Hierarchical Extraction and Multi-Feature Optimization of Complex Crop Planting Structures in the Hetao Irrigation District Based on Multi-Source Remote Sensing Data

Urbanization and expansion in each city of emerging countries have become an essential function of Earth’s surface, with the majority of people migrating from rural to urban regions. The various urban category characteristics have emphasized the great importance of understanding and creating suitable land evaluations in the future. The overall objective of this study is to classify the urban zone utilizing building height which is estimated using Sentinel-1 synthetic aperture radar (SAR) and various satellite-based indexes of Sentinel-2A. The first objective of this research is to estimate the building height of the Sentinel-1 SAR in Nonthaburi, Thailand. A new indicator, vertical-vertical-horizontal polarization (VVH), which can provide a better performance, is produced from the dual-polarization information, vertical-vertical (VV), and vertical-horizontal (VH). Then, the building height model was developed using indicator VVH and the reference building height data. The root means square error (RMSE) between the estimated and reference height is 1.413 m. Then, the second objective is to classify three classes of urban types, which are composed of residential buildings, commercial buildings, and other buildings, including vegetation, waterbodies, car parks, and so on. Spectral indices such as normalized difference vegetation index (NDVI), normalized difference water index (NDWI), and normalized difference built up the index (NDBI) are extracted from the Sentinel-2A data. To classify the urban types, a three-machine learning classifier, support vector machine (SVM), random forest (RF), and k-nearest neighbor (KNN) were developed. The classification uses randomly trained data from each 500 m focus study which are divided into a 100 × 100 m grid. Different models are examined using different variables, for example, classification using only building height and only spectral indices. The indices and estimated building height were used to classify the urban types. Not only the average of various satellite-based indices and building height of 100 × 100 m grid was used, but also the minimum, maximum, mean, and standard deviation were calculated from NDVI, NDWI, NDBI, and building height. There are a total of 16 variables used in the model. Eventually, the principal components analysis (PCA) was used to reduce the variables and get better performance of the models. SVM showed better accuracy than the other two, RF and KNN. The accuracies of SVM, RF, and KNN are 0.86, 0.75, and 0.76, respectively.

Crop identification in large irrigation districts is important for crop yield estimation, hydrological simulation, and agricultural water management. Remote sensing provides an opportunity to visualize crops in the regional scale. However, the use of coarse resolution remote sensing images for crop identification usually causes great errors due to the presence of mixed pixels in regions with complex planting structure of crops. Therefore, it is preferable to use remote sensing data with high spatial and temporal resolutions in crop identification. This study aimed to map multi-year distributions of major crops (maize and sunflower) in Hetao Irrigation District, the third largest irrigation district in China, using HJ-1A/1B CCD images with high spatial and temporal resolutions. The Normalized Difference Vegetation Index (NDVI) series obtained from HJ-1A/1B CCD images was fitted with an asymmetric logistic curve to find the NDVI characteristics and phenological metrics for both maize and sunflower. Nine combinations of NDVI characteristics and phenological metrics were compared to obtain the optimal classifier to map maize and sunflower from 2009 to 2015. Results showed that the classification ellipse with the NDVI characteristic of the left inflection point in the NDVI curve and the phenological metric from the left inflection point to the peak point normalized, with mean values of corresponding grassland indexes achieving the minimum mean relative error of 10.82% for maize and 4.38% for sunflower. The corresponding Kappa coefficient was 0.62. These results indicated that the vegetation and phenology-based classifier using HJ-1A/1B data could effectively identify multi-year distribution of maize and sunflower in the study region. It was found that maize was mainly distributed in the middle part of the irrigation district (Hangjinhouqi and Linhe), while sunflower mainly in the east part (Wuyuan). The planting sites of sunflower had been gradually expanded from Wuyuan to the north part of Hangjinhouqi and Linhe. These results were in agreement with the local economic policy. Results also revealed the increasing trends of both maize and sunflower planting areas during the study period.

Dual-polarized (VV and VH) Sentinel-1 Synthetic-Aperture Radar (SAR) Ground Range Detected (GRD) data are available in 9-m spatial resolution and 12-day repeat orbit. A constellation of two satellites, Sentinel 1A and Sentinel 1B, capture these data with ascending and descending orbits, thus increasing the revisit time at the equator to every six days. Those specifications allow creating dense cross-orbit time-series data with a relatively high spatial resolution, beneficial for identifying land-covers and land-uses with unique temporal dynamics, such as paddies. This study was intended to assess the accuracy of time-series dual-polarized cross-orbit Sentinel 1A and 1B GRD data for mapping paddy extents. The monthly median value of these data was processed in Google Earth Engine and used as inputs in the paddy identification in Magelang District using bagging random forests (RF) and extreme gradient boosting (XGB) algorithms. Variables were ranked based on importance and selected using recursive feature elimination (RFE) and RF model to reduce the data dimensionality and understand the variable importance corresponding to a different month of the year. The resulting variable importance demonstrates better contributions of VV polarization and ascending orbit to the mapping model, and the producer’s and user’s accuracies achieved by RF classifier were 75% and 93.9%. For these reasons, an ascending (ASC) dataset provides better accuracy than its descending (DSC) counterpart and the combination of both (ASC+DSC). The user’s accuracy of the paddy identified using the RF model with ascending Sentinel 1-data is 4% and 6% higher than the XGB models built using ASC and cross-orbit (ASC+DSC) datasets, respectively.

To identify factors affecting and key environmental factors of the Oncomelania hupensis snail density in the Yangtze River Delta region using machine learning methods. Administrative village-level O. hupensis snail survey data in the Yangtze River Delta (including Shanghai Municipality, Jiangsu Province, Zhejiang Province and Anhui Province) from 2011 to 2021 were retrieved from the Information Management System for Parasitic Disease Control of Chinese Center for Disease Control and Prevention. Environmental factor data were captured from the Google Earth Engine platform, including elevation, slope, terrain, normalized difference vegetation index (NDVI), vegetation type, soil type, total petroleum hydrocarbon (TPH), ammonium nitrogen, inorganic nitrogen, dissolved oxygen, pH of water, chemical oxygen demand (COD) and inorganic phosphorus, and climatic factor data in the study region were retrieved from the Copernicus Climate Data Store, including annual precipitation, aridity index and annual mean temperature (AMT). O. hupensis snail survey data in the Yangtze River Delta region from 2011 to 2021 were randomly divided into a training set (70%) and a test set (30%), and five machine learning models were selected for machine learning model construction and comparative analysis of the O. hupensis snail density using the software R 4.3.0, including random forest (RF), eXtreme gradient boosting (XGBoost), support vector machine (SVM), gradient boosting machine (GBM) and neural network (NN). The XGBoost model was employed to construct a predictive model for the O. hupensis snail density, and the impact of each environmental factor on O. hupensis snail distribution was quantified. The SHapley Additive exPlanations (SHAPs) values were calculated to estimate the average contribution of each variable to the model prediction, and the core environmental factors affecting the O. hupensis snail population density were screened. Among the five machine learning models, the XGBoost model exhibited the optimal comprehensive performance, with the coefficient of determination (R2) of 0.855, mean squared error (MSE) of 0.188, root mean squared error (RMSE) of 0.434 and mean absolute error (MAE) of 0.155, respectively. Analysis of factors affecting the O. hupensis snail density with the XGBoost model showed that among the 16 environmental factors, the top four high-impact factors ranked by SHAPs values included annual precipitation, elevation, aridity index and NDVI, with cumulative SHAPs contributions of 75%, which was higher than that of other environmental factors. If NDVI was higher than 0.6, the O. hupensis snail density increased with NDVI and peaked if NDVI was 0.8 (1.60 snails/0.1 m2). The O. hupensis snail density increased with elevation if the elevation ranged from 14 to 40 m, and slowly rose if the annual precipitation ranged from 900 to 1 300 mm, and then increased rapidly to the peak (1.52 snails/0.1 m2) if the annual precipitation ranged from 1 300 to 1 500 mm. In addition, the O. hupensis snail density increased rapidly to the maximum (1.60 snails/0.1 m2) if the aridity index ranged from 0.8 to 1.1, and decreased gradually if the aridity index exceeded 1.1. The XGBoost model shows excellent performance in prediction of the O. hupensis snail density and identification of key environmental factors in the Yangtze River Delta region. Annual precipitation, elevation, aridity index and NDVI are key environmental factors affecting the distribution and density of O. hupensis snails in the Yangtze River Delta region.

The couplings of convolutional neural networks (CNN) with random forest (RF), support vector machine (SVM), long short-term memory (LSTM), and extreme gradient boosting (XGBoost) ensemble algorithms were used to construct novel ensemble computational models (CNN-LSTM, CNN-XG, CNN-SVM, and CNN-RF) for flood hazard mapping in the monsoon-dominated catchment, Bangladesh. The results revealed that geology, elevation, the normalized difference vegetation index (NDVI), and rainfall are the most significant parameters in flash floods based on the Pearson correlation technique. Statistical method such as the area under the curve (AUC) was used to evaluate model performance. The CNN-RF model could be a promising tool for precisely predicting and mapping flash floods as it is outperformed the other models (AUC = 1.0). Furthermore, to meet sustainable development goals (SDGs), a blockchain-based technology is proposed to create a decentralized flood management tool for help seekers and help providers during and post floods. The suggested tool accelerates emergency rescue operations during flood events.

<p><strong>Potential of Sentinel-1 and Sentinel-2 data for Mapping Irrigated areas at plot scale</strong></p><p>Hassan Bazzi <sup>1</sup>, Nicolas Baghdadi <sup>1</sup>, Dino Ienco <sup>1</sup>, Mehrez Zribi <sup>2</sup>, Hatem Belhouchette <sup>3</sup></p><p>Irrigation plays a significant role in agricultural production in order to meet the global food requirement under changing climatic conditions. To fulfill the high demand for food with an ever-increasing global population, better planning of irrigation is required. Therefore, more focus is being set on the assessment of irrigation performance for improving water management in order to achieve higher water productivity and increase agricultural water sustainability.</p><p>In the context of mapping irrigated areas, we propose an innovative approach to map irrigated areas using Sentinel-1 (S1) SAR (Synthetic Aperture Radar) and Sentinel-2 (S2) optical time series. Our proposed approach is based on the use of S1 and/or S2 time series combined with statistical and mathematical functions such as principal component analysis (PCA) and wavelet transformation (WT). The proposed approach was tested over the Catalonia region, Spain with a dataset containing 126 000 irrigated and 67 000 non-irrigated plots. The novelty of our study resides in eliminating the ambiguity between irrigation and rainfall by comparing between the SAR backscattering signal of each plot and that of the corresponding grid (10 km × 10 km). The potential of S2 images to classify irrigated areas by means of NDVI time series was also investigated in this study. Random forest (RF) and convolutional neural network (CNN) approaches were used to build up classification models using the PCA or WT parameters in three different scenarios: The first using only S1 data, the second using only S2 data, and the third using both S1 and S2 data.</p><p>The RF classifiers built using the PCA or WT on the S1 time series perform well in mapping irrigated areas with an accuracy of 90.7% and 89.1% respectively. However, the CNN classification on the S1 temporal series produces a significant overall accuracy of 94.1%. The overall accuracy obtained using the NDVI time series in RF classifier reached 89.5% while that in the CNN reached 91.6%. Finally, the combined use of the SAR and optical data enhanced the accuracy of the RF classification but did not significantly change the overall accuracy of the CNN model.</p>

Wheat (Triticum aestivum L.) is one of the world’s primary food crops, and timely and accurate yield prediction is essential for ensuring food security. There has been a growing use of remote sensing, climate data, and their combination to estimate yields, but the optimal indices and time window for wheat yield prediction in arid regions remain unclear. This study was conducted to (1) assess the performance of widely recognized remote sensing indices to predict wheat yield at different growth stages, (2) evaluate the predictive accuracy of different yield predictive machine learning models, (3) determine the appropriate growth period for wheat yield prediction in arid regions, and (4) evaluate the impact of climate parameters on model accuracy. The vegetation indices, widely recognized due to their proven effectiveness, used in this study include the Normalized Difference Vegetation Index (NDVI), the Enhanced Vegetation Index (EVI), and the Atmospheric Resistance Vegetation Index (ARVI). Moreover, four machine learning models, viz. Decision Trees (DTs), Random Forest (RF), Gradient Boosting (GB), and Bagging Trees (BTs), were evaluated to assess their predictive accuracy for wheat yield in the arid region. The whole wheat growth period was divided into three time windows: tillering to grain filling (December 15–March), stem elongation to grain filling (January 15–March), and heading to grain filling (February–March 15). The model was evaluated and developed in the Google Earth Engine (GEE), combining climate and remote sensing data. The results showed that the RF model with ARVI could accurately predict wheat yield at the grain filling and the maturity stages in arid regions with an R2 > 0.75 and yield error of less than 10%. The grain filling stage was identified as the optimal prediction window for wheat yield in arid regions. While RF with ARVI delivered the best results, GB with EVI showed slightly lower precision but still outperformed other models. It is concluded that combining multisource data and machine learning models is a promising approach for wheat yield prediction in arid regions.

Evaluating machine learning-based PM2.5 estimation using integrated high-resolution datasets across NDVI levels in an urban-industrialized region.

Glass forming ability prediction of bulk metallic glasses based on fused strategy

Spatio-temporal flood frequency dynamics in Kaziranga National Park using multi-year Sentinel-1 SAR and vegetation indices

The precise mapping of coastal wetlands holds great significance in the context of monitoring carbon sequestration and storage within coastal ecosystems, particularly in light of climate change and human-induced activities. Time series and multi-source remote sensing data offers distinct advantage in the spatial and temporal mapping of land use, particularly in wetland systems, encompassing various types of vegetation. The primary aim of this study was to delineate the spatial distribution of land use within the Liao River Delta (LRD) wetland. This was achieved by employing a stacking ensemble model that integrated time series GaoFen-1 (GF-1) optical imagery, GaoFen-3 (GF-3) synthetic aperture radar (SAR) imagery, and Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) imagery. The first step involved the application of the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) to fuse the GF-1 NDVI and MODIS NDVI datasets, resulting in the production of time series NDVI data. Subsequently, the parameters pertaining to vegetation phenology were obtained by employing the threshold method on time series NDVI data. We compiled feature datasets that encompassed GF-1 spectral bands, spectral indices, phenological parameters, and GF-3 SAR features. In order to mitigate data redundancy, the Recursive Feature Elimination and Cross-Validation (RFECV) model was employed to identify and select significant features. Finally, the stacking ensemble model was constructed by combining five base models [K-Nearest Neighbors (KNN), Random Forest (RF), Adaptive Boosting (AdaBoost), Extreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM)] to perform wetland classification. The findings showed the following: (1) ESTARFM was able to successfully fuse GF-1 NDVI and MODIS NDVI data, resulting in a spatiotemporal fusion with a coefficient of determination (R2) of 0.85 and a root mean square error (RMSE) of 0.07. (2) In the process of wetland classification, the RFECV algorithm was employed to select relevant features. Specifically, 75 spectral band features, 89 spectral index features, 13 SAR features, and 7 phenological parameters were identified as significant for this task. (3) A stacking ensemble model was constructed using the aforementioned multi-source features. This model exhibited a robust and consistent performance in wetland classification, achieving the highest overall accuracy of 94.33%. Notably, this accuracy improvement ranged from approximately 0.09% to 10.02% when compared to the individual base models. Thus, the present study has the potential to be utilized in the context of fine-scale wetland monitoring, thereby offering valuable assistance to the field of wetland environmental research.

Although the goal of rectal cancer treatment is to restore gastrointestinal continuity, some patients with rectal cancer develop a permanent stoma (PS) after sphincter-saving operations. Although many studies have identified the risk factors and causes of PS, few have precisely predicted the probability of PS formation before surgery. To validate whether an artificial intelligence model can accurately predict PS formation in patients with rectal cancer after sphincter-saving operations. Patients with rectal cancer who underwent a sphincter-saving operation at Taipei Medical University Hospital between January 1, 2012, and December 31, 2021, were retrospectively included in this study. A machine learning technique was used to predict whether a PS would form after a sphincter-saving operation. We included 19 routinely available preoperative variables in the artificial intelligence analysis. To evaluate the efficiency of the model, 6 performance metrics were utilized: accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and area under the receiving operating characteristic curve. In our classification pipeline, the data were randomly divided into a training set (80% of the data) and a validation set (20% of the data). The artificial intelligence models were trained using the training dataset, and their performance was evaluated using the validation dataset. Synthetic minority oversampling was used to solve the data imbalance. A total of 428 patients were included, and the PS rate was 13.6% (58/428) in the training set. The logistic regression (LR), Gaussian Naïve Bayes (GNB), Extreme Gradient Boosting (XGB), Gradient Boosting (GB), random forest, decision tree and light gradient boosting machine (LightGBM) algorithms were employed. The accuracies of the logistic regression (LR), Gaussian Naïve Bayes (GNB), Extreme Gradient Boosting (XGB), Gradient Boosting (GB), random forest (RF), decision tree (DT) and light gradient boosting machine (LightGBM) models were 70%, 76%, 89%, 93%, 95%, 79% and 93%, respectively. The area under the receiving operating characteristic curve values were 0.79 for the LR model, 0.84 for the GNB, 0.95 for the XGB, 0.95 for the GB, 0.99 for the RF model, 0.79 for the DT model and 0.98 for the LightGBM model. The key predictors that were identified were the distance of the lesion from the anal verge, clinical N stage, age, sex, American Society of Anesthesiologists score, and preoperative albumin and carcinoembryonic antigen levels. Integration of artificial intelligence with available preoperative data can potentially predict stoma outcomes after sphincter-saving operations. Our model exhibited excellent predictive ability and can improve the process of obtaining informed consent.

Mapping water bodies with a high accuracy is necessary for water resource assessment, and mapping them rapidly is necessary for flood monitoring. Poyang Lake is the largest freshwater lake in China, and its wetland is one of the most important in the world. Poyang Lake is affected by floods from the Yangtze River basin every year, and the fluctuation of the water area and water level directly or indirectly affects the ecological environment of Poyang Lake. Synthetic Aperture Radar (SAR) is particularly suitable for large-scale water body mapping, as SAR allows data acquisition regardless of illumination and weather conditions. The two-satellite Sentinel-1 constellation, providing C-Band SAR data, passes over the Poyang Lake about five times a month. With its high temporal-spatial resolution, the Sentinel-1 SAR data can be used to accurately monitor the water body. After acquiring all the Sentinel-1 (1A and 1B) SAR data, to ensure the consistency of data processing, we propose the use of a Python and SeNtinel Application Platform (SNAP)-based engine (SARProcMod) to process the data and construct a Poyang Lake Sentinel-1 SAR dataset with a 10 m resolution. To extract water body information from Sentinel-1 SAR data, we propose an automatic classification engine based on a modified U-Net convolutional neural network (WaterUNet), which classifies all data using artificial sample datasets with a high validation accuracy. The results show that the maximum and minimum water areas in our study area were 2714.08 km2 on 20 July 2020, and 634.44 km2 on 4 January 2020. Compared to the water level data from the Poyang gauging station, the water area was highly correlated with the water level, with the correlation coefficient being up to 0.92 and the R2 from quadratic polynomial fitting up to 0.88; thus, the resulting relationship results can be used to estimate the water area or water level of Poyang Lake. According to the results, we can conclude that Sentinel-1 SAR and WaterUNet are very suitable for water body monitoring as well as emergency flood mapping.

In this paper, some algorithms for the rain rate from Sentinel-1 (S-1) synthetic aperture radar (SAR) images are proposed to increase the accuracy of rainfall retrieval during a tropical cyclone (TC). The proposed algorithms contain the extreme gradient boosting (XGBoost), random forest (RF), and support vector regression (SVR). The SAR rain rate retrieval results using the XGBoost are validated against the Global Precipitation Measurement (GPM) data, yielding a root mean square error (RMSE) of 2.43 mm h–1, correlation (COR) of 0.92, and scatter index (SI) of 0.65, which are better than the 5.3 mm h–1 RMSE, 0.51 COR, and 1.38 SI achieved using the re-tuned empirical model CRAIN2_S1, the RF values of 2.50 RMSE, 0.91 COR, and 0.67 SI, and the SVR model with a RMSE of 4.18, a COR of 0.73, and a SI of 1.11. It is concluded that the trained XGBoost is recommended for use in SAR rain rate retrieval during TCs.

The knowledge of water surface changes provides invaluable information for water resources management and flood monitoring. However, the accurate identification of water bodies is a long-term challenge due to human activities and climate change. Sentinel-1 synthetic aperture radar (SAR) data have been drawn, increasing attention to water extraction due to the availability of weather conditions, water sensitivity and high spatial and temporal resolutions. This study investigated the abilities of random forest (RF), Extreme Gradient Boosting (XGB) and support vector machine (SVM) methods to identify water bodies using Sentinel-1 imageries in the upper stream of the Yangtze River, China. Three sets of hyper-parameters including default values, optimized by grid searches and genetic algorithms, were examined for each model. Model performances were evaluated using a Sentinel-1 image of the developed site and the transfer site. The results showed that SVM outperformed RF and XGB under the three scenarios on both the validated and transfer sites. Among them, SVM optimized by genetic algorithm obtained the best accuracy with precisions of 0.9917 and 0.985, kappa statistics of 0.9833 and 0.97, F1-scores of 0.9919 and 0.9848 on validated and transfer sites, respectively. The best model was then used to identify the dynamic changes in water surfaces during the 2020 flood season in the study area. Overall, the study further demonstrated that SVM optimized using a genetic algorithm was a suitable method for monitoring water surface changes with a Sentinel-1 dataset.