Moisture Products Research Articles

Improving radio frequency heating uniformity in cauliflower by changing density in different zones

Radio frequency (RF) treatment is one of the most promising approaches for disinfesting, drying, and pasteurizing agricultural products. However, the inhomogeneous heating of RF treatment is the primary barrier for practical applications. To improve the RF heating uniformity, a 27.12 MHz, 6 kW pilot-scale RF system was applied to heat the cauliflower by changing its density in different zones of a rectangular container. The results proved that the RF heating rate and the average mass change rate of the cauliflower decreased with increasing electrode gap and bulk density of the cauliflower. The RF heating uniformity of the cauliflower was improved by increasing the electrode gap and the bulk density of the cauliflower during RF heating. With increasing of the bulk density of sample at the edges in the container, the RF heating rate and heating uniformity index of the sample firstly decreased and then increased when the sample density at the center and the corners were 405.5 and 516.8 kg/m3, respectively. The mass change rate of the sample presented an inverse relationship with the heating rate and heating uniformity index during RF treatment. The best heating uniformity and highest mass change rate were obtained when the bulk density of the cauliflowers at the center, edges, and corners in the container was 405.5, 461.1, and 516.8 kg/m3, respectively. The results of this study can offer effective method for improving the RF heating uniformity in high moisture products.

A Review of Earth’s Surface Soil Moisture Retrieval Models via Remote Sensing

Soil moisture is essential parameter in the Earth’s surface. The information provided by soil moisture plays a vital role in agricultural production, eco-environmental protection, water and land resources management, etc. Meanwhile, the accurate monitoring of the spatial and temporal distribution of soil moisture is of great significance for the engineering geological assessment and geological disaster prevention. Monitoring and retrieving soil moisture via remote sensing data and mathematical models are the main research methods at present and the crucial issue is how to eliminate the influence of other surface and soil parameters like roughness and soil bulk density, and the interference of vegetated areas to electromagnetic waves. Nowadays, many branches of retrieval methods have been developed, and researchers are integrating multiple models to improve the retrieval accuracy. This paper summarizes the present research status and progress of soil moisture retrieval via remote sensing based on four kinds of models: empirical model, semi-empirical model, physical model, and machine learning. The soil moisture products are summarized and listed at the same time. The difficulties and issues in the present research are discussed and the future outlook is explored.

Machine Learning Downscaling of SoilMERGE in the United States Southern Great Plains

SoilMERGE (SMERGE) is a root-zone soil moisture (RZSM) product that covers the entire continental United States and spans 1978 to 2019. Machine learning techniques, Random Forest (RF), eXtreme Gradient Boosting (XGBoost), and Gradient Boost (GBoost) downscaled SMERGE to spatial resolutions straddling the field scale domain (100 to 3000 m). Study area was northern Oklahoma and southern Kansas. The coarse resolution of SMERGE (0.125 degree) limits this product’s utility. To validate downscaled results in situ data from four sources were used that included: United States Department of Energy Atmospheric Radiation Measurement (ARM) observatory, United States Climate Reference Network (USCRN), Soil Climate Analysis Network (SCAN), and Soil moisture Sensing Controller and oPtimal Estimator (SoilSCAPE). In addition, RZSM retrievals from NASA’s Airborne Microwave Observatory of Subcanopy and Surface (AirMOSS) campaign provided a nearly spatially continuous comparison. Three periods were examined: era 1 (2016 to 2019), era 2 (2012 to 2015), and era 3 (2003 to 2007). During eras 1 and 2, RF outperformed XGBoost and GBoost, whereas during era 3 no model dominated. Performance was better during eras 1 and 2 as opposed to the pre-L band era 3. Improvements across all eras, regions, and models realized from downscaling included an increase in correlation from 0.03 to 0.42 and a decrease in ubRMSE from −0.0005 to −0.0118 m3/m3. This study demonstrates the feasibility of SMERGE downscaling opening the prospect for the development of a long-term RZSM dataset at a more desirable field-scale resolution with the potential to support diverse hydrometeorological and agricultural applications.

Fertilizer nitrogen use by cereal crops in arid conditions of Buryatia

The results of research in a long-term (1982–2021) stationary experiment to determine the coefficients of nitrogen use of mineral fertilizers by the crops of grain and fallow crop rotation in the conditions of the dry-steppe zone of Buryatia are presented. Chestnut loamy sand soil on the studied variants was characterized by very low content of humus, total nitrogen and nitrates before sowing of the crops. During the research period, the prevalence of dry years was noted. Nitrogen use coefficients of mineral fertilizers in the conditions of the dry-steppe zone of Buryatia on average for 39 years of research were 69% for wheat, 54% for oats and 90% for oats for grain haylage. The variation of the coefficients was significantly influenced by summer moistening conditions. They were 32% under extreme and severe drought, 57% under moderate and weak drought, and increased significantly (up to 124%) under favorable moisture conditions. The utilization of nitrogen of solid mineral fertilizers was maximum in the third crop (oats for grain haylage) and more responsive to the atmospheric moistening. The smallest consumption coefficients and the range of its changes were observed in oats for grain. The first crop of the rotation (fallow wheat) occupied an intermediate position in this assessment. The data on the influence of soil moisture and hydrothermal conditions of separate critical periods of vegetation on the variation of the coefficient of mineral fertilizer nitrogen utilization (N40) by wheat, oats, and oats for grain haylage were obtained. The correlation analysis showed that the most critical period for nitrogen consumption in wheat is June (r = 0.61), in oats – July (r = 0.51) and July–August for oats for grain haylage (r = 0.50–0.52). Multiple linear models of dependence of N fertilizer use coefficients on productive moisture content and hydrothermal conditions were made.

Assessment of the soil buffer capacity in the green plantings of the megalopolis parks

Land degradation poses a serious challenge to food security, livelihood sustainability, ecosystem services and biodiversity conservation, while inefficient land management leads to widespread loss of soil biodiversity. The soil buffer capacity is actively involved in the mechanisms of implementing such an ecosystem service as the development and stabilization of soil fertility. Buffering determines the share of the soil potential that determines the processes of immobilization (deposition) and mobilization (release, loss) of a particular element of fertility – first of all, mineral nutrients required by plants, productive moisture, thermal energy of the soil, gas composition of soil air, acidity. The soils in parks on the territory of megacities are an integral component of them determining the conditions for the growth and development of green spaces. We assessed the acid-base (pH) buffer capacity of urban areas in a park area covered with tree stands of such introduced tree species as horse chestnut (Aesculus hippocastanum), sugar maple (Acer saccharum), small-leaved elm (Ulmus parvifolia), common hackberry (Celtis occidentalis), honey locust (Gleditsia triacanthos) and Japanese pagoda tree (Styphnolobium japonicum) growing on the territory of the T. G. Shevchenko Park (Dnipro city, Ukraine). Soil samples were collected under the crowns of these tree species. The acid-base buffering capacity of urban soils was determined by the Arrhenius method, which includes adding certain volumes of acid and alkaline components to the sample, and further calculating the buffering area within acid and base intervals. Buffering areas were calculated using the Simpson formula. The results were processed with statistical methods (arithmetic mean and standard deviation were calculated, and the difference in means was found according to the Tukey's comparison test). Initially the samples of the studied soils were found to have mostly a slightly alkaline reaction of the soil solution. Within the acid range of external influences, the buffer capacity of urban soils under introduced tree species was determined to establish the following descending series: horse chestnut, common hackberry, small-leaved elm, honey locust, Japanese pagoda tree, sugar maple. The same series was established for the total acid-base buffering capacity. The results show the greater acid-base buffer capacity of soils sampled under horse chestnut, common hackberry, small-leaved elm and honey locust trees, and therefore these soils were resistant to degradation compared to the soils sampled under Japanese pagoda tree and sweet maple. The environment-forming role of introduced tree species in the implementation of buffer properties of urban soils in the park on the megalopolis territory can be assumed.

Intercomparison of recent microwave satellite soil moisture products on European ecoregions

Several satellite microwave-based soil moisture (SM) products have become available in recent years, making possible to produce different datasets at continental and/or a global scale. Numerous variables, including geographic heterogeneity, weather and climate, and land cover, have an impact on these products' accuracy. In this study, we account for all these variables by using the ecoregions as the scale of analysis and conducting a thorough comparison of satellite products with ground-based SM measurements. The whole Europe has been selected as study area, considering it as the aggregation of several ecoregions, each of them classified as an homogeneous zones, in terms of climate, vegetation and potentially investigable soil cover. Selected SM satellite data includes: i) the National Aeronautics and Space Administration (NASA) SMAP L4 V5; ii) the European Space Agency (ESA) SMOS-IC V2.0; iii) the H115 and H116 SM products in time-series format generated by analyzing data collected by the Advanced Scatterometer (ASCAT) aboard MetOp satellites; iv) the CGLS SSM 1 km obtained by the radar onboard ESA’s SENTINEL-1 platforms; and v) the European Space Agency’s Climate Change Initiative for Soil Moisture (ESA CCI SM) “COMBINED” ESA CCI SM v06.1 product, created by merging satellite-based passive/active microwave measurements. Such a variety, in terms of technologies and main features, of publicly available online SM products (in their up-to-date version), allowed for a comprehensive intercomparison against in situ measurements of the International Soil Moisture Network (ISMN), that spreads across all of Europe within the above-mentioned terrestrial ecoregion, using four different metrics, i.e., r, bias, ubRMSE and r/rANOM.Overall the intercomparison has underlined how SMAP L4 and ESA CCI show the best performance regardless of the considered metric. ASCAT has achieved best performances among non-modeled/blended products, while SMOS-IC has showed slightly better performance than the CGLS SSM 1 km. Using ecoregions allowed to further characterize the differences of each satellite products and identify the areas where all these products are more reliable (e.g., group (1) made by ecoregions as the Cantabrian Mixed forests or the Balkan Mixed forests; or group 2 made by ecoregions which belong to Mediterranean area) or less performing (e.g., the Central European mixed forests and the Pontic steppe). Hence, these classes have been further analyzed exploiting the ERA5-Land data, in terms of the SM, air temperature, precipitation, evaporation and Leaf area index. The research has provided helpful insights into how the performance of particular SM satellite products changes based on both the characteristics of the product under consideration and the area under investigation (i.e., one ecoregion compared to another).

A novel finer soil strength mapping framework based on machine learning and remote sensing images

Soil strength is an important factor for assessing the vehicle trafficability in the wilds and making reliable off-road path planning. Rating Cone index (RCI) has been widely used as an indicator of soil strength for mobility assessment. Currently, regional RCI are mainly obtained by using Soil Moisture Strength Prediction (SMSP) Model based on the Unified Soil Classification System (USCS) soil types and soil moisture at critical depth. However, USCS classification is inaccessible directly in most soil databases. And current soil moisture products are of coarse spatial resolution and only reliable in soil surface layer. It is costly to arrange ground survey for USCS soil type and the fine soil moisture in deeper layers cannot be obtained directly. To fill this gap, a novel framework is proposed to generate finer RCI-based soil strength map. To gain USCS soil classification in an un-surveyed study site, RF, GBDT, XGBoost and LCE ensemble learning models were trained with data from gSSURGO Database which contains USCS soil types and soil properties, and then make prediction on data from SoilGrids Database by using same properties. Then, to obtain the finer soil moisture of critical depth, these tree-based models were constructed with ground observation data, Sentinel-2 images, topographical data and soil properties with higher spatial resolution. The SMSP model is finally used to generate the finer daily soil strength map for the study site. The proposed framework highly improved the resolution and reliability of existing soil strength map generating methods, and can provide detailed soil strength information.

Prediction of the capacitance of the corn drying process parameter using adaptive- neuro-fuzzy intelligent technique with experimental validation

In this study, innovative, intelligent software based on Adaptive Neuro-Fuzzy Inference System (ANFIS) has been developed to effectively estimate the corn drying process parameters using data from getting experimental study. For this purpose, laboratory-scale corn drier experimental setup was established to get corn drying parameters (air temperature in the different locations, air humidity, and capacitance value). The twelve sensors measure the drying parameters (s1, s2, s3, …, s12 ) such as inlet air temperature T1 with ±0.5 °C sensitivity, relative humidity RH1 with ±0.1 RH, inlet temperatures T2 and Tw2 with the ±0.5 °C, dryer chamber temperature Ti (i = 3,…,7) with the ±0.5 °C, outlet temperatures T8 and Tw8 with the ±0.5 °C, product moisture content with ±0.05 gr. Finally, depending on the twelve sensor parameters, the capacitance value was measured using a capacitive sensor with ±0.05% sensitivity. Accordingly, an Adaptive Neuro-Fuzzy Inference System architecture was developed with twelve inputs and one output. For the training of the Adaptive Neuro-Fuzzy Inference System, 52 data sets from laboratory studies were used, and Adaptive Neuro-Fuzzy Inference System was successfully trained with absolute fraction of variance 0.998 and mean squared error 0.8007. For the testing the performance of the Adaptive Neuro-Fuzzy Inference System 51 dataset was used and testing performance of the ANFIS getting with the absolute fraction of variance 0.9963 and mean squared error 1.47. Finally, the performance of the Adaptive Neuro-Fuzzy Inference System compared with the artificial neural network (ANN), and it is clearly seen that the performance of the fuzzy-neuro hybrid algorithm is higher than the core neuro algorithm with %0.97 for the training and %31.5 for the testing. By forecasting the moisture content of corn under various operating conditions, the Adaptive Neuro-Fuzzy Inference System model can be used to improve the operation of the corn dryer, resulting in increased efficiency, lower energy usage, and higher-quality dried corn.

Spatio-temporal changes in global root zone soil moisture from 1981 to 2017

Root zone soil moisture (RZSM) has a direct impact on ecosystem function, vegetation growth and food security, and plays a vital role in global climate system, water and carbon cycles. However, large variations and uncertainties still exist in RZSM across the globe under the warming climate. In this study, we applied comparison map profile (CMP), Theil-Sen regression and partial correlation analysis to investigate the spatial and temporal changes of RZMS and its driving factors from 1981 to 2017 by using three soil moisture products–ERA5, GLDAS and MERRA-2. Results showed that RZSM derived from three products presented a similar spatial pattern that the highest RZSM values occurred in tropical forest and cold areas, followed by subtropical, while the relatively low RZSM values were observed in arid and semiarid regions. Globally, RZSM decreased in all of three datasets with a rate of −0.14 × 10-3 m3 m−3 yr−1 on average (p < 0.001), which was largely correlated with temperature anomalies. Spatially, the RZSM trends greatly varied, with 21–31% of global land areas experiencing a significant decreasing trend and 7–24% for an increasing trend, respectively, confirming their different sensitivities to climate change. Temperature-driven RZSM dominated 19–29% of global land areas and was primarily distributed in northern high-latitude areas. The areas dominated by evapotranspiration were mainly in arid and semiarid areas, accounting for 29–44% of global land areas. Precipitation dominates the remaining 36–45% of global land areas mainly in eastern America and Europe, suggesting variations in the dominance of environmental factors on the spatial patterns of RZSM trend. Our findings will deepen our understanding of the impacts of climate change on the long-term trend of global soil moisture, and will be greatly critical to global soil water resource protection and management under the warming climate.

EFFECTIVENESS OF PRECISION AGRICULTURE ELEMENTS IN POTATO PRODUCTION IN THE WESTERN SIBERIAN FOREST-STEPPE

Theresultsofcomprehensiveresearchconductedin 2020-2022 usinggeoinformationtechnologies in potato cultivation at ZAO Plemzavod "Irmens" in the Ordynsky District of the Novosibirsk Region, located in the forest-steppe zone of Western Siberia, are presented. Potatoes require specific environmental factors to achieve high yields with good product quality. Using satellite navigation systems ensures an increase in potato yield and product quality. The research aims to develop elements of potato cultivation technology using innovative satellite navigation systems. The experiments were conducted in the soil and climate zone of drained forest-steppe in the northern forest-steppe area of the Priobye foothills. The soil of the experimental plots is alkaline black soil with a humus content of 6.32%, total nitrogen content of 0.28%, phosphorus content of 0.23%, and potassium content of 1.19%. The concentration of quickly hydrolysable nitrogen was 10.3, exchangeable phosphorus was 20.7, and exchangeable potassium was 14.4 mg/100 g of soil, with a pH of Early potato varieties Gala and Riviera were cultivated in the experiments. During the research period, aggregated data were obtained, and regression equations between yield indicators, the sum of productive soil moisture reserves, and precipitation in interphase periods of potato varieties were calculated. Correlation coefficients were determined for specific interphase periods. The dependence of potato yield on precipitation indicators in interphase periods was established. The significance of precision agriculture elements in ensuring early potato varieties' high product and quality was noted.

Drying, Physical, Nutritional and Bioactive Characteristics of Apple Peel Flour Subjected to Blanching and Ultrasound Pretreatments.

The aim of this work was to study the effect of blanching and ultrasound pretreatments on drying and quality characteristics of apple peel. Blanching was conducted in boiling water, ultrasound in a water bath, and drying in a batch tray dryer. The product obtained was ground into a flour, and assessed for color, water activity, proximate composition, sugars, and bioactive compounds. Results showed that effective moisture diffusivity increases with a decrease in product moisture content, being such dependence well described by a second order polynomial model. Average drying rate was higher and product moisture content was lower for blanched and sonicated flour, especially for the former. Such result suggests that blanching and ultrasound enhance water removal during drying of apple peel. Physical properties were significantly affected by pretreatments, being more intense red color and lower water activity obtained for sonicated flour. Nutritional and bioactive properties were also significantly affected by pretreatments, being lower sugar, higher protein, fiber, catechin and epicatechin content observed for blanched flour. Summarizing, blanching and ultrasound pretreatments improve drying of apple peel, both regarding process efficiency and product quality.

Retrieval and evaluation of surface soil moisture from CYGNSS using blended microwave soil moisture products

The Global Navigation Satellite System-Reflectometry (GNSS-R) can estimate land surface soil moisture (SSM) as a viable and promising approach. However, it has some large uncertainty in retrieving SSM. In this study, the SSM is retrieved from different Cyclone GNSS (CYGNSS) SSM retrieval models formed with different SSM reference data products, including two blended microwave SSM products from the European Space Agency's Climate Change Initiative (CCI) and the National Oceanic and Atmospheric Administration's Soil Moisture Operational Product System (SMOPS), and a single microwave sensor-derived Soil Moisture Active Passive (SMAP) Level-3 product. The performance of the developed retrieval models, characterized by spatial resolutions of 36 km × 36 km and 0.25° × 0.25°, is evaluated using K-fold cross-validation. Furthermore, the accuracy of these models is validated against ground measurements acquired from Chinese automated soil moisture observation network. In order to alleviate the impact of spatial mismatching between the predicted gridded SSM and the point-scale in-situ measurements, a cumulative distribution function (CDF) rescaling strategy is applied. The results indicated that all models are effective at capturing spatial variations in SSM, and the SMOPS-based model achieves the highest correlation coefficient (0.930) and the lowest root mean square error (RMSD, 0.028 cm3/cm3), followed by the CCI-based model (0.906 and 0.042 cm3/cm3). The SMAP-based model performs poorly in the comparison. The suboptimal performance of models evaluated with Chinese automated soil moisture measurements is largely attributed to the insufficient calibration of the original reference data in the region.

IMERG Precipitation Improves the SMAP Level-4 Soil Moisture Product

Abstract The NASA Soil Moisture Active Passive (SMAP) mission Level-4 Soil Moisture (L4_SM) product provides global, 9-km resolution, 3-hourly surface and root-zone soil moisture from April 2015 to the present with a mean latency of 2.5 days from the time of observation. The L4_SM algorithm assimilates SMAP L-band (1.4 GHz) brightness temperature (Tb) observations into the NASA Catchment land surface model as the model is driven with observation-based precipitation. This paper describes and evaluates the use of satellite- and gauge-based precipitation from the NASA Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement (IMERG) products in the L4_SM algorithm beginning with L4_SM Version 6. Specifically, IMERG is used in two ways: (i) The L4_SM precipitation reference climatology is primarily based on IMERG-Final (Version 06B) data, replacing the Global Precipitation Climatology Project Version 2.2 data used in previous L4_SM versions, and (ii) the precipitation forcing outside of North America and the high latitudes is corrected to match the daily totals from IMERG, replacing the gauge-only, daily product or uncorrected weather analysis precipitation used there in earlier L4_SM versions. The use of IMERG precipitation improves the anomaly time series correlation coefficient of L4_SM surface soil moisture (versus independent satellite estimates) by 0.03 in the global average and by up to ∼0.3 in parts of South America, Africa, Australia, and East Asia, where the quality of the gauge-only precipitation product used in earlier L4_SM versions is poor. The improvements also reduce the time series standard deviation of the Tb observation-minus-forecast residuals from 5.5 K in L4_SM Version 5 to 5.1 K in Version 6. Significance Statement Soil moisture links the land surface water, energy, and carbon cycles. NASA Soil Moisture Active Passive (SMAP) satellite observations and observation-based precipitation data are merged into a numerical model of land surface water and energy balance processes to generate the global, 9-km resolution, 3-hourly Level-4 Soil Moisture (L4_SM) data product. The product is available with ∼2.5-day latency to support Earth science research and applications, such as flood prediction and drought monitoring. We show that a recent L4_SM algorithm update using satellite- and gauge-based precipitation inputs from the NASA Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement (IMERG) products improves the temporal variations in the estimated soil moisture, particularly in otherwise poorly instrumented regions in South America, Africa, Australia, and East Asia.

Comprehensive validation of seven root zone soil moisture products at 1153 ground sites across China

ABSTRACTRoot zone soil moisture (RZSM) is a critical parameter for drought monitoring, agriculture, and vegetation growth. In situ measurements of RZSM are scarce, and therefore, RZSM products are derived mainly through modeling approaches. This study evaluated seven RZSM products, including ERA5, MERRA2, CFSR, SMAP level 4, GLDAS-NOAH2.1, GLDAS-Catchment2.2, and SMOS CATDS Level 4, using 1153 ground sites across China. The evaluations aimed to address which products perform better, how well they capture the seasonal variations, and how they perform under different land surface and climate conditions. Results showed that MERRA2 and NOAH had the best performance, with MERRA2 having a better ability to capture surplus and deficit soil moisture conditions. The study also revealed that precipitation forcing plays a vital role in improving RZSM simulations. Conversely, ERA5 overestimates RZSM, and SMOS exhibits a dry bias. From the time series variability, all datasets except SMOS can capture the temporal dynamics of RZSM well. Moreover, the accuracy of the RZSM products varies significantly in tropical climate zones. This study provides crucial insights for improving land surface and hydrological models in estimating RZSM, which will help in drought management and agriculture production.

Effect of excitation parameters on the motion characteristics of the tailings slurry during vibration dewatering

Fine flotation tailings slurry (FFTS) are potential mines rich in a large number of useful minerals, and the large-scale stockpiling of tailings slurry have caused serious resource waste and environmental pollution. High efficiency dewatering is urgent demand of the FFTS disposal and utilization. In this work, the influence of excitation parameters on the FFTS motion characteristics and dewatering performance was explored during the continuous dewatering by the vibration screen. The results showed that the slurry concentration along the screen surface showed a trend of increasing in steps, and a typical three-region differential dewatering phenomenon appeared. The increase of vibration intensity was conducive to speeding up the motion velocity of the FFTS and avoiding the accumulation of the tailings particles on the screen surface. And the layer thickness on the screen was reduced, which was beneficial to shorten the percolation distance of the liquid. There was a competitive coordination mechanism between percolation distance and dewatering time on the dewatering performance. When the velocity was less than 0.07 m/s, the moisture was reduced by the reduction of the layer thickness, and the dewatering time was not the main factor. When the velocity exceeded 0.07 m/s, the influence of the layer thickness on the dewatering was weakened, the material could not obtain sufficient dewatering time. The dewatering results showed that with the optimal conditions of f = 16 Hz, A = 5 mm and α = 45°, the product moisture reached 19.37 % and the discharge speed was 4.69 t/m2·h. A case study indicated that dewatering for 6000 t FFTS could be completed by 3 vibration screens working 7.67 h per day, and the volume of FFTS was reduced from 2125 m3 to 831.25 m3. The work might provide theoretical and technical support for efficient dewatering of FFTS with environmental benefits.

Enhanced Soil Moisture Retrieval through Integrating Satellite Data with Pedotransfer Functions in a Complex Landscape of Ethiopia

Remotely sensed soil moisture products potentially provide a valuable resource for monitoring agricultural drought and assessing food security. The agriculture dominated countries of Eastern Africa experience high inter-annual variability of rainfall, but the monitoring and assessment of the predominantly rainfed agriculture systems is hindered by an absence of ground-based observations. This study evaluates the accuracy of three soil moisture products: ASCAT SWI 12.5 km, SMAP soil moisture data 9 km (SPL3SMP_E), and enhanced surface soil moisture map derived through integrating ASCAT SWI and Pedotransfer Functions (PTFs) (ASCAT_PTF_SM), in Ethiopia, through comparison with in situ-observed soil moisture datasets. Additionally, a new water retention PTF, developed for Ethiopian soils, is integrated with a high-resolution soil property dataset to enhance the spatial resolution of the soil moisture product. The results show that the new integrated dataset performs better in terms of unbiased root mean square error (ubRMSE = 0.0398 m3/m3) and bias (0.0222 m3/m3) in comparison with ASCAT SWI 12.5 km (ubRMSE = 0.0.0771 m3/m3, bias = 0.1065 m3/m3). SMAP is found to have limitations during the wet season, overestimating soil moisture. The finer spatial resolution of the data allows for a better depiction of heterogeneity of soil moisture across the landscape and can be used to identify water-related issues and improve hydrological models for agricultural water management.

Impact of SAR-based vegetation attributes on the SMAP high-resolution soil moisture product

NASA's Soil Moisture Active Passive (SMAP) mission employs L-band radiometer observations with a spatial resolution of ~33 km to produce global soil moisture products at various spatial resolutions and regular intervals. Among these products is the SMAP-Sentinel active-passive high-resolution soil moisture, available at spatial resolutions of 1 km and 3 km. The SMAP-Sentinel product combines data from the SMAP L-band radiometer and the Copernicus Sentinel-1A and Sentinel-1B C-band Synthetic Aperture Radar (SAR) observations. The SMAP-Sentinel high-resolution soil moisture product enables various hydrological and agricultural applications that were previously beyond the capability of coarser resolution radiometer-only products. However, the assessment of SMAP-Sentinel product over SMAP core validation sites (CVS) has revealed high errors and biases, particularly in agricultural regions. We suspect that the discrepancy may be related to the vegetation attribute parameters (e.g., vegetation optical depth, τ) used in the SMAP-Sentinel baseline Single-Channel Algorithm (SCA) microwave emission model (i.e., τ-ω model) for soil moisture retrievals, which might be out of sync due to reliance on NDVI climatology in the τ estimation. In this study, we leveraged and evaluated the advantage of using vegetation attribute information inherently available through the SMAP ratiometer and Sentinel- 1A/1B SAR backscatter as an alternative to NDVI climatology-derived τ in the SCA to enhance soil moisture retrievals. The SAR cross-polarization (vh) backscatter potentially contains vegetation attribute information that can be correlated with the coarse resolution τ derived from SMAP observations. Therefore, we hypothesized that including Sentinel- 1A/1B vh observations to derive high-resolution (1 km and 3 km) τ, and subsequently incorporating it into the SCA, can significantly improve the accuracy of the SMAP-Sentinel soil moisture product. We established a statistical relationship between Sentinel- 1A/1B SAR backscatter and the SMAP Dual-Channel Algorithm (DCA)-based τ retrieval at ~33 km, for a specific region and different landcover classes at a global extent, using seven years of observations (2015–2022). Further, we developed an algorithm to downscale the SMAP DCA τ of ~33 km to 1 km and 3 km resolutions based on the developed statistical relationships, enabling us to capture the actual temporal variability in vegetation attributes (i.e., τ) at higher resolutions. The downscaled τ values were then incorporated into the SCA to retrieve SMAP-Sentinel high-resolution (1 km and 3 km) soil moisture. The validation analysis of high-resolution soil moisture retrievals demonstrates that the inclusion of the downscaled τ approach significantly improved the performance of the SMAP-Sentinel product by reducing both bias and error standard deviation compared to the retrievals based on τ derived from the NDVI climatology.

Multi-Source Soil Moisture Data Fusion Based on Spherical Cap Harmonic Analysis and Helmert Variance Component Estimation in the Western U.S.

Soil moisture (SM) is a vital climate variable in the interaction process between the Earth's atmosphere and land. However, global soil moisture products from various satellite missions and land surface models are affected by inherently discontinuous observations and coarse spatial resolution, which limits their application at fine spatial scales. To address this problem, this paper integrates three diverse types of datasets from in situ, satellites, and models through Spherical cap harmonic analysis (SCHA) and Helmert variance component estimation (HVCE) to produce 1 km of spatio-temporally continuous SM products with high accuracy. First, this paper eliminates the bias between different datasets and in situ sites and resamples the datasets before data fusion. Then, multi-source SM data fusion is performed based on the SCHA and HVCE methods. Finally, this paper evaluates the fused products from three aspects, including the performance of representative sites under different climate types, the overall performance of validation sites, and the comparison with other products. The results show that the fused products have better performance than other SM products. In the representative sites, the minimal correlation coefficient (R) of the fused products is above 0.85, and the largest root mean square error (RMSE) is below 0.040 m3 m-3. For all validation sites, the R and RMSE of the fused products are 0.889 and 0.036 m3 m-3, respectively, while the R for other products is below 0.75 and the RMSE is above 0.06 m3 m-3. In comparison to other SM products, the fused products exhibit superior performance, generally align more closely with in situ measurements, and possess the ability to accurately and finely capture the spatial and temporal variability of surface SM.

The synergies of SMAP enhanced and MODIS products in a random forest regression for estimating 1 km soil moisture over Africa using Google Earth Engine

ABSTRACT Due to the coarse scale of soil moisture products retrieved from passive microwave observations (SMPMW), several downscaling methods have been developed to enable regional scale applications. However, it can be challenging for users to access final data products and algorithms, as well as managing different data sources and formats, various data processing methods, and the complexity of the workflows from raw data to information products. Here, the Google Earth Engine (GEE), which as of late offers SMPMW, is used to implement a workflow for retrieving 1 km SM at a depth of 0–5 cm using MODIS optical/thermal measurements, the SMPMW coarse scale product, and a random forest regression. The proposed method was implemented on the African continent to estimate weekly SM maps. The results of this study were evaluated against in-situ measurements of three validation networks. Overall, in comparison to the original SMPMW product, which was limited by a spatial resolution of only 9 km, this method is able to estimate SM at 1 km spatial resolution with acceptable accuracy (an average correlation coefficient of 0.64 and a ubRMSD of 0.069 m3/m3). The results show that the proposed method in GEE provides a precise estimation of SM with a higher spatial resolution across the entire continent.

Influence of the harvesting method on the processes of moisture accumulation and yield capacity

BACKGROUND: A feature of the climate of the steppe zones of Western Siberia is the frequent recurrence of droughts. Accumulation and preservation of solid precipitation in the form of snow on field in winter is a relevant task, since this directly affects the accumulation of productive moisture in soil by the time of sowing and, as a result, the formation of the future harvest.&#x0D; AIMS: To determine the influence of the snow cover level, moisture reserves and productive moisture in the soil on a stubble background, harvested with a thresher for direct combining and a rippler on the yield capacity of spring wheat.&#x0D; METHODS: The field experiment was carried out in 2021-2022 in the steppe zone of the Omsk region at the Novouralskoye Scientific and Production Enterprise of the Omsk Agrarian Scientific Center. In the autumn of 2021, sections of the field were marked out for measurements: wheat stubble field after the thresher for direct combining, wheat stubble field after the rippler. In March 2022, indicators of the height of the snow cover and moisture reserves in the snow were determined on the marked areas using the VS-43 balance snow gauge. In the spring, wheat was sown with the machine-tractor unit, consisting of the RSM 2400 tractor and the Amazone Condor 15001-C sowing facility. In autumn, the chosen field sections were harvested with the Sampo-250 seed-production harvester.&#x0D; RESULTS: It was found that after harvesting with the thresher for direct combining with a stubble background of about 15 cm, there is a decrease in the moisture reserve in the one-meter-depth layer by almost 2 times, down to 48.6 mm, by the spring period. The moisture reserve on a high stubble background, after the operation of the rippler, on the contrary, was able to persist, as there were 96.6 mm of productive moisture in the one-meter-depth layer by the time of sowing. The average grain yield on the field section sown after the rippler was 1.16 t/ha. The average grain yield on the field section sown after harvesting with the thresher for direct combining was 1.06 t/ha. Thus, the grain increase was 0.1 t/ha or 9.4%.&#x0D; CONCLUSIONS: The carried out field experiment indicates that, when harvesting with a rippler, there is a twofold increase in productive moisture by the time of sowing, while the increase in the yield of spring soft wheat grain was 9.4%.