Research on soil moisture inversion method based on GA-BP neural network model

Purpose In order to analyze the text complexity of Chinese and foreign academic English writings, the artificial neural network (ANN) under deep learning (DL) is applied to the study of text complexity. Firstly, the research status and existing problems of text complexity are introduced based on DL. Secondly, based on Back Propagation Neural Network (BPNN) algorithm, analyzation is made on the text complexity of Chinese and foreign academic English writings. And the research establishes a BPNN syntactic complexity evaluation system. Thirdly, MATLAB2013b is used for simulation analysis of the model. The proposed model algorithm BPANN is compared with other classical algorithms, and the weight value of each index and the model training effect are further analyzed by statistical methods. Finally, L2 Syntactic Complexity Analyzer (L2SCA) is used to calculate the syntactic complexity of the two libraries, and Mann–Whitney U test is used to compare the syntactic complexity of Chinese English learners and native English speakers. The experimental results show that compared with the shallow neural network, the deep neural network algorithm has more hidden layers and richer features, and better performance of feature extraction. BPNN algorithm shows excellent performance in the training process, and the actual output value is very close to the expected value. Meantime, the error of sample test is analyzed, and it is found that the evaluation error of BPNN algorithm is less than 1.8%, of high accuracy. However, there are significant differences in grammatical complexity among students with different English writing proficiency. Some measurement methods cannot effectively reflect the types and characteristics of written language, or may have a negative relationship with writing quality. In addition, the research also finds that the measurement of syntactic complexity is more sensitive to the language ability of writing. Therefore, BPNN algorithm can effectively analyze the text complexity of academic English writing. The results of the research provide reference for improving the evaluation system of text complexity of academic paper writing. Design/methodology/approach In order to analyze the text complexity of Chinese and foreign academic English writings, the artificial neural network (ANN) under deep learning (DL) is applied to the study of text complexity. Firstly, the research status and existing problems of text complexity are introduced based on DL. Secondly, based on Back Propagation Neural Network (BPNN) algorithm, analyzation is made on the text complexity of Chinese and foreign academic English writings. And the research establishes a BPNN syntactic complexity evaluation system. Thirdly, MATLAB2013b is used for simulation analysis of the model. The proposed model algorithm BPANN is compared with other classical algorithms, and the weight value of each index and the model training effect are further analyzed by statistical methods. Finally, L2 Syntactic Complexity Analyzer (L2SCA) is used to calculate the syntactic complexity of the two libraries, and Mann–Whitney U test is used to compare the syntactic complexity of Chinese English learners and native English speakers. The experimental results show that compared with the shallow neural network, the deep neural network algorithm has more hidden layers and richer features, and better performance of feature extraction. BPNN algorithm shows excellent performance in the training process, and the actual output value is very close to the expected value. Meantime, the error of sample test is analyzed, and it is found that the evaluation error of BPNN algorithm is less than 1.8%, of high accuracy. However, there are significant differences in grammatical complexity among students with different English writing proficiency. Some measurement methods cannot effectively reflect the types and characteristics of written language, or may have a negative relationship with writing quality. In addition, the research also finds that the measurement of syntactic complexity is more sensitive to the language ability of writing. Therefore, BPNN algorithm can effectively analyze the text complexity of academic English writing. The results of the research provide reference for improving the evaluation system of text complexity of academic paper writing. Findings In order to analyze the text complexity of Chinese and foreign academic English writings, the artificial neural network (ANN) under deep learning (DL) is applied to the study of text complexity. Firstly, the research status and existing problems of text complexity are introduced based on DL. Secondly, based on Back Propagation Neural Network (BPNN) algorithm, analyzation is made on the text complexity of Chinese and foreign academic English writings. And the research establishes a BPNN syntactic complexity evaluation system. Thirdly, MATLAB2013b is used for simulation analysis of the model. The proposed model algorithm BPANN is compared with other classical algorithms, and the weight value of each index and the model training effect are further analyzed by statistical methods. Finally, L2 Syntactic Complexity Analyzer (L2SCA) is used to calculate the syntactic complexity of the two libraries, and Mann–Whitney U test is used to compare the syntactic complexity of Chinese English learners and native English speakers. The experimental results show that compared with the shallow neural network, the deep neural network algorithm has more hidden layers and richer features, and better performance of feature extraction. BPNN algorithm shows excellent performance in the training process, and the actual output value is very close to the expected value. Meantime, the error of sample test is analyzed, and it is found that the evaluation error of BPNN algorithm is less than 1.8%, of high accuracy. However, there are significant differences in grammatical complexity among students with different English writing proficiency. Some measurement methods cannot effectively reflect the types and characteristics of written language, or may have a negative relationship with writing quality. In addition, the research also finds that the measurement of syntactic complexity is more sensitive to the language ability of writing. Therefore, BPNN algorithm can effectively analyze the text complexity of academic English writing. The results of the research provide reference for improving the evaluation system of text complexity of academic paper writing. Originality/value In order to analyze the text complexity of Chinese and foreign academic English writings, the artificial neural network (ANN) under deep learning (DL) is applied to the study of text complexity. Firstly, the research status and existing problems of text complexity are introduced based on DL. Secondly, based on Back Propagation Neural Network (BPNN) algorithm, analyzation is made on the text complexity of Chinese and foreign academic English writings. And the research establishes a BPNN syntactic complexity evaluation system. Thirdly, MATLAB2013b is used for simulation analysis of the model. The proposed model algorithm BPANN is compared with other classical algorithms, and the weight value of each index and the model training effect are further analyzed by statistical methods. Finally, L2 Syntactic Complexity Analyzer (L2SCA) is used to calculate the syntactic complexity of the two libraries, and Mann–Whitney U test is used to compare the syntactic complexity of Chinese English learners and native English speakers. The experimental results show that compared with the shallow neural network, the deep neural network algorithm has more hidden layers and richer features, and better performance of feature extraction. BPNN algorithm shows excellent performance in the training process, and the actual output value is very close to the expected value. Meantime, the error of sample test is analyzed, and it is found that the evaluation error of BPNN algorithm is less than 1.8%, of high accuracy. However, there are significant differences in grammatical complexity among students with different English writing proficiency. Some measurement methods cannot effectively reflect the types and characteristics of written language, or may have a negative relationship with writing quality. In addition, the research also finds that the measurement of syntactic complexity is more sensitive to the language ability of writing. Therefore, BPNN algorithm can effectively analyze the text complexity of academic English writing. The results of the research provide reference for improving the evaluation system of text complexity of academic paper writing.

Soil moisture is one of the critical variables in maintaining the global water cycle balance. Moreover, it plays an essential role in climate change, crop growth, and environmental disaster event monitoring, and continuous monitoring of soil moisture is of great significance. The inversion of soil moisture detection technique using Global Navigation Satellite System Satellite-based Reflectance Signal (GNSS-R) to obtain high accuracy soil moisture is a hot topic of current research. To address the recent study, it only considers a limited number of variables related to the Cyclone Global Navigation Satellite System (CYGNSS). Still, it does not consider the effect of real-time variables rainfall and temperature on CYGNSS. To this end, this paper uses the GA-BP neural network model to obtain soil moisture by combining soil moisture data from ground stations with CYGNSS data-related variables, surface environmental data, rainfall, and air temperature. Analysis of the effect of meteorological data rainfall and temperature on the inversion of soil moisture. The experimental results show that the GA-BP neural network model with rainfall and temperature can better describe the correlation between multi-source variables and soil moisture with R of 0.9821 and RMSE of 0.0206 cm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> /cm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> . Rainfall and temperature have contributed benefits to GNSS-R soil moisture inversion.

Cross-cultural learning competence, a critical skill in our globally interconnected world, is advanced through the application of the Backpropagation (BP) neural network algorithm. This innovative approach involves leveraging neural network techniques to model and enhance individuals' abilities to navigate and understand diverse cultural contexts. The BP neural network algorithm facilitates personalized learning experiences by adapting to individuals' cultural backgrounds and preferences. This research explores a comprehensive approach for assessing and enhancing cross-cultural learning competence among Chinese college students, integrating the Word Embedding Multilingual Model with the Back Propagation Neural Network (WEMM-BPNN) algorithm. Recognizing the importance of global competencies in higher education, our study focuses on leveraging advanced neural network techniques to evaluate and elevate students' cross-cultural learning abilities. The WEMM-BPNN model combines the power of word embedding and multilingual considerations, tailoring the learning experience to individual cultural backgrounds. Through a meticulous analysis of cross-cultural data and linguistic patterns, the algorithm refines its recommendations for personalized learning strategies. The research aims not only to assess the current state of cross-cultural learning competence but also to provide targeted interventions to enhance students' intercultural understanding and adaptability. By merging linguistic models with neural network algorithms, this study offers a pioneering approach to cultivating cross-cultural competencies, contributing valuable insights to the ongoing discourse on globalized education.

Although vegetation community information such as grazing gradient, biomass, and density have been well characterized in typical grassland communities with Stipa grandis and Leymus chinensis as dominant species, their impact on the soil moisture (SM) inversion is still unclear. This study investigated the characteristics of a grassland vegetation community at different grazing gradients and growing seasons and its impact on SM inversion using remote sensing data. The water cloud model (WCM) was used for SM inversion, and both field and remote sensing data collected from 2019 to 2021 were used for calibration and prediction. The study found that the calibrated WCM achieved prediction results of SM inversion with average R2 values of 0.41 and 0.38 at different grazing gradients and growing seasons, respectively. Vegetation biomass and height were significantly correlated with vegetation indexes, and the highest model prediction accuracy was achieved for biomass and height around 121.1 g/m2 [102.3–139.9] and 18.6 cm [17.3–19.8], respectively. Generally, NDWI1 produced higher SM estimation accuracy than NDWI2. The growing season of vegetation also affects the accuracy of the WCM to retrieve SM, with the highest accuracy achieved in mid-growing season I. Therefore, the developed WCM with optimal height and biomass of vegetation communities can enhance the SM prediction capacity; it thus can be potentially used for SM prediction in typical grasslands.

Forecasting and alert of atmospheric bioaerosol concentration profile based on adaptive genetic algorithm back propagation neural network, atmospheric parameter and fluorescence lidar

Rapid and unplanned urbanization leads to temperature rise, urban vegetation decrease and built-up land increase, forming an urban heat island (UHI). This study investigated the effects of changes in vegetation and built-up land on land surface temperature (LST) in summer, based on remotely sensed images. LST was first retrieved by means of the Radiative Transfer Model (RTM). Scatter-plots and an univariate linear regression model (ULRM) were first employed to independently measure the influence of NDVI on LST, and of NDBI on LST, respectively. In order to assess the effects of changes in vegetation and built-up land on LST, a multivariate linear regression model (MLRM) was finally employed to improve the accuracy of the predicted model in the identification of the joint effect of both the normalized difference vegetation index (NDVI) and the normalized difference built-up index (NDBI) on LST. The result from the case from the Hanoi Metropolitan Area (HMA), Vietnam using Landsat-5 TM and Landsat-8 OLI/TIRS time-series images during the 1996-2016 period shows that there exists a negative effect of built-up land and a positive effect of vegetation on LST. In addition, indications of intensifying UHI effects were detected in the HMA, especially tending to expand faster and wider to the parts of western, north-western and south-western HMA during the 1996-2007 period. These findings suggest that vegetation weakens the effect of UHIs, whereas, built-up land greatly strengthens the effect of UHIs in the HMA.

By utilizing the data observed at Dunhuang during August and September 2000 in the “Field Experiment on Interaction between Land and Atmosphere in the Arid Region of Northwest China (FEILARNC)”, the characteristics of the soil moisture, temperature, and atmospheric humidity are analyzed. It is found that the thickness of the soil temperature active layer is about 5 cm and much thinner than is typical, that not only the atmospheric humidity gradient is often inverted but also the soil moisture gradient in the shallow layer in the Gobi near oasis, that the diurnal variation of soil moisture can be divided into the four stages that are called the wet stage, the losing-water stage, the dry stage, and the attaining-water stage. It is shown that in soil moisture profiles, the depth of the soil moisture active layer is about 10 cm and soil moisture inversion is the main feature in the shallow layer during the wet stage. Such a feature as soil moisture inversion indicates that soil in the shallow layer can inhale moisture from the air through condensation in the nighttime and exhale moisture to the air through evaporation in the daytime. The condensation and evaporation constitute together the full respiration process of moisture on the ground. The formation of soil moisture inversion is related with the state of soil temperature and moisture, the intensity of atmospheric humidity inversion, and the atmospheric thermodynamic stability.

Inversing soil moisture from remote sensing data is difficult for the problem is usually nonlinear and ill-posed.To enhance the accuracy of this inversion problem and reduce the effect of surface roughness, a least square-support machine (LS-SVM) based inversion approach is used to retrieve the soil moisture from the radar backscattering coefficients.Starting from the generation of data set by using Integral Equation Model (IEM), wide range of soil moisture and surface roughness are simulated.The kernel and capacity parameter of LS-SVM are optimized through the training process.Then, to assess the effectiveness of the proposed approach, testing data added with Gaussian distributed noise is processed by the suitably defined model.Concerning the robustness of the approach, selected training data is applied when the model is established, and the soil moisture is inversed again.Along this process, the comparison between BP neural network and LS-SVM based method is conducted.

The accuracy of soil moisture retrieval based on traditional microwave remote sensing models in the Qinghai Tibet Plateau (QTP) is unstable due to its unique plateau climate. However, considering the impact of multiple multi-scale factors effectively improves the accuracy and stability of soil moisture inversion. This article uses Sentinel-1 and seasonal climate data to analyze factors and influencing mechanisms of soil moisture in the QTP. First, an artificial neural network (ANN) was used to conduct a significance analysis to screen significant influencing factors to reduce the redundancy of the experimental design and insert information. Second, the normalization effect of each factor on the soil moisture inversion was determined, and the factors with significant normalization influences were input to fit the model. Third, different fitting methods combined the semi-empirical models for soil moisture inversion. The decision tree Chi-square Automatic Interaction Detector (CHAID) analyzed the model accuracy, and the Pearson correlation coefficient between the sample and measured data was tested to further validate the accuracy of the results to obtain an optimized model that effectively inverts soil moisture. Finally, the influencing mechanisms of various factors in the optimization model were analyzed. The results show that: (1) The terrain factors, such as elevation, slope gradient, aspect, and angle, along with climate factors, such as temperature and precipitation, all have the greatest normalized impact on soil moisture in the QTP. (2) For spring (March), summer (June), and autumn (September), the greatest normalized factor of soil moisture is the terrain factor. In winter (December), precipitation was the greatest factor due to heavy snow cover and permafrost. (3) Analyzing the impact mechanism from various factors on the soil moisture showed a restricted relationship between the inversion results and the accuracy of the power fitting model, meaning it is unsuitable for general soil moisture inversion. However, among the selected models, the accuracy of the linear fit was generally higher than 79.2%, the Pearson index was greater than 0.4, and the restricted relationship between the inversion results and accuracy was weak, making it suitable for the general inversion of soil moisture in the QTP.

Pavement cracks are an important hidden danger for driving safety. Rapid and accurate detection of road crack types and effective treatment can greatly reduce the incidence of traffic accidents, which has important practical significance. Based on the advantages of scale invariant feature transform (SIFT) and back-propagation neural network (BP) algorithm, this paper proposes a method of road crack detection and recognition for four types of cracks, such as cracks, longitudinal cracks, transverse cracks and massive cracks.Firstly, the crack image is denoised and enhanced; Then scale invariant feature transform method is used to extract the feature point information of the route crack image; Finally, Back propagation neural network is used for training and recognition. The results show that this method can quickly and accurately identify the crack type, in which the accuracy of training set is 98.79%, and the accuracy of recognition set is 86.47%. The results show that the combination of scale invariant feature transform and back propagation neural network algorithm can effectively improve the efficiency of pavement crack type detection.

Analyzing the Regional Soil-Vegetation-Atmosphere Interaction Using Both the Eagleson and Budyko's Water Balance Models

Predicting the harvest time of wheat in large areas is important for guiding the scheduling of wheat combine harvesters and reducing losses during harvest. In this study, Zhumadian, Zhengzhou and Anyang, the main winter-wheat-producing areas in Henan province, were selected as the observation points, and the main producing areas were from south to north. Based on Landsat 8 satellite remote sensing images, the changes in NDVI (Normalized Difference Vegetation Index), EVI (Enhanced Vegetation Index), and NDWI (Normalized Difference Water Index) were analyzed at different growth stages of winter wheat in 2020. Multiple regression analysis and Back Propagation (BP) neural network machine learning methods were used to establish prediction models for the harvest time of winter wheat at different growth stages. The results showed that the prediction model based on a BP neural network had high accuracy. The RMSE, MAE and MAPE of the training set and the test set were 0.531 and 0.5947, 0.3001 and 0.3104, 0.0114% and 0.0119%, respectively. The prediction model of winter wheat harvest date based on BP neural network was verified in the main winter wheat producing areas of Henan province in 2020 and 2021. The average errors were 1.67 days and 2.13 days, which were less than 3 days, meeting the needs for winter wheat production and harvest. The grain water content of winter wheat at harvest time calculated by the prediction model reached the grain water standard of the wheat combine harvester. Therefore, the prediction of the winter wheat harvest time can be realized based on multiple remote sensing indicators.

Assessment of calibration methods for nitrogen estimation in wet and dry soil samples with different wavelength ranges using near-infrared spectroscopy

PDF HTML阅读 XML下载 导出引用 引用提醒 1988-2013年基于BP神经网络的植被叶面积指数遥感定量反演 DOI: 10.5846/stxb201703290547 作者: 作者单位: 南京林业大学,南京林业大学,南京林业大学,南京林业大学,南京林业大学,南京林业大学 作者简介: 通讯作者: 中图分类号: 基金项目: 国家重点研发计划资助项目（2017YFC0505505）；国家自然科学基金项目（31200534）；江苏高校优势学科建设工程资助项目；2014年江苏省普通高校学术学位研究生科研创新计划项目 Quantitative inversion of long sequential leaf area index using remote sensing based on BP neural network from 1988 to 2013 Author: Affiliation: Key Laboratory of Soil and Water Conservation and Ecological Restoration in Jiangsu Province,Collaborative Innovation Center of Sustainable Forestry in Southern China of Jiangsu Province,Nanjing Forestry University,Nanjing,Key Laboratory of Soil and Water Conservation and Ecological Restoration in Jiangsu Province,Collaborative Innovation Center of Sustainable Forestry in Southern China of Jiangsu Province,Nanjing Forestry University,Nanjing,Key Laboratory of Soil and Water Conservation and Ecological Restoration in Jiangsu Province,Collaborative Innovation Center of Sustainable Forestry in Southern China of Jiangsu Province,Nanjing Forestry University,Nanjing,Key Laboratory of Soil and Water Conservation and Ecological Restoration in Jiangsu Province,Collaborative Innovation Center of Sustainable Forestry in Southern China of Jiangsu Province,Nanjing Forestry University,Nanjing,Key Laboratory of Soil and Water Conservation and Ecological Restoration in Jiangsu Province,Collaborative Innovation Center of Sustainable Forestry in Southern China of Jiangsu Province,Nanjing Forestry University,Nanjing,Key Laboratory of Soil and Water Conservation and Ecological Restoration in Jiangsu Province,Collaborative Innovation Center of Sustainable Forestry in Southern China of Jiangsu Province,Nanjing Forestry University,Nanjing Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:叶面积指数（Leaf Area Index，LAI）高度综合了植被水平覆盖状况和垂直结构，以及枯枝落叶层厚薄和地下生物量多少，是植被影响土壤侵蚀的主要方面。区域尺度的时间序列叶面积指数揭示了区域土壤侵蚀的演化过程。因此，及时准确地掌握区域尺度上长时间序列的植被LAI，对研究土壤侵蚀动态变化与植被的关系至关重要。选择南京市1988-2013年10期遥感影像，基于反向传播（Back Propagation，BP）神经网络构建LAI反演模型，进行了长时间序列的叶面积指数反演。结合2009和2010年LAI实测值，验证与探讨了该模型的评价精度与适应性。结果表明：（1）该模型拟合度较高，2009和2010年平均相对误差、均方根误差、相关系数分别是0.2395和0.2174，0.2962和0.2581，0.7713和0.6844，各项精度评价指标均较好；（2）统计分析去除耕地后全市LAI变化，低植被覆盖（LAI<2）面积不断增加，高植被覆盖区（LAI>3）面积先减少后增加，耕地面积不断减少，符合南京市的发展变化规律；（3）主城区LAI年际变化与其他学者得到的南京市植被盖度变化趋势一致，反演结果的时序性较高。本文提出的基于反向传播神经网络模型反演长时间序列LAI是可行的，为区域尺度土壤侵蚀定量遥感监测提供新途径。 Abstract:Leaf Area Index(LAI) can reflect the horizontal coverage, vertical structure of vegetation, the thickness of the litter layer and the amount of underground biomass, which is the main aspect of vegetation affecting soil erosion. It is very important to monitor the changes in the amount of soil erosion, for useful information to guide the planning of soil and water conservation, protect the soil and water resources and control the soil erosion. Therefore, the method by which we obtain high quality and long sequential LAI at a regional scale is very important for analyzing the relationship between the dynamic changes in soil erosion and vegetation. Previous studies showed that the neural network had an incomparable superiority in terms of complex, nonlinear data fitting and pattern recognition, and had been successfully applied to inverse the LAI in Nanjing based on the multi-spectral remote sensing data derived from the Landsat 8 Operational Land Imager(OLI), four types of vegetation indices(Normalized Difference Vegetation Index, NDVI; Ratio Vegetation Index, RVI; Soil-adjusted Vegetation Index, SAVI; Modified Soil adjusted Vegetation Index, MSAVI), and measured LAI data. The results showed that the accuracy of retrieval was good. In this paper, we used the Back Propagation(BP) neural network model to inverse the LAI in Nanjing during 1988-2013 based on the data derived from Landsat 8 OLI and Landsat 5 Thematic Mapper(TM). Based on the measured values of LAI in 2009 and 2010, the evaluation accuracy and adaptability of the model were verified and discussed. The results showed that:(1) The model had a fitting of higher degree, and average relative errors(MAPE), root mean square errors(RMSE), and correlation coefficients(R) in 2009 and 2010 of 0.2395 and 0.2174, 0.2962 and 0.2581, and 0.7713 and 0.6844, respectively. Each accuracy evaluation index was good.(2) Following statistical analysis, we found that the low vegetation coverage area(LAI < 2) exhibited an increasing trend, the high vegetation coverage area(LAI > 3) presented a first decreasing and then increasing trend, while the cultivated land area decreased with the rapid development in Nanjing.(3) To analyze accurately the LAI, we extracted the LAI in the main urban area, and found that there was a relatively high inversion value, and the inter-annual change in LAI was consistent with the change in vegetation coverage in Nanjing reported by other studies. Therefore, we could see that the BP neural network model had a high accuracy for the time series LAI inversion. It provides a new way for quantitative remote sensing monitoring of regional soil erosion. Moreover, because of other potential limiting factors, such as the errors produced by the BP neural network model, the large area of the inversion area, the complexity of vegetation types and community structure, etc., the inversion accuracy of LAI through remote sensing still needs to be explored, and the inversion method improved. We will try to establish a multi-angle LAI inversion method to construct the coupling model of LAI and soil erosion or quantitative fusion of multi-source remote sensing images in the future study. 参考文献 相似文献 引证文献

Soil moisture (SM) inversion using Global Navigation System Reflectometry techniques has garnered significant attention in recent years. However, current Cyclone Global Navigation Satellite System (CYGNSS) SM products generally suffer from low spatial resolution, which limits their application in regional and local scale studies. Therefore, this study proposed a SM downscaling method based on multi-model fusion, aimed at enhancing the spatial resolution of CYGNSS data from 36 km to 3 km. The method integrates CYGNSS observations, SM active passive (SMAP) (36 km) SM data, and auxiliary environmental variables to construct four individual machine learning models: random forest, light gradient boosting machine, categorical boosting, and gradient boosting trees as well as the Stacking fusion model. The results of each model downscaling SM comparison with International SM Network stations show that the Stacking model outperforms the single model in correlation coefficient (R), root mean square error, and mean absolute error, while simultaneously exhibiting superior inversion accuracy. In the spatial correlation analysis using moderate resolution imaging spectroradiometer enhanced vegetation index and VIIRS land surface temperature products, the Stacking model results have the highest correlation and more detailed spatial distribution. In addition, the Stacking model downscaled SM also showed good spatial consistency and accuracy in the comparison and verification with SMAP/Sentinel-1 SM (3 km). The results show that the Stacking model, which integrates multiple machine learning algorithms, significantly improves the spatial resolution of the CYGNSS SM products and effectively improves the inversion accuracy, which is of certain reference value for the development of high-resolution SM products.