Moisture Products Research Articles

Evaluation of Several Satellite-Based Soil Moisture Products in the Continental US.

Satellite-based soil moisture products are suitable for large-scale regional monitoring due to the accessibility. Five soil moisture products including SMAP, ESA CCI, and AMSR2 (ascending, descending, and average) were selected in the continental United States (US) from 2016 to 2021. To evaluate the performance of the products and assess their applicability, ISMN (International Soil Moisture Network) data were used as the in situ measurement. PBIAS (Percentage of BIAS), R (Pearson correlation coefficient), RMSE (Root Mean Square Error), ubRMSE (unbiased RMSE), MAE (Mean Absolute Error), and MBE (Mean Bias Error) were selected for evaluation. The performance of five products over six observation networks and various land cover types was compared, and the differences were analyzed at monthly, seasonal, and annual scales. The results show that SMAP had the smallest deviation with the ISMN data because PBIAS was around -0.13, and MBE was around -0.02 m3/m3. ESA CCI performed the best in almost all aspects; its R reached around 0.7, and RMSE was only around 0.07 m3/m3 at the three time scales. The performance of the AMSR2 products varied greatly across the time scales, and increasing errors and deviations showed from 2016 to 2020. The PBO_H2O and USCRN networks could reflect soil moisture characteristics in the continental US, while iRON performed poorly. The evaluation of the networks was closely related to spatial distributions. All products performed better over grasslands and shrublands with R, which was greater than 0.52, and ubRMSE was around 0.1 m3/m3, while products performed worse over forests, where PBIAS was less than -0.62, and RMSE was greater than 0.2 m3/m3, except for ESA CCI. From the boxplot, SMAP was close to the ISMN data with differences less than 0.004 m3/m3 between the median and lower quartiles.

Simulating carbon and water fluxes using a coupled process-based terrestrial biosphere model and joint assimilation of leaf area index and surface soil moisture

Abstract. Reliable modeling of carbon and water fluxes is essential for understanding the terrestrial carbon and water cycles and informing policy strategies aimed at constraining carbon emissions and improving water use efficiency. We designed an assimilation framework (LPJ-Vegetation and soil moisture Joint Assimilation, or LPJ-VSJA) to improve gross primary production (GPP) and evapotranspiration (ET) estimates globally. The integrated model, LPJ-PM (LPJ-PT-JPLSM Model) as the underlying model, was coupled from the Lund–Potsdam–Jena Dynamic Global Vegetation Model (LPJ-DGVM version 3.01) and a hydrology module (i.e., the updated Priestley–Taylor Jet Propulsion Laboratory model, PT-JPLSM). Satellite-based soil moisture products derived from the Soil Moisture and Ocean Salinity (SMOS) and Soil Moisture Active and Passive (SMAP) and leaf area index (LAI) from the Global LAnd and Surface Satellite (GLASS) product were assimilated into LPJ-PM to improve GPP and ET simulations using a proper orthogonal decomposition (POD)-based ensemble four-dimensional variational assimilation method (PODEn4DVar). The joint assimilation framework LPJ-VSJA achieved the best model performance (with an R2 ( coefficient of determination) of 0.91 and 0.81 and an ubRMSD (unbiased root mean square deviation) reduced by 40.3 % and 29.9 % for GPP and ET, respectively, compared with those of LPJ-DGVM at the monthly scale). The GPP and ET resulting from the assimilation demonstrated a better performance in the arid and semi-arid regions (GPP: R2 = 0.73, ubRMSD = 1.05 g C m−2 d−1; ET: R2 = 0.73, ubRMSD = 0.61 mm d−1) than in the humid and sub-dry humid regions (GPP: R2 = 0.61, ubRMSD = 1.23 g C m−2 d−1; ET: R2 = 0.66; ubRMSD = 0.67 mm d−1). The ET simulated by LPJ-PM that assimilated SMAP or SMOS data had a slight difference, and the SMAP soil moisture data performed better than SMOS data. Our global simulation modeled by LPJ-VSJA was compared with several global GPP and ET products (e.g., GLASS GPP, GOSIF GPP, GLDAS ET, and GLEAM ET) using the triple collocation (TC) method. Our products, especially ET, exhibited advantages in the overall error distribution (estimated error (μ): 3.4 mm per month; estimated standard deviation of μ: 1.91 mm per month). Our research showed that the assimilation of multiple datasets could reduce model uncertainties, while the model performance differed across regions and plant functional types. Our assimilation framework (LPJ-VSJA) can improve the model simulation performance of daily GPP and ET globally, especially in water-limited regions.

Ecological plasticity of winter wheat yield when cultivating on different predecessors in arid conditions of Eastern Caucasus

Relevance. The selection of the best winter wheat precursors that provide low yield variability in contrasting years is an important task of farming.Methods. Studies were carried out in 2017–2021 at the Prikumskaya Experimental Breeding Station in a 6-field crop rotation, where winter wheat was placed on bare and full fallow and semifallow. The aim of the research was to evaluate the precursors in different nutritional backgrounds from the position of low variability of yield, ecological stability and high adaptability to the conditions of the arid zone of the Stavropol Territory.Results. The hydro-thermal coefficient of summer-autumn period was 0,22 and of spring-summer period — 0,06 units drier than the average summer value. The pre-sowing period was especially unfavorable (HTC = 0.22). The stocks of productive moisture in a meter layer of soil in autumn were considerably below the norm on bare fallow in 40% of years of experiment and on other predecessors — in 80% of years. Yield of winter wheat varied from 2.43 to 4.46 t/ha on bare fallow on control, 2.86 to 4.93 t/ha — on fertilized variant, 1.98 to 4.72 and 2.15 to 5.35 t/ha— on ful fallow, 0.59 to 2.19 and 0.64 to 2.44 t/ha — on semifallow. And the environmental conditions index ranged from –0.51 to +1.52 and –0.64 to+1.43; from –0.90 to+1.84 and –1.10 to +2.10; from –0.72 to + 0.88 and –1.19 to +0.79 respectively. Maximum yields in all nutritional backgrounds were obtained on bare and full fallow. Only 1 year out of 5 was favorable as it had positive environmental conditions index values. The environmental plasticity ranged from 1.67 to 3.04. The highest yield variability (47.1%) and maximum stress tolerance (–1.6 –1.98 t/ha) was observed for the half fallow Semifallow. Bare fallow was the most flexible under stress conditions of cultivation as it had the highest yield (2.94–3.50 t/ha) and the lowest variability (26.4%) compared to other precursors.

Developing a framework for urban flood modeling in Data-poor regions

Forecasting floods is an effective non-engineering measure for flood control. However, for urbanized regions, the lack of drainage data and complex underlying surface conditions make flood forecasting extremely difficult. This paper proposes a framework for urban flood modeling in data-poor regions to address these issues. The drainage capacity of the pipe system was represented by the constant infiltration rate, which was calibrated by coupling satellite soil moisture product and measured flood data to better reflect real drainage conditions. Subsequently, given the complex underlying surface conditions, this framework divides the land use/cover into several dominant urban land use types, which were assigned unique runoff generation and flow confluence parameters in urbanized regions. The framework also takes the impact of dense buildings on water flow into consideration. A building treatment method for structured grids was proposed, which can consider the anisotropy of buildings and scale of the grid. The results showed that it is feasible to estimate drainage capacity using the constant infiltration approach, and the process of soil infiltration is crucial for urban flood forecasting. Compared with the highest resolution publicly available land use/cover dataset, the urban land use types extracted from high-resolution remote sensing images can reveal the runoff generation and flow confluence mechanism more accurately in urbanized regions. This study verified the necessity of considering the impact of buildings in urban flood modeling. The building treatment method of this framework can identify synergistic effects of local terrain and building boundaries on water flow. The results provide a reference for effective flood forecasting in urban regions with poor data.

Evaluating surface soil moisture characteristics and the performance of remote sensing and analytical products in Central Asia

Reanalysis and remotely sensed soil moisture (SM) products are important for monitoring hydrology and the terrestrial carbon cycle, especially in water-limited arid regions. However, it is essential to assess the reliability of SM products before they are used. In this study, surface SM from the European Reanalysis-Interim v5 (ERA5), Essential Climate Variables (ECV), the Famine Early Warning Systems Network (FEWS) Land Data Assimilation System (FLDAS), and the Global Land Data Assimilation System (GLDAS) were assessed based on in-situ data and the Three-Cornered Hat (TCH) approach in a typical arid region, Central Asia (CA). The validation based on in-situ data indicated that all three products underestimated the observed surface SM, except for ERA5. The ERA5 SM was closest to the in-situ observations, averaging a higher correlation coefficient of 0.59, followed by the GLDAS, ECV, and FLDAS. The TCH method revealed low uncertainties (<0.05 m3/m3) for all four SM products, and high uncertainties were found at high altitudes. For ERA5 and ECV SM products, the uncertainty is high in the shrublands and forests, respectively. FLDAS and GLDAS SM products performed better in terms of uncertainty compared to ERA5 and ECV, with an average uncertainty below 0.03 m3/m3, except for forests. The above assessment results indicate that, compared with the other three SM products, ERA5 has a better performance and more potential for application in CA. There was an upward trend in surface SM from 1982 to 2018 in CA. The relationship between the four SM products and aridity index and drought events showed that GLDAS could capture the hydrothermal conditions and drought events, followed by FLDAS, ERA5, and ECV. Our research is intended to provide some reference for the application of gridded SM products to ecosystem and water resources monitoring in arid regions.

Geospatial Weather Affected Terrain Conditions and Hazards (GeoWATCH) description and evaluation

Accurately estimating soil moisture at fine resolutions – scales approaching a few meters – has been a target of a broad community involved in soil moisture research and application development, from agriculture support, improving land-atmosphere interaction in weather and climate prediction, and hydrologic modeling, to a variety of military applications. The US Army interest in high spatial-resolution soil moisture products is based on the need to better understand soil impacts on broad range of applications, including but not limited to movement and maneuver and logistics. The Geospatial Weather-Affected Terrain Conditions and Hazards (GeoWATCH) application was developed to support Army needs for high resolution soil moisture products. This paper describes the GeoWATCH application, development of the high-resolution soil moisture analysis and prediction capability, and linkages to downstream Army applications that deliver weather-informed content to support decision making. This paper also describes product evaluation and validation efforts to understand application performance.

Satellite-based estimation of daily suspended sediment load using hybrid intelligent models

ABSTRACT This study uses a combination of support vector regression models, particle swarm optimization, and grey wolf optimization algorithms to predict suspended sediment load. For this purpose, The Satellite Precipitation of Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) and Global Land Data Assimilation System (GLDAS) soil moisture products are utilized as the predictors. The prediction models are evaluated based on various visual and quantitative indicators. The Taylor and radar diagrams confirm that the support vector regression-particle swarm optimization best agrees with the observed values. Moreover, the obtained quantitative indices show that the support vector regression-particle swarm optimization model offers better performance than other models used in the present study. The values of the best indices are: Pearson correlation coefficient of 0.997, relative root mean square error of 13.17, percentage bias of 4.05, and Nash-Sutcliffe efficiency of 0.995.

An Examination of the SMAP Operational Soil Moisture Products Accuracy at the Tibetan Plateau

Surface soil moisture (SSM) plays an essential role in the Earth’s water cycle and land surface processes as well as in vegetative growth, ecological health, and ecosystem properties. Particularly, information on this parameter’s spatiotemporal variability at the Tibetan Plateau is of key importance to the study of climate and the impact of climate change due to it is distinctive characteristics in this area. The present study assesses the operational SSM products provided by the SMAP (Soil Moisture Active and Passive) satellite at the Tibetan Plateau, Naqu observational station, China. In particular, the globally distributed Level 3 operational products, SPL3SMP_36km and the Enhanced Passive SSM Product SPL3SMP_9km, are evaluated in two-phases. SSM and the surface temperature estimates by SPL3SMP_36km and SPL3SMP_9km are compared against corresponding ground data available at the Naqu observation network. All in all, the examined products captured the SSM dynamics in the studied area. The results showed that precipitation is the key driving source of SSM variability. SSM fluctuated significantly and was dependent on precipitation in the studied region. Statistical metrics, such as the root mean square error (RMSE), varied for SPL3SMP_36km and SPL3SMP_9km in the ranges of 0.036–0.083 m3/m3 and 0.074–0.097 m3/m3, respectively. The unbiased RMSE (ubRMSE) was higher than the SMAP uncertainty limit (0.04 m3/m3) in most cases. This study establishes some of the causes for the different performances of SMAP products, mainly, the ancillary input dataset parameterizations, and, specifically, the surface temperature parameterization schemes of SMAP retrieval algorithm is analyzed and discussed. Our research findings highlight, among others, the usefulness of those SSM products from SMAP, particularly in mesoscale studies, providing additional useful insights into the use of those products in practice in China and globally.

Performance assessment of SM2RAIN-NWF using ASCAT soil moisture via supervised land cover-soil-climate classification

The SM2RAIN algorithm developed a simple analytical relationship by inverting the soil-water equation to estimate rainfall through the knowledge of soil moisture. The recently developed SM2RAIN-NWF algorithm offers an improvement in estimating rainfall by integrating the SM2RAIN algorithm and the net water flux (NWF) model. The Advanced Scatterometer (ASCAT) soil moisture products were used to estimate rainfall and evaluate the reliability of the SM2RAIN-NWF algorithm compared to the SM2RAIN on a national scale. Besides, the impact of Land cover-Soil texture-Climate (LSC) characteristics and the intensity of rainfall (four classes of intensity) on the performance of algorithms were discussed. Five performance metrics, including Correlation Coefficient (R), Kling–Gupta (KGE), Root Mean Square Error (RMSE), False Alarm Ration (FAR), and Probability of Detection (POD) were used to validate the estimated cumulative 5-, 14-, and 30-day rainfall. Furthermore, the effect of evapotranspiration (ET) and drainage terms were investigated in the performance of rainfall estimation through the SM2RAIN-NWF algorithm for the first time on a national scale. Results showed the rainfall estimations through the SM2RAIN-NWF algorithm improved approximately up to 7.5% in each accumulation (e.g. rainfall aggregation intervals (AGGR) 5 to 14 and 14 to 30) based on R and KGE indices. In addition, the SM2RAIN-NWF improved rainfall estimations up to 50% based on the KGE index in the southern half of Iran (arid and semi-arid climate) compared to the SM2RAIN estimates. The comprehensive evaluation and uncertainty analysis of rainfall estimations under the supervised classification of 11 LSC and 4 rainfall classes also showed the calibration of the SM2RAIN-NWF was highly affected by environmental and climatic circumstances. Uncertainty analysis showed the SM2RAIN-NWF algorithm can estimate rainfall more consistently in the five LSC classes namely 1) barren-clay loam-arid-desert, 2) barren-loam-arid-steppe, 3) barren-clay loam-arid-steppe, 4) urban-clay loam-arid-desert, and 5) urban-loam-arid-steppe. Similarly, estimating rainfall in the region with precipitation under 267 mm/year can be retrieved more reliably through the SM2RAIN-NWF algorithm. Results obtained from the ET analysis revealed an insignificant (<4%) effect of this term in improving the performance of the SM2RAIN-NWF algorithm. Also, the NWF equation confirmed the paramount role of the drainage term in enhancing the accuracy of rainfall estimates compared to the SM2RAIN method.

History and Development of the USAF Agriculture Meteorology Modeling System and Resulting USAF–NASA Strategic Partnership

Abstract The USAF Weather (AFW) supports a number of military and U.S. government agencies by providing authoritative weather analysis and forecast products for any location globally, including soil moisture analyses. The long history of supporting soil moisture products and partnering with other U.S. government agencies led to the partnering between the U.S. Air Force (USAF) and NASA Goddard Space Flight Center, resulting in a merger of those organizations’ modeling systems, collaborative development of the Land Information System (LIS), and operational fielding of the system within the USAF 557th Weather Wing [557 WW; formerly, Headquarters Air Force Weather Agency (HQ AFWA)]. In 2009, the USAF implemented the NASA LIS and later made it the primary software system to generate global soil hydrology and energy budget products. The implementation of LIS delivered a significant upgrade over the existing Land Data Assimilation System (LDAS) the USAF operated, the Agriculture Meteorology (AGRMET) system. Implementation enabled the rapid integration of new LDAS technology into USAF operations, and led to a long-term NASA–USAF partnership resulting in continued development, integration, and implementation of new LIS capabilities. This paper documents both the history of the USAF Weather organization capabilities enabling the generation of soil moisture and other land surface analysis products, and describes the USAF–NASA partnership leading to the development of the merged LIS-AGRMET system. The article also presents a successful example of a mutually beneficial partnership that has enabled cutting-edge land analysis capabilities at the USAF, while transitioning NASA software and satellite data into USAF operations.

Reconstruct SMAP brightness temperature scanning gaps over Qinghai-Tibet Plateau

• SMAP brightness temperature ( TB ) scanning gaps were compensated. • Daily variation of TB was analyzed in function of influencing factors. • Seamless TB benefits the refinements of SMAP soil moisture products. Soil Moisture Active Passive (SMAP) satellite was used to monitor global soil moisture and freeze–thaw state using surface brightness temperature (TB). However, due to the limitation of scanning width and shifting track, the daily TB observation had stripe gaps in varying degrees, which imped scientific applications of SMAP official product. To solve this issue, we proposed a temporal fitting algorithm to produce daily seamless TB data over Qinghai-Tibet Plateau (QTP) from 2015 to 2021. This method was composed of two steps: (1) overall time trend fitting which used a spline function to fit the overall mean time trend for each pixel from 2015 to 2021; (2) daily variation correction which accounted for several influential factors including surface temperature, precipitation, and vegetation to establish an Ordinary Least Squares (OLS) model for predicting daily variation of TB in numerous time slices. Consequently, the missing TB observation was reconstructed by combining the overall temporal trend and daily variation. A 10-fold cross-validation and product comparison were carried out to validate the robustness of the proposed method. For cross-validation, we randomly removed available SMAP observations in certain periods as unavailable, and then compared the reconstructed values with the removed practical ones. The cross-validation results indicated the reconstructed TB agreed well with actual SMAP TB, with a high overall coefficient of determination (R 2 = 0.94) and low RMSE (RMSE = 3.7 K), implying the desirable performance of our proposed method. By comparison with Soil Moisture and Ocean Salinity (SMOS) TB, the reconstructed data had an approximate performance with actual SMAP TB. Furthermore, the effects of surface temperature and precipitation on TB in different periods (frozen and unfrozen) over QTP were also analyzed. Our method has the potential to generate seamless SMAP products.

Evaluating Root-Zone Soil Moisture Products from GLEAM, GLDAS, and ERA5 Based on In Situ Observations and Triple Collocation Method over the Tibetan Plateau

Abstract Root-zone soil moisture (RZSM) is an important variable in land–atmosphere interactions, notably affecting the global climate system. Contrary to satellite-based acquisition of surface soil moisture, RZSM is generally obtained from model-based simulations. In this study, in situ observations from the Naqu and Pali networks that represent different climatic conditions over the Tibetan Plateau (TP) and a triple collocation (TC) method are used to evaluate model-based RZSM products, including Global Land Evaporation Amsterdam Model (GLEAM) (versions 3.5a and 3.5b), Global Land Data Assimilation System (GLDAS) (versions 2.1 and 2.2), and the fifth-generation European Centre for Medium-Range Weather Forecasts reanalysis (ERA5). The evaluation results based on in situ observations indicate that all products tend to overestimate but could generally capture the temporal variation, and ERA5 exhibits the best performance with the highest R (0.875) and the lowest unbiased RMSE (ubRMSE; 0.015 m3 m−3) against in situ observations in the Naqu network. In the TC analysis, similar results are obtained: ERA5 has the best performance with the highest TC-derived R (0.785) over the entire TP, followed by GLEAM v3.5a (0.746) and GLDAS-2.1 (0.682). Meanwhile, GLEAM v3.5a and GLDAS-2.1 outperform GLEAM v3.5b and GLDAS-2.2 over the entire TP, respectively. Besides, possible error causes in evaluating these RZSM products are summarized, and the effectiveness of TC method is also evaluated with two dense networks, finding that TC method is reliable since TC-derived R is close to ground-derived R, with only 6.85% mean relative differences. These results using both in situ observations and TC method may provide a new perspective for the soil moisture product developers to further enhance the accuracy of model-based RZSM over the TP. Significance Statement The purpose of this study is to better understand the quality and applicability of GLEAM, GLDAS, and ERA5 RZSM products over the TP using both in situ observations and the triple collocation (TC) method, making it better applied to climate and hydrological research. This study provides four standard statistical metrics evaluation based on in situ observations, as well as the reliable metric, that is, correlation coefficient (R) derived from TC method, and highlights that TC-based evaluation could supplement the ground-based validation, especially over the data-scarce TP region.

Measurement of Grain and Grain Products Moisture Content by Ultrahigh-Frequency Method: Influence of Grain Density Inhomogeneity on the Mass Ratio of Moisture

Measurement of Grain and Grain Products Moisture Content by Ultrahigh-Frequency Method: Influence of Grain Density Inhomogeneity on the Mass Ratio of Moisture

A 1 km daily soil moisture dataset over China using in situ measurement and machine learning

Abstract. High-quality gridded soil moisture products are essential for many Earth system science applications, while the recent reanalysis and remote sensing soil moisture data are often available at coarse resolution and remote sensing data are only for the surface soil. Here, we present a 1 km resolution long-term dataset of soil moisture derived through machine learning trained by the in situ measurements of 1789 stations over China, named SMCI1.0 (Soil Moisture of China by in situ data, version 1.0). Random forest is used as a robust machine learning approach to predict soil moisture using ERA5-Land time series, leaf area index, land cover type, topography and soil properties as predictors. SMCI1.0 provides 10-layer soil moisture with 10 cm intervals up to 100 cm deep at daily resolution over the period 2000–2020. Using in situ soil moisture as the benchmark, two independent experiments were conducted to evaluate the estimation accuracy of SMCI1.0: year-to-year (ubRMSE ranges from 0.041 to 0.052 and R ranges from 0.883 to 0.919) and station-to-station experiments (ubRMSE ranges from 0.045 to 0.051 and R ranges from 0.866 to 0.893). SMCI1.0 generally has advantages over other gridded soil moisture products, including ERA5-Land, SMAP-L4, and SoMo.ml. However, the high errors of soil moisture are often located in the North China Monsoon Region. Overall, the highly accurate estimations of both the year-to-year and station-to-station experiments ensure the applicability of SMCI1.0 to study the spatial–temporal patterns. As SMCI1.0 is based on in situ data, it can be a useful complement to existing model-based and satellite-based soil moisture datasets for various hydrological, meteorological, and ecological analyses and models. The DOI link for the dataset is http://dx.doi.org/10.11888/Terre.tpdc.272415 (Shangguan et al., 2022).

Relationship of potato yield and factors of influence on the background of herbological protection

Abstract The latest technologies for growing crops, including potatoes, are based on the use of modern mathematical models that can fairly accurately identify the impact of various factors of natural and technological nature on the object of study. Yield modeling makes it possible to adjust resource consumption indicators to obtain the maximum economic effect and minimize the negative impact on the environment. It was found that the lowest weediness of potato agrocenosis (24 pcs/m2) was formed by the complex application of Hezagard (4 L ha−1) and Panthera (1 L ha−1). The lowest level of actual weeds infestation had a positive effect on the yield of tubers – 27.6 t ha−1 (+26.6% to control) and was obtained in the variant of herbicide application. The results of correlations of potato tuber yield from factors such as weediness, density and productive moisture reserves in the arable soil layer are highlighted. It was found that the highest yield of 27.6 t ha−1 (+26.6% compared to the control) was obtained in the variant with the application of herbicides Hezagard (4 L ha−1) and Panthera (1 L ha−1). According to the results of multiple regression, it was found that the coefficient of multiple correlation is R = 0.9985, and the coefficient of determination is R = 0.997, i.e., the relationship between potato yield and experimental factors is quite close.

Spatial downscaling of satellite soil moisture products based on apparent thermal inertia: Considering the effect of vegetation condition

Many applications such as irrigation management and flood predictions require soil moisture (SM) estimates at fine-resolutions. Soil moisture estimates of current satellite missions are normally at coarse spatial resolutions, such as 25–36 km for Soil Moisture and Ocean Salinity (SMOS) SM data. Many spatial downscaling approaches have been developed to get finer resolution SM. Apparent thermal inertia (ATI), as a reliable approximation of thermal inertia (TI) that could be derived from daily fine-resolution optical/thermal remote sensing (RS) data, has been testified to be highly related to SM. However, the empirical relationship between ATI and SM varies with vegetation cover density. The main purpose of this study is to develop an ATI-based SM downscaling method that is free of vegetation cover effects. The study was conducted in Naqu areas in the central Tibetan Plateau (TP) in southwest China, where spatially intensive in-situ SM measurements were routinely obtained. European Space Agency’s Climate Change Initiative (ESA CCI) soil moisture combined product (version 4.2) was used for this study. The fine-resolution (1 km) ATI data were derived from the Collection 6 MODerate resolution Imaging Spectroradiometer (MODIS) products. A modified quantile-quantile (Q-Q) adjustment method was applied to correct the bias of the CCI SM data before downscaling. A non-linear regression model between bias-corrected CCI SM and fine resolution ATI was built to disaggregate the bias-corrected SM to spatial resolution of 1 km. In this model, Enhanced Vegetation Index (EVI) was introduced to globally and quantitatively adjust the vegetation effect in the relationship between SM and ATI. The bias-corrected CCI SM and the downscaled 1 km resolution SM were evaluated using the in-situ SM measurements obtained from the Multi-scale Soil Moisture and Temperature Monitoring Network on the central TP (CTP-SMTMN) in the study area. The results indicate that the proposed modified Q-Q adjustment method effectively reduced the bias of the original CCI SM data (bias = 0 m3/m3). The downscaled 1 km resolution SM shows fine scale spatial variability of soil moisture and preserves the accuracy (R = 0.552) and temporal variability of the bias-corrected CCI SM. The accuracies of the downscaled SM over areas with different vegetation cover show quite good consistency. The uncertainties of the proposed SM spatial downscaling method were comprehensively analyzed. The proposed ATI-based SM downscaling algorithm is applicable to generating representative and consistent high spatial resolution SM over areas with different vegetation cover density.

Retrieving Soil Moisture in the Permafrost Environment by Sentinel-1/2 Temporal Data on the Qinghai–Tibet Plateau

Soil moisture (SM) products presently available in permafrost regions, especially on the Qinghai–Tibet Plateau (QTP), hardly meet the demands of evaluating and modeling climatic, hydrological, and ecological processes, due to their significant bias and low spatial resolution. This study developed an algorithm to generate high-spatial-resolution SM during the thawing season using Sentinel-1 (S1) and Sentinel-2 (S2) temporal data in the permafrost environment. This algorithm utilizes the seasonal backscatter differences to reduce the effect of surface roughness and uses the normalized difference vegetation index (NDVI) and the normalized difference moisture index (NDMI) to characterize vegetation contribution. Then, the SM map with a grid spacing of 50 m × 50 m in the hinterland of the QTP with an area of 505 km × 246 km was generated. The results were independently validated based on in situ data from active layer monitoring sites. It shows that this algorithm can retrieve SM well in the study area. The coefficient of determination (R2) and root-mean-square error (RMSE) are 0.82 and 0.06 m3/m3, respectively. This study analyzed the SM distribution of different vegetation types: the alpine swamp meadow had the largest SM of 0.26 m3/m3, followed by the alpine meadow (0.23), alpine steppe (0.2), and alpine desert (0.16), taking the Tuotuo River basin as an example. We also found a significantly negative correlation between the coefficient of variation (CV) and SM in the permafrost area, and the variability of SM is higher in drier environments and lower in wetter environments. The comparison with ERA5-Land, GLDAS, and ESA CCI showed that the proposed method can provide more spatial details and achieve better performance in permafrost areas on QTP. The results also indicated that the developed algorithm has the potential to be applied in the entire permafrost regions on the QTP.

An assessment of L-band surface soil moisture products from SMOS and SMAP in the tropical areas

Recently, large efforts have been made to develop surface soil moisture (SSM) products based on observations from passive microwave L-band satellites at the global scale. Despite vast previous efforts to assess satellite SSM products based on in situ data, the performance of L-band SSM products remains still little known over the tropical areas including the rainforests. To close this knowledge gap, a comprehensive evaluation of five L-band SSM products from the Soil Moisture and Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP) satellites, including SMOS-IC, SMAP SCA-V, DCA, and MTDCA, as well as the recently developed SMAP INRAE-BORDEAUX (SMAP-IB) was conducted in tropical areas from 2015 to 2020. The investigation was implemented by using in situ observations, and expanded by the Triple Collocation Analysis (TCA) and double instrumental variable (IVd) methods. The results revealed that all the five L-band SSM products show a good capacity to estimate SSM in most moderately vegetated areas of the tropical areas while they exhibit some uncertainties in dense vegetation (e.g., rainforests). The SSM climatology of the five L-band products in rainforests agreed generally well with that of in situ measurements especially for SMOS-IC (R = 0.75), followed by SMAP-IB (R = 0.72). Notably, the newly developed SMAP-IB shows satisfactory performance in the tropics with the highest R of 0.73 and the smallest unbiased root mean square difference (ubRMSD) of 0.041 m3/m3 from the study based on in situ SM data, and R of >0.81 from the combined TCA/IVd method in most vegetation conditions. SMAP-DCA SSM demonstrates comparable performance to SMAP-SCA-V (R = 0.66) from the in situ based analysis. This study is expected to deepen our understanding on the skill of L-band SSM products in tropical areas and further promote possible upgrades in algorithms and applications in these regions.

Downscaling of SMAP Soil Moisture Data by Using a Deep Belief Network

The spatial resolution of current soil moisture (SM) products is generally low, consequently limiting their applications. In this study, a deep belief network-based method (DBN) was used to downscale the Soil Moisture Active Passive (SMAP) L4 SM product. First, the factors affecting soil surface moisture were analyzed, and the significantly correlated ones were selected as predictors for the downscaling model. Second, a DBN model was trained and used to downscale the 9 km SMAP L4 SM to 1 km in the study area on 25 September 2019. Validation was performed using original SMAP L4 SM data and in situ measurements from SM and temperature wireless sensor network with 34 sites. Finally, the DBN method was compared with another commonly used machine learning model-random forest (RF). Results showed that (1) the downscaled 1 km SM data are in good agreement with the original SMAP L4 SM data and field measured data, and (2) DBN has a higher correlation coefficient and a lower root mean square error than those of RF. The coefficients of determination for fitting the two models with the measured data at the site were 0.5260 and 0.4816, with relative mean square errors of 0.0303 and 0.0342 m3/m3, respectively. The study also demonstrated the applicability of the DBN method to AMSR SM data downscaling besides SMAP. The proposed method can provide a framework to support future hydrological modeling, regional drought monitoring, and agricultural research.

ВЛИЯНИЕ ПРЕДПОСЕВНОЙ ОБРАБОТКИ ПОЧВЫ НА УРОЖАЙНОСТЬ ЯРОВОЙ ПШЕНИЦЫ В УСЛОВИЯХ ЛЕСОСТЕПИ ПОВОЛЖЬЯ

The purpose of the research is to study the influence of early spring tillage techniques on the formation of spring wheat yield. The work was carried out in 2017-2019 in Ulyanovsk region. The soil area is represented by leached chernozem, medium-sized with a humus content of 6.22%, mobile phosphorus and potassium according to Chirikov – respectively 198 and 121 mg/kg, pHsol – 6.4 units, the sum of absorbed bases - 46.4 mg.-eq./100 g. The scheme of the field experiment provided for the study of the following options for pre-sowing tillage against the background of the use of N50P16K16: 1. Harrowing + cultivation; 2. Harrowing; 3. Cultivation. The soil density (1.10...1.15g/cm3) was found in favorable values for wheat, and did not worsen its condition. The reserves of productive moisture in the arable and meter layer of the soil before sowing were significantly higher than the harrowing option – 54.3 and 152.4 mm. The absence of pre-sowing cultivation did not affect the processes of nitrification in the soil. There is a tendency to a higher biological activity of the soil (33.3%) after harrowing. In general, the experimental plots were relatively clean of weeds, so there was no significant difference in the options. The yield of spring wheat in all variants of the experiment was almost the same 2.96...2.98 t/ ha. The lowest costs were observed on the harrowing option – 16614 rubles/ha, which was 6.1% lower than the classical pre-sowing tillage, and the cost of this option was 5508 rubles per ton of grain or 7.5% lower. The net income for the harrowing option amounted to 3,586 rubles per hectare, which is 55.7% higher than the classic option. The reduction in production costs led to an increase in profit and profitability of this option – 21.8%.