Spatial Variability Of Soil Moisture Research Articles

SMAP-HydroBlocks, a 30-m satellite-based soil moisture dataset for the conterminous US

Soil moisture plays a key role in controlling land-atmosphere interactions, with implications for water resources, agriculture, climate, and ecosystem dynamics. Although soil moisture varies strongly across the landscape, current monitoring capabilities are limited to coarse-scale satellite retrievals and a few regional in-situ networks. Here, we introduce SMAP-HydroBlocks (SMAP-HB), a high-resolution satellite-based surface soil moisture dataset at an unprecedented 30-m resolution (2015–2019) across the conterminous United States. SMAP-HB was produced by using a scalable cluster-based merging scheme that combines high-resolution land surface modeling, radiative transfer modeling, machine learning, SMAP satellite microwave data, and in-situ observations. We evaluated the resulting dataset over 1,192 observational sites. SMAP-HB performed substantially better than the current state-of-the-art SMAP products, showing a median temporal correlation of 0.73 ± 0.13 and a median Kling-Gupta Efficiency of 0.52 ± 0.20. The largest benefit of SMAP-HB is, however, the high spatial detail and improved representation of the soil moisture spatial variability and spatial accuracy with respect to SMAP products. The SMAP-HB dataset is available via zenodo and at https://waterai.earth/smaphb.

A soil moisture‐based framework for guiding the number and location of soil moisture sensors in agricultural fields

AbstractSoil moisture information is a key variable for guiding in‐season management decisions in rainfed and irrigated agricultural systems. However, methods for deciding the number and location of soil moisture sensors (SMS) per field still remain poorly explored in the scientific literature. The goal of this study was to evaluate a quantitative framework based on soil moisture‐based management zones (MZs) to determine the minimum number and tentative deployment location of SMS. Multiple spatially intensive (n > 100 observations) surveys of near‐surface (0–12 cm) soil moisture were conducted during the fallow periods and early growing seasons of 2017, 2018, and 2019 on three agricultural fields using a calibrated handheld soil water reflectometer. The fuzzy C‐means (FCM) clustering method was used to delineate MZs based on the soil moisture surveys, and the silhouette clustering evaluation method was used to identify the optimal number of MZs per field. Then, a sensor location index that considered the distance to the MZ boundaries and the FCM membership grade was developed to identify the tentative optimal deployment location of SMS. The proposed method effectively identified field areas with distinct soil moisture regimes and revealed the complex soil moisture spatial patterns that were not captured with elevation or soil texture alone. Dividing the fields using soil moisture‐based MZ reduced the intrazone soil moisture spatial variability by about 50% compared with that of the entire field. In the three studied fields, a total of two SMS were sufficient to capture the salient soil moisture spatial regimes.

Are spatial patterns of soil moisture at plot scales generalisable across catchments, climates, and other characteristics? A synthesis of synoptic soil moisture across theMid‐Atlantic

AbstractThe spatial variation of soil moisture over very small areas (<100 m2) can have nonlinear impacts on cycling and flux rates resulting in bias if it is not considered, but measuring this variation is difficult over extensive temporal and spatial scales. Most studies examining spatial variation of soil moisture were conducted at hillslope (0.01 km2) to multi‐catchment spatial scales (1000 km2). They found the greatest variation at mid wetness levels and the smallest variation at wet and dry wetness levels forming a concave down relationship. There is growing evidence that concave down relationships formed between spatial variation of soil moisture and average soil moisture are consistent across spatial scales spanning several orders of magnitude, but more research is needed at very small, plot scales (<100 m2). The goal of this study was to characterise spatial variation in shallow soil moisture at the plot scale by relating the mean of measurements collected in a plot to the standard deviation (SD). We combined data from a previous study with thousands of new soil moisture measurements from 212 plots in eight catchments distributed across the US Mid‐Atlantic Region to (1) test for a generalisable mean–SD relationship at plot scales, (2) characterise how landcover, land use, season, and hillslope position contribute to differences in mean–SD relationships, and (3) use these generalised mean–SD relationships to quantify their impacts on catchment scale nitrification and denitrification potential. Our study found that 98% of all measurements formed a generalised mean–SD relationship like those observed at hillslope and catchment spatial scales. The remaining 2% of data comprised a mean–SD relationship with greater spatial variation that originated from two riparian plots reported in a previous study. Incorporating the generalised mean–SD relationship into estimates of nitrification and denitrification potential revealed strong bias that was even greater when incorporating mean–SD observations from the two riparian plots with significantly greater spatial variation.

Is the soil moisture precipitation feedback enhanced by heterogeneity and dry soils? A comparative study

AbstractThe interaction between the land surface and the atmosphere is a crucial driver of atmospheric processes. Soil moisture and precipitation are key components in this feedback. Both variables are intertwined in a cycle, that is, the soil moisture – precipitation feedback for which involved processes and interactions are still discussed. In this study the soil moisture – precipitation feedback is compared for the sempiternal humid Ammer catchment in Southern Germany and for the semiarid to subhumid Sissili catchment in West Africa during the warm season, using precipitation datasets from the Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS), from the German Weather Service (REGNIE) and simulation datasets from the Weather Research and Forecasting (WRF) model and the hydrologically enhanced WRF‐Hydro model. WRF and WRF‐Hydro differ by their representation of terrestrial water flow. With this setup we want to investigate the strength, sign and variables involved in the soil moisture – precipitation feedback for these two regions. The normalized model spread between the two simulation results shows linkages between precipitation variability and diagnostic variables surface fluxes, moisture flux convergence above the surface and convective available potential energy in both study regions. The soil moisture – precipitation feedback is evaluated with a classification of soil moisture spatial heterogeneity based on the strength of the soil moisture gradients. This allows us to assess the impact of soil moisture anomalies on surface fluxes, moisture flux convergence, convective available potential energy and precipitation. In both regions the amount of precipitation generally increases with soil moisture spatial heterogeneity. For the Ammer region the soil moisture – precipitation feedback has a weak negative sign with more rain near drier patches while it has a positive signal for the Sissili region with more rain over wetter patches. At least for the observed moderate soil moisture values and the spatial scale of the Ammer region, the spatial variability of soil moisture is more important for surface‐atmosphere interactions than the actual soil moisture content. Overall, we found that soil moisture heterogeneity can greatly affect the soil moisture – precipitation feedback.

Spatial Variability of Soil Moisture under Typical Vegetation Types on Karst Dolomite Slope in Dry Season

Spatial Variability of Soil Moisture under Typical Vegetation Types on Karst Dolomite Slope in Dry Season

Spatial interpolation of red bed soil moisture in Nanxiong basin, South China

Soil moisture is an important indicator for monitoring land degradation and plays an important role in soil biogeochemistry. Mapping the spatial distribution of soil moisture provides fundamental information for soil management and agricultural production. In this study, 225 sampling points in the study area, Nanxiong basin, were investigated to map the spatial distribution of soil moisture in the typical ecological degradation red beds area. Four interpolation methods including inverse distance weighting (IDW), ordinary kriging (OK), radial basis function (RBF) and empirical bayesian kriging (EBF) were used to estimate the continuous soil moisture distribution. The results showed that the soil moisture of the study area ranged from 8.12% to 32.82% with an average of 18.42% and a median of 18.20%. The annual average temperature and soil bulk density had significantly negative correlations with soil moisture, of-0.53 and − 0.31, respectively. The regional soil moisture had a moderate variation with the variation coefficient of 21.66% and strong spatial dependence with the nugget-to-sill ratios of 32.23%. The IDW method obtained a more accurate estimation on the spatial variability of soil moisture in the Nanxiong basin. The spatial distribution of soil moisture in the study area was drawn by different interpolation methods. The results show that the red bed degradation in the eastern part of the Nanxiong basin is more severe than in other areas, in which the geomorphic characteristics of red bed desert have appeared with the lowest soil moisture in this study. In addition, the spatial heterogeneity of soil moisture is also pertinent to human disturbance and land use. The results of spatial soil moisture distribution are of great significance for monitoring land degradation and agricultural drought in the red bed area.

Ecohydrological modelling in a deciduous boreal forest: Model evaluation for application in non‐stationary climates

AbstractSoil moisture is an important driver of growth in boreal Alaska, but estimating soil hydraulic parameters can be challenging in this data‐sparse region. Parameter estimation is further complicated in regions with rapidly warming climate, where there is a need to minimize model error dependence on interannual climate variations. To better identify soil hydraulic parameters and quantify energy and water balance and soil moisture dynamics, we applied the physically based, one‐dimensional ecohydrological Simultaneous Heat and Water (SHAW) model, loosely coupled with the Geophysical Institute of Permafrost Laboratory (GIPL) model, to an upland deciduous forest stand in interior Alaska over a 13‐year period. Using a Generalized Likelihood Uncertainty Estimation parameterisation, SHAW reproduced interannual and vertical spatial variability of soil moisture during a five‐year validation period quite well, with root mean squared error (RMSE) of volumetric water content at 0.5 m as low as 0.020 cm3/cm3. Many parameter sets reproduced reasonable soil moisture dynamics, suggesting considerable equifinality. Model performance generally declined in the eight‐year validation period, indicating some overfitting and demonstrating the importance of interannual variability in model evaluation. We compared the performance of parameter sets selected based on traditional performance measures such as the RMSE that minimize error in soil moisture simulation, with one that is designed to minimize the dependence of model error on interannual climate variability using a new diagnostic approach we call CSMP, which stands for Climate Sensitivity of Model Performance. Use of the CSMP approach moderately decreases traditional model performance but may be more suitable for climate change applications, for which it is important that model error is independent from climate variability. These findings illustrate (1) that the SHAW model, coupled with GIPL, can adequately simulate soil moisture dynamics in this boreal deciduous region, (2) the importance of interannual variability in model parameterisation, and (3) a novel objective function for parameter selection to improve applicability in non‐stationary climates.

Temporal and Spatial Variations of Soil Moisture Over Xinjiang Based on Multiple GLDAS Datasets

Under the global warming, as the typical arid region of Central Asia, the Xinjiang Uygur Autonomous Region (Xinjiang) has been experienced the remarkable warming and increased precipitation based on large previous studies. The arid and semiarid ecosystem of Xinjiang is very sensitive and vulnerable to climate change and water resource variations. However, the sparse and highly unevenly distributed in-situ stations in this region provide limited data for understanding of the soil moisture variations. In this study, the spatial and temporal changes and variations of soil moisture were explored at annual and seasonal time scales during the period of 2000–2017. The soil moisture data are from the Global Land Data Assimilation System (GLDAS) models, including four GLDAS 1 models: CLM, Mosaic, VIC and Noah 2.7 and one GLDAS 2.1 model: Noah 3.3. Major results show that 1) Noah 3.3 and VIC have the significant positive trends of annual soil moisture with the values of 2.64°mm/a and 0.98°mm/a. The trend of CLM is significant negative. The other two models Mosaic and Noah 2.7 have the weak positive trends. The temporal variations of seasonal soil moisutre are similar the annual soil moisture for each of the model. 2) For the spatial characteristics of the soil mositure variations, CLM displays the negative trends over large part of Xinjiang. Mosaic and VIC have the similar spatial characteristics of the linear trends. Noah 3.3 has the significant positive trends over almost Xinjiang which is different with Noah 2.7. All the five models have the positive trends over KLM. Our results have a better understanding of the soil moisture variations across Xinjiang, and they also enhance the reconginzing of the complex hydrological circulation in the arid regions.

Watershed scale patterns and controlling factors of ecosystem respiration and methane fluxes in a Tibetan alpine grassland

Ecosystem respiration (Re) and methane (CH4) fluxes are two important soil-atmosphere carbon exchange processes that have been well documented at the local scale. However, the spatial patterns and controlling factors of these processes remain unclear in the Tibetan alpine permafrost region at the watershed scale. We conducted a two-year field monitoring study of Re and CH4 fluxes at three altitudinal positions on shady and sunny slopes in a Tibetan alpine grassland to determine the spatial variability in the two processes and to identify their underlying mechanisms. The microbial factor had a controlling effect on Re spatial variability in alpine grassland watersheds. The lower soil temperature and soil organic matter content at the upper slope positions on the shady slopes inhibited Re because they reduced the soil microbial activity. We found that the alpine grassland to be a net sink of atmospheric CH4, and the average CH4 flux rates exhibited large spatial variance ranging from −1.60 to −10.48 µg CH4 m−2 h−1 within the watersheds. The spatial variability in soil volumetric water content explained 76% of the variation in CH4 fluxes within the watersheds. We suggest that the influence of permafrost on hydrologic conditions may increase the spatial variability of soil moisture (measured as soil volumetric water content and water-filled pore space) in alpine grassland watersheds, and generally form poorly drained landscape at the lower slope position where Re and CH4 uptake are inhibited. Our results highlight the indirect effects of terrain and permafrost on Re and CH4 fluxes through their effects on biophysiochemical factors. We recommend that the spatial variability in Re and CH4 fluxes at the watershed scale of Tibetan alpine grassland should be given more attention in earth system models, especially the variability of CH4 fluxes with altitudinal position.

Estimation of root zone soil moisture from ground and remotely sensed soil information with multisensor data fusion and automated machine learning

Root zone soil moisture (RZSM) estimation and monitoring based on high spatial resolution remote sensing information such as obtained with an Unmanned Aerial System (UAS) is of significant interest for field-scale precision irrigation management, particularly in water-limited regions of the world. To date, there is no accurate and widely accepted model that relies on UAS optical surface reflectance observations for RZSM estimation at high spatial resolution. This study is aimed at the development of a new approach for RZSM estimation based on the fusion of high spatial resolution optical reflectance UAS observations with physical and hydraulic soil information integrated into Automated Machine Learning (AutoML). The H2O AutoML platform includes a number of advanced machine learning algorithms that efficiently perform feature selection and automatically identify complex relationships between inputs and outputs. Twelve models combining UAS optical observations with various soil properties were developed in a hierarchical manner and fed into AutoML to estimate surface, near-surface, and root zone soil moisture. The addition of independently measured surface and near-surface soil moisture information to the hierarchical models to improve RZSM estimation was investigated. The accuracy of soil moisture estimates was evaluated based on a comparison with Time Domain Reflectometry (TDR) sensors that were deployed to monitor surface, near-surface and root zone soil moisture dynamics. The obtained results indicate that the consideration of physical and hydraulic soil properties together with UAS optical observations improves soil moisture estimation, especially for the root zone with a RMSE of about 0.04 cm3 cm−3. Accurate RZSM estimates were obtained when measured surface and near-surface soil moisture data was added to the hierarchical models, yielding RMSE values below 0.02 cm3 cm−3 and R and NSE values above 0.90. The generated high spatial resolution RZSM maps clearly capture the spatial variability of soil moisture at the field scale. The presented framework can aid farm scale precision irrigation management via improving the crop water use efficiency and reducing the risk of groundwater contamination.

Plant productivity and nutrient uptake as affected by tillage and site‐position in furrow‐irrigated rice

AbstractFurrow‐irrigated rice (Oryza sativa L.) is a relatively new production system that has been shown to have increased water‐use efficiency and reduced operational costs. However, due to spatial variations in soil moisture, field studies are necessary to evaluate potential resulting variations in rice growth and yield in the furrow‐irrigated production system. This study was conducted to evaluate the effects of tillage practice (conventional tillage, CT; no‐tillage, NT) and site position (up‐, mid‐, and down‐slope) on aboveground biomass, yield, and plant nutrient uptake from rice grown in a silt‐loam soil in a direct‐seeded, furrow‐irrigated production system in eastern Arkansas. Field research was conducted from May to September during the 2018 and 2019 rice growing seasons. Total aboveground dry matter, vegetative dry matter, and yield were unaffected (P > .05) by tillage treatment or site position in either growing season. Vegetative tissue nutrient concentrations and uptake were generally at least 10% greater (P < .05) under CT than NT but did not substantially differ by site position during the drier growing season (2018). During the wetter growing season (2019), vegetative tissue nutrient concentrations and uptake were generally at least 20% greater (P < .05) at the up‐ than at the mid‐ or down‐slope positions. Results suggest that different approaches, in terms of soil and nutrient management per site position, in combination with tillage practices, may need to be considered for implementation to maximize nutrient uptake and reduce losses, thus improving the agronomic suitability of the furrow‐irrigated rice production system.

Spatiotemporal variation of soil moisture in Northern China based on climate change initiative data

AbstractIn view of the sparse, uneven distribution and poor data continuity of measured soil moisture sites in China and the lack of large‐scale research, the soil moisture data set of the European Space Agency (ESA) Climate Change Initiative (CCI) was applied to carry out spatial‐temporal variation analysis of soil moisture in five agricultural ecological zones in Northern China. The Pearson correlation coefficient and multiple‐component metric Kling–Gupta efficiency were used to verify the validity of remote sensing data and to analyze the temporal and spatial variation of soil moisture with the concept of temporal and spatial persistence. The ESA CCI remote sensing soil moisture data had good spatial and temporal adaptability for the whole year in the Huang‐Huai‐Hai region, in summer in Northeast China, in spring in Northwest China, in summer and autumn in the Loess Plateau, and in summer and autumn in the Inner Mongolian Plateau. The annual mean soil volume water content in Northern China from 1991 to 2016 was 0.06–0.39 m3 m−3. Spatially, soil moisture in Northern China has been gradually increasing from west to east. In this area, seasonally, soil moisture was the highest in summer, followed by autumn and then winter and spring. Northeast China, the Loess Plateau, and the Huang‐Huai‐Hai region are important grain‐producing areas in China. The soil moisture in these areas fluctuates widely with seasonal changes and shows an obvious drying trend.

鼎湖山不同演替阶段森林土壤水分时空变异研究

PDF HTML阅读 XML下载 导出引用 引用提醒 鼎湖山不同演替阶段森林土壤水分时空变异研究 DOI: 10.5846/stxb201912022603 作者: 作者单位: 作者简介: 通讯作者: 中图分类号: 基金项目: 国家自然科学基金项目（31600353，41430529）；广东省自然科学基金项目（2016A030310450）；广东省林业科技创新平台项目（2020-KYXM-09） Temporal and spatial variability of soil moisture in a forest succession series in Dinghushan Author: Affiliation: Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:土壤水分作为森林生态系统水分蓄库的主体，森林土壤水分储量及其时空动态与变异对揭示区域植被恢复与气候变化背景下的森林生态系统水文过程响应与服务功能变化机制具有重要意义。本研究以南亚热带地区典型森林植被演替序列马尾松人工林（Pinus massoniana coniferous forest，PF）-马尾松针阔叶混交林（mixed Pinus massoniana/broad-leaved forest，MF）-季风常绿阔叶林（monsoon evergreen broad-leaved forest，MEBF）为研究对象，依托中国生态系统研究网络森林样地建设与监测统一规范对鼎湖山森林生态系统定位站站区内分布的上述森林类型土壤水分的长期定位观测（2005-2015年），通过分析各演替阶段森林土壤不同土层（0-15、15-30、30-45、45-60、60-75和75-90 cm）土壤体积含水量观测数据，探究该区域森林植被恢复过程中的土壤水分变化及其时空变异。结果表明：在雨热同期且干湿季明显的南亚热带地区，鼎湖山森林土壤储水量及其时间动态受降雨量的影响显著，森林土壤层对降雨具有强烈的调蓄和稳定作用，伴随PF→MF→MEBF自然演替进程，调蓄水分能力逐步增强。林型间，由初期阶段PF到顶级群落MEBF，森林土壤水分储量逐渐提高，且演替后期林型相对于早期林型，土壤储水量均呈现为较小的年际与年内变幅。干、湿季而言，干季时林型间的土壤储水量差异大于湿季，干季时MEBF和MF土壤含水量分别是PF的1.33倍和1.11倍。从土壤含水量的干、湿季期间变异来看，不同林型各土层土壤含水量的变异系数大小均表现为干季大于湿季；垂直剖面方向上，突出表现为无论干湿季MEBF各层土壤含水量变异均比其他两种林型较为缓和，充分体现了MEBF优越的土壤水分时空调配能力。整体上，伴随PF→MF→MEBF自然演替进程，土壤水分储量及其稳定性逐步提升。 Abstract:Soil moisture is the main component of water storage in forest ecosystems. The soil water reserve of forests and its spatiotemporal dynamics and variations are of great significance for revealing the response of hydrological processes and the change mechanism of service functions in forest ecosystems under the background of regional vegetation restoration and climate change. In this study, we focused on the typical forests in lower subtropical China: a Pinus massoniana forest (PF), mixed Pinus massoniana/broad-leaved forest (MF), and monsoon evergreen broad-leaved forest (MEBF), which formed a natural successional sequence in this region. Based on the unified standards for the construction and monitoring of forest plots by the Chinese Ecosystem Research Network, long-term positioning observation of soil moisture in the above forest types of the Dinghushan Forest Ecosystem Research Station was conducted during the 2005-2015 period. Through analysis of the measured soil volumetric water content in different soil layers (0-15, 15-30, 30-45, 45-60, 60-75 and 75-90 cm) of the above mentioned forests at each stage of succession, the change in soil water storage and its spatiotemporal variation in the process of forest vegetation restoration in this region were explored. The data showed that in the southern subtropical regions with simultaneous rain and heat and distinct dry and wet seasons, soil water storage and its seasonal dynamics in Dinghushan were significantly affected by rainfall, and the soil layer had a strong regulatory and stabilizing effect on rainfall. With natural succession from the planted (PF) to the climax (MEBF) stage, the ability to regulate and store water gradually increased. Among forest types, from the initial stage (PF) to the top community (MEBF), the soil water storage of the forest ecosystem gradually increased, and compared with the early forest types, the soil water storage of late succession forest types showed small interannual and intra-annual variation. At a seasonal scale, the difference in soil water storage between forest types in the dry season was greater than that in the wet season. The soil water content of MEBF and MF in the dry season was 1.33 times and 1.11 times that of PF, respectively. According to the variation in the soil water content in dry and wet seasons, the coefficient of variation of the soil water content in each soil layer of the different forest types was greater in the dry season than in the wet season. In the vertical direction, this coefficient of variation highlighted that the variation in the soil water content in each layer of MEBF was more moderate than that in the other two forest types in both the dry and wet seasons, which reflected the superior water regulation ability of MEBF. On the whole, with natural succession from the planted (PF) to the climax (MEBF) forest, the soil water storage and its stability improved. 参考文献 相似文献 引证文献

A New Drought Index for Soil Moisture Monitoring Based on MPDI-NDVI Trapezoid Space Using MODIS Data

The temperature vegetation dryness index (TVDI) has been commonly implemented to estimate regional soil moisture in arid and semi-arid regions. However, the parameterization of the dry edge in the TVDI model is performed with a constraint to define the maximum water stress conditions. Mismatch of the spatial scale between visible and thermal bands retrieved from remotely sensed data and terrain variations also affect the effectiveness of the TVDI. Therefore, this study proposed a new drought index named the condition vegetation drought index (CVDI) to monitor the temporal and spatial variations of soil moisture status by substituting the land surface temperature (LST) with the modified perpendicular drought index (MPDI). In situ soil moisture observations at crop and pasture sites in Victoria were used to validate the effectiveness of the CVDI. The results indicate that the dry and wet edges in the parameterization scheme of the CVDI formed a better-defined trapezoid shape than that of the TVDI. Compared with the MPDI and TVDI for soil moisture monitoring at crop sites, the CVDI exhibited a performance superior to the MPDI and TVDI in most days where the coefficients of determination (R2) achieved can reach to 0.67 on DOY023, 137, 274 and 0.71 on DOY 322 and reproduced more accurate spatial and seasonal variation of soil moisture. Moreover, the CVDI showed higher correlation with the Australian Water Resource Assessment Landscape (AWRA-L) soil moisture product on temporal scales. The R2 can reach to 0.69 and the root mean square error (RMSE) is also much better than that of the MPDI and TVDI. Overall, it can be concluded that the CVDI appears to be a feasible method and can be successfully used in regional soil moisture monitoring.

Study of the geostatistical grid maths operation method of quantifying water movement in soil layers of a cotton field*

AbstractThe precise quantification of water movement in different field soil layers is important for variable‐rate irrigation and fertilization. To this end, a field experiment based on a geostatistical approach was conducted in southern Anyang, Henan, China, to examine soil water movement in a cotton field with drip irrigation by measuring the soil water content (SWC) data on a regular 100 × 80 cm grid. The result shows that the temporal and spatial variation of soil moisture decreased with the increase of soil depth at different points, and apparent spatial heterogeneity was observed under different soil conditions through a semivariogram analysis. The variation of soil moisture at different soil profiles was affected by the cotton plants and drip lines in non‐irrigation and irrigation periods respectively. The level of the change at 20 cm was more than 70 cm in the horizontal direction in the two periods, however the degree of change at 40 cm in the irrigation period was more than 70 cm in the vertical direction, while contrary results were shown in the non‐irrigation period. Moreover, the results indicated that the trapezoidal rule could be applied to quantify the volume of water movement precisely.

Vertical variation in shallow and deep soil moisture in an apple orchard in the loess hilly–gully area of north China

AbstractDeep soil (>2 m) moisture is relatively stable water that supports tree growth and water management in the loess hilly–gully area of China. Characterizing the spatial variation of deep soil moisture is important for water management in apple orchards. Using a space‐for‐time substitution approach, we analysed the spatial variation of soil moisture content (θ) in 0–8 m soil profiles in a dry year (2015, precipitation: 392 mm) in rainfed apple (Malus pumila Mill.) orchards of various ages (mainly established in 2009, 2006, 2003, 1997 and 1994). The soil moisture surveys revealed: (a) four soil layers according to soil moisture distribution characteristics at 0–8.0 m depth: shallow rapidly changing layer (0–0.6 m), rainfall redistribution layer (0.6–2.0 m), transition layer (2.0–4.0 m) and stable layer (4.0–8.0 m); (b) the most critical zone for water consumption is the 0.6–2.0 m soil layer, with the lowest θ in the orchards, while water in the 4.0–8.0 m soil layer remained stable; and (c) significant (p < .01) positive correlations between θs in the 0.6–2.0 m, 2.0–4.0 m and 4.0–8.0 m layers. In addition, the θs showed a significant (p < .01) negative relationship with orchard age in the deep soil layers (>2 m), and especially in the 0.6–2.0 m soil layer. Therefore, effective water management through various water regulation measures at the 0.6–2.0 m soil layer is essential for the sustainable development of apple orchards and reducing drought stress on the Loess Plateau and, potentially, other semi‐arid orchards.

Integrating Invasive and Non-invasive Monitoring Sensors to Detect Field-Scale Soil Hydrological Behavior

The Critical Zone Observatory (CZO) in the Alento River Catchment (ARC; southern Italy) has been collecting data for some decades and recently became part of the TERENO (TERrestrial ENvironmental Observatories) long-term ecosystem infrastructure network. In 2016, cosmic-ray neutron probes (CRNP, non-invasive proximal sensors) together with SoilNet wireless sensor networks (invasive ground-based sensors) were installed in two sub-catchments (MFC2 and GOR1), characterized by different weather, topographic, pedological, and land-use conditions. The SoilNet sensors are measuring soil apparent permittivity (converted into soil moisture), electrical conductivity, and temperature at two soil depths of 0.15 m and 0.30 m in 20 locations around the CRNP. The SoilNet sensor network includes also soil matric pressure potential sensors at the same soil depths and locations. A major aim of this study is to compare the hydrological response of the two experimental sites to climate forcings (rainfall minus potential evapotranspiration, hereby defined as rainfall deficit) through the use of the monthly Soil Moisture Index (SMI) computed from daily values of soil moisture measured by the CRNPs. For both sites, strong correlations are detected between monthly rainfall deficit and SMI, even if the cropland site (MFC2) is characterized by more extreme dry and wet conditions. For these two different sites, our investigations also aim at identifying the dominant controls governing the seasonal dynamics of spatial soil moisture patterns. The majority of the spatial variance in the cropland site is explained by terrain attributes, under both wet and dry conditions, whereas the second-order statistical moment of soil moisture in the forested site is mostly explained by topographic factors under wet conditions during the rainy season. For both sites, our data show that soil texture exerts a minimal impact on the spatial variations of soil moisture.

DEEP PHENOTYPING CONSIDERING TILE DRAINAGE FROM UAS-BASED MULTISPECTRAL IMAGERY BY CONVOLUTIONAL NEURAL NETWORKS

Abstract. Subsurface agriculture tile lines can greatly impact plant phenotypic characteristics through spatial variation of soil moisture, plant nutrient, and plant rooting depth. Therefore, location of subsurface tile lines plays a critical role in supporting the above ground plant phentoyping and needs to be considered in plant phenotyping analysis. Unnamed Aerial Systems (UAS) imagery together with deep learning methods can develop strong relations between the vegetation spectra and soil parameters.Here, we consider the capability of deep convolutional neural networks (CNN) to evaluate crop quality based on biomass production derived from soil moisture differences by using UAS-based multispectral imagery over soybean breeding fields. Results are still being evaluated, with particular attention to the temporal and spatial resolution of the data required to apply our approach.

Estimating Regional Soil Moisture Distribution Based on NDVI and Land Surface Temperature Time Series Data in the Upstream of the Heihe River Watershed, Northwest China

Temporal and spatial variability of soil moisture has an important impact on hydrological processes in mountainous areas. Understanding such variability requires soil moisture datasets at multiple temporal and spatial scales. Remote sensing is a very effective method to obtain surface (~5 cm depth) soil moisture at the regional scale but cannot directly measure soil moisture at deep soil layers (>5 cm depth) currently. This study chose the upstream of the Heihe River Watershed in the Qilian Mountain Ranges in Northwest China as the study area to estimate the profile soil moisture (0–70 cm depth) at the regional scale using satellite Vegetation Index (NDVI) and Land Surface Temperature (LST) products. The study area was divided into 31 zones according to the combination of altitude, vegetation and soil type. Long-term in situ soil moisture observation stations were set up at each of the zones. Soil moisture probe, ECH2O, was used to collect soil moisture at five layers (0–10, 10–20, 20–30, 30–50 and 50–70 cm) continuously. Multiple linear regression equations of time series MODIS (Moderate-resolution Imaging Spectroradiometer) NDVI, LST and soil moisture were developed for each of the five soil layers at the 31 zones to estimate the soil moisture (0–70 cm) on a regional scale with a spatial resolution of 1 km2 and a temporal resolution of 16-d from October, 2013 to September, 2016. The correlation coefficient R of the regression equations was between 0.47 and 0.94, the RMSE was 0.03, indicating that the estimation method based on the MODIS NDVI and LST data was suitable and could be applied to alpine mountainous areas with complex topography, soil and vegetation types. The overall pattern of soil moisture spatial distribution indicated that soil moisture was higher in the eastern region than in the western region, and the soil moisture content in the whole study area was 14.5%. The algorithm and results provide novel applications of remote sensing to support soil moisture data acquisition and hydrological research in mountainous areas.

Temporal and Spatial Variation of Soil Moisture and Its Possible Impact on Regional Air Temperature in China

Soil moisture is closely related to the hydrosphere, atmosphere, and biosphere, which makes it one of the most significant climate variables. Using data from the National Environmental Forecasting Center (NCEP), this paper analyzes the temporal and spatial characteristics of soil moisture at a depth of 0–10 cm in China for the period of 1948 to 2014. In addition, the soil moisture’s possible interaction with air temperature is explored. Mainly using statistical analysis, the results showed that annual soil moisture decreased significantly (p < 0.01) in most areas. The tendency of decreasing soil moisture was relatively higher in spring and autumn than that in summer and winter. As to the national annual average soil moisture, there was a sudden change in the 1970s. The soil moisture had a relatively high value with a larger deviation before the abrupt change, but after that, the soil moisture was at a relatively low level with a smaller deviation. It was also found that the soil moisture at 0–10 cm showed a negative correlation with the 2-m air temperature above ground in the northern part of China, where the speed of the temperature rise was higher. The results are expected to help improve the understanding of the link between regional soil moisture variation and climate change.