Search Results (59)

The updated MODIS Collection 6.1 (C6.1) Dark Target (DT) aerosol optical depth (AOD) is extensively utilized in aerosol-climate studies in China. Nevertheless, the long-term accuracy of this data remains under-evaluated, especially for the forested areas. This study was undertaken to substantiate the accuracy of MODIS Terra (MOD04) and Aqua (MYD04) at 3 km resolution AOD retrievals at six forested sites in China from 2004 to 2022. The results revealed that MODIS C6.1 DT MOD04 and MYD04 datasets display good correlation (R = 0.75), low RMSE (0.20, 0.18), but significant underestimation, with only 53.57% (Terra) and 52.20% (Aqua) of retrievals within expected error (EE). Both the Terra and Aqua struggled in complex terrain (Gongga Mt.) and high aerosol loads (AOD > 1). In northern sites, MOD04 outperformed MYD04 with better correlation and a relatively high number of retrievals percentage within EE. In contrast, MYD04 outperformed MOD04 in central region with better R (0.69 vs. 0.62), and high percentage within EE (68.70% vs. 63.62%). Since both products perform well in the central region, MODIS C6.1 DT products are recommended for this region. In southern sites, MOD04 product performs relatively better than MYD04 with a marginally higher percentage within EE. However, MYD04 shows better correlation, although a higher number of retrievals fall below EE compared to MOD04. Seasonal biases, driven by snow and dust, were pronounced at northern sites during winter and spring. Southern sites faced issues during biomass burning seasons and complex terrain further degraded accuracy. MOD04 demonstrated a marginally superior performance compared to MYD04, yet both failed to achieve the global validation benchmark (66% within). The proposed results highlight critical limitations of current aerosol retrieval algorithms in forest and mountainous landscapes, necessitating methodological refinements to improve satellite-based derived AOD accuracy in ecological sensitive areas. Full article

Reanalysis and satellite retrieval are two primary approaches for obtaining large-scale and long-term Aerosol Optical Depth (AOD) datasets. This study evaluates and compares the accuracy, long-term stability, and error characteristics of the MERRA-2, MODIS combined Dark Target and Deep Blue (DT&DB), and VIIRS DB AOD products globally and regionally. The results indicate that the MERRA-2 AOD exhibits the highest accuracy with an expected error (EE, ±0.05 ± 20%) of 83.24% and mean absolute error (MAE) of 0.056, maintaining a stability of 0.010 per decade. However, since the MERRA-2 AOD ceased assimilating observations other than the MODIS AOD in 2014, its accuracy decreased by approximately 5.6% in the EE metric after 2014. The VIIRS Deep Blue (DB) AOD product, with an EE of 79.43% and stability of 0.016 per decade, is slightly less accurate and stable compared to the MERRA-2 AOD. The MODIS DT&DB AOD demonstrates an EE of 76.75% and stability of 0.011 per decade. Regionally, the MERRA-2 AOD performs acceptably in most areas, especially in low-aerosol-loading regions, with an EE > 86% and stability ~0.02 per decade. The VIIRS DB AOD excels in high-aerosol-loading regions, such as the Indian subcontinent, with an EE of 69.14% and a stability of 0.049 per decade. The performance of the MODIS DT&DB AOD falls between that of VIIRS DB and MERRA-2 across most regions. Overall, each product meets the accuracy and stability metrics globally, but users need to select the appropriate product for analysis based on the validation results of the accuracy and stability in different regions. Full article

Soil, a significant natural resource, plays a crucial role in supporting various ecosystems and serves as the foundation of Pakistan’s economy due to its primary use in agriculture. Hence, timely monitoring of soil type and salinity is essential. However, traditional methods for identifying soil types and detecting salinity are time-consuming, requiring expert intervention and extensive laboratory experiments. The objective of this study is to propose a model that leverages MODIS Terra data to identify soil types and detect soil salinity. To achieve this, 195 soil samples were collected from Lahore, Kot Addu, and Kohat, dating from October 2022 to November 2022. Simultaneously, spectral data of the same regions were obtained to spatially map soil types and salinity of bare land. The spectral reflectance of band values, salinity indices, and vegetation indices were utilized to classify the soil types and predict soil salinity. To perform the classification and regression tasks, the study employed three popular techniques in the research community: Random Forest (RF), Ada Boost (AB), and Gradient Boosting (GB), along with Decision Tree (DT), K-Nearest Neighbor (KNN), and Extra Tree (ET). A 70–30 test train validation split was used for the implementation of these techniques. The efficacy of the multi-class classification models for soil types was evaluated using accuracy, precision, recall, and f1-score. On the other hand, the regression models’ performances were evaluated and compared using R-squared (R${}^2$), Mean Squared Error (MSE), Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE). The results demonstrated that Random Forest outperformed other methods for both predicting soil types (accuracy = 65.38, precision = 0.60, recall = 0.57, and f1-score = 0.57) and predicting salinity (R${}^2$ = 0.90, MAE = 0.56, MSE = 0.98, RMSE = 0.97). Finally, the study designed a web portal to enable real-time prediction of soil types and salinity using these models. This web portal can be utilized by farmers and decision-makers to make informed decisions regarding soil, crop cultivation, and agricultural planning. Full article

Satellite aerosol products are pivotal in studies of regional air quality and global climate change. Compared with accurate in situ observations, satellite measurements provide valuable large-scale atmospheric information. However, limitations such as clouds and retrieval assumptions result in a significant number of missing values in satellite aerosol optical depth (AOD) products, which severely hampers the representativeness. To address this issue, spatial interpolation of the AOD data is necessary to improve data coverage. In this study, one year of AOD observation data from the MODIS C6.1 version was applied to analyze the spatiotemporal correlated characteristics. The statistical parameters were used as dynamic interpolation weights to develop a novel interpolation method called empirical correlation weighting (ECW) based on MODIS AOD over Northern China in 2016. The ECW interpolation results were obtained at a 0.05° resolution (~5 km). The results showed that the spatial coverage of the Deep Blue (DB) and Dark Target (DT) products increased from 43.88% to 70.65% and from 15.04% to 32.62%, respectively. The reconstruction of the ECW method illustrated good agreement with original values in three cases and in two experimental areas. The mean absolute error (MAE) and root mean square error (RMSE) in the two experiments were 0.1171 and 0.0809, and 0.1212 and 0.0838, respectively, indicating that the ECW exhibited the better accuracy than ordinary Kriging (OK) and Thin Plate Spline (TPS). The AERONET validation results indicated that the values of RMSE and MAE were slightly higher after interpolation compared with those before interpolation, maintaining relatively low values, 0.241 and 0.257, 0.140 and 0.150, respectively. Full article

The accurate determination of aerosol optical depth (AOD) is of great importance for climate change research and environmental monitoring. To understand the applicability of the MODIS aerosol product inversion algorithm in Gansu Province, this work uses ground-based solar photometer AOD observation data to validate the MODIS C6 version of the AOD product. Additionally, the retrieval accuracy of MODIS C6 Deep Blue (DB) algorithm AOD products and Deep Blue and Dark Target Fusion (DB–DT combined) algorithm AOD products for Gansu Province when setting different spatial sampling windows is compared and analyzed. Meanwhile, the monitoring effects of these two AOD algorithms in typical polluted atmospheric conditions in Gansu Province are compared. The results show that (1) the correlation between the MODIS AOD products of the two algorithms and the ground-based observation data decreases with an increasing spatial sampling window size. When the spatial sampling window of the two algorithms is set at 30 km × 30 km, it is more representative of the AOD value in Gansu Province, thus reflecting local characteristics. (2) When the spatial sampling window is set at 30 km × 30 km, the inversion effect of the DB algorithm AOD is better than that of the DB–DT combined algorithm AOD on different underlying surfaces. (3) The seasonal variability in the inversion accuracy of the DB algorithm AOD is less than that of the DB–DT combined algorithm, and it has inversion advantages in spring, autumn and winter, while the DB–DT combined algorithm outperforms the DB algorithm only in winter. The inversion effect of the two algorithms on AOD is influenced by the spatial sampling window setting. (4) Both the DB algorithm AOD and the DB–DT combined algorithm AOD can monitor the distribution of AOD in the central and western regions of Gansu, especially for high values of AOD under polluted atmospheric conditions, which represents a good monitoring effect. However, the two algorithms perform poorly in monitoring the southeast region of Gansu, while there is a discontinuous AOD distribution in the northwest region of Gansu. Overall, the MODIS DB algorithm AOD product has higher applicability in Gansu Province. This work provides a good reference for local air pollution and climate prediction. Full article

The latest epidemiological studies have revealed that the adverse health effects of PM_2.5 have impacts beyond respiratory and cardio-vascular diseases and also affect the development of the brain and metabolic diseases. The need for accurate and spatio-temporally resolved PM_2.5 data has thus been substantiated. While the selective information provided by station measurements is mostly insufficient for area-wide monitoring, satellite data have been increasingly applied to comprehensively monitor PM_2.5 distributions. Although the accuracy and reliability of satellite-based PM_2.5 estimations have increased, most studies still rely on a single sensor. However, several datasets have become available in the meantime, which raises the need for a systematic analysis. This study presents the first systematic evaluation of four satellite-based AOD datasets obtained from different sensors and retrieval methodologies to derive ground-level PM_2.5 concentrations. We apply a random forest approach and analyze the effect of the resolution and coverage of the satellite data and the impact of proxy data on the performance. We examine AOD data from the Moderate resolution Imaging spectroradiometer (MODIS) onboard Terra and Aqua satellites, including Dark Target (DT) algorithm products and the Multi-Angle Implementation of Atmospheric Correction (MAIAC) product. Additionally, we explore more recent datasets from the Sea and Land Surface Temperature Radiometer (SLSTR) onboard Sentinel-3a and from the Tropospheric Monitoring Instrument (TROPOMI) operating on the Sentinel-5 precursor (S5p). The method is demonstrated for Germany and the year 2018, where a dense in situ measurement network and relevant proxy data are available. Overall, the model performance is satisfactory for all four datasets with cross-validated R² values ranging from 0.68 to 0.77 and excellent for MODIS AOD reaching correlations of almost 0.9. We find a strong dependency of the model performance on the coverage and resolution of the AOD training data. Feature importance rankings show that AOD has less weight compared to proxy data for SLSTR and TROPOMI. Full article

Satellite remote sensing plays an important role in wildfire detection. Methods using the brightness and temperature difference of remote sensing images to determine if a wildfire has occurred are one of the main research directions of forest fire monitoring. However, common wildfire detection algorithms are mainly based on a fixed brightness temperature threshold to distinguish wildfire pixels and non-wildfire pixels, which reduces the applicability of the algorithm in different space–time regions. This paper presents an adaptive wildfire detection algorithm, DBTDW, based on a dynamic brightness temperature threshold. First, a regression dataset, MODIS_DT_Fire, was constructed based on moderate resolution imaging spectroradiometry (MODIS) to determine the wildfire brightness temperature threshold. Then, based on the meteorological information, normalized difference vegetation index (NDVI) information, and elevation information provided by the dataset, the DBTDW algorithm was used to calculate and obtain the minimum brightness temperature threshold of the burning area by using the Planck algorithm and Otsu algorithm. Finally, six regression models were trained to establish the correlation between factors and the dynamic brightness temperature threshold of wildfire. The root-mean-square error (RMSE) and mean absolute error (MAE) were used to evaluate the regression performance. The results show that under the XGBoost model, the DBTDW algorithm has the best prediction effect on the dynamic brightness temperature threshold of wildfire (leave-one-out method: RMSE/MAE = 0.0730). Compared with the method based on a fixed brightness temperature threshold, the method proposed in this paper to adaptively determine the brightness temperature threshold of wildfire has higher universality, which will help improve the effectiveness of satellite remote fire detection. Full article

The Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol optical depth (AOD) has been widely used in atmospheric environment and climate change research. Based on data of the Aerosol Robotic Network and Sun–Sky Radiometer Observation Network in the Yangtze River Delta, the retrieval accuracies of MODIS C6.1 Dark Target (DT), Deep Blue (DB), and C6.0 Multi-angle Implementation of Atmospheric Correction (MAIAC) products under different land cover types, aerosol types, and observation geometries were analyzed. About 65.64% of MAIAC AOD is within the expected error (Within EE), which is significantly higher than 41.43% for DT and 56.98% for DB. The DT product accuracy varies most obviously with the seasons, and the Within EE in winter is more than three times that in spring. The DB and MAIAC products have low accuracy in summer but high in other seasons. The accuracy of the DT product gradually decreases with the increase in urban and water land-cover proportion. After being corrected by bias and mean relative error, the DT accuracy is significantly improved, and the Within EE increases by 24.12% and 32.33%, respectively. The observation geometries and aerosol types were also examined to investigate their effects on AOD retrieval. Full article

Aerosols suspended in the atmosphere negatively affect air quality and public health and promote global climate change. The Guanzhong area in China was selected as the study area. Air quality data from July 2018 to June 2021 were recorded daily, and 19 haze periods were selected for this study. The Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model was used to simulate the air mass transport trajectory during this haze period to classify the formation process. The spatial distribution of the aerosol optical depth (AOD) was obtained by processing Moderate-resolution Imaging Spectroradiometer (MODIS) data using the dark target (DT) method. Three factors were used to analyze the AOD spatial distribution characteristics based on the perceptual hashing algorithm (PHA): GDP, population density, and topography. Correlations between aerosols and the wind direction, wind speed, and precipitation were analyzed using weather station data. The research results showed that the haze period in Guanzhong was mainly due to locally generated haze (94.7%). The spatial distribution factors are GDP, population density, and topography. The statistical results showed that wind direction mainly affected aerosol diffusion in Guanzhong, while wind speed (r = −0.63) and precipitation (r = −0.66) had a significant influence on aerosol accumulation and diffusion. Full article

Given the advantages of remote sensing, an increasing number of satellite aerosol optical depths (AOD) have been utilized to evaluate near-ground PM_2.5. However, the spatiotemporal relationship between AODs and PM_2.5 still lacks a comprehensive investigation, especially in some regions with severe pollution within China. Here, we investigated the spatiotemporal relationships between several satellite AODs and the near-surface PM_2.5 concentration across China and its 14 representative regions during 2016–2018 using the correlation coefficient (R), the PM_2.5/AOD ratio (η), the geo-detector (q), and the different aerosol-dominated regimes. The results showed that the MODIS AOD from the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm strongly correlates with PM_2.5 (R > 0.6) in China, particularly in the Chengyu (CY), Beijing-Tianjin-Hebei (BTH), and Yangtze River Delta (YRD) regions. The close correlations (R = 0.7) exist between PM_2.5 and MODIS and VIIRS AOD from the deep blue (DB) algorithm in the CY, BTH, and YRD regions. Under the key aerosols affecting China (e.g., sulfate and dust), there is a strong correlation (R > 0.5) between the PM_2.5 and MODIS and VIIRS AODs from the MAIAC and DB algorithms, with the higher concentration of ground-level PM_2.5 per unit of these AODs (η > 130). The MAIAC AOD (Terra/Aqua) can better explain the spatial distribution (q > 0.4) of PM_2.5 than those of AODs from the dark target (DT) and DB algorithms applied to the MODIS over China and its specific regions across seasons. The performance of the Advanced Himawari Imager (AHI) AOD (R > 0.5, q > 0.3) was close to that of the MAIAC AOD during the spring and summer; however, it was far less than the MAIAC AOD in the autumn and winter seasons. The investigation provides instructions for estimating the near-surface PM_2.5 concentration based on AOD in different regions of China. Full article

Longer-term projections indicate that today’s developing and rising nations will account for roughly 60% of the global GDP by 2030. There is tremendous financial growth and advancement in developing countries, resulting in a high demand for personal loans from citizens. Depending on their needs, many people seek personal loans from banks. However, it is difficult for banks to predict which consumers will pay their bills and which will not since the number of bank frauds in many countries, notably India, is growing. According to the Reserve Bank of India, the Indian banking industry uncovered INR 71,500 in the scam in the fiscal year 2018–2019. The average lag time between the date of the occurrence and its recognition by banks, according to the statistics, was 22 months. This is despite harsher warnings from both the RBI and the government, particularly in the aftermath of the Nirav Modi debacle. To overcome this issue, we demonstrated how to create a predictive loan model that identifies problematic candidates who are considerably more likely to pay the money back. In step-by-step methods, we illustrated how to handle raw data, remove unneeded portions, choose appropriate features, gather exploratory statistics, and finally how to construct a model. In this work, we created supervised learning models such as decision tree (DT), random forest (RF), and k-nearest neighbor (KNN). According to the classification report, the models with the highest accuracy score, f-score, precision, and recall are considered the best among all models. However, in this work, our primary aim was to reduce the false-positive parameter in the classification models’ confusion matrix to reduce the banks’ non-performing assets (NPA), which is helpful to the banking sector. The data were graphed to help bankers better understand the customer’s behavior. Thus, using the same method, client loyalty may also be anticipated. Full article

The techniques and analyses employed by remote sensing provide key information about atmospheric particle properties at regional and global scales. However, limitations in optical spectral models used to represent the different types of aerosols in the atmosphere and their effects (direct and indirect) are still one of the major causes of sources of uncertainties and substantial impacts in climate prediction. There are no studies yet in South America, especially in the Amazon Basin, that have evaluated the advantages, disadvantages, inconsistencies, applicability, and suitability of the MODIS sensor (Moderate Resolution Imaging Spectroradiometer) destined for monitoring the ambient aerosol optical thickness over rivers and continents. In this study, the results of the DT (Dark Target) algorithm for products with 3 km and 10 km resolutions were systematically evaluated for six sites in the Amazon rainforest. The comparisons between the products were carried out with the AERONET (Aerosol Robotic Network) measurements, which were used as reference. Statistical parameters between AERONET vs. MODIS were also evaluated based on biomass burning records in the site regions. Here, the DT 10 km product showed satisfactory performance for the Amazon region, with observations between the expected error (EE) limits above 66%, in addition to R > 0.8 and RMSE < 0.3. However, the regional analysis for the two sites in the central and southern regions of the Amazon basin did not have the same performance, where the results showed an EE of 24 and 47%, respectively. The DT 3 km product did not perform well in any site, with an EE below 50%. Both products overestimated the AOD, but the 3 km product overestimated it approximately four times more due to its algorithm setup. Thus, we recommend the 10 km product for general analysis in Amazonia. Regional biomass burning records showed a direct relationship with the AERONET vs. MODIS DT with overestimation of both products. All variations between products and sites were justified based on the difficulty of retrieving surface reflectance and the model selected for local aerosols. Improvements in the optical spectral model currently implemented in the algorithms, with more realistic representations of the main types of the aerosol present in the Amazon Basin, may contribute to better performance among the evaluated products. Full article

Information on crop spatial distribution is essential for agricultural monitoring and food security. Classification with remote-sensing time series images is an effective way to obtain crop distribution maps across time and space. Optimal features are the precondition for crop classification and are critical to the accuracy of crop maps. Although several approaches are available for extracting spectral, temporal, and phenological features for crop identification, these methods depend heavily on domain knowledge and human experiences, adding uncertainty to the final crop classification. This study proposed a novel Genetic Programming (GP) approach to learning high-level features from time series images for crop classification to address this issue. We developed a new representation of GP to extend the GP tree’s width and depth to dynamically generate either fixed or flexible informative features without requiring domain knowledge. This new GP approach was wrapped with four classifiers, i.e., K-Nearest Neighbor (KNN), Decision Tree (DT), Naive Bayes (NB), and Support Vector Machine (SVM), and was then used for crop classification based on MODIS time series data in Heilongjiang Province, China. The performance of the GP features was compared with the traditional features of vegetation indices (VIs) and the advanced feature learning method Multilayer Perceptron (MLP) to show GP effectiveness. The experiments indicated that high-level features learned by GP improved the classification accuracies, and the accuracies were higher than those using VIs and MLP. GP was more robust and stable for diverse classifiers, different feature numbers, and various training sample sets compared with classification using VI features and the classifier MLP. The proposed GP approach automatically selects valuable features from the original data and uses them to construct high-level features simultaneously. The learned features are explainable, unlike those of a black-box deep learning model. This study demonstrated the outstanding performance of GP for feature learning in crop classification. GP has the potential of becoming a mainstream method to solve complex remote sensing tasks, such as feature transfer learning, image classification, and change detection. Full article

Sea level rise is an important and topical issue in the South Pacific region and needs an urgent assessment of trends for informed decision making. This paper presents mean sea level trend assessment using harmonic analysis and a hybrid deep learning (DL) model based on the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) technique, Convolutional Neural Network (CNN), Gated Recurrent Unit (GRU) and Neighbourhood Component Analysis (NCA) to build a highly accurate sea level forecasting model for three small islands (Fiji, Marshall Island and Papua New Guinea (PNG)) in the South Pacific. For a 20-year period, the estimated mean sea level rise per year from the harmonic computation is obtained: 112 mm for PNG, 98 mm for Marshall Island and 52 mm for Fiji. The DL procedure uses climate and environment-based remote sensing satellite (MODIS, GLDAS-2.0, MODIS TERRA, MERRA-2) predictor variables with tide gauge base mean sea level (MSL) data for model training and development for forecasting. The developed CEEMDAN-CNN-GRU as the objective model is benchmarked by comparison to the hybrid model without data decomposition, CNN-GRU and standalone models, Decision Trees (DT) and Support Vector Regression (SVR). All model performances are evaluated using reliable statistical metrics. The CEEMDAN-CNN-GRU shows superior accuracy when compared with other standalone and hybrid models. It shows an accuracy of >96% for correlation coefficient and an error of <1% for all study sites. Full article

Due to the significant spatial variation of the performance of Moderate Resolution Imaging Spectrometer (MODIS) aerosol optical depth (AOD) retrievals, validation is very important for applications of MODIS AOD products at regional scales. This study presents a comparative analysis of Collection 6.1 MODIS AOD retrievals and ground measurements from five local sites and one Aerosol Robotic Network (AERONET) site in the Yangtze River Delta (YRD) region, which significantly complements the previous validation that utilized limited AERONET measurements. Generally, MODIS AOD retrievals showed a reasonable agreement with collocated ground measurements (R² > 0.7), with 66% of Dark Target (DT) 10 km retrievals, 56% of Deep Blue (DB) 10 km retrievals, and 69% of DT 3 km retrievals falling within the expected error (EE = ±(0.05 + 0.2 × AOD)). Nevertheless, it was found that the DT AOD retrievals tended to be overestimated over urbanized and lakeside sites, while the DB AOD retrievals tended to be underestimated over all ground sites except for lakeside sites. Such patterns appeared to be linked with the systematic biases of the single-scattering albedo estimation in the AOD retrieval algorithms. Another significant finding of this study is that the uncertainties of the MODIS AOD retrievals were highly correlated with the land cover proportions of urbanized features and water (LCP_UW) in the surrounding region, especially for the DT products. An empirical correction method based on these correlations could substantially reduce the uncertainties of DT AOD products over high LCP_UW areas. The results not only highlight the significant impacts of both urban and water areas on the MODIS AOD retrieval algorithms but also create new possibilities to correct such impacts once the universal correlations between LCP_UW and the uncertainty measures are established. Full article