranges from 1.24 to 1.58 ∘C, the mean absolute error (MAE) varies from 1.23 to 1.37 ∘C and the Pearson coefficient (R2) ranges from 0.93 to 0.99. The new dataset adequately captures the spatiotemporal variations in LST at annual, seasonal and monthly scales. From 2003 to 2017, the overall annual mean LST in China showed a weak increase. Moreover, the positive trend was remarkably unevenly distributed across China. The most significant warming occurred in the central and western areas of the Inner Mongolia Plateau in the Northwest Region, and the average annual temperature change is greater than 0.1 K (R>0.71, P<0.05), and a strong negative trend was observed in some parts of the Northeast Region and South China Region. Seasonally, there was significant warming in western China in winter, which was most pronounced in December. The reconstructed dataset exhibits significant improvements and can be used for the spatiotemporal evaluation of LST in high-temperature and drought-monitoring studies. The data are available through Zenodo at https://doi.org/10.5281/zenodo.3528024 (Zhao et al., 2019).

scholarly journals Sub3DNet1.0: a deep-learning model for regional-scale 3D subsurface structure mapping

2021 ◽  
Vol 14 (6) ◽  
pp. 3421-3435
Author(s):  
Zhenjiao Jiang ◽  
Dirk Mallants ◽  
Lei Gao ◽  
Tim Munday ◽  
Gregoire Mariethoz ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Land Surface ◽  
Regional Scale ◽  
Learning Model ◽  
Generic Structure ◽  
Squared Error ◽  
The Neural Network ◽  
Deep Learning Model ◽  
Surface Observations

Abstract. This study introduces an efficient deep-learning model based on convolutional neural networks with joint autoencoder and adversarial structures for 3D subsurface mapping from 2D surface observations. The method was applied to delineate paleovalleys in an Australian desert landscape. The neural network was trained on a 6400 km2 domain by using a land surface topography as 2D input and an airborne electromagnetic (AEM)-derived probability map of paleovalley presence as 3D output. The trained neural network has a squared error <0.10 across 99 % of the training domain and produces a squared error <0.10 across 93 % of the validation domain, demonstrating that it is reliable in reconstructing 3D paleovalley patterns beyond the training area. Due to its generic structure, the neural network structure designed in this study and the training algorithm have broad application potential to construct 3D geological features (e.g., ore bodies, aquifer) from 2D land surface observations.

scholarly journals Simulating carbon exchange using a regional atmospheric model coupled to an advanced land-surface model

2008 ◽  
Vol 5 (5) ◽  
pp. 4161-4207 ◽  
Author(s):  
H. W. Ter Maat ◽  
R. W. A. Hutjes
Keyword(s):  
Carbon Dioxide ◽  
Land Surface ◽  
Large Scale ◽  
Regional Scale ◽  
Atmospheric Model ◽  
Surface Fluxes ◽  
Heat Fluxes ◽  
Surface Model ◽  
Carbon Exchange ◽  
Regional Atmospheric Modelling System

Abstract. A large scale mismatch exists between our understanding and quantification of ecosystem atmosphere exchange of carbon dioxide at local scale and continental scales. This paper will focus on the carbon exchange on the regional scale to address the following question: What are the main controlling factors determining atmospheric carbon dioxide content at a regional scale? We use the Regional Atmospheric Modelling System (RAMS), coupled with a land surface scheme simulating carbon, heat and momentum fluxes (SWAPS-C), and including also sub models for urban and marine fluxes, which in principle include the main controlling mechanisms and capture the relevant dynamics of the system. To validate the model, observations are used which were taken during an intensive observational campaign in the central Netherlands in summer 2002. These included flux-site observations, vertical profiles at tall towers and spatial fluxes of various variables taken by aircraft. The coupled regional model (RAMS-SWAPS-C) generally does a good job in simulating results close to reality. The validation of the model demonstrates that surface fluxes of heat, water and CO2 are reasonably well simulated. The comparison against aircraft data shows that the regional meteorology is captured by the model. Comparing spatially explicit simulated and observed fluxes we conclude that in general simulated latent heat fluxes are underestimated by the model to the observations which exhibit large standard deviation for all flights. Sensitivity experiments demonstrated the relevance of the urban emissions of carbon dioxide for the carbon balance in this particular region. The same test also show the relation between uncertainties in surface fluxes and those in atmospheric concentrations.

Sign in / Sign up

Export Citation Format

Share Document