Soil Moisture Fields Research Articles

Impacts of compound hot–dry extremes on US soybean yields

Abstract. The US agriculture system supplies more than one-third of globally traded soybean, and with 90 % of US soybean produced under rainfed agriculture, soybean trade is particularly sensitive to weather and climate variability. Average growing season climate conditions can explain about one-third of US soybean yield variability. Additionally, crops can be sensitive to specific short-term weather extremes, occurring in isolation or compounding at key moments throughout crop development. Here, we identify the dominant within-season climate drivers that can explain soybean yield variability in the US, and we explore the synergistic effects between drivers that can lead to severe impacts. The study combines weather data from reanalysis and satellite-informed root zone soil moisture fields with subnational crop yields using statistical methods that account for interaction effects. On average, our models can explain about two-thirds of the year-to-year yield variability (70 % for all years and 60 % for out-of-sample predictions). The largest negative influence on soybean yields is driven by high temperature and low soil moisture during the summer crop reproductive period. Moreover, due to synergistic effects, heat is considerably more damaging to soybean crops during dry conditions and is less problematic during wet conditions. Compounding and interacting hot and dry (hot–dry) summer conditions (defined by the 95th and 5th percentiles of temperature and soil moisture respectively) reduce yields by 2 standard deviations. This sensitivity is 4 and 3 times larger than the sensitivity to hot or dry conditions alone respectively. Other relevant drivers of negative yield responses are lower temperatures early and late in the season, excessive precipitation in the early season, and dry conditions in the late season. We note that the sensitivity to the identified drivers varies across the spatial domain. Higher latitudes, and thus colder regions, are positively affected by high temperatures during the summer period. On the other hand, warmer southeastern regions are positively affected by low temperatures during the late season. Historic trends in identified drivers indicate that US soybean production has generally benefited from recent shifts in weather except for increasing rainfall in the early season. Overall, warming conditions have reduced the risk of frost in the early and late seasons and have potentially allowed for earlier sowing dates. More importantly, summers have been getting cooler and wetter over the eastern US. Nevertheless, despite these positive changes, we show that the frequency of compound hot–dry summer events has remained unchanged over the 1946–2016 period. In the longer term, climate models project substantially warmer summers for the continental US, although uncertainty remains as to whether this will be accompanied by drier conditions. This highlights a critical element to explore in future studies focused on US agricultural production risk under climate change.

Improved Estimates of Pentad Precipitation Through the Merging of Independent Precipitation Data Sets

AbstractThree independent, quasi‐global, gridded data sets of precipitation (a rain gauge‐based data set, the satellite‐only component of the NASA Integrated Multi‐satellitE Retrievals for Global Precipitation Measurement mission (IMERG) Final Run precipitation product, and precipitation estimates derived from NASA Soil Moisture Active Passive (SMAP) soil moisture retrievals), are objectively combined into a single pentad precipitation data set at 36‐km resolution using a unique approach based on extended triple collocation. The quality of each of the four data sets is then evaluated against independent observations. When a global land surface model at 36‐km resolution is integrated four times, once utilizing the merged precipitation forcing and once with each of the three contributing data sets, the near‐surface soil moisture variations produced with the merged forcing validate best against independent satellite‐based soil moisture fields. In addition, the merged data set is found to be more consistent, relative to each contributor, with estimates of air temperature variations across the globe. The merged data set thus appears to draw successfully on the complementary strengths of each contributor: the particularly high quality of the rain gauge‐based data set in areas of high gauge density, the more uniform accuracy across the globe of the IMERG data, and the moderate accuracy, particularly in semi‐arid regions, of the soil moisture retrieval‐based data.

Changes of soil moisture during apple growth based on TVDI index

Based on MODIS data, soil moisture data and field survey data from 2014 to 2018, the consistency of temperature vegetation drought index (TVDL), normalized vegetation water content index (NDWL), vegetation water supply index (VSWI) and soil moisture at 15cm depth (SM) in apple growth in Fuxian county was investigated. Results showed that the spatial and temporal consistency between VSWI and SM calculated by the enhanced vegetation index (EVI) was best; the sensitivity of remote sensing indexes to soil moisture was different in different apple growth stages. The sensitivity of VSWI was the most obvious in different growth stages, and the sensitivity of soil moisture was higher than that of germination, flowering, fruit expansion and maturity. The research findings were consistent with the law of water demand in different growth stages of apple in Fuxian county and the characteristics of precipitation and drought in Fuxian county. The present results could provide a reference for soil moisture monitoring of apple growth by remote sensing. Bangladesh J. Bot. 50(3): 955-961, 2021 (September) Special

Machine‐Learning Characterization of Tectonic, Hydrological and Anthropogenic Sources of Active Ground Deformation in California

AbstractTectonic, hydrological and industrial processes coexist in the dynamic natural environments. However, our knowledge of ground deformation associated with tectonic, hydrological and anthropogenic processes and their interactions remains limited. California represents a natural laboratory that hosts the San Andreas fault system, Central Valley and other aquifer systems, and extensive human extraction of natural resources. The attendant multi‐scale ground deformation that has been mapped using Copernicus Sentinel‐1 Synthetic Aperture Radar (SAR)‐satellite constellation from four ascending and five descending tracks during 2015–2019. We consider the secular horizontal surface velocities and strain rates, constrained from GNSS measurements and tectonic models, as proxies for tectonic processes, and seasonal displacement amplitudes from interferometric SAR (InSAR) time series as proxies for hydrological processes. We synergize 23 types of multidisciplinary datasets, including ground deformation, sedimentary basins, precipitation, soil moisture, topography, and hydrocarbon production fields, using a machine learning algorithm–random forest, and we succeed in predicting 86%–95% of the representative data sets. High strain rates along the SAF system mainly occur in areas with a low‐to‐moderate vegetation fraction (∼0.3), suggesting a correlation of rough/high‐relief coastal range morphology and topography with the active faulting, seasonal and orographic rainfall, and vegetation growth. Linear discontinuities in the long‐term, seasonal amplitude and phase of the surface displacement fields coincide with some fault strands, the boundary zone between the sediment‐fill Central Valley and bedrock‐dominated Sierra Nevada, and the margins of the inelastically deforming aquifer in the San Joaquin Valley, suggesting groundwater flow interruptions, contrasting elastic properties, and heterogeneous hydrological units.

Quasi-3D mapping of soil moisture in agricultural fields using electrical conductivity sensing

Knowledge of real time spatial distribution of soil moisture has great potential to improve yield and profit in agricultural systems. Recent advances in non-invasive electromagnetic induction (EMI) techniques have created an opportunity to determine soil moisture content with high-resolution and minimal soil intrusion. So far, EMI has mainly been used for homogenous soil conditions, which are not common in agriculture and results are mainly validated by excavated pits or calibration models using soil samples on a transect. This study converts apparent electrical conductivity data recorded with a Dualem-1Hs EM-metre for two surveys of variable moisture conditions (dry and wet season) with 2475 and 2174 data points over 5.4 ha, in a field with a contrasting vertical soil profile into spatiotemporal management zones. A least square inversion algorithm was used to determine electrical conductivities for individual soil layers of 0–0.5 m, 0.5–0.8 m and 0.8–1.6 m. Soil samples from the depth of 0.5 m and 0.8 m were used for soil moisture calibrations. A laboratory experiment under controlled conditions developed electric conductivity vs volumetric water content relations with power law functions for required soil depth slices with R2 values between 0.98 and 0.99. Subsequently, EMI data were converted to volumetric water contents for each layer and predictions were spatially displayed. Median change between the measured apparent conductivity and inverted values range from 6 to 17 mS m-1 resulting in 3–7% difference in volumetric water prediction. These EMI based soil moisture predictions were compared with neutron moisture metre measurements, with Pearson R values of 0.74 and 0.95 for the wet and dry season surveys, respectively. The method is robust and offers a comparatively fast method to estimate the soil moisture status in fields and subsequently make informed management decisions.

Machine Learning for Predicting Field Soil Moisture Using Soil, Crop, and Nearby Weather Station Data in the Red River Valley of the North

Precise soil moisture prediction is important for water management and logistics of on-farm operations. However, soil moisture is affected by various soil, crop, and meteorological factors, and it is difficult to establish ideal mathematical models for moisture prediction. We investigated various machine learning techniques for predicting soil moisture in the Red River Valley of the North (RRVN). Specifically, the evaluated machine learning techniques included classification and regression trees (CART), random forest regression (RFR), boosted regression trees (BRT), multiple linear regression (MLR), support vector regression (SVR), and artificial neural networks (ANN). The objective of this study was to determine the effectiveness of these machine learning techniques and evaluate the importance of predictor variables. The RFR and BRT algorithms performed the best, with mean absolute errors (MAE) of <0.040 m3 m−3 and root mean square errors (RMSE) of 0.045 and 0.048 m3 m−3, respectively. Similarly, RFR, SVR, and BRT showed high correlations (r2 of 0.72, 0.65 and 0.67 respectively) between predicted and measured soil moisture. The CART, RFR, and BRT models showed that soil moisture at nearby weather stations had the highest relative influence on moisture prediction, followed by 4-day cumulative rainfall and PET, subsequently followed by bulk density and Ksat.

Effects of Zanthoxylum bungeanum planting on soil hydraulic properties and soil moisture in a karst area

Zanthoxylum bungeanum plantations in karst areas in China are important for economic reasons and for restoring the ecological environment. However, the lack of water resources in karst areas will affect the growth of Zanthoxylum bungeanum and the restoration of the ecological environment. Studying the influence of Zanthoxylum bungeanum on soil hydraulic properties and soil moisture in karst areas is important to optimize management of Zanthoxylum bungeanum planting and for ecosystem vegetation restoration. In this study, 4-year-old and 11-year-old Zanthoxylum bungeanum forest sampling sites were selected, and abandoned land was used as a control site; 0–50 cm undisturbed soil samples were collected to determine the soil hydraulic properties, as well as the basic physical and chemical properties. Soil volumetric water content sensors were installed at 10, 25, 40 and 55 cm depths in the soil profile at each sampling site to measure the soil moisture dynamics (from May 12, 2018, to October 3, 2018). The results showed that Zanthoxylum bungeanum had a significant impact on the shallow soil hydraulic properties, i.e., reduced soil bulk density, increased field moisture capacity, porosity and soil moisture, and enhanced soil water holding capacity; these changes increased with the time since planting. Additionally, the rainfall responses and rainfall replenishment of soil water at the three sites were significantly different. The dynamic change in soil moisture in the shallow layer was mainly affected by precipitation and atmospheric temperature (evaporation), which resulted in rapid changes under dry and wet conditions, while soil moisture in the deep layer was mainly affected by precipitation recharge, which resulted in a long retention time. With the increase in time since the planting of Zanthoxylum bungeanum, the hysteresis of the soil moisture response to rainfall strengthened, and the soil water storage and stability improved. This study shows that the planting of Zanthoxylum bungeanum improved the shallow soil hydraulic properties in karst areas and can play a positive role in water conservation.

Evaluating the Neural Network Ensemble Method in Predicting Soil Moisture in Agricultural Fields

Soil is an important element in the agricultural domain because it serves as the media that bridges the water consumption and supply processes. In this study, a neural network ensemble (NNE) method was employed to predict the soil moisture to eliminate the effects of random initial parameters of neural network (NN) on model accuracy. The constructed NNE model predicts daily root zone soil moisture continuously for the whole crop growing season and the water consumption and supply processes were separately modeled. The soil profile was divided into multiple layers and modeled separately. Weather data (including air temperature, humidity, wind speed, net radiation, and precipitation), rooting depth, and the hesternal soil moisture of each layer were used as the input. A calibrated root zone water quality model for maize (Zea mays L.) was used to generate training and evaluation data. The result showed that with 100 randomly initialized NN models, the NNE model achieved an average R2 of 0.96 and nRMSE of 5.93%, suggesting that the NNE model learned the soil moisture dynamics well and sufficiently improved the robustness of soil moisture prediction with high accuracy.

Comparison of soil moisture indices and field measurements in hilly agricultural lands of SW Hungary

The retention of surface runoff and the preservation of soil moisture are among the most important water-related ecosystem services. In addition to field monitoring, advanced remote sensing techniques have been devised to reveal soil moisture dynamics on agricultural land. In our study we compare two soil moisture indices, TWI and SAVI, in three agricultural areas with different land use types. The SAVI has been found suitable to point out spatial variation on the moisture conditions of the vadose zone.

Multiyear analysis of the dependency of the planting date on rainfall and soil moisture in paddy fields in Cambodia, 2003\u20132019

The dependencies of the planting date on rainfall and soil moisture in paddy fields in Cambodia were analyzed to quantify farmers’ empirical knowledge regarding their decision of the planting date. Remote sensing data from multiple satellites covering the 2003–2019 period were analyzed. The planting dates in rain-fed paddies ranged from April to August, with large spatial variations and year-to-year fluctuations. In years when planting was suppressed in April and May, planting was extensively enhanced in June and August compared to normal years, and vice versa. Over the northeastern side of Tonle Sap Lake and south of Phnom Penh city, the areas planted in April and May were found to have positive correlations with rainfall and soil moisture, suggesting that wetter-than-average conditions encouraged farmers to plant earlier in the season. In contrast, this relationship was unclear on the western side of Tonle Sap Lake, where the rainfall amounts were larger throughout the year than in other areas in Cambodia. In this region, the relationship between the planting area and soil water availability was either unclear or was even slightly negative from June to August. Since more frequent dry spells have been detected after the onset of the rainy season in recent years, further studies and disseminations of potential changes in dry spells are important for the agronomic adaptation of planting dates under climate change.

Comparison of N2O Emissions From Cold Waterlogged and Normal Paddy Fields

Paddy fields are major sources of atmospheric N2O. Soil temperature and moisture strongly affect N2O emissions from rice fields. However, N2O emissions from cold-waterlogged paddy fields (CW), an important kind of paddy soil in China, are not well studied so far. It is unclear whether the N2O emissions from cold-waterlogged paddy fields are the same as normal paddy fields (NW). We investigated the N2O emission characteristics from the CW and NW paddy fields under with (R1) and without (R0) rice in Tuku Village, Baisha Town, Yangxin County (YX site, monitoring in 2013) and Huandiqiao Town, Daye City (DY site, monitoring in 2014); compared the difference and influencing factors between the CW and NW paddy fields at two sites in South China. The results showed that the N2O emissions from NWR0 were 13.4 times higher than from CWR0, and from NWR1 were 10.3 times higher than from CWR1 in the YX site. The N2O emissions from NWR0 were 2.4 times higher than from CWR0, and from NWR1 were 17.3 times higher than from CWR1 in the DY site. The structural equation models (SEMs) showed that the N2O emissions are mainly driven by rice planting and soil moisture in the NW fields at the annual scale, while soil temperature in the CW fields. Overall, N2O emissions from cold waterlogged paddy fields are significantly lower than those of normal paddy fields due to the low temperature and higher water content; however, there are dinitrogen emissions from cold waterlogged paddy fields denitrification should be further examined.

Herbicide Resistance to Metsulfuron-Methyl in Rumex dentatus L. in North-West India and Its Management Perspectives for Sustainable Wheat Production

Herbicide resistance in weeds is a global threat to sustaining food security. In India, herbicide-resistant Phalaris minor was the major problem in wheat for more than two decades, but the continuous use of metsulfuron-methyl (an ALS inhibitor) to control broadleaf weeds has resulted in the evolution of ALS inhibitor-resistant Rumex dentatus L. This review summarizes the current scenario of herbicide resistance in R. dentatus, along with its ecology and management perspectives. Studies have provided valuable insights on the emergence pattern of R. dentatus under different environments in relation to tillage, cropping systems, nutrients, and irrigation. Moreover, R. dentatus has exhibited higher emergence under zero tillage, with high infestation levels in rice-wheat compared to other wheat-based cropping systems (sorghum-wheat). Alternative herbicides for the management of resistant R. dentatus include pendimethalin, 2,4-D, carfentrazone, isoproturon, and metribuzin. Although the pre-emergence application of pendimethalin is highly successful in suppressing R. dentatus, but its efficiency is questionable under lower field soil moisture and heavy residue load conditions. Nevertheless, the biological data may be utilized to control R. dentatus. Therefore, herbicide rotation with suitable spray techniques, collecting weed seeds at differential heights from wheat, crop rotation, alternate tillage practices, and straw retention are recommended for addressing the resistance issue in R. dentatus in North India conditions. Overall, we discuss the current state of herbicide resistance in R. dentatus, the agronomic factors affecting its population, its proliferation in specific cropping systems (rice-wheat), and management strategies for containing an infestation of a resistant population.

Spatial-temporal distribution characteristics of soil moisture in dryland tobacco fields in the Wuling mountain area

Spatial-temporal distribution characteristics of soil moisture in dryland tobacco fields in the Wuling mountain area

Towards new soil water flow equations using physics‐constrained machine learning

AbstractThe Richardson–Richards equation (RRE) is a widely used partial differential equation (PDE) for modeling moisture dynamics in unsaturated soil. However, field soil moisture observations do not always satisfy RRE. In this paper, we introduce a new physically constrained machine learning (PCML) approach to derive governing soil water flow PDE directly from moisture observations. This paper is viewed as a feasibility study and reports results of our first attempt in developing the PCML approach. Here, we rely on noisy synthetic soil moisture data obtained from the linear RRE subject to real flux boundary conditions. The linear RRE was used as a reference PDE to check the PCML‐derived PDEs, where the PCML performance was shown to be highly dependent upon the sample size in time and space. Results presented here confirm the feasibility of deriving soil water flow governing PDEs directly from soil moisture observations using PCML.

An assessment of different methods to determine specific yield for estimating groundwater recharge using lysimeters

Estimation of groundwater recharge is considered crucial for the management of groundwater resources. The groundwater level fluctuation (GLF) method is a widely used approach to estimate groundwater recharge due to its simplicity and ease of implementation. However, the main source of uncertainty is the specific yield for the GLF method. Although there have been a lot of methods for determining specific yield, the performance of specific yield on the estimation of recharge remains unclear. We set up three lysimeters with different water table depths in the Guanzhong Basin, China. Soil moisture content along with the soil profile, water table depths, and rainfall data were measured continuously. These data provide us with accurately observed recharge and allow us to analyze the performance of specific yields from different methods in estimating recharge. The main results are: (1) The constant specific yield from the pumping tests, which is equal to the ultimate specific yield, significantly overestimated the observed recharge rates independent of water table depths. (2) The constant specific yield obtained from saturated soil moisture content minus field capacity tended to overestimate recharge under the shallow water table depths (less than 2 m), and vice versa; (3) The depth-dependent specific yield using the measured soil moisture content along with soil profile can obtain reliable recharge across all water table depths. (4) The accuracy of the depth-dependent specific yield obtained by the soil water retention curve relies on reliable parameters. The parameters α and n have to be taken into account carefully for determing the specific yield. Our results are important for the application of the GLF method to estimate recharge. More importantly, it is valuable for the sustainable management of groundwater recourses.

Delimitation of low topsoil moisture content areas in a vineyard using remote sensing imagery (Sentinel-1 and Sentinel-2) in a Mediterranean-climate region

Irrigation can be responsible for salt accumulation in the root zone of grapevines when late autumn and winter precipitation is not enough to leach salts from the soil upper horizons, turning the soil unsuitable for grape production. The aim of this work is to present a novel methodology to outline areas, within a drip-irrigated vineyard, with a low soil moisture content (SMC) during, and after, an 11-month agricultural drought. Soil moisture (SM) field measurements were performed in two plots at the vineyard, followed by a geostatistical method (indicator kriging) to estimate the SM class probabilities according to a threshold value, enlarging the training set for the classification algorithms. The logistic regression (LR) and Random Forest (RF) methods used the features of the Sentinel-1 and Sentinel-2 images and terrain parameters to classify the SMC probabilities at the vineyard. Both methods classified the highest SMC probabilities above 14% that is located close to the stream at the lower altitudes. The RF method performed very well in classifying the topsoil zones with a lower SMC during the autumn-winter period. This delineation allows the prevention of the occurrence of areas affected by salinisation, indicating which areas will need irrigation management strategies to control the salinity, especially under climate change, and the expected increase in droughts.

Rancang Bangun Internet of Things (IoT) Pengaturan Kelembaban Tanah untuk Tanaman Berbasis Mikrokontroler

Soil moisture is needed in rice fields for plant growth, with attention to soil moisture in rice fields, it can increase fertility in the land which impacts existing plants. But in regulating soil moisture requires a level that is following the existing plants, because each plant has different humidity settings. This research will design a tool to adjust soil moisture in rice fields automatically to maintain soil moisture according to plant needs. This tool is designed to use a Soil Moisture sensor which can later be used as input data to the ESP8266 to drive a Servo Motor as a soil moisture regulator, a Servo Motor as an irrigation door drive. In making the design of irrigation arrangements, moisture data from each plant is needed because each plant has a different moisture content, from the results of the study, it was found that the soil moisture of kale plants was ± 49% - 68%, and soil moisture of corn plants ± 48% - 63%.Keywords:Automatic Watering, ESP8266, Soil Moisture Sensor.

Assessment of surface soil moisture from ALOS PALSAR-2 in small-scale maize fields using polarimetric decomposition technique

Surface soil moisture knowledge is important, especially in agriculture and irrigation management. Properties of microwave remote sensing like penetration power and longer wavelength facilitate retrieval of surface soil moisture. ALOS PALSAR-2, quad polarized data are used to retrieve surface soil moisture using polarization decomposition techniques in a marginal farmer small-scale maize field. The focus of the study is to explore the utility of ALOS PALSAR-2 in retrieving surface soil moisture using the polarization decomposition technique. The demonstration of the study is carried out in Malavalli village, southern India, an agricultural predominant area. The study involves field soil moisture sampling in synchronous with satellite pass, measuring soil properties, preprocessing of SAR data, polarization decomposition, proportional analysis, regression analysis, model calibration and validation. Van Zyl decomposition gave the highest surface scattering component (43%) and reduced volumetric scattering component compared to Yamaguchi and Freeman–Durden decomposition. Surface scattering component of Yamaguchi decomposition gave a good coefficient of determination (R2 = 0.8029) with field-measured surface soil moisture. The semi-empirical model (SEM) was developed using surface scattering component and depolarization ratio with adjusted R2 = 0.75 at 95% confidence interval. On its comparison with existing soil moisture models, it is observed that the developed model is performing well with RMSE and AEmax of 1.81 and 2.88, respectively. Implying the applicability of ALOS PALSAR-2 in soil moisture retrieval in marginal farmer small-scale maize fields gave satisfactory results of accuracy.

Assimilation of the SCATSAR-SWI with SURFEX: Impact of Local Observation Errors in Austria

AbstractThe proper determination of soil moisture on different scales is important for applications in a variety of fields. We aim to develop a high-level soil moisture product with high temporal and spatial resolution by assimilating the multilayer soil moisture product SCATSAR-SWI (Scatterometer Synthetic Aperture Radar Soil Water Index) into the surface model SURFEX. In addition, we probe the impact of the findings on the numerical weather prediction (NWP) in Austria. The data assimilation system consists of the NWP model AROME and the SURFEX Offline Data Assimilation, which provide atmospheric forcing and soil moisture fields as mutual input. To address the known sensitivity of the employed simplified extended Kalman filter to the specification of errors, we compute the observation error variances of the SCATSAR-SWI locally using Triple Collocation Analysis and implement them into the assimilation system. The verification of the forecasted 2 m temperature and relative humidity against measurements of Austrian weather stations shows that the actual impact of the local error approach on the atmospheric forecast is slightly positive to neutral compared to the standard error approach, depending on the time of the year. The direct verification of the soil moisture analysis against a gridded water balance product reveals a degradation of the unbiased root-mean-square error for small observation errors.

Design and Development of Soil Moisture Sensor

Water is a very valuable and stimulating force for irrigation. The Optimum usage of water is an hourly requirement. Efficient irrigation helps to conserve water, increase plant yields, reduce fertilizer reliance, and improve the quality of crops. Various techniques are available to measure soil moisture content, both laboratory and field, including remote sensing, but the fastest and better one is with the use of soil moisture sensor electronic devices. The range of soil moisture sensors has its own benefits and drawbacks. The goal of this work is to design and develop a module for the measurement of soil moisture and temperature levels, as well as ambient temperature and humidity by using frequency concepts. The sensor is made of a corrosion resistant element and it is rugged, battery operated, low power and long range sensor using IoT. A “GND BLE Mobile Application (gSense100)” has been developed, which includes everything related to the BLE technology and soil moisture sensor, where the app uses BLE technology to transmit all sensor values to the consumer.