Soil Moisture Observations Research Articles

Optimal plant water use strategies explain soil moisture variability

Plant responses to water stress influence water and carbon cycles and can lead to feedbacks on climate yet characterizing these responses at ecosystem levels remains uncertain. Quantifying ecosystem-level water use strategies is complex due to challenges of upscaling plant traits and disentangling confounding environmental factors, ultimately limiting our ability to understand and anticipate global change in ecosystem dynamics and ecohydrological fluxes. We reduce the dimensionality of this problem and quantify plant water use strategies by combining plant traits with soil and climate variables into parameter groups that synthesize key eco-physiological tradeoffs. Using a parsimonious soil water balance framework, we explore variations in plant water uptake capacity, water stress responses, and water use performance via these non-dimensional parameter groups. The group characterizing the synchronization of plant water transport and atmospheric water demand emerges as the primary axis of variation in water use strategies and interacts with the group representing plant hydraulic risk tolerance, especially in arid conditions when plant water transport is limiting. Next, we show that specific plant water use strategies maximize plant water uptake (leading to carbon gain benefits) weighted by risks of water stress (leading to higher costs of water use). A model-data comparison demonstrates that these ecohydrologically optimal parameter groups capture observed soil moisture variability in 40 ecosystems and beyond aridity, rainfall frequency is an important environmental control for plant water use strategies. The emerging parsimonious link between ecohydrological performance and non-dimensional parameters provides a tractable representation of plant water use strategies, relevant to parameterize global models while accounting for ecological and evolutionary constraints on the water cycle.

Soil moisture-constrained East Asian Monsoon meridional patterns over China from observations

As an internal forcing of the earth climate system, soil moisture (SM) significantly influences the water and energy cycle by controlling evapotranspiration and terrestrial solar energy. The current study used observed precipitation, remotely sensed SM, and reanalysis of atmosphere and land parameters to assess the East Asian Monsoon (EAM) precipitation variability due to meridional SM oscillations across China. A generalized linear method, namely coupled manifold technique (CMT) for assessing the reciprocal forcing between two climate fields and numerical simulations are applied to SM and EAM precipitation. We find that the EAM precipitation interannual variability between north and south China significantly correlates with SM meridional oscillation. The CMT results further showed that SM forcing has a significant (99% confidence) influence on the EAM precipitation explaining about 0.40 of the variance ratio in north and south China. The EAM and SM composite analysis show that the wetter (drier) north (south) oscillates the EAM precipitation over the north (south) of China and vice versa due to SM thermal controls. We then used control and sensitivity simulations with SM observations to further validate the findings implying that SM can potentially improve the interannual EAM forecast skills. The model results show that a wetter (drier) north (south) results in negative (positive) sensible heat (latent heat) anomalies that impact the boundary layer and propagate to change the meridional atmospheric heating profile. When positive (negative) SM anomalies exist over northern (southern) China, the zonal easterlies and extratropical westerlies move to north China causing above-normal precipitation that descends into southern China, suppressing subtropical westerlies and precipitation in southern China. On the contrary, a dry (wet) north (south) favors intensified subtropical westerlies and precipitation in southern China. The findings have dire implications for the water and energy cycle of the region in the projected wetting and drying patterns of the north (south).

Spatial and Temporal Variation/ Variability of the Estimated and Measured Soil Moisture in Northern Jordan

The study employed the adjusted Thornthwaite-Mather model (1955) to estimate soil moisture in Irbid, Ras Muneef, and Almafraq stations for the period (1977-2015). Besides, fieldwork to collect soil moisture from 22 sites for four months (November 2019 to February 2020) was conducted. The results show that both estimated and observed soil moisture increased toward the northwest of the study area, that the highest average soil moisture reached 196.8 mm in February in Ras Munif following rainfall and soil characteristics such as texture, porosity, permeability, and soil water capacity. In addition, the content of soil moisture tends to be more stable when the soil is at or near its field capacity. However, the instability of soil moisture decreases when the soil is dry, Soil moisture reaches its lowest level from May to October, and the lowest average moisture is recorded in October at a rate of up to 0.5 mm, in Mafraq. It has also been found that the soil moisture in the northwest slopes is slightly higher than those in the northeast slopes. The annual mean of true evapotranspiration ranged between (5.4 and 54 mm); The highest annual averages were recorded in Irbid Station for all months, which exceeded the other stations; It reached (40 mm) in the year 2009. Ultimately, the study recommended that it is necessary to integrate different studies such as agro-meteorological, hydrological, and studies to establish a geodatabase to support future agricultural planning.

Soil Moisture Assimilation Improves Terrestrial Biosphere Model GPP Responses to Sub-Annual Drought at Continental Scale

Due to the substantial gross exchange fluxes with the atmosphere, the terrestrial carbon cycle plays a significant role in the global carbon budget. Drought commonly affects terrestrial carbon absorption negatively. Terrestrial biosphere models exhibit significant uncertainties in capturing the carbon flux response to drought, which have an impact on estimates of the global carbon budget. Through plant physiological processes, soil moisture tightly regulates the carbon cycle in the environment. Therefore, accurate observations of soil moisture may enhance the modeling of carbon fluxes in a model–data fusion framework. We employ the Carbon Cycle Data Assimilation System (CCDAS) to assimilate 36-year satellite-derived surface soil moisture observations in combination with flask samples of atmospheric CO2 concentrations. We find that, compared to the default model, the performance of optimized net ecosystem productivity (NEP) and gross primary productivity (GPP) has increased with the RMSEs reduced by 1.62 gC/m2/month and 10.84 gC/m2/month, which indicates the added value of the ESA-CCI soil moisture observations as a constraint on the terrestrial carbon cycle. Additionally, the combination of soil moisture and CO2 concentration in this study improves the representation of inter-annual variability of terrestrial carbon fluxes as well as the atmospheric CO2 growth rate. We thereby investigate the ability of the optimized GPP in responding to drought by comparing continentally aggregated GPP with the drought index. The assimilation of surface soil moisture has been shown to efficiently capture the influences of the sub-annual (≤9 months drought durations) and large-scale (e.g., regional to continental scales) droughts on GPP. This study highlights the significant potential of satellite soil moisture for constraining inter-annual models of the terrestrial biosphere’s carbon cycle and for illustrating how GPP responds to drought at a continental scale.

ChinaCropSM1 km: a fine 1 km daily soil moisture dataset for dryland wheat and maize across China during 1993–2018

Abstract. Soil moisture (SM) is a key variable of the regional hydrological cycle and has important applications for water resource and agricultural drought management. Various global soil moisture products have been mostly retrieved from microwave remote sensing data. However, currently there is rarely spatially explicit and time-continuous soil moisture information with a high resolution at the national scale. In this study, we generated a 1 km soil moisture dataset for dryland wheat and maize in China (ChinaCropSM1 km) over 1993–2018 through a random forest (RF) algorithm based on numerous in situ daily observations of soil moisture. We independently used in situ observations (181 327 samples) from the agricultural meteorological stations (AMSs) across China for training (164 202 samples) and others for testing (17 125 samples). An irrigation module was first developed according to crop type (i.e., wheat, maize), soil depth (0–10, 10–20 cm) and phenology. We produced four daily datasets separately by crop type and soil depth, and their accuracies were all satisfactory (wheat r 0.93, ubRMSE 0.033 m3 m−3; maize r 0.93, ubRMSE 0.035 m3 m−3). The spatiotemporal resolutions and accuracy of ChinaCropSM1 km were significantly better than those of global soil moisture products (e.g., r increased by 116 %, ubRMSE decreased by 64 %), including the global remote-sensing-based surface soil moisture dataset (RSSSM) and the European Space Agency (ESA) Climate Change Initiative (CCI) SM. The approach developed in our study could be applied to other regions and crops in the world, and our improved datasets are very valuable for many studies and field management, such as agricultural drought monitoring and crop yield forecasting. The data are published in Zenodo at https://doi.org/10.5281/zenodo.6834530 (wheat0–10) (Cheng et al., 2022a), https://doi.org/10.5281/zenodo.6822591 (wheat10–20) (Cheng et al., 2022b), https://doi/org/10.5281/zenodo.6822581 (maize0–10) (Cheng et al., 2022c) and https://doi.org/10.5281/zenodo.6820166 (maize10–20) (Cheng et al., 2022d).

From field observations to temporally dynamic soil surface roughness retrievals in the U.S. Corn Belt

Soil surface roughness in the U.S. Corn Belt varies outside of the growing season due to rainfall and soil management activities such as tillage. This is not consistent with the assumption that roughness is a static parameter in the Soil Moisture Active Passive (SMAP) soil moisture retrieval algorithm. SMAPVEX16-IA, an extensive field campaign focused on calibration of SMAP retrievals in croplands, occurred during a period when roughness is theoretically at a minima. Satellite-scale measurements of rms height and correlation length collected via pinboard, gridboard, and lidar methodologies resulted in a ensemble of model roughness coefficients that, at h = [0.13, 0.97], is significantly rougher than the original SMAP assumption in croplands of h = 0.108. Comparing simulated brightness temperatures using the ensemble h to SMAP observations resulted in a best-fit angular sensitivity coefficient of N=−2 for both horizontal and vertical polarizations. Satellite-scale h is then retrieved for a four-year study period (excluding growing seasons) via the single channel algorithm from SMAP brightness temperatures and in situ observations of soil moisture and temperature. Roughness retrievals are highly variable in the spring as rainfall events decrease h followed by an increase during soil dry-down; h steadily increases in the fall post-harvest as fields are tilled across the network. Retrievals of h are effectively independent of polarization and demonstrate the potential to simultaneously retrieve soil moisture and roughness in the absence of crops. These soil roughness retrievals could provide new information on tillage and other soil management activities.

Estimating karst groundwater recharge from soil moisture observations – a new method tested at the Swabian Alb, southwest Germany

Abstract. Understanding groundwater recharge processes is important for sustainable water resource management. Experimental approaches to study recharge in karst areas often focus on analysing the aquifer response using a disintegration of its outlet signals, but only a few approaches directly investigate the recharge processes that occur at the surface of the system. Soil moisture measurements have a high potential to investigate water infiltration to deeper soil depth or epikarst with an easy and not too intrusive installation. They can yield long-term measurements with high temporal resolution. Using these advantages, we developed and tested a method to estimate recharge based on soil moisture measurements. The method consists of the extraction of linked events in rainfall, soil moisture, and discharge time series, as well as a subsequent fitting of the parameters of a simple drainage model to calculate karst recharge from soil moisture metrics of individual events. The fitted parameters could be interpreted in physically meaningful terms and were related to the properties of the karstic system. The model was tested and validated in a karst catchment located in southwest Germany with hourly precipitation, soil moisture, and discharge data of 8 years duration. The soil moisture measurements were distributed among grassland (n = 8) and woodland areas (n = 7) at 20 cm depth. A threshold of about 35 % (± 8 %) of volumetric water content was necessary to initiate effective infiltration. Soil moisture averaged during the wetting period of each event was the best metric for the prediction of recharge. The model performed reasonably well, estimating recharge during single rainfall events. It was also capable of simulating 88 % of the average annual recharge volume despite considerable differences in the performance between years. The event-based approach is potentially applicable to other karstic systems where soil moisture and precipitation measurements are available to predict karst groundwater recharge.

Impacts of Spatiotemporal Gaps in Satellite Soil Moisture Data on Hydrological Data Assimilation

Soil moisture modeling is necessary for many hydrometeorological and agricultural applications. One of the ways in which the modeling of soil moisture (SM) can be improved is by assimilating SM observations to update the model states. Remotely sensed SM observations are prone to being riddled with data discontinuities, namely in the horizontal and vertical spatial, and temporal, dimensions. In this study, a set of synthetic experiments were designed to assess how much impact each of these individual components of spatiotemporal gaps can have on the modeling performance of SM, as well as streamflow. The results show that not having root-zone SM estimates from satellite derived observations is most impactful in terms of the modeling performance. Having temporal gaps and horizontal spatial gaps in the satellite SM data also impacts the modeling performance, but to a lesser degree. Real-data experiments with the remotely sensed Soil Moisture Active Passive (SMAP) product generally brought improvements to the SM modeling performance in the upper soil layers, but to a lesser degree in the bottom soil layer. The updating of the model SM states with observations also resulted in some improvements in the streamflow modeling performance during the synthetic experiments, but not during the real-data experiments.

Field observations of soil moisture, suction and movement of cornfield in tropical highland with and without vetiver system

An increasing large number of cornfield have been cultivated in highland of many Southeast Asian countries. In most cases, this corn plantation is done without proper soil & water conservation such as vetiver system (VS), thus causing land degradation and shallow slide. This study is aimed at investigating the field behaviour of slopes with corn plantation with and without vetiver system in Chiang Mai, Northern Thailand. Tensiometers, soil moisture sensors, in-place inclinometers and tiltmeters were installed at a slope in two locations, with and without vetiver system from surface to 2m depth. The monitoring results show that for the vetiver treatment, the soil moisture tended to be higher and response faster to rainfall than the cornfield without vetiver. However, the pore-water pressure in the root zone (about 0.5m) was higher for the vetiver treatment than the cornfield which only has the root depth of about 0.2m. For cornfield without vetiver, the seasonal variation of the soil moisture and pore-water pressure was also larger. Tiltmeter indicates a larger surface movement in the cornfield without VS. However, the inclinometer readings suggested that for slope with VS there was some cyclic movement toward hillside at the surface. This could be an indication of non-uniform settlement of soil layers or influence of groundwater rising.

Examining the variation of soil moisture from cosmic-ray neutron probes footprint: experimental results from a COSMOS-UK site

Utilising cosmic-ray neutron probes is a relatively new approach in obtaining larger area soil moisture and various operational monitoring networks have been established worldwide utilising this technology to measure operationally this parameter. One such network located in the United Kingdom (UK) is the Cosmic-ray Soil Moisture Observing System, so-called COSMOS-UK, established in 2013. The present study aims at investigating the true footprint and the variations within the footprint detectable area at the COSMOS-UK sites using as a case study one such site located in Riseholme, UK. At the selected experimental site extensive fieldwork was conducted in July 2017 that allowed examining the agreement among the soil moisture data retrieved by the Time Domain Transmissometer (TDT) sensors and the corresponding estimates from the COSMOS-UK network station probe. The COSMOS-UK site footprint was compared using GPS-aided information from ground instrumentation, assisted by drone imagery acquisition and the implementation of geospatial interpolation methods in a Geographical Information System (GIS) environment. Altogether, this information was used for assessing the soil moisture footprint extent from the COSMOS-UK site. The COSMOS-UK station footprint was representative for an area shorter in size than the alleged footprint of 600 m diameter, as generally proposed in various relevant investigations. The COSMOS network slightly overestimated soil moisture content measured by the TDT sensor probes installed in the area. Our study findings although concern specifically the studied experimental site contribute towards efforts aiming at assessing the COSMOS-UK soil moisture measurement footprint showcasing the added value of geospatial analysis in this direction.

Internet of Things (IoT) for Soil Moisture Detection Using Time Series Model

Technology in agriculture has been widely and massively applied. One of them is automation technology and the use of big data through the Internet of Things (IoT). The use of IoT allows a process to run automatically without human intervention. Extreme weather changes and narrow land use are one of the main problems in agriculture. The development of IoT devices has been widely developed regarding this subject. One of them is a soil moisture detection system. This study aims to build an IoT soil moisture detection system. The system will use a sensor as input which is then processed in a microcontroller device and the prediction results are sent to the IoT cloud platform. Prediction results are obtained using a time series model and then its performance is evaluated using RMSE. This model was chosen because the structure of the observed soil moisture data is based on time. The results of this study indicate that the soil moisture IoT system can work well. This is supported by the results of the prediction evaluation value of the RMSE = 1.175682x10-5 model which is very small.

NASA’s NMME-based S2S hydrologic forecast system for food insecurity early warning in southern Africa

In situ hydrologic monitoring over regions most susceptible to food insecurity can be a challenge in current times due to various socio-economic and political issues in combination with environmental factors such as ongoing famine or drought. Hydrologic monitoring and initializing forecasts based on remotely sensed and analyzed data can contribute significantly to early warning in such regions. Routine hydrologic forecasts, as provided by NASA’s Hydrologic Forecasting and Analysis System (NHyFAS), are a recent addition to early warning systems. A custom instance of NHyFAS, termed FLDAS-Forecast, is used by FEWS NET’sLand Data Assimilation System (FLDAS). The FLDAS-Forecast’s dynamic forecasting component was originally set up with Goddard Earth Observing System (GEOS) forecast inputs and has been recently expanded with precipitation forecast forcing from the North American Multi-Model Ensemble (NMME). This paper describes the improvements in seasonal hydrologic forecasts produced with this updated system. Evaluations in this study focus on soil moisture across southern Africa’s growing season. Soil moisture forecasts are benchmarked and evaluated relative to climatology-based forecasts and historic runs, which are driven by observation-based meteorological forcing fields, and they are verified with remotely sensed observations of soil moisture and vegetation. Through multiple deterministic and probabilistic skill assessments, we show that using the larger ensemble of NMME precipitation inputs in the forecast system results in higher quality hydrologic forecasts than are allowed by climatology- or GEOS-only-based forecasts. Further, the near-real-time NMME-based rootzone soil moisture forecasts were able to correctly predict developing drought conditions over southern Africa through late 2019 and into early 2020.

Using solar radiation data in soil moisture diagnostic equation for estimating root-zone soil moisture.

The soil moisture daily diagnostic equation (SMDE) evaluates the relationship between the loss function coefficient and the summation of the weighted average of precipitation. The loss function coefficient uses the day of the year (DOY) to approximate the seasonal changes in soil moisture loss for a given location. Solar radiation is the source of the energy that drives the complex and intricates of the earth-atmospheric processes and biogeochemical cycles in the environment. Previous research assumed DOY is the approximation of other environmental factors (e.g., temperature, wind speed, solar radiation). In this article, two solar radiation parameters were introduced, i.e., the actual solar radiation and the clear sky solar radiation and were incorporated into the loss function coefficient to improve its estimation. This was applied to 2 years of continuous rainfall, soil moisture data from USDA soil climate network (SCAN) sites AL2053, GA2027 MS2025, and TN2076. It was observed that the correlation coefficient between the observed soil moisture and B values (which is the cumulated average of rainfall to soil moisture loss) increased on average by 2.3% and the root mean square errors (RMSEs) for estimating volumetric soil moisture at columns 0-5, 0-10, 0-20, 0-50, 0-100 cm reduced on average by 8.6% for all the study sites. The study has confirmed that using actual solar radiation data in the soil moisture daily diagnostic equation can improve its accuracy.

An Assessment of the National Water Model’s Ability to Reproduce Drought Series in the Northeastern United States

Abstract Portions of the northeastern United States (NE) have experienced drought every year since 2016. The U.S. Drought Monitor (USDM) has played an important role in drought characterization and management by providing weekly drought maps across the entire United States, including the NE. Unfortunately, the USDM lacks consistency between input variables leading to difficulties in defining boundaries between drought categories. This paper evaluates the National Water Model’s (NWM) ability to model streamflow and soil moisture, two important hydrological products that are frequently incorporated in drought indices. Using a 26-yr NWM retrospective simulation, comparisons were conducted between NWM output and observations of streamflow and soil moisture, as well as between drought categories derived from the NWM and observations and the USDM. Results indicate that NWM provides moderate predictions of streamflow at NE stations when comparing to historical observations, that NWM streamflow estimators are generally upwardly biased, and performance is worse at lower streamflow magnitudes. The NWM’s ability to predict soil moisture is worse than streamflow, with again a positive bias at most sites and strong variations in anomaly correlation across sites. When predicting drought categories, NWM streamflow is as strong a predictor of USDM drought categories as observed streamflow. Extending the NWM streamflow series using a maintenance of variance technique and only past records provides slight improvements over drought categories derived from the entire 26-yr retrospective simulation. Output from the NWM appears to have some skill in characterizing drought in the NE and provides a spatial resolution to improve the designation of drought boundaries.

Microwave remote sensing for agricultural drought monitoring: Recent developments and challenges

Agricultural droughts are extreme events which are often a result of interplays between multiple hydro-meteorological processes. Therefore, assessing drought occurrence, extent, duration and intensity is complex and requires the combined use of multiple variables, such as temperature, rainfall, soil moisture (SM) and vegetation state. The benefit of using information on SM and vegetation state is that they integrate information on precipitation, temperature and evapotranspiration, making them direct indicators of plant available water and vegetation productivity. Microwave remote sensing enables the retrieval of both SM and vegetation information, and satellite-based SM and vegetation products are available operationally and free of charge on a regional or global scale and daily basis. As a result, microwave remote sensing products play an increasingly important role in drought monitoring applications. Here, we provide an overview of recent developments in using microwave remote sensing for large-scale agricultural drought monitoring. We focus on the intricacy of monitoring the complex process of drought development using multiple variables. First, we give a brief introduction on fundamental concepts of microwave remote sensing together with an overview of recent research, development and applications of drought indicators derived from microwave-based satellite SM and vegetation observations. This is followed by a more detailed overview of the current research gaps and challenges in combining microwave-based SM and vegetation measurements with hydro-meteorological data sets. The potential of using microwave remote sensing for drought monitoring is demonstrated through a case study over Senegal using multiple satellite- and model-based data sets on rainfall, SM, vegetation and combinations thereof. The case study demonstrates the added-value of microwave-based SM and vegetation observations for drought monitoring applications. Finally, we provide an outlook on potential developments and opportunities.

Climatological Drought Monitoring in Switzerland Using EUMETSAT SAF Satellite Data

Climatological drought monitoring in Switzerland relies heavily on station-based precipitation and temperature data. Due to the high spatial variability and complexity of droughts, it is important to complement station-based drought indices with gridded information and to couple multiple drought indicators within the monitoring system. Here, long-term satellite-based drought parameters from the EUMETSAT SAF network are analyzed in terms of dry anomalies within their climatology’s, namely ASCAT soil water index (SWI), CM SAF land surface temperature (LST), complemented with NOAA vegetation data, and LSA SAF Meteosat evapotranspiration data. The upcoming EUMETSAT SAF climate data records on land surface temperature and evapotranspiration will cover for the first time the WMO climatological 30-year reference period. This study is the first study investigating the potential of those long-term data records for climate monitoring of droughts in Europe. The satellite datasets are compared with the standardized precipitation index (SPI), soil moisture observations from the SwissSMEX measurement network, with a modelled soil moisture index (SMI) based on observations, and with evapotranspiration measurements, focusing on the temporal dynamics of the anomalies. For vegetation and surface temperature, the dry years of 2003, 2015, and 2018 are clearly visible in the satellite data. CM SAF LSTs show strong anomalies at the beginning of the drought period. The comparison of in situ and modelled soil moisture and evapotranspiration measurements with the satellite parameters shows strong agreement in terms of anomalies. The SWI indicates high anomaly correlations of 0.56 to 0.83 with measurements and 0.63 to 0.76 with the SMI at grassland sites. The Meteosat evapotranspiration data strongly agree with the measurements, with anomaly correlations of 0.63 and 0.67 for potential and actual evapotranspiration, respectively. Due to the prevailing humid climate conditions at the considered sites, evapotranspiration anomalies during the investigated dry periods were mostly positive and thus not water limited, but were also a driver for soil moisture drought. The results indicate that EUMETSAT SAF satellite data can well complement the station-based drought monitoring in Switzerland with spatial information.

A pixel-wise calculation of soil evaporative efficiency with thermal/optical remote sensing and meteorological reanalysis data for downscaling microwave soil moisture

Soil evaporative efficiency (SEE) was estimated as the relative difference of soil temperature (Ts) to its maximum (Tsmax) and minimum (Tsmin) values at ‘minimum and maximum’ soil moisture (SM), i.e., SEE= (Tsmax−Ts)/(Tsmax−Tsmin). This thermal indicator of SM (SMI) has been proven effective in downscaling satellite microwave SM in some local areas. However, the Tsmax and Tsmin were usually determined in an empirical way which is not conducive to a wider range of applications or global downscaling. In addition to the Ts-based SMI (SEE), solo-LST (Land Surface Temperature)-based SMI and composite index of LST and optical SMIs have also been widely used in downscaling microwave SM. However, they have rarely been evaluated with spatially distributed SM at a similar spatial resolution, leading to a vague understanding of their pros and cons in the downscaling. In this study, we first implemented a pixel-wise and theoretically calculating of SEE with LST/FVC space and meteorological reanalysis data. Then, a critical evaluation with satellite, aircraft, and in-situ SM observations was made via the SMPAVEX15 and SMAPVEX16 aircraft-based field experiments. Results indicated that the thermal SMIs presented a better performance than the optical SMIs for depicting the variations of SM. Among the thermal SMIs, Ts-based SEE showed better performance than the solo-LST-based index and the composite index temperature-vegetation-soil moisture dryness index (TVMDI). The results demonstrate the validity and necessity of obtaining the Ts-based SEE for downscaling satellite microwave SM with thermal/optical data. The provided pixel-wise method was validated in this study for calculating the Ts-based SEE, which is suggested for future downscaling of satellite microwave SM with microwave and thermal/optical fusion. However, the disadvantages of cloud contamination, the decoupling effect, and uncertainties brought by complex meteorological conditions and reanalysis data urgently need more research efforts.

Short‐term effects of organic matter and compaction manipulations on soil temperature, moisture, and soil respiration for 2 years in the Oregon Cascades

AbstractUnderstanding the factors controlling nutrient dynamics can help guide forest management plans to promote their long‐term productivity. We used experimental treatments with three levels of biomass removals and two levels of compaction to monitor the impacts to soil biophysical characteristics in an intensively managed forest. Soil temperature, moisture, and respiration observations began 6 mo after treatment installation completion and continued for 2 yr. Compaction had few consistent significant effects on measured variables, and there were negligible differences in volumetric soil water content between whole tree (WT) and bole only (BO) harvesting. Compared with BO, the 10‐cm average and maximum growing season temperatures in WT significantly increased by 1.2 and 2.5 °C, respectively. The effects of WT removals resulted in whole profile (10–100 cm) increases in the average and maximum growing season soil temperatures. The WT removals resulted in an increase of 1.4 times more soil growing degree days (SGDD) at 10 cm and 1.6 times more at 100 cm compared with BO. Despite favorable temperature and moisture conditions, differences in soil respiration could not be explained by biomass or compaction treatments. The uncut reference forest was consistently cooler and drier, but respired more CO2 throughout both years of observation compared with treated areas. The large physical disturbance of forest harvesting on the site likely masked any incremental treatment differences by homogenizing the microbial response in the ensuing 2‐yr study period. Future research should continue to investigate whether these soil biophysical changes influence site productivity or more sensitive indices of soil C dynamics.

Assessment of Spatial Heterogeneity of Soil Moisture in the Critical Zone of Gully Consolidation and Highland Protection

The Gully Consolidation and Highland Protection (GCHP) project (such as for gully head landfills), a consideration of soil and water conservation measures, has been explored and developed continuously in recent decades in the Chinese Loess Plateau. Using high-precision images taken by drones to extract the topography of the basin, the changes in vegetation and land use were also analyzed. The observation of soil moisture in the critical gully head area of the GCHP was carried out. The results indicated that the critical zones of the GCHP project implementation include the gully head landfill, the gully, and the highland farming area. The soil moisture of the landfill area was 6.91% and 23.61% higher than that of the gully and farming area, respectively, with obvious spatial heterogeneity. The soil sand content in the gully head landfill area was higher than that in the gully area and the agricultural area of the plateau. The main reason for the high soil moisture in the gully head landfill area is that the terrain at the outlet is low, and it is at the lowest point of the whole basin. Analyzing the spatial distribution of soil moisture can point out the direction for the monitoring, prevention, and treatment of geological disasters, such as landslides and debris flow, induced by water erosion. This study will help to understand in detail the spatial heterogeneity and influencing factors of soil moisture under the implementation of the GCHP and improve the GCHP project management system.

Soil Moisture and Water Transport through the Vadose Zone and into the Shallow Aquifer: Field Observations in Irrigated and Non-Irrigated Pasture Fields

Reliable estimates of soil moisture and other field observations (e.g., precipitation, irrigation) are critical to quantify the seasonal variability of surface water and groundwater relationships. This is especially important in pasture-based agroecosystems that rely on surface water diversions and precipitation inputs for agricultural production. The objectives of this study were to (1) quantify soil water balance components in irrigated and non-irrigated pasture fields in western Oregon, USA and (2) evaluate soil moisture and shallow aquifer recharge relationships in irrigated vs. non-irrigated pasture fields. Four monitoring stations in each field were used to measure soil water content in the upper 0.8 m profile and shallow groundwater levels. A soil water balance (SWB) approach was used to determine deep percolation based on field measurements of several other hydrology variables (e.g., irrigation and soil moisture). The water table fluctuation method (WTFM) was used to estimate shallow aquifer response to irrigation and precipitation inputs. Results from this study add to the understanding of seasonal water transport through the vadose zone and into the shallow aquifer in agroecological systems with fine-textured soils in the Pacific Northwest region of the United States.