Multisource Remote Sensing Data Research Articles

Monitoring of Extreme Drought in the Yangtze River Basin in 2022 Based on Multi-Source Remote Sensing Data

The Yangtze River Basin experienced a once-in-a-century extreme drought in 2022 due to extreme weather, which had a serious impact on the local agricultural production and ecological environment. In order to investigate the spatial distribution and occurrence of the extreme drought events, this study used multi-source remote sensing data to monitor the extreme drought events in the Yangtze River Basin in 2022. In this study, the gravity satellite data product CSR_Mascon was used to calculate the GRACE Drought Intensity Index (GRACE-DSI), which was analyzed and compared with the commonly used meteorological drought indices, relative soil humidity, and soil water content data. The results show that (1) terrestrial water storage change data can well reflect the change in water storage in the Yangtze River Basin. Throughout the year, the average change in terrestrial water storage in the Yangtze River Basin from January to June is higher than the average value of 33.47 mm, and the average from July to December is lower than the average value of 48.17 mm; (2) the GRACE-DSI responded well to the intensity and spatial distribution of drought events in the Yangtze River Basin region in 2022. From the point of view of drought area, the Yangtze River Basin showed a trend of extreme drought increasing first, and then decreasing in the area of different levels of drought, and the range of drought reached a maximum in September with a drought area of 175.87 km2, which accounted for 97.71 per cent of the total area; at the same time, the area of extreme drought was the largest, with an area of 85.69 km2; (3) the spatial and temporal variations of the GRACE-DSI and commonly used meteorological drought indices were well correlated, with correlation coefficients above 0.750, among which the correlation coefficient of the SPEI-3 was higher at 0.937; (4) the soil moisture and soil relative humidity products from the CLDAS, combined with soil moisture products from the GLDAS, reflect the starting and ending times of extreme drought events in the Yangtze River Basin in 2022 well, using the information from the actual stations. In conclusion, gravity satellite data, analyzed in synergy with data from multiple sources, help decision makers to better understand and respond to drought.

Forest Structure Mapping of Boreal Coniferous Forests Using Multi-Source Remote Sensing Data

Forest Structure Mapping of Boreal Coniferous Forests Using Multi-Source Remote Sensing Data

Innovative Decision Fusion for Accurate Crop/Vegetation Classification with Multiple Classifiers and Multisource Remote Sensing Data

Obtaining accurate and real-time spatial distribution information regarding crops is critical for enabling effective smart agricultural management. In this study, innovative decision fusion strategies, including Enhanced Overall Accuracy Index (E-OAI) voting and the Overall Accuracy Index-based Majority Voting (OAI-MV), were introduced to optimize the use of diverse remote sensing data and various classifiers, thereby improving the accuracy of crop/vegetation identification. These strategies were utilized to integrate crop/vegetation classification outcomes from distinct feature sets (including Gaofen-6 reflectance, Sentinel-2 time series of vegetation indices, Sentinel-2 time series of biophysical variables, Sentinel-1 time series of backscatter coefficients, and their combinations) using distinct classifiers (Random Forests (RFs), Support Vector Machines (SVMs), Maximum Likelihood (ML), and U-Net), taking two grain-producing areas (Site #1 and Site #2) in Haixi Prefecture, Qinghai Province, China, as the research area. The results indicate that employing U-Net on feature-combined sets yielded the highest overall accuracy (OA) of 81.23% and 91.49% for Site #1 and Site #2, respectively, in the single classifier experiments. The E-OAI strategy, compared to the original OAI strategy, boosted the OA by 0.17% to 6.28%. Furthermore, the OAI-MV strategy achieved the highest OA of 86.02% and 95.67% for the respective study sites. This study highlights the distinct strengths of various remote sensing features and classifiers in discerning different crop and vegetation types. Additionally, the proposed OAI-MV and E-OAI strategies effectively harness the benefits of diverse classifiers and multisource remote sensing features, significantly enhancing the accuracy of crop/vegetation classification.

Drought Monitoring of Winter Wheat in Henan Province, China Based on Multi-Source Remote Sensing Data

Characterized by soil moisture content and plant growth, agricultural drought occurs when the soil moisture content is lower than the water requirement of plants. Microwave remote sensing observation has the advantages of all-weather application and sensitivity to soil moisture change. However, microwave remote sensing can only invert 0~5 cm of soil surface moisture, so it cannot effectively reflect the drought situation of farmland. Therefore, this study took Henan Province as the study area, used soil moisture active and passive (SMAP) satellite soil moisture data, employed NDVI, LST, and ET as the independent variables, and took the drought grade on the sample as the dependent variable. Using the 2017–2019 data as the training set and the 2020 data as the testing set, a random forest drought monitoring model with comprehensive influence of multiple factors was constructed based on the training set data. In the process of model training, the cross-validation method was employed to establish and verify the model. This involved allocating 80% of the sample data for model construction and reserving 20% for model verification. The results demonstrated an 85% accuracy on the training set and an 87% accuracy on the testing set. Additionally, two drought events occurring during the winter wheat growing period in Henan Province were monitored, and the validity of these droughts was confirmed using on-site soil moisture and the vegetation supply water index (VSWI). The findings indicated a high incidence of agricultural drought in the southwestern part of Henan Province, while the central and northern regions experienced a lower incidence during the jointing to heading and filling stages. Subsequently, leveraging the results from the random forest drought monitoring, this study conducted a time series analysis using the Mann–Kendall test and a spatial analysis employing Moran’s I index to examine the temporal and spatial distribution of agricultural drought in Henan Province. This analysis aimed to unveil trends in soil moisture changes affecting agricultural drought, as observed via the SMAP satellite (NASA). The results suggested a possible significant spatial auto-correlation in the occurrence of agricultural drought.

A Comparison of Multiple DEMs and Satellite Altimetric Data in Lake Volume Monitoring

Lake volume variation is closely related to climate change and human activities, which can be monitored by multi-source remote-sensing data from space. Although there are usually two routine ways to construct the lake volume by the digital elevation model (DEM) or satellite altimetric data combined with the lake area, rarely has a comparison been made between the two methods. Therefore, we conducted a comparison between the two methods in Texas for 14 lakes with abundant validation data. First, we constructed the lake hypsometric curve by five commonly applied DEMs (SRTM, ASTER, ALOS, GMTED2010, and NED) or satellite altimetric products combined with the gauge lake area. Second, the lake volume was estimated by combining the hypsometric curve with the gauge lake area time series. Finally, the estimation error has been quantitatively calculated. The results show that the relative lake volume estimation error (rVSD) of the altimetric data (4%) is only 10–18% of that of the DEMs (22–41%), and the DEM with the highest resolution (NED) has the least rVSD with an average of 22%. Therefore, for large-scale lake monitoring, we suggest the application of satellite altimetric data with the lake area to estimate the lake volume of large lakes, and the application of high-resolution DEM with the lake area to calculate the lake volume of small lakes that are gapped by satellite altimetric data.

A Statistical Model for Multisource Remote-Sensing Data Streams of Wildfire Aerosols Optimal Depth

Increasingly frequent wildfires have significant impact on solar energy production as the atmospheric aerosols generated by wildfires diminish the incoming solar radiation. Atmospheric aerosols can be measured by aerosol optical depth (AOD), and multiple spatiotemporal AOD data streams are available from geostationary satellites. Although these multisource remote-sensing data streams are measurements of the same underlying AOD process, they often present heterogeneous characteristics, such as different measurement biases and errors, data missing rates, etc. It is usually not known which data source provides more accurate measurements, and simply averaging multiple data streams is not always the best practice. Hence, this paper proposes a statistical model that is capable of inferring the underlying true AOD process by simultaneously integrating heterogeneous multisource remote-sensing data streams and the advection-diffusion equation that governs the dynamics of the AOD process. A bias correction process is included in the model to account for the bias of the physics model and the truncation error of the Fourier series. The proposed approach is applied to California wildfires AOD data obtained from the GOES East (GOES-16) and GOES West (GOES-17) satellites operated by the National Oceanic and Atmospheric Administration. Comprehensive numerical examples are provided to demonstrate the predictive capabilities and model the interpretability of the proposed approach. Funding: This work was funded by the U.S. National Science Foundation [Grant 2143695]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/ijds.2021.0058 . Data Ethics & Reproducibility Note: Our code requires packages that are not available on CodeOcean (for example, the Rfast package in R). Hence, code and sample data are provided on GitHub: https://github.com/gz-wei/Physics-Informed-Statistical-Modeling-for-Wildfire-Aerosols-Propagation .

Estimation of PM2.5 Concentration across China Based on Multi-Source Remote Sensing Data and Machine Learning Methods

Long-term exposure to high concentrations of fine particles can cause irreversible damage to people’s health. Therefore, it is of extreme significance to conduct large-scale continuous spatial fine particulate matter (PM2.5) concentration prediction for air pollution prevention and control in China. The distribution of PM2.5 ground monitoring stations in China is uneven with a larger number of stations in southeastern China, while the number of ground monitoring sites is also insufficient for air quality control. Remote sensing technology can obtain information quickly and macroscopically. Therefore, it is possible to predict PM2.5 concentration based on multi-source remote sensing data. Our study took China as the research area, using the Pearson correlation coefficient and GeoDetector to select auxiliary variables. In addition, a long short-term memory neural network and random forest regression model were established for PM2.5 concentration estimation. We finally selected the random forest regression model (R2 = 0.93, RMSE = 4.59 μg m−3) as our prediction model by the model evaluation index. The PM2.5 concentration distribution across China in 2021 was estimated, and then the influence factors of high-value regions were explored. It is clear that PM2.5 concentration is not only related to the local geographical and meteorological conditions, but also closely related to economic and social development.

Spatial and Temporal Variation Characteristics of Ecological Environment Quality in China from 2002 to 2019 and Influencing Factors

Since the beginning of the new century, there has been a notable enhancement in China’s ecological environment quality (EEQ), a development occurring in tandem with climate change and the extensive ecological restoration projects (ERPs) undertaken in the country. However, comprehensive insights into the spatial and temporal characteristics of China’s EEQ, and its responses to both climate change and human activities over the past two decades, have remained largely elusive. In this study, we harnessed a combination of multi-source remote-sensing data and reanalysis data. We employed Theil–Sen median trend analysis, multivariate regression residual analysis, and the Hurst index to examine the impacts and changing patterns of climatic factors and human activities on China’s EEQ during the past two decades. Furthermore, we endeavored to forecast the future trajectory of EEQ. Our findings underscore a significant improvement in EEQ across most regions of China between 2002 and 2019, with the most pronounced enhancements observed in the Loess Plateau, Northeast China, and South China. This transformation can be attributed to the combined influence of climatic factors and human activities, which jointly accounted for alterations in EEQ across 78.25% of China’s geographical expanse. Human activities (HA) contributed 3.93% to these changes, while climatic factors (CC) contributed 17.79%. Additionally, our projections indicate that EEQ is poised to continue improving in 56.70% of China’s territory in the foreseeable future. However, the Loess Plateau, Tarim Basin, and Inner Mongolia Plateau are anticipated to experience a declining trend. Consequently, within the context of global climate change, the judicious management of human activities emerges as a critical imperative for maintaining EEQ in China. This study, bridging existing gaps in the literature, furnishes a scientific foundation for comprehending the evolving dynamics of EEQ in China and informs the optimization of management policies in this domain.

Assessing the Current and Future Potential Distribution of Solanum rostratum Dunal in China Using Multisource Remote Sensing Data and Principal Component Analysis

Accurate information concerning the spatial distribution of invasive alien species’ habitats is essential for invasive species prevention and management, and ecological sustainability. Currently, nationwide identification of suitable habitats for the highly destructive and potentially invasive weed, Solanum rostratum Dunal (S. rostratum), poses a series of challenges. Simultaneously, research on potential future invasion areas and likely directions of spread has not received adequate attention. This study, based on species occurrence data and multi-dimensional environmental variables constructed from multi-source remote sensing data, utilized Principal Component Analysis (PCA) in combination with the Maxent model to effectively model the current and future potential habitat distribution of S. rostratum in China, while quantitatively assessing the various factors influencing its distribution. Research findings indicate that the current suitable habitat area of S. rostratum covers 1.3952 million km2, all of which is located in northern China. As the trend of climate warming persists, the potential habitat suitability range of S. rostratum is projected to shift southward and expand in the future; while still predominantly located in northern China, it will have varying degrees of expansion at different time frames. Notably, during the period from 2040 to 2061, under the SSP1-2.6 scenario, the habitat area exhibits the most significant increase, surpassing the current scenario by 19.23%. Furthermore, attribution analysis based on PCA inverse transformation reveals that a combination of soil, climate, spatial, humanistic, and topographic variables collectively influence the suitability of S. rostratum habitats, with soil factors, in particular, playing a dominant role and contributing up to 75.85%. This study identifies target areas for the management and control of S. rostratum, providing valuable insights into factor selection and variable screening methods in species distribution modeling (SDM).

A Decision Rule and Machine Learning-Based Hybrid Approach for Automated Land-Cover Type Local Climate Zones (LCZs) Mapping Using Multi-Source Remote Sensing Data

A Decision Rule and Machine Learning-Based Hybrid Approach for Automated Land-Cover Type Local Climate Zones (LCZs) Mapping Using Multi-Source Remote Sensing Data

Tightly Coupled Tomography Model for Atmospheric Water Vapor Based on Multisource Remote-Sensing and GNSS Data

Tightly Coupled Tomography Model for Atmospheric Water Vapor Based on Multisource Remote-Sensing and GNSS Data

Multiscale Spectral–Spatial Attention Residual Fusion Network for Multisource Remote Sensing Data Classification

Multiscale Spectral–Spatial Attention Residual Fusion Network for Multisource Remote Sensing Data Classification

Multifrequency Graph Convolutional Network With Cross-Modality Mutual Enhancement for Multisource Remote Sensing Data Classification

Multifrequency Graph Convolutional Network With Cross-Modality Mutual Enhancement for Multisource Remote Sensing Data Classification

Characterizing the Water Storage Variation of Kusai Lake by Constructing Time Series from Multisource Remote Sensing Data

In September 2011, Zhuonai Lake (ZL) in the northeast of Hoh Xil (HX) on the Qinghai–Tibet Plateau (QTP) broke out. The outburst event seriously changed the environmental hydraulics in this region. Due to the insufficient temporal resolution of observations, it is challenging to assess the impact of this event on short-period variations of water volumes in three lakes downstream of ZL. Combining multisource remote sensing data, we constructed long and high-temporal-resolution time series for the lake level, area, and lake water storage (LWS) of Kusai Lake (KL) to characterize the variabilities before and after the outburst. The water level, area, and LWS time series contain 1051 samples from 1990 to 2022, with uncertainties of 0.16 m, 2.5 km2, and 0.016 km3, respectively. The accuracies verified using the Database for Hydrological Time Series of Inland Waters (DAHITI) are 0.26 m, 2.64 km2, and 0.08 km3 for water level, area, and LWS, respectively. We characterized the LWS variations during the past 30 years based on the high temporal resolution LWS time series. Before the outburst, the 1-year and 3.5-year variations dominated the LWS time series, and there was no obvious semi-annual signal. After the outburst, the 3.5-year variation disappeared, and a strong semi-annual oscillation was observed. From 2012 to 2015, the periodic LWS variations in KL were disturbed by the ZL outburst and the subsequent outflow of KL led by the outburst. Regular cyclic signals have been restored since 2016, with an amplified annual fluctuation. By analysis, precipitation, evaporation, and glacier area change are excluded as driving factors of the pattern change in LWS variations of KL. It can be concluded that the altered recharge pattern of KL triggered by the outburst directly resulted in the observed changes in TWS behavior. For the first time, we identified the periodic patterns of LWS variations of KL during the past 30 years and revealed that the ZL outburst event significantly influenced these patterns. This finding contributes to the comprehensive understanding of the effects of the ZL outburst on downstream lake dynamics. Furthermore, the presented procedure for constructing long and high-resolution time series of LWS allows for monitoring and characterizing the short-period variabilities of Tibetan lakes that lack hydrological data.

A Robust Index Based on Phenological Features to Extract Sugarcane from Multisource Remote Sensing Data

Sugarcane is a major crop for sugar and biofuel production. Historically, mapping large sugarcane fields meticulously depended heavily on gathering comprehensive and representative training samples. This process was time-consuming and inefficient. Addressing this drawback, this study proposed a novel index, the Normalized Difference Vegetation Index (NDVI)-Based Sugarcane Index (NBSI). NBSI analyzed the temporal variation of sugarcane’s NDVI over a year. Leveraging the distinct growth phases of sugarcane (transplantation, tillering, rapid growth and maturity) four measurement methodologies, f(W1), f(W2), f(V) and f(D), were developed to characterize the features of the sugarcane growth period. Utilizing imagery from Landsat-8, Sentinel-2, and MODIS, this study employed the enhanced gap-filling (EGF) method to reconstruct NDVI time-series data for seven counties in Chongzuo, Guangxi Zhuang Autonomous Region, during 2021, subsequently testing NBSI’s ability to extract sugarcane. The results demonstrate the efficiency of NBSI with simple threshold settings: it was able to map sugarcane cultivation areas, exhibiting higher accuracy when compared to traditional classifiers like support vector machines (SVM) and random forests (RF), with an overall accuracy (OA) of 95.24% and a Kappa coefficient of 0.93, significantly surpassing RF (OA = 85.31%, Kappa = 0.84) and SVM (OA = 85.87%, Kappa = 0.86). This confirms the outstanding generalizability and robustness of the proposed method in Chongzuo. Therefore, the NBSI methodology, recognized for its flexibility and practicality, shows potential in enabling the extensive mapping of sugarcane cultivation. This heralds a new paradigm of thought in this field.

Ecological Security Assessment of “Grain-for-Green” Program Typical Areas in Northern China Based on Multi-Source Remote Sensing Data

The Inner Mongolia segment of the Yellow River basin (IMYRB) is a typical area for ecological restoration in China. At the end of the 20th century, influenced by climate and human activities, such as mining, grazing, and farmland abandonment, the ecological security of the IMYRB was under more significant pressure. To alleviate the pressure on natural ecosystems and improve the fragile ecological situation, China implemented the “Grain-for-Green” (GFG) project in 1999. However, the evolutionary characteristics of the ecological security of the IMYRB in the first two decades of the 21st century are still lacking. Quantitative and long-term ecological security information of “Grain-for-Green” is needed. Based on this, this study used the “Pressure (P)-State (S)-Response (R)” method and proposed an ecological security assessment and early warning system based on multi-source remote sensing data. The evaluation results indicated a significant improvement in ecological security in the IMYRB from 2000 to 2020. Compared to 2000, the ecological security of the IMYRB had improved significantly in 2020, with an increase of 11.02% (ES > 0.65) and a decrease of 8.89% (ES < 0.35). For the early warning aspect of ecological security, there was a 26.31% growth in non-warning areas, with a 5% decrease in warning areas. Based on the analysis of ecologically critical factors, we proposed the implications for future ecological management as follows. (1) In ecologically fragile areas such as the IMYRB, continued implementation of the GFG was necessary. (2) Vegetation restoration should be scientific and tailored adaptive. (3) The protection of arable land also showed necessity. (4) The grazing management skills should be upgraded. Our study demonstrated that the ecological benefits derived from the “GFG” project are not immediate but cumulative and persistent. The continuous implementation of “GFG” will likely alleviate the pressure exerted by human activities on the natural environment.

NCGLF[formula omitted]: Network combining global and local features for fusion of multisource remote sensing data

The fusion of multisource remote sensing (RS) data has demonstrated significant potential in target recognition and classification tasks. However, there is limited emphasis on capturing both high- and low-frequency information from these data sources. Additionally, effectively integrating multisource data remains a challenging task, as the absence of redundancy and discriminant information hampers the applications of RS data. In this paper, we propose a fusion network called network combining global and local features (NCGLF2) that integrates global and local features (GLF) extracted from multisource RS data. This approach effectively leverages the capabilities of convolutional neural networks (CNNs) to extract high frequency features while utilizing transformer architecture to replicate low frequency information and remote correlations. Firstly, a scale information aggregation (SIA) module extracts multiscale shallow layer features from the input data sources. Secondly, a structural information learning transformer (SIL-Trans) module captures low frequency features, while an invertible neural network (INN) module learns high frequency information. Finally, a GLF fusion module maximizes the complementary characteristics of multisource RS data and GLF to effectively fuse high- and low-frequency information. Our experimental results with three benchmark datasets indicate that NCGLF2 outperforms existing state-of-the-art approaches in terms of feature representation and compatibility with diverse data types. The code is available at https://github.com/renqi1998/NCGLF2.

Estimation of sugarcane evapotranspiration from remote sensing and limited meteorological variables using machine learning models

Rapid and accurate crop evapotranspiration (ETc) estimation is essential for water resource management of irrigated lands. Nevertheless, due to the significant uncertainties in the inputs of conventional ETc equations and the inaccessibility of their proper spatial and temporal distribution values, precision agriculture requires a more accurate evaluation of ETc using efficient methods. Combining remote sensing (RS) data and machine learning (ML) techniques has provided a considerable capacity for estimating ETc, which can address these challenges. This study aims to develop a framework for ETc estimation based on the four most popular tree-based ML algorithms, namely M5-pruned (M5P), random forest regression (RFR), gradient-boosted regression trees (GBRT), and extreme gradient boosting (XGBoost). For this purpose, multisource RS data and meteorological and ground measurements (Meteo-GM) were used during three growing seasons (2018–2021) in irrigated croplands of the Sugarcane & By-Products Development Company in Khuzestan, southwest Iran. Since the unavailability of optical sensors, e.g., Landsat-8 data, in cloudy conditions restricts their enduring application, an alternative approach employing the Sentinel-1 Synthetic Aperture Radar (SAR) data for ETc estimation was also proposed. A set of 23 scenarios of various unique combinations of the available parameters was developed to ensure that a proper decision could be made for ETc estimation under any conditions. Eventually, the contribution of the input variables to estimate ETc was evaluated using the Sobol sensitivity analysis. The results showed that the RFR algorithm provided the most accurate ETc estimation in all scenarios, followed by the XGBoost, GBRT, and M5P algorithms (R2 = 0.92–0.99, RMSE = 2.02–0.32 mm d-1). The Meteo-GM models’ performances were improved by incorporating optical and thermal infrared (TIR) (R2 = 0.99, RMSE = 0.32 mm d-1) and SAR (R2 = 0.98, RMSE = 0.65 mm d-1) RS data. In addition, Sobol’s sensitivity analysis revealed that maximum temperature, wind speed, pan evaporation, leaf area index, moisture stress index, and inverse dual-pol diagonal distance were the most influential input variables in the ETc estimation. This study can be used as a reference to simplify the ETc calculations using a novel application of ML as a helpful basis for precision irrigation management from the point to the regional scale in any weather conditions.

Quantitative Assessment of Factors Influencing the Spatiotemporal Variation in Carbon Dioxide Fluxes Simulated by Multi-Source Remote Sensing Data in Tropical Vegetation

Vegetation plays a vital role in the global carbon cycle, a function of particular significance in regulating carbon dioxide fluxes within tropical ecosystems. Therefore, it is crucial to enhance the precision of carbon dioxide flux estimates for tropical vegetation and to explore the determinants influencing carbon sequestration. In this study, Landsat series images and Sentinel-2 Multispectral Instrument satellite data were used to invert vegetation biophysical parameters, thereby improving the timeliness and resolution of state variables from the boreal ecosystem productivity simulator (BEPS). The BEPS model at a 30 m resolution was developed to accurately capture tropical vegetation carbon dioxide fluxes across Hainan Island (HN) over the preceding two decades. The impacts of climate variations and anthropogenic activities on the carbon dioxide fluxes of tropical vegetation were further quantified using quantile regression models and a land-use transfer matrix. Results indicate significant increases in both net primary productivity (NPP) and net ecosystem productivity (NEP) in HN during the period 2000–2020, by 5.81 and 4.29 g C/m2 year, respectively. Spatial trends in vegetation carbon dioxide fluxes exhibited a consistent decline from inland regions to coastal zones. Anthropogenic activities were the dominant factor in the reduced stability of coastal NPP, while the post-2005 vegetation restoration promoted the southward expansion of high NPP (>1200 g C/m2) in the central part of HN. NPP in this tropical island was more sensitive to temperature than to precipitation, with a 1 °C temperature increase resulting in 4.1 g C/m2 reduction in dry-season NPP compared to wet-season NPP. Upgrades of cropland quality and grassland restoration have improved NPP yields, and land use transfers have resulted in a 0.301 Tg C net increase in NPP. This study provides new insight into the improvement of the carbon dioxide flux model at a finer scale for tropical vegetation and highlights ecological construction as an adaptation strategy to enhance the carbon sinks of tropical vegetation under negative climate change conditions.

Identification of Surface Deformation-Sensitive Features under Extreme Rainfall Conditions in Zhengzhou City Based on Multi-Source Remote Sensing Data

Extreme precipitation is one of the most prevalent meteorological disasters occurring today. Its occurrence not only causes significant social and economic losses but also indirectly affects surface deformation, creating safety hazards for diverse ground features. Although there are presently high-precision, comprehensive tools such as continuous scattering interferometry to observe surface deformation, it takes a long time to locate potentially vulnerable objects. A monitoring scheme for surface deformation anomalies was devised to address the timeliness issue of identifying sensitive surface features under extreme rainfall conditions. An SAR image of Sentinel-1A is used to derive the surface deformation in three years before and after a rainstorm in the main urban area of Zhengzhou, and the anomaly surface deformation objects after extreme precipitation are screened to determine the surface deformation-sensitive objects. The results indicate that, in the past three years, a 22.14 km2 area in Zhengzhou City has experienced a settlement speed greater than 10 mm/yr. Under the influence of the “7–20” rainstorm in the main urban area of Zhengzhou City, among them, the area of highly sensitive agricultural land for deformation is 2,581,215 m2, and there are 955 highly sensitive houses for deformation, with an excellent recognition effect. This method is effective in rapidly locating surface deformation-sensitive or potentially damaged features; it can provide a reference for the vulnerability and risk assessment of buildings.