Surface Soil Moisture Research Articles

Inversion of Farmland Soil Moisture Based on Multi-Band Synthetic Aperture Radar Data and Optical Data

Surface soil moisture (SSM) plays an important role in agricultural and environmental systems. With the continuous improvement in the availability of remote sensing data, satellite technology has experienced widespread development in the monitoring of large-scale SSM. Synthetic Aperture Radar (SAR) and optical remote sensing data have been extensively utilized due to their complementary advantages in this field. However, the limited information from single-band SARs or single optical remote sensing data has restricted the accuracy of SSM retrieval, posing challenges for precise SSM monitoring. In contrast, multi-source and multi-band remote sensing data contain richer and more comprehensive surface information. Therefore, a method of combining multi-band SAR data and employing machine learning models for SSM inversion was proposed. C-band Sentinel-1 SAR data, X-band TerraSAR data, and Sentinel-2 optical data were used in this study. Six commonly used feature parameters were extracted from these data. Three machine learning methods suitable for small-sample training, including Genetic Algorithms Back Propagation (GA-BP), support vector regression (SVR), and Random Forest (RF), were employed to construct the SSM inversion models. The differences in SSM retrieval accuracy were compared when two different bands of SAR data were combined with optical data separately and when three types of data were used together. The results show that the best inversion performance was achieved when all three types of remote sensing data were used simultaneously. Additionally, compared to the C-band SAR data, the X-band SAR data exhibited superior performance. Ultimately, the RF model achieved the best accuracy, with a determinable coefficient of 0.9186, a root mean square error of 0.0153 cm3/cm3, and a mean absolute error of 0.0122 cm3/cm3. The results indicate that utilizing multi-band remote sensing data for SSM inversion offers significant advantages, providing a new perspective for the precise monitoring of SSM.

Differentiable modeling for soil moisture retrieval by unifying deep neural networks and water cloud model

Machine learning has been widely used in high-spatial-resolution surface soil moisture (SSM) retrieval studies, but in recent years, this purely data-driven retrieval method has been controversial due to its lack of physical interpretability and generalization ability. Physical retrieval models based on the theory of radiative transfer equations respect physical laws, but their retrieval accuracy is usually affected by many insufficient accurate inputs, the complex model structure, and parameter adjustment method. In order to explore the retrieval method of unifying these two types of models, in this study, a differentiable model (DM) was constructed to realize the soil moisture retrieval at 10 m resolution based on Sentinel data. The differentiable soil moisture retrieval model takes the water cloud model (WCM) as the skeleton, and united the WCM and neural networks by implementing differentiable programming of the WCM in a machine learning platform. The differentiability makes the retrieval model trained by the gradient descent method the same as the neural network, which allows the retrieval model to be physical while obtaining more accurate retrieval results. Luan River Basin, Shandian River Basin, Maqu, and Lake Tahoe study areas with various land cover types and climate types were selected for model evaluation, and the performances of DM were close to that of the random forest model with Pearson correlation coefficient (R) of 0.747, 0.853, 0.838 and 0.792 in four study areas, respectively. While in the assessment of extrapolation capability of retrieval models, the DM showed its strong generalization ability and retrieval performance that exceeded that of the other retrieval models, with R of 0.786, unbiased root mean square error (ubRMSE) of 5.523 vol% and bias of 0.054 vol%. The DM synthesizes the advantages of both physical and machine learning models while providing high-resolution SSM estimates with acceptable accuracy for the study areas. This study creates favorable conditions for the realization of large-scale soil moisture retrieval with high resolution and high accuracy, and provides new ideas for the combination of machine learning and physical knowledge in other retrieval studies.

Enhancing water-carbon fluxes and yield predictions of winter wheat using irrigation and data assimilation techniques in a land surface model

Croplands are essential to land–atmosphere interactions and have a great impact on water balance, carbon fluxes, regional weather and climate, and food supply. This study utilized the Noah-MP land surface model, enhanced by a dynamic crop growth process known as Noah-MP-Crop, and employed a multi-pass data assimilation scheme (MLDAS) to assimilate leaf area index, surface soil moisture, and solar-induced chlorophyll fluorescence into the model for refining model’s simulations of land state variables (soil moisture and leaf biomass) and optimize the model vegetation parameters [specific leaf area (SLA) and maximum carboxylation rate normalized to 25 °C (Vcmax25)]. In conjunction with the irrigation module in this model, four experiments were conducted at the local observation-site scale to estimate and validate water-carbon fluxes [actual evapotranspiration (ETa) and gross primary productivity (GPP)] and crop yield within the winter wheat growing phase, which included open-loop simulation with and without irrigation (OLirr and OL), data assimilation with and without irrigation (DAirr and DA), separately. In-situ observations from the Yucheng Station in 2006 were used to evaluate these four experiments. Results indicated that the combined experiment (DAirr) provided the best agreement with the observed ETa, GPP, and yield. This superior performance is mainly attributed to the accurate vegetation information and soil moisture profile provided by the combined experiments with the model. Results indicated that the DAirr experiment performed better than the OL experiment in ETa estimation, with a higher correlation coefficient (R) of 0.85 compared to 0.71, a reduced root mean square error (RMSE) from 1.51 to 1.06 mm day−1, a lower normalized RMSE (NRMSE) from 43.21 % to 23.39 %, and a decreased mean absolute error (MAE) from 1.16 to 0.78 mm day−1. Similarly, DAirr increased R from 0.71 to 0.87 and cut down RMSE (MAE) from 5.81 (4.32) to 3.48 (2.26) g m−2 day−1 and NRMSE from 70.39 % to 18.20 % for GPP estimation. Moreover, winter wheat yield from DAirr (770.9 g m−2) agreed better with ground measurements (819.7 g m−2) than that from OL (400.6 g m−2). Additionally, results revealed that assimilating all three variables simultaneously is superior to single-variable assimilation and significantly outperforms the OL approach. All in all, this study has provided initial insights into the feasibility of assimilating three variables at the site scale into the model for improving the estimation accuracy of water-carbon fluxes and winter wheat yield, which laid a foundation for subsequent regional simulations.

Soil Moisture, Soil NOx and Regional Air Quality in the Agricultural Central United States

AbstractAgricultural soils containing nitrogen‐rich fertilizers are a substantial source of reactive nitrogen to the atmosphere with potential to impact air quality. One form of reactive nitrogen, nitrogen oxides (NOx = NO + NO2), are a harmful air pollutant and form secondary pollutants, including particulate matter (PM) and ozone (O3). Soil nitrogen oxide emissions (SNOx) are heavily influenced by environmental conditions, however the understanding of the influence of environmental drivers on the behavior of SNOx is limited. Here, we implement a modified soil moisture‐dependent SNOx parameterization into the Weather Research and Forecasting model coupled with Chemistry (WRF‐Chem) and investigate the impact on regional air quality in the central U.S. Evaluating against TROPOspheric Monitoring Instrument (TROPOMI) column NO2 observations, WRF‐Chem columns better capture the TROPOMI column magnitudes earlier in the growing season when using the updated SNOx parametrization, with modeled column bias improved to −1.1% over the most heavily fertilized regions. Evaluating against Environmental Protection Agency (EPA) surface NO2 observations, the relationship between surface NO2 and soil moisture is better represented in agriculturally‐dominant regions when using the updated parameterization, with greatest surface NO2 concentrations at moderate soil moisture and lower concentrations at wetter or drier soil conditions. In simulations, these SNOx lead to increased O3 in select urban regions, with more than double the occurrences of O3 exceeding the EPA 8‐hr O3 standard of 70 ppb.

European vineyards and their cultural landscapes exposed to record drought and heat

CONTEXTEuropean vineyards, producing over 50% of the world's wine and hosting significant cultural landscapes, face threats from climate change and related severe weather events. The summer of 2022, especially July, posed significant challenges due to extreme drought and high temperatures. However, this period also provided an opportunity to study how such events might involve viticulture in Europe and to explore mitigation solutions. OBJECTIVEThe objectives are (1) to characterize the severity of the extreme event of July 2022 in European wine regions regarding primary climatic parameters, (2) map vineyards at risk due to agricultural drought and high land surface temperature, and (3) discuss the role of various water-related interventions for mitigating similar events. METHODSAfter identifying the locations of European vineyards using the Corine Land Cover 2018 (CLC2018), open-access satellite data were employed to: (1) assess anomalies in Maximum Air Temperature (NATm), Land Surface Temperature (LST), Precipitation (P), and Soil Moisture (SM) in July 2022 compared to long-term averages; (2) identify regions at higher risk that experienced extreme agricultural drought (Vegetation Health Index, VHI = Extreme) and LST > 35 °C. RESULTS AND CONCLUSIONSIn July 2022, European vineyards experienced an average increase of 11% in NATm, a 9% rise in LST, and a reduction of 47% in P and 30% in SM compared to historical averages. 18% of European vineyards were at risk of drought and excessive heat, particularly in Portugal (31%), France (27%), and Italy (21%), including 10 viticultural cultural landscapes. Findings highlight the urgent need for long-term sustainable water management practices over emergency interventions. This research supports informed decision-making, emphasizing that climate resilience is necessary for preserving the cultural heritage of European wine-growing areas. SIGNIFICANCEThis research provides an overview of the dynamics of extreme events on viticulture at a continental scale, promoting climate-aware viticultural systems and offering insight scalable to global viticulture under changing climatic conditions.

Insights on agricultural nitrate leaching from soil block mesocosms.

Quantifying nitrate leaching in agricultural fields is often complicated by inability to capture all water draining through a specific area. We designed and tested undisturbed soil monoliths (termed "soil block mesocosms") to achieve complete collection of drainage. Each mesocosm measures 1.5m ×1.5m ×1.2 m and is enclosed by steel on the sides and bottom with a single outlet to collect drainage. We compared measurements from replicate mesocosms planted to corn (Zea mays L.) with a nearby field experiment with tile-drained plots ("drainage plots"), and with drainage from nearby watersheds from 2020 through 2022 under drought conditions. Annual mesocosm drainage volumes were 6.5-24.6cm greater than from the drainage plots, likely because the mesocosms were isolated from the subsoil and could not store groundwater below the drain depth, whereas the drainage plots accumulated infiltration as groundwater. Thus, we obtained consistent nitrate leaching measurements from the mesocosms even when some drainage plots yielded no water. Despite drainage volume differences, mean flow-weighted nitrate concentrations were similar between mesocosms and drainage plots in 2 of 3 years. Mesocosm annual drainage volume was 8.7cm lower to 16.7cm higher than watershed drainage, likely due to lagged influences of groundwater. Corn yields were lower in mesocosms than drainage plots in 2020, but with irrigation, yields were similar in subsequent years. Mean 2020 surface soil moisture and temperature were similar between the mesocosms and nearby fields. Based on these comparisons, the mesocosms provide a robust method to measure nitrate leaching with lower variability than field plots.

Global critical soil moisture thresholds of plant water stress

During extensive periods without rain, known as dry-downs, decreasing soil moisture (SM) induces plant water stress at the point when it limits evapotranspiration, defining a critical SM threshold (θcrit). Better quantification of θcrit is needed for improving future projections of climate and water resources, food production, and ecosystem vulnerability. Here, we combine systematic satellite observations of the diurnal amplitude of land surface temperature (dLST) and SM during dry-downs, corroborated by in-situ data from flux towers, to generate the observation-based global map of θcrit. We find an average global θcrit of 0.19 m3/m3, varying from 0.12 m3/m3 in arid ecosystems to 0.26 m3/m3 in humid ecosystems. θcrit simulated by Earth System Models is overestimated in dry areas and underestimated in wet areas. The global observed pattern of θcrit reflects plant adaptation to soil available water and atmospheric demand. Using explainable machine learning, we show that aridity index, leaf area and soil texture are the most influential drivers. Moreover, we show that the annual fraction of days with water stress, when SM stays below θcrit, has increased in the past four decades. Our results have important implications for understanding the inception of water stress in models and identifying SM tipping points.

Using the AIEM and Radarsat-2 SAR to Retrieve Bare Surface Soil Moisture

Taking the Jiefangzha irrigation area of the Inner Mongolia Autonomous Region as the research area, the response relationships between the backscattering coefficient and radar frequency, radar incidence angle, root-mean-square height, correlation length, and soil water content under different conditions were simulated using advanced integral equations. The backscattering characteristics of exposed surfaces in cold and dry irrigation areas were discussed, and the reasons for the different effects were analyzed. Based on this, surface roughness models and statistical regression moisture inversion models were constructed through co-polarized backscatter coefficients and combined surface roughness. The correlation between the inverted surface roughness values and the measured values was R2 = 0.7569. The correlation between the soil moisture simulation values and the measured values was R2 = 0.8501, with an RMSE of 0.04. The findings showed a strong correlation between the values from the regression simulation and the measured data, indicating that the model can be applied to soil moisture inversion and has a good inversion accuracy. Compared with previous studies in the same area, the inversion model proposed in this paper has a higher accuracy and is more suitable for the inversion of soil moisture in the Jiefangzha irrigation area. These findings can support research on the water cycle and water environment assessment in the region.

Deciphering the Biophysical Impact of Permafrost Greening on Summer Surface Offset

AbstractSatellite observations have shown widespread greening during the last few decades over the northern permafrost region, but the impact of vegetation greening on permafrost thermal dynamics remains poorly understood, hindering the understanding of permafrost‐vegetation‐climate feedbacks. Summer surface offset (SSO), defined as the difference between surface soil temperature and near‐surface air temperature in summer (June‐August), is often predicted as a function of surface thermal characteristics for permafrost modeling. Here we examined the impact of leaf area index (LAI), detected by satellite as a proxy to permafrost vegetation dynamics, on SSO variations from 2003 to 2021 across the northern permafrost region. We observed latitude‐ and biome‐dependent patterns of SSO changes, with a pronounced increase in Siberian shrublands and a decrease in Tibetan grasslands. Based on partial correlation and sensitivity analyses, we found a strong LAI signal (∼30% of climatic signal) on SSO with varying elevation‐ and canopy height‐dependent patterns. Positive correlations or sensitivities, that is, increases in LAI lead to higher SSO, were distributed in relatively cold and wet areas. Biophysical effects of permafrost greening on surface albedo, evapotranspiration, and soil moisture (SM) could link the connection between LAI and SSO. Increased LAI substantially reduced surface albedo and enhanced evapotranspiration, influenced energy redistribution, and further controlled interannual variability of SSO. We also found contrasting effects of LAI on surface SM, consequently leading to divergent impacts on SSO. The results offer a fresh perspective on how greening affects the thermal balance and dynamics of permafrost, which is enlightening for improved permafrost projections.

Spatio‐Temporal Machine Learning for Regional to Continental Scale Terrestrial Hydrology

AbstractIntegrated hydrologic models can simulate coupled surface and subsurface processes but are computationally expensive to run at high resolutions over large domains. Here we develop a novel deep learning model to emulate subsurface flows simulated by the integrated ParFlow‐CLM model across the contiguous US. We compare convolutional neural networks like ResNet and UNet run autoregressively against our novel architecture called the Forced SpatioTemporal RNN (FSTR). The FSTR model incorporates separate encoding of initial conditions, static parameters, and meteorological forcings, which are fused in a recurrent loop to produce spatiotemporal predictions of groundwater. We evaluate the model architectures on their ability to reproduce 4D pressure heads, water table depths, and surface soil moisture over the contiguous US at 1 km resolution and daily time steps over the course of a full water year. The FSTR model shows superior performance to the baseline models, producing stable simulations that capture both seasonal and event‐scale dynamics across a wide array of hydroclimatic regimes. The emulators provide over 1,000× speedup compared to the original physical model, which will enable new capabilities like uncertainty quantification and data assimilation for integrated hydrologic modeling that were not previously possible. Our results demonstrate the promise of using specialized deep learning architectures like FSTR for emulating complex process‐based models without sacrificing fidelity.

Variational Assimilation of the SMAP Surface Soil Moisture Retrievals into an Integrated Urban Land Model

Variational Assimilation of the SMAP Surface Soil Moisture Retrievals into an Integrated Urban Land Model

Improved Representation of Vegetation Soil Moisture Coupling Enhances Soil Moisture Data Assimilation in Water‐Limited Regimes: A Case Study Over Texas

AbstractAssimilating remotely sensed surface soil moisture (SSM) into land surface models (LSM) is widely used to improve model representations of soil moisture (SM). However, the efficacy of SSM data assimilation (DA) has been found to be limited, particularly in resolving root‐zone soil moisture (RZSM). This study investigates how the representation of vegetation phenology, modulates the efficacy of SSM DA in enhancing the realism of RZSM simulations. To this end, two sets of climatological leaf area index (LAI) are implemented in Noah‐MP LSM over the state of Texas: (a) Noah‐MP default based on long‐term MODIS observations, (b) an alternative LAI adapted from AVHRR products. The former are found to exhibit conspicuous phase errors whereas the latter are more consistent with observed seasonal cycle. Two sets of DA experiments were performed accordingly, wherein SMAP L3 SSM is assimilated into Noah‐MP equipped with each LAI product from 2015 to 2019, Validation of the resulting products against in‐situ data reveals that (a) using the AVHRR‐based LAI, the Noah‐MP outperforms the baseline in reproducing the dynamics of RZSM, and the outperformance is particularly evident over the warm season and water‐stressed western Texas; (b) using the alternative LAI enhances the ability of DA to improve the accuracy of Noah‐MP RZSM, and to a lesser extent, SSM; and (c) gains in SM attained through improvement of LAI and application of DA is most pronounced over regions featuring tight vertical SM coupling. Additional model mechanistic limitations that need to be overcome to improve efficacy of DA are discussed.

Numerical Estimation of Surface Soil Moisture by Machine Learning Algorithms in Different Climatic Types

Numerical Estimation of Surface Soil Moisture by Machine Learning Algorithms in Different Climatic Types

Construction and Validation of Surface Soil Moisture Inversion Model Based on Remote Sensing and Neural Network

Surface soil moisture (SSM) reflects the dry and wet states of soil. Microwave remote sensing technology can accurately obtain regional SSM in real time and effectively improve the level of agricultural drought monitoring, and it is of great significance for agricultural precision irrigation and smart agriculture construction. Based on Sentinel-1, Sentinel-2, and Landsat-8 images, the effect of vegetation was removed by the water cloud model (WCM), and SSM was retrieved and validated by a radial basis function (RBF) neural network model in bare soil and vegetated areas, respectively. The normalized difference vegetation index (NDVI) calculated by Landsat-8 (NDVI_Landsat-8) had a better effect on removing the influence the of vegetation layer than that of NDVI_Sentinel-2. The RBF network model, established in a bare area (R = 0.796; RMSE = 0.029 cm3/cm3), and the RBF neural network model, established in vegetated areas (R = 0.855; RMSE = 0.024 cm3/cm3), have better simulation effects on SSM than a linear SSM inversion model with single polarization. The introduction of surface parameters to the RBF neural network model can improve the accuracy of the model and realize the high-accuracy inversion of SSM in the study area.

Feature Variable Selection Based on VIS-NIR Spectra and Soil Moisture Content Prediction Model Construction

The hydrological cycle, surface energy balance, and the management of water resources are all significantly impacted by soil moisture. Because it governs the physical processes of evapotranspiration and rainfall penetration, surface soil moisture is a significant climatic variable. In this work, visible-near infrared (VIS-NIR) bands were used to compare and analyze the spectra of loess samples with varying moisture concentrations. The investigation looked at how changes in the soil moisture content impacted the response of the soil spectra. The researchers used a genetic algorithm (GA), interval combination optimization (ICO), and competitive adaptive reweighted sampling (CARS) to filter feature variables from full-band spectral data. To forecast the moisture content of loess on the soil surface, models like partial least squares regression (PLSR), support vector machine (SVM), and random forest (RF) were created. The findings indicate that: (1) the most reliable spectrum preprocessing technique is the first derivative (FD), which can significantly enhance the model’s prediction power and spectral characteristic information. (2) The feature band selection method’s prediction effect of soil moisture content is typically superior to that of full-spectrum data. (3) The random forest (RF) prediction model for soil moisture content with the highest accuracy was built by combining the genetic algorithm (GA) with the FD preprocessed spectra. The results may provide a new understanding on how to use VIS-NIR to measure soil moisture content.

Ensembles of multiple models for soil moisture retrieval from remote sensing data over agricultural areas: A deep learning-based framework

In agricultural areas, most surface soil moisture (SM) retrieval models are unstable in terms of their accuracy and performance during crop growth. As a result, there is no consensus on which model performs optimally during the agricultural season. This is because of the uncertainties associated with model physics, initial conditions, model inversion processes, input data, vegetation attenuation and soil characteristics. To better deal with these practical concerns, we propose a simple, but robust SM retrieval method of using combination of multiple models based on deep learning and multi-model ensemble approach, called the DL-MME method, which makes use of the ‘collective intelligence’ and ‘wisdom of crowds’ concepts. The advantages of this method are: (1) robustness to model selection, and (2) robustness to model calibration during the growing season. In addition, this method is less dependent on one type of data across various agricultural areas compared to the single model approach. Firstly, the coupled water cloud model (WCM) and soil backscattering models (Oh model or advanced integral equation model (AIEM)) with different vegetation descriptors were calibrated and validated during the growing season in sugarcane and winter wheat fields for Sentinel-1 backscattering coefficients (VV and VH). SM was also retrieved by employing the trapezoid model (OPTRAM) with different parameters from Sentinel-2 images. To optimize SM retrieval computations, we used the outputs from optical and SAR models, auxiliary features, and reliable in situ SM measurements as inputs to a deep learning convolutional neural network (DL-CNN). For sugarcane and wheat fields in the early stages of crop growth, WCM models retrieved more accurate time-series SM than optical models. OPTRAM soil moisture retrievals showed greater accuracy in the late crop growing season. Time-series SM retrieval accuracy using DL-MME was higher than for the optical and semi-empirical SAR models. According to the results of the in situ validation for wheat (sugarcane) fields, the minimum MAE by an optimal combination of models was around 0.01 (0.02) m3m−3 (RMSE = 0.036 (0.074) m3m−3; R = 0.87 (0.71)). The findings demonstrate that our method is reliable and feasible for SM retrieval. Additionally, our method provides a way to select an optimal model for retrieving time-series SM during the crop growing season.

Modeling the impact of measured and projected climate and management systems on agricultural fields: Surface runoff, soil moisture, and soil erosion.

As global climate change poses a challenge to crop production, it is imperative to prioritize effective adaptation of agricultural systems based on a scientific understanding of likely impacts. In this study, we applied an integrated watershed modeling framework to examine the impacts of projected climate on runoff, soil moisture, and soil erosion under different management systems in Central Oklahoma. The proposed model uses measured climate data and three downscaled ensembles from the Coupled Model Intercomparison Project Phase 6 (CMIP6) at the water resources and erosion watershed to understand the impact of climate change and various climate conditions under three management systems: (1) continuous winter wheat (Triticum aestivum) under conventional tillage (WW-CT; baseline system), (2) continuous winter wheat under no-till (WW-NT), and (3) cool and warm season forage cover crop mixes under no-till (CC-NT). The study indicates that the occurrence of agricultural drought is projected to increase while erosion rates will remain unchanged under the WW-CT. In contrast, climate simulations imposed on the WW-NT and CC-NT systems significantly reduce runoff and sediment while preserving soil moisture levels. Especially, implementing the CC-NT system can bolster food security and foster sustainable farming practices in Central Oklahoma in the face of a changing climate.

Surface soil moisture from combined active and passive microwave observations: Integrating ASCAT and SMAP observations based on machine learning approaches

The fusion of active and passive microwave measurements is expected to provide more robust surface soil moisture (SSM) mapping across various environmental conditions compared to the use of a single sensor. Thus, the integration of the newest L-band passive (i.e., Soil Moisture Active Passive, SMAP) and the active (i.e., the Advanced Scatterometer, ASCAT) observations provides an opportunity for SSM mapping with improved accuracy. However, this integration remains largely underexplored. In this context, the integration of SMAP brightness temperature (TB) and ASCAT backscattering coefficients for global-scale SSM estimation was investigated, by fully considering the potential error sources in conventional radiative transfer models (RTMs) as well as other SSM linked factors. Based on ground measurements from globally distributed dense networks with mitigated mismatch issues and spatial/temporal independent evaluation strategies, this study: (i) comprehensively evaluated four classical machine learning approaches, including Random Forest (RF), Long-Short Term Memory (LSTM), Support Vector Machine (SVM), and Cascaded Neural Network (CNN), and chose the best performing RF method to implement the final integration of SSM; (ii) compared the integration retrievals to those made using data from a single sensor (SMAP or ASCAT) with the same machine learning framework, as well as to the SMAP passive, ASCAT active, and ESA CCI active-passive combined SSM products. The results show the integration retrievals achieve satisfactory performance by obtaining an averaged unbiased root mean squared error (ubRMSE) of 0.042 m3/m3 and a temporal correlation of 0.756, which are superior to machine learning based SSM estimated from a single active or passive sensor, and also outperform the SMAP, ASCAT, and ESA CCI products. Moreover, the temporal resolution is evidently improved compared to the SMAP and ASCAT SSM products, with a temporal ratio exceeding 60% for most areas across the globe. Therefore, blending active and passive measurements affords a more reliable SSM mapping with increased sampling at the global scale, and could contribute to improved hydro-ecological applications.

Response of Soil Moisture to Four Rainfall Regimes and Tillage Measures under Natural Rainfall in Red Soil Region, Southern China

Investigating the effects of natural rainfall on the soil moisture for sloping farmland is extremely important for comprehending a variety of hydrological processes. Rainfall regimes can elicit different responses to soil moisture at the depth of soil layers, and the responses may differ depending on the landscape position of the sloping farmland. This paper utilized the surface runoff natural rainfall and soil moisture measured on sloping farmland with conventional tillage and contour tillage in the red soil area of China to investigate the influence of natural rainfall events on soil moisture content and to evaluate if the response results were consistent across the four rainfall regimes. Natural rainfall events were classified into different four regimes in line with rainfall duration, rainfall amount, and the maximum 30 min rainfall intensity (I30) by the k-means clustering method, including advanced, intermediate, uniform and delayed regimes. The result showed that the advanced regime was the predominant one in the study area, which represented 45.9% of the total rainfall events. The rainfall regimes influenced the surface runoff coefficient and runoff depth on sloping farmland for conventional and contour tillage, with the uniform regime generating the highest runoff coefficient (5.20% and 5.82%) and runoff depth (2.00% and 2.05%), respectively. For the conventional tillage, soil moisture at a depth of 0–20 cm increased appreciably when the rainfall amount was larger than 20 mm and larger than 30 mm for the advanced regime. For the contour tillage, soil moisture in 20–40 cm layer increased for the advanced regime when the rainfall amount was larger than 15 mm and 30 mm, while the values were modified with rainfall duration and the antecedent dry day (ADD). A longer rainfall duration had a more obvious effect on soil moisture increasing in deep soil. At a depth of 20 cm, the ADD showed an appreciably positive correlation with the increment in soil moisture, which means that a brief ADD produces a compounding effect for a natural rainfall event. The present results underline the complexity of the effect of the rainfall events on soil moisture under two tillage measures for different rainfall regimes and suggest that the advanced regime significantly affects the distinction of soil moisture and the contour tillage can effectively reduce soil water loss and enhance water storage on the sloping farmland with red soil, presenting a theoretical basis for local soil and water conservation research and sloping farmland conservation tillage management.

Diagnosing drivers of PM2.5 simulation biases in China from meteorology, chemical composition, and emission sources using an efficient machine learning method

Abstract. Chemical transport models (CTMs) are widely used for air pollution modeling, which suffer from significant biases due to uncertainties in simplified parameterization, meteorological fields, and emission inventories. Accurate diagnosis of simulation biases is critical for the improvement of models, interpretation of results, and management of air quality, especially for the simulation of fine particulate matter (PM2.5). In this study, an efficient method with high speed and a low computational resource requirement based on the tree-based machine learning (ML) method, the light gradient boosting machine (LightGBM), was designed to diagnose CTM simulation biases. The drivers of the Community Multiscale Air Quality (CMAQ) model biases are compared to observations obtained by simulating PM2.5 concentrations from the perspectives of meteorology, chemical composition, and emission sources. The source-oriented CMAQ was used to diagnose the influences of different emission sources on PM2.5 biases. The model can capture the complex relationship between input variables and simulation bias well; meteorology, PM2.5 components, and source sectors can partially explain the simulation bias. The CMAQ model underestimates PM2.5 by −19.25 to −2.66 µg m−3 in 2019, especially in winter and spring and during high-PM2.5 events. Secondary organic components showed the largest contribution to the PM2.5 simulation bias for different regions and seasons (13.8 %–22.6 %) of all components. Relative humidity, cloud cover, and soil surface moisture were the main meteorological factors contributing to PM2.5 bias in the North China Plain, Pearl River Delta, and northwestern China, respectively. Primary and secondary inorganic components from residential sources showed the two largest contributions to this bias (12.05 % and 12.78 %), implying large uncertainties in this sector. The ML-based methods provide valuable complements to traditional-mechanism-based methods for model improvement, with high efficiency and low reliance on prior information.