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The rapid and accurate extraction of water information from satellite imagery has been a crucial topic in remote sensing applications and has important value in water resources management, water environment monitoring, and disaster emergency management. Although the OLI-2 sensor onboard Landsat-9 is similar to the well-known OLI onboard Landsat-8, there were significant differences in the average absolute percentage change in the bands for water detection. Additionally, the performance of Landsat-9 in water body extraction is yet to be fully understood. Therefore, it is crucial to conduct comparative studies to evaluate the water extraction performance of Landsat-9 with Landsat-8. In this study, we analyze the performance of simultaneous Landsat-8 and Landsat-9 data for water body extraction based on eight common water indices (Normalized Difference Water Index (NDWI) and Modified Normalized Difference Water Index (MNDWI), Augmented Normalized Difference Water Index (ANDWI), Water Index 2015 (WI2015), tasseled cap wetness index (TCW), Automated Water Extraction Index for scenes with shadows (AWEIsh) and without shadows (AWEInsh) and Multi-Band Water Index (MBWI)) to extract water bodies in seven study sites worldwide. The Otsu algorithm is utilized to automatically determine the optimal segmentation threshold for water body extraction. The results showed that (1) Landsat-9 satellite data can be used for water body extraction effectively, with results consistent with those from Landsat-8. The eight selected water indices in this study are applicable to both Landsat-8 and Landsat-9 satellites. (2) The NDWI index shows a larger variability in accuracy compared to other indices when used on Landsat-8 and Landsat-9 imagery. Therefore, additional caution should be exercised when using the NDWI for water body analysis with both Landsat-8 and Landsat-9 satellites simultaneously. (3) For Landsat-8 and Landsat-9 imagery, ratio-based water indices tend to have more omission errors, while difference-based indices are more prone to commission errors. Overall, ratio-based indices exhibit greater variability in overall accuracy, whereas difference-based indices demonstrate lower sensitivity to variations in the study area, showing smaller overall accuracy fluctuations and higher robustness. This study can provide necessary references for the selection of water indices based on the newest Landsat-9 data. The results are crucial for guiding the combined use of Landsat-8 and Landsat-9 for global surface water mapping and understanding its long-term changes. [ABSTRACT FROM AUTHOR]

More specifically, remote sensing of watersheds encompasses a wide range of applications that involve acquiring and utilizing hydrological, ecological, and environmental information in watersheds [[15], [17]], including watershed mapping [[18]], monitoring of underlying surface elements in the watershed [[19]], inversion of water cycle parameters [[20]], water resource assessment [[21]], acquisition of watershed ecological environment parameters [[22]], monitoring natural disasters [[23]], analyzing upstream and downstream [[24]], supporting water governance priorities [[25]], water resource development [[26]], and irrigation water management [[27]]. Data Barriers and Data Sharing Addressing data barriers and promoting data sharing is a critical perspective in remote sensing for watershed management. The advent of remote sensing has revolutionized watershed research, providing unprecedented insights into watershed dynamics and spatiotemporal patterns [[10]]. Similarly, merging remote sensing data with in situ measurements, socio-economic data, and other geospatial information can enhance the accuracy and reliability of watershed analyses. [Extracted from the article]

The accurate estimation of leaf chlorophyll content (LCC) is a significant foundation in assessing litchi photosynthetic activity and possible nutrient status. Hyperspectral remote sensing data have been widely used in agricultural quantitative monitoring research for the non-destructive assessment of LCC. Variable selection approaches are crucial for analyzing high-dimensional datasets due to the high danger of overfitting, time-intensiveness, or substantial computational requirements. In this study, the performance of five machine learning regression algorithms (MLRAs) was investigated based on the hyperspectral fractional order derivative (FOD) reflection of 298 leaves together with the variable combination population analysis (VCPA)-genetic algorithm (GA) hybrid strategy in estimating the LCC of Litchi. The results showed that the correlation coefficient (r) between the 0.8-order derivative spectrum and LCC had the highest correlation coefficients (r = 0.9179, p < 0.01). The VCPA-GA hybrid strategy fully utilizes VCPA and GA while compensating for their limitations based on a large number of variables. Moreover, the model was developed using the selected 14 sensitive bands from 0.8-order hyperspectral reflectance data with the lowest root mean square error in prediction (RMSEP = 5.04 μ g · cm − 2 ). Compared with the five MLRAs, validation results confirmed that the ridge regression (RR) algorithm derived from the 0.2 order was the most effective for estimating the LCC with the coefficient of determination (R2 = 0.88), mean absolute error (MAE = 3.40   μ g · cm − 2 ), root mean square error (RMSE = 4.23   μ g · cm − 2 ), and ratio of performance to inter-quartile distance (RPIQ = 3.59). This study indicates that a hybrid variable selection strategy (VCPA-GA) and MLRAs are very effective in retrieving the LCC through hyperspectral reflectance at the leaf scale. The proposed methods could further provide some scientific basis for the hyperspectral remote sensing band setting of different platforms, such as an unmanned aerial vehicle (UAV) and satellite. [ABSTRACT FROM AUTHOR]

Soil moisture content (SMC) is a basic condition for crop growth, and a key parameter for crop yield prediction and drought monitoring. An advantage of large-scale synchronous observation using remote sensing technology is that it provides the possibility of dynamic monitoring of soil moisture content in a large area. This study aimed to explore the feasibility of accurately estimating soil moisture content at a regional scale by combining ground hyper-spectral data with multispectral remote sensing (Sentinel-2) data. The results showed that the different mathematical transformations increased the correlation between soil spectral reflectance and SMC to varying degrees. Hyper-spectral optimized index normalized difference index (NDI) ((B769~797 – B848~881/B769~797 + B848~881); (B842 – B740/B842 + B740)) derived from the transformed reflectance (the first-order derivate of reciprocal-logarithm (Log (1/R))′, second-order derivate of reciprocal-logarithm (Log (1/R)) ′′) showed significant correlation (correlation coefficient (r) = 0.61; r = 0.47) with SMC, and the correlation coefficient values higher than difference index (DI) and ratio index (RI). From the performance of 12 prediction models which were taken optimized indices as independent variables, the central wavelength reflectance model (Log (1/R))′′ and the average wavelength reflectance model ((Log (1/R)) ′ presented higher validation coefficients (coefficient of determination (R2) = 0.61, root mean square error (RMSE) = 4.09%, residual prediction deviation (RPD) = 1.82; R2 = 0.69, RMSE = 3.48%, RPD = 1.91) compared with other models. When verifying the accuracy, the model yields R2 values of 0.619 and 0.701. These results indicated that the two-band hyper-spectral optimized indices (NDI) as an optimal indicator for quickly and accurately soil moisture content estimation. Combining the ground hyper-spectral data and satellite remote sensing image regional scale soil moisture content prediction provides a scientific reference for land-space integrated soil moisture content remote sensing monitoring. [ABSTRACT FROM AUTHOR]

Soil salinization is one of the most important causes for land degradation and desertification and is an important threat to land management, farming activities, water quality, and sustainable development in arid and semi-arid areas. Soil salinization is often characterized with significant spatiotemporal dynamics. The salt-affected soil is predominant in the Ebinur Lake region in the Northwestern China. However, detailed local soil salinity information is ambiguous at the best due to limited monitoring techniques. Nowadays, the availability of Multi-Spectral Instrument (MSI) onboard Sentinel-2, offers unprecedented perspectives for the monitoring and mapping of soil salinity. The use of MSI data is an innovative attempt for salinity detection in arid land. We hypothesize that field observations and MSI data and MSI data-derived spectral indices using the partial least square regression (PLSR) approach will yield fairly accurate regional salinity map. Based on electrical conductivity of 1:5 soil:water extract (EC) of 72 ground-truth measurements (out of 116 sample sites) and various spectral parameters, such as satellite band reflectance, published satellite salinity indices, red-edge indices, newly constructed two-band indices, and three-band indices from MSI data, we built a few inversion models in an attempt to produce the regional salinity maps. Different algorithms including Pearson correlation coefficient method (PCC), variable importance in projection (VIP), Gray relational analysis (GRA), and random forest (RF) were applied for variable selection. The results suggest that both the newly proposed normalized difference index (NDI) [(B12 − B7) / (B12 + B7)] and three-band index (TBI4) [(B12 − B3) / (B3 − B11)] show a better correlation with validation data and could be applied to estimate the soil salinity in the Ebinur Lake region. The established models were validated using the remaining 44 independent ground-based measurements. The RF-PLSR model performed the best across the five models with R2 V , RMSE V , and RPD of 0.92, 7.58 dS m−1, and 2.36, respectively. The result from this model was then used to map the soil salinity over the study area. Our analyses suggest that soil salinization changes quite significantly in different seasons. Specifically, soil salinity in the dry season was higher than in the wet season, mostly in the lake area and nearby shores. We contend that the results from the study will be useful for soil salinization monitoring and land reclamation in arid or semi-arid regions outside the current study area. • The introduction of red-edge bands can enhance the sensitivities of the indices to soil salinity. • Three-band index [(B12 − B3) / (B3 − B11)] shows a best correlation (r = 0.544) with measured EC. • RF-PLSR model was proved a suitable method for soil salinity estimating and mapping. • The study shows a large variability in soil salinity in dry and wet seasons. [ABSTRACT FROM AUTHOR]

Abstract In arid and semi-arid climatic areas, lakes are extremely essential for fragile ecological environment and regional sustainable development. Ebinur Lake is an important component of the ecological barrier of Junggar Basin, Xinjiang Uyghur Autonomous Region (XUAR), China. Due to the tremendous changes in Ebinur Lake and surrounding marshes during the last decades, Ebinur Lake becomes a representative ecological degradation region in northwestern China. The detection of the intra-annual variations of water body and its responses to changing environment are critical for regional ecological security and rehabilitation of degraded ecosystem. To extract more accurate water information using Synthetic Aperture Radar (SAR) data and further fill the gap of inter-month dynamic monitoring of Ebinur Lake, a new SAR water index (modified Sentinel 1A water index, MSWI) was proposed based on the relationship between normalized difference water index (NDWI) imageries and Sentinel 1A data. The dynamic thresholds of classification were selected using Otsu method and the results showed that the classification results were acceptable with the optimal overall accuracy of 99.94% and kappa coefficient of 0.9971, respectively. We conduct a time series analysis of surface areas of Ebinur Lake using S1A data from February 9th, 2017 to February 4th, 2018. The maximum lake surface area was 965.29 km2 in April 22nd, 2017, while the minimum value was 750.37 km2 in September 1st, 2017, and the mean area was 831.51 km2. The seasonal variations showed the stages of "sharp rising" – "significant decreasing" – "gradual stabilizing" in the study period. The water surface area was highly correlated with inflow water volume (correlation coefficient = 0.72, P < 0.001). The variation of Ebinur Lake's water surface area is crucial to monitor the resulting eco-environmental impacts under the changing environmental conditions in the arid and semi-arid areas. Graphical abstract Unlabelled Image Highlights • Proposed a new SAR water index (MSWI). • The 12-day interval spatial-temporal distributions of Ebinur Lake. • Variation of lake in arid areas and its eco-environmental impacts. • Examined the driving forces of the dynamic variations. [ABSTRACT FROM AUTHOR]

Since hyperspectral satellite images (HSIs) usually hold low spatial resolution, improving the spatial resolution of hyperspectral imaging (HSI) is an effective solution to explore its potential for remote sensing applications, such as land cover mapping over urban and coastal areas. The fusion of HSIs with high spatial resolution multispectral images (MSIs) and panchromatic (PAN) images could be a solution. To address the challenging work of fusing HSIs, MSIs and PAN images, a novel easy-to-implement stepwise fusion approach was proposed in this study. The fusion of HSIs and MSIs was decomposed into a set of simple image fusion tasks through spectral grouping strategy. HSI, MSI and PAN images were fused step by step using existing image fusion algorithms. According to different fusion order, two strategies ((HSI+MSI)+PAN and HSI+(MSI+PAN)) were proposed. Using simulated and real Gaofen-5 (GF-5) HSI, MSI and PAN images from the Gaofen-1 (GF-1) PMS sensor as experimental data, we compared the proposed stepwise fusion strategies with the traditional fusion strategy (HSI+PAN), and compared the performances of six fusion algorithms under three fusion strategies. We comprehensively evaluated the fused results through three aspects: spectral fidelity, spatial fidelity and computation efficiency evaluation. The results showed that (1) the spectral fidelity of the fused images obtained by stepwise fusion strategies was better than that of the traditional strategy; (2) the proposed stepwise strategies performed better or comparable spatial fidelity than traditional strategy; (3) the stepwise strategy did not significantly increase the time complexity compared to the traditional strategy; and (4) we also provide suggestions for selecting image fusion algorithms using the proposed strategy. The study provided us with a reference for the selection of fusion strategies and algorithms in different application scenarios, and also provided an easy-to-implement solution and useful references for fusing HSI, MSI and PAN images. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicle (UAV)-based hyperspectral remote sensing is an important monitoring technology for the soil moisture content (SMC) of agroecological systems in arid regions. This technology develops precision farming and agricultural informatization. However, hyperspectral data are generally used in data mining. In this study, UAV-based hyperspectral imaging data with a resolution o 4 cm and totaling 70 soil samples (0–10 cm) were collected from farmland (2.5 × 104 m2) near Fukang City, Xinjiang Uygur Autonomous Region, China. Four estimation strategies were tested: the original image (strategy I), first- and second-order derivative methods (strategy II), the fractional-order derivative (FOD) technique (strategy III), and the optimal fractional order combined with the optimal multiband indices (strategy IV). These strategies were based on the eXtreme Gradient Boost (XGBoost) algorithm, with the aim of building the best estimation model for agricultural SMC in arid regions. The results demonstrated that FOD technology could effectively mine information (with an absolute maximum correlation coefficient of 0.768). By comparison, strategy IV yielded the best estimates out of the methods tested (R2val = 0.921, RMSEP = 1.943, and RPD = 2.736) for the SMC. The model derived from the order of 0.4 within strategy IV worked relatively well among the different derivative methods (strategy I, II, and III). In conclusion, the combination of FOD technology and the optimal multiband indices generated a highly accurate model within the XGBoost algorithm for SMC estimation. This research provided a promising data mining approach for UAV-based hyperspectral imaging data. [ABSTRACT FROM AUTHOR]

The degradation of watersheds creates immense pressure on water quality, especially in arid and semiarid regions. Total suspended solids (TSS) provide essential information to water environmental quality assessments. However, the calibration of direct retrieval models requires complicated preparations and further increases uncertainties. Here, we hypothesized that a common remote sensing index (NDVI, normalized difference vegetation index) could be used to estimate TSS concentrations in water due to the effects of canopy cover. To address this hypothesis, we collected 65 water samples from the Ebinur Lake Watershed, northwest China, to investigate the potential relationships between TSS concentrations and Sentinel-2-based NDVI at various scales (100, 200, 300, 400, and 500 m). Subsequently, we established a classical measurement error (CME) model for the estimation of TSS concentrations. The results showed that TSS concentration is negatively related to the NDVI value at all buffer distances. The 300 m scale mean NDVI value showed the most effective explanation of the variations in TSS concentrations (R2 = 0.83, P-value < 0.001), which indicated that the TSS concentration can be assessed by NDVI. The CME model showed that NDVI values played an important role in the assessment of TSS concentrations in surface water. Furthermore, the results of both leave-one-out cross-validation and the accuracy measure suggested that this specific method is satisfactory. Compared with previous statistical and field monitoring results, the proposed method is promising for cost-effective monitoring of TSS concentrations in water, especially in data-poor watersheds. This specific method may provide the basis for the conservation and management of nonpoint source pollution in arid regions. • A novel method is proposed to assess TSS concentrations under data scarce conditions. • Sentintinel-2 MSI derived NDVI is an effective tool to evaluate watershed condition. • TSS concentration is negatively related with NDVI at five different spatial scales. • 300 m could be considered as the most appropriate scale for assessing TSS concentrations. [ABSTRACT FROM AUTHOR]

In arid and semi-arid regions, water-quality problems are crucial to local social demand and human well-being. However, the conventional remote sensing-based direct detection of water quality parameters, especially using spectral reflectance of water, must satisfy certain preconditions (e.g., flat water surface and ideal radiation geometry). In this study, we hypothesized that drone-borne hyperspectral imagery of emergent plants could be better applied to retrieval total nitrogen (TN) concentration in water regardless of preconditions possibly due to the spectral responses of emergent plants on nitrogen removal and water purification. To test this hypothesis, a total of 200 groups of bootstrap samples were used to examine the relationship between the extracted TN concentrations from the drone-borne hyperspectral imagery of emergent plants and the experimentally measured TN concentrations in Ebinur Lake Oasis using four machine learning (ML) models (Partial Least Squares (PLS), Random Forest (RF), Extreme Learning Machine (ELM), and Gaussian Process (GP)). Through the introduction of the fractional order derivative (FOD), we build a decision-level fusion (DLF) model to minimize the regression results' biases of individual ML models. For individual ML model, GP performed the best. Still, the amount of uncertainty in individual ML models renders their performance to be subpar. The introduction of the DLF model greatly minimizes the regression results' biases. The DLF model allows to reduce potential uncertainties without sacrificing accuracy. In conclusion, the spectral response caused by nitrogen removal and water purification on emergent plants could be used to retrieve TN concentration in water with a DLF model framework. Our study offers a new perspective and a basic scientific support for water quality monitoring in arid regions. Image 1 • An indirect remote sensed method for retrieving total nitrogen concentration in water are proposed. • Fractional order derivative is an effective data mining technology for drone-borne hyperspectral data. • Decision-level fusion (DLF) model allows to reduce potential uncertainties without sacrificing accuracy. • Combined use of Bootstrap and DLF model is effective when dealing with small sample size. [ABSTRACT FROM AUTHOR]

Accurate assessment of soil salinization is considered as one of the most important steps in combating global climate change, especially in arid and semi-arid regions. Multi-spectral remote sensing (RS) data including Landsat series provides the potential for frequent surveys for soil salinization at various scales and resolutions. Additionally, the recently launched Sentinel-2 satellite constellation has temporal revisiting frequency of 5 days, which has been proven to be an ideal approach to assess soil salinity. Yet, studies on detailed comparison in soil salinity tracking between Landsat-8 OLI and Sentinel-2 MSI remain limited. For this purpose, we collected a total of 64 topsoil samples in an arid desert region, the Ebinur Lake Wetland National Nature Reserve (ELWNNR) to compare the monitoring accuracy between Landsat-8 OLI and Sentinel-2 MSI. In this study, the Cubist model was trained using RS-derived covariates (spectral bands, Tasseled Cap transformation-derived wetness (TCW), and satellite salinity indices) and laboratory measured electrical conductivity of 1:5 soil:water extract (EC). The results showed that the measured soil salinity had a significant correlation with surface soil moisture (Pearson's r = 0.75). The introduction of TCW generated satisfactory estimating performance. Compared with OLI dataset, the combination of MSI dataset and Cubist model yielded overall better model performance and accuracy measures (R 2 = 0.912, RMSE = 6.462 dS m−1, NRMSE = 9.226%, RPD = 3.400 and RPIQ = 6.824, respectively). The differences between Landsat-8 OLI and Sentinel-2 MSI were distinguishable. In conclusion, MSI image with finer spatial resolution performed better than OLI. Combining RS data sets and their derived TCW within a Cubist framework yielded accurate regional salinity map. The increased temporal revisiting frequency and spectral resolution of MSI data are expected to be positive enhancements to the acquisition of high-quality soil salinity information of desert soils. Unlabelled Image • Differences between Landsat-8 OLI and Sentinel-2 MSI are distinguishable. • Satellite derived surface soil moisture is significantly correlated with soil salinity. • Cubist is a satisfactory approach for soil salinity mapping (RPIQ = 6.824). • MSI image with finer spatial resolution performs better than OLI. • We need to pay more attention to the environmental covariates. [ABSTRACT FROM AUTHOR]

[Display omitted] • The growth of exotic mangrove was significantly faster than that of native species. • The morphological patterns can be used to indicate the hotspots of mangroves. • Sonneratia species was found in the forest gap and forest edge of native mangroves. • The spread of Sonneratia species is fast in bare tidal flat and tidal creek zones. • Invasive potentials of Sonneratia mangrove in Shenzhen Bay should not be ignored. Mangroves are essential forest communities in tropical and subtropical coastal zones, providing unique ecological functions and significant social and economic value. Accurate and efficient monitoring of mangroves is crucial for their protection and management. The impact of exotic species on native species is still debated, especially for mangroves, the spatiotemporal dynamics of introduced species and native mangrove species need to be closely monitored. This study aimed to investigate how exotic mangrove species affects the spatial dynamics of mangroves in Shenzhen Bay, China. Yearly Landsat images from 2000 to 2011 and Chinese high spatial resolution images during 2012–2022 were obtained and the pixel-based and multiscale object-based methods were used to obtain mangrove distribution. The results showed that: (1) Overall, mangroves in the study area increased from 281.51 ha to 526.43 ha from 2000 to 2022, with different spatiotemporal patterns in Shenzhen (+5.14%) and Hong Kong (+2.38%); (2) rapid spread of Sonneratia species was one of the main contributions to the growth of mangrove hotspots, and growth hotspots were mainly concentrated in the estuary delta; and (3) Sonneratia mangrove species was dominant in interspecific competition, not only invading the habitat of local mangroves, but also preventing the expansion of native mangrove growth. The results provide a reference for fine-scale and accurate mangrove mapping with high spatial and temporal resolutions, and highlight the need to pay more attentions to the spread and invasive potential of exotic mangrove species in the study area, as well as cooperation in adjacent reserves. [ABSTRACT FROM AUTHOR]

[Display omitted] • For population modelling, remote sensing nighttime light data and nighttime LBS data were utilized. • Population maps of the Tibetan Plateau at the city level were modelled by geographically weighted regression. • The crowd exposure frequency characterized by nighttime LBS data contributed significantly to population modelling. • The model predicted more accurate population maps than three mainstream international population datasets. • The operational mechanism of the new demographic model was revealed. Population mapping is one of the fundamental materials for regional sustainability studies. Most scholars applied remote sensing data with excessive indicators to fit the population distribution. Nevertheless, over-complex models were lack of accuracy. This paper proposed a population model in the Qinghai-Tibet Plateau as a study area; the model has consisted of Human Activity Extent and Crowd Exposure Frequency. Performed remote sensing land cover data, nighttime light data, and LBS geographic big data as candidate indicators for exploratory regression experiments eventually developed an optimal population model assembled by nighttime LBS data and nighttime light data. The model fits significantly better at the city level (R2values of 0.9922) and reduces the error compared with other studies and publicly available datasets (%RMSE values of 6.83%). For the first time, the model proposes that Crowd Exposure Frequency based on nighttime LBS data can provide effective population simulation in the global, nighttime light data gain compensation for it. Nighttime light data plays a dominant role in densely populated areas; at the same time, nighttime LBS revised its overestimation. They modified each other to make the model accuracy significantly elevated. The modelling framework allows dynamic and low-cost population estimates of ecologically vulnerable areas and thus serves sustainable regional development. [ABSTRACT FROM AUTHOR]

Aerosols are an important contributor to global atmospheric environmental changes and have critical effects on the global climate system and human health. Central Asia is one of the most important sources of dust aerosols in the world and produces a significant portion of global aerosols. Central Asia is also a scarce aerosol-data area, so it is of great significance to obtain and verify new aerosol data from this region. In this study, based on the aerosol optical depth (AOD) data from remote sensing (MYD04_L2) and ground-based observations (AERONET and Microtops II), the applicability of multiangle implementation of atmospheric correction (MAIAC) AOD in Central Asia was comprehensively analyzed, and the variation in AOD in Central Asia over the last 20 years was analyzed by the information entropy method. The results indicate that MAIAC AOD has good application prospects in Central Asia and can effectively compensate for the lack of observational data from Central Asia. MAIAC AOD exhibits excellent spatiotemporal consistency with MYD04 deep blue (DB) AOD and has a better ability than MYD04 DB AOD to describe local fine-scale features. Furthermore, MAIAC AOD demonstrates high consistency with ground-based AOD observations, showing high R (0.737) and low RMSE (0.067) values and having 65.2% of samples falling within the expected error (EE) envelope. When employing the ground-based AOD observations as a bridge, MAIAC exhibits superiority to MYD04 DB in both the richness number of valid high-quality retrievals and the retrieval accuracy of various evaluation indicators. The annual variation in AOD in Central Asia exhibits a unimodal distribution, with AOD being largest in April, followed by March and May, and comparable rangeability. Based on information entropy, interannual variation in AOD exists in most areas of Central Asia, with AOD in the Taklimakan Desert area being significantly increased and that in northern Central Asia (Kazakhstan) showing a downward trend. • MAIAC and MYD04 DB AODs exhibit good spatiotemporal consistency over Central Asia. • A good agreement between the AOD values from MAIAC AOD and AERONET was found. • Spatial and temporal variations are evaluated against MAIAC products. • The AOD annual variation in Central Asia is characterized by a unimodal distribution. [ABSTRACT FROM AUTHOR]