Time Series Of Satellite Data Research Articles

Hydrological Factor and Land Use/Land Cover Change Explain the Vegetation Browning in the Dosso Reserve, Niger

The West Sahel is facing significant threats to its vegetation and wildlife due to the land degradation and habitat fragmentation. It is crucial to assess the regional vegetation greenness dynamics in order to comprehensively evaluate the effectiveness of protection in the natural reserves. This study analyzes the vegetation greenness trends and the driving factors in the Dosso Partial Faunal Reserve in Niger and nearby unprotected regions—one of the most important habitats for endemic African fauna—using satellite time series data from 2001 to 2020. An overall vegetation browning trend was observed throughout the entire region with significant spatial variability. Vegetation browning dominated in the Dosso Reserve with 17.7% of the area showing a significant trend, while the area with significant greening was 6.8%. In a comparison, the nearby unprotected regions to the north and the east were found to be dominated by vegetation browning and greening, respectively. These results suggest that the vegetation protection practice was not fully effective throughout the Dosso Reserve. The dominant drivers were also diagnosed using the Random Forest model-based method and the Partial Dependence Plot tool, showing that water availability (expressed as soil moisture) and land use/land cover change were the most critical factors affecting vegetation greenness in the study region. Specifically, soil moisture stress and specific land management practices associated with logging, grazing, and land clearing appeared to dominate vegetation browning in the Dosso Reserve. In contrast, the vegetation greening in the central Dosso Reserve and the nearby unprotected region to the east was probably caused by the increase in shrubland/forest, which was related to the effective implementation of protection. These findings improve our understanding of how regional vegetation greenness dynamics respond to environmental changes in the Dosso Reserve and also highlight the need for more effective conservation planning and implementation to ensure sustainable socio-ecological development in the West Sahel.

Linearly interpolating missing values in time series helps little for land cover classification using recurrent or attention networks

Satellite time series data, widely used for land cover classification, often contain missing values due to cloud contamination, which can negatively affect classification. Numerous strategies have been developed to reconstruct the missing values to produce regular time series for machine learning classifiers, among which the compositing followed by the linear interpolation is most widely used. However, the classification improvement of linear interpolation for land cover classification has not been examined. Recently developed deep learning models such as long short term memory (LSTM) and Transformer allow such examination as they can classify time series with missing values. In this study, we compared the time series composites with missing values (without linear interpolation) and the linearly interpolated time series composites (without missing values) for land cover classification. About 18 thousand Harmonized Landsat Sentinel-2 (HLS) images acquired over Amur River Basin of China (890,308 km2) in 2021 were composited to 14 16-day periods. Two time series composites were classified, i.e., (i) the 16-day composites without interpolation that have on average 15.35% 16-day periods with missing values and (ii) the linearly interpolated 16-day composites with no missing values. The classifications showed that (1) between classifications with and without linear interpolation there was < 0.2% overall accuracy differences for the bidirectional LSTM (Bi-LSTM) and < 0.5% for the Transformer both of which were smaller than model training randomness; and (2) the computation time can be saved using composites without linear interpolation. The findings suggested that it is unnecessary to use the time-consuming linear interpolation in Bi-LSTM and Transformer-based land cover classifications. The findings were confirmed by experiments for sensitivity to the number of cloud-free composites and to different classification legends using crop type classifications. It implied the linear interpolation algorithm cannot reconstruct reliable time series for land cover classifications and historical use of such method is more about mitigating the inability of traditional classifiers to handle missing values rather than improving classifications. Linear interpolation is not necessary for LSTM and Transformer with capability to handle missing values. The training datasets and developed codes in this study are made publicly available.

Early Season Forecasting of Corn Yield at Field Level from Multi-Source Satellite Time Series Data

Crop yield forecasting during an ongoing season is crucial to ensure food security and commodity markets. For this reason, here, a scalable approach to forecast corn yields at the field-level using machine learning and satellite imagery from Sentinel-2 and Landsat missions is proposed. The model, evaluated on 1319 corn fields in the U.S. Corn Belt from 2017 to 2022, integrates biophysical parameters from Sentinel-2, Land Surface Temperature (LST) from Landsat, and agroclimatic data from ERA5 reanalysis dataset. Resampling the time series over thermal time significantly enhances predictive performance. The addition of LST to our model further improves in-season yield forecasting, through its capacity to detect early drought, which is not immediately visible to optical sensors such as the Sentinel-2. Finally, we propose a new two-stage machine learning strategy to mitigate early season partially available data. It consists in extending the current time series on the basis of complete historical data and adapting the model inference according to the crop progress.

Sea Surface Temperature Prediction in China Sea Based on SAM-LSTM Approach

Sea Surface Temperature (SST), a critical environmental element in the ocean, significantly impacts the global atmosphere-ocean energy balance and holds the potential to trigger severe weather like droughts, floods, and El Niño events. Therefore, the prediction of future SST dynamics is crucial to identifying these extreme events and mitigating the damage they caused. In this study, we introduce a time series prediction method based on the Self-Attention Mechanism-Long Short-Term Memory (SAM-LSTM) model. In addition, the historical time-series satellite data of SST anomaly (SSTA) is used instead of SST itself considering that the fluctuations of SST are very small compared to their absolute magnitudes. The Seasonal-Trend decomposition using Loess (STL) method is adopted to decompose the complex non-linear SSTA time series into trend components, seasonal components, and residual components. Then, the deseasonalized time series data at 6 locations in the Bohai Sea are used to train and valid the developed SAM-LSTM model. After that, the validated models are applied to the Yellow Sea, East China Sea, and South China Sea. The experimental results show that the combination of STL time series decomposition and SAM-LSTM can achieve high-precision prediction of daily SSTA than LSTM. This suggests that the methodology used in this paper has a good application for short-term daily SST prediction.

The potential of optical and SAR time-series data for the improvement of aboveground biomass carbon estimation in Southwestern China’s evergreen coniferous forests

ABSTRACT Accurate assessments of forest biomass carbon are invaluable for managing forest resources, evaluating effects on ecological protection, and achieving goals related to climate change and sustainable development. Currently, the integration of optical and synthetic aperture radar (SAR) data has been extensively utilized in estimating forest aboveground biomass carbon (AGC), while it is limited by using single-phase remote sensing images. Time-series data, which capture the interannual dynamic growth and seasonal variations of photosynthetic phenology in forests, can sufficiently describe forest growth characteristics. However, there remains a gap in research focusing on utilizing satellite-based time-series data for AGC estimation, especially for SAR sensors. This study investigated the potential of satellite-based optical and SAR time-series data for estimating AGC. Here, we undertook nine quantitative experiments of AGC estimation from Landsat 8 and Sentinel-1 and tested several regression algorithms (including multiple linear regression (MLR), random forests (RF), artificial neural network (ANN), and extreme gradient boosting (XGBoost)) to explore the contributions of spatiotemporal features to AGC estimation. The results suggested that the XGBoost algorithm was suitable for AGC estimation with explanatory solid power and stable performance. The temporal features representing forest growth trends and periodic change characteristics (such as coefficients of continuous wavelet transform) were more valuable for AGC estimation than spatial features for both sensor types, accounting for around 40% ~50% of the variance compared to 17% ~25%. The combination of optical and SAR time-series data produced the best performance (R2 = 0.814, RMSE = 18.789 Mg C/ha, rRMSE = 26.235%), compared with when utilizing optical or SAR time-series data alone (optical: R2 of 0.657 and rRMSE of 35.317%; SAR: R2 of 0.672 and rRMSE of 34.701%). Feature importance analysis also verified that temporal features of optical vegetation indices, SWIR 1/2 bands, and SAR backscatter from VV polarization were the most critical variables for AGC estimation. Furthermore, incorporating temporal features into the modeling is illustrated to be effective in reducing saturation effects within high-biomass forests. This study demonstrated the superiority of time-series data for forest carbon estimation. While the applicability of this methodology has only been investigated in evergreen coniferous forests, it may provide a viable approach needed to make full use of increasingly better and free satellite time-series data to estimate forest AGC with high accuracy, supporting policy making of forest management and sustainable development.

Summer snowline altitude gradients in Western Norway are influenced by maritime climate

ABSTRACT Mountain snowline dynamics are relatively underreported with few studies exploring spatial snowline dynamics. Whilst clear regional-scale relationships between snowline location and temperature exist in European mountains, recent research at higher latitudes reports no response to climate change. In maritime mountains, snowlines occupy complex environmental gradients. Using timeseries of satellite data from Landsat missions 5–8 (151 images between 1984 and 2021), we explored sub-regional summer snowline dynamics across the maritime-continental climate gradient in the Western Norwegian mountains. We characterize spatio-temporal snowline altitude dynamics and investigate the climate factors altering snowline patterns. Summer snowline altitudes were found to increase inland at around double the rate of the 0°C summer isotherm. Data from the European Centre for Medium-Range Weather Forecasts (ECMWF) land component of the fifth generation of European Reanalysis (ERA5-Land), showed a potential ‘maritime-mountain’ effect with coastal orographic snowfall and cloud cover-induced surface solar downwelling radiation amplifying maritime-continental snowline altitude gradients alongside surface atmospheric temperature. This was replicated in the Canadian Rocky Mountains. Between 1984 and 2021, we found spatial summer snowline gradients in Norway decreased and propose multiple climate forcings are responsible, potentially masking links between snowlines and climate change. Although non-significant, the data also suggest regional summer snowline altitudes increased. This study demonstrates the complex spatial heterogeneity in snow-climate relationships and highlights how long-term snow dynamics can be queried using fine-grain (Landsat) resolution satellite data. We share our approach through a Google Earth Engine web-app that rapidly executes spatial snowline analyses for global mountain regions via a graphical user interface.

Time series analysis of landsat data for urban heat island monitoring in Semarang City

The urban heat island (UHI) condition has received significant attention because it has the potential to negatively impact life quality and comfort, especially livability and level of comfort. Semarang City, the capital of Central Java, has been attracting many people to live and causing land use conversion to built-up areas. This study aims to observe the land surface temperature (LST) trend and to analyse the UHI spatiotemporal pattern in Semarang based on satellite time series data within 10 years. Land cover and LST were extracted from Landsat data of 2008, 2013, and 2019. Based on the results, it was observed that the vegetated area has reduced from 20.347 Ha to 7.241 Ha, while the average LST value has increased from 29.24°C to 31.31°C in 2008 and 2019 respectively. We also found that the UHI pattern, which was initially detected only in a few areas in 2008 and was focused in the city centre, has gradually spread to the east and south of Semarang in 2019. The result also shows the spatiotemporal trend of land cover conversion and UHI areas, which can be beneficial for mitigation planning in the future.

Enhancing Satellite Image Sequences through Multi-Scale Optical Flow-Intermediate Feature Joint Network

Satellite time-series data contain information in three dimensions—spatial, spectral, and temporal—and are widely used for monitoring, simulating, and evaluating Earth activities. However, some time-phase images in the satellite time series data are missing due to satellite sensor malfunction or adverse atmospheric conditions, which prevents the effective use of the data. Therefore, we need to complement the satellite time series data with sequence image interpolation. Linear interpolation methods and deep learning methods that have been applied to sequence image interpolation lead to large errors between the interpolation results and the real images due to the lack of accurate estimation of pixel positions and the capture of changes in objects. Inspired by video frame interpolation, we combine optical flow estimation and deep learning and propose a method named Multi-Scale Optical Flow-Intermediate Feature Joint Network. This method learns pixel occlusion and detailed compensation information for each channel and jointly refines optical flow and intermediate features at different scales through an end-to-end network together. In addition, we set a spectral loss function to optimize the network’s learning of the spectral features of satellite images. We have created a time-series dataset using Landsat-8 satellite data and Sentinel-2 satellite data and then conducted experiments on this dataset. Through visual and quantitative evaluation of the experimental results, we discovered that the interpolation results of our method retain better spectral and spatial consistency with the real images, and that the results of our method on the test dataset have a 7.54% lower Root Mean Square Error than other approaches.

Evaluating the Potential of Sentinel-2 Time Series Imagery and Machine Learning for Tree Species Classification in a Mountainous Forest

Accurate and reliable information on tree species composition and distribution is crucial in operational and sustainable forest management. Developing a high-precision tree species map based on time series satellite data is an effective and cost-efficient approach. However, we do not quantitatively know how the time scale of data acquisitions contributes to complex tree species mapping. This study aimed to produce a detailed tree species map in a typical forest zone of the Changbai Mountains by incorporating Sentinel-2 images, topography data, and machine learning algorithms. We focused on exploring the effects of the three-year time series of Sentinel-2 within monthly, seasonal, and yearly time scales on the classification of ten dominant tree species. A random forest (RF) and support vector machine (SVM) were compared and employed to map continuous tree species. The results showed that classification with monthly datasets (overall accuracy (OA): 83.38–87.45%) outperformed that with seasonal and yearly datasets (OA:72.38–85.91%), and the RF (OA: 81.70–87.45%) was better than the SVM (OA: 72.38–83.38%) at processing the same datasets. Short-wave infrared, the normalized vegetation index, and elevation were the most important variables for tree species classification. The highest classification accuracy of 87.45% was achieved by combining RF, monthly datasets, and topography information. In terms of single species’ accuracy, the F1 scores of the ten tree species ranged from 62.99% (Manchurian ash) to 97.04% (Mongolian Oak), and eight of them obtained high F1 scores greater than 87%. This study confirmed that monthly Sentinel-2 datasets, topography data, and machine learning algorithms have great potential for accurate tree species mapping in mountainous regions.

Large-scale spatio-temporal yield estimation via deep learning using satellite and management data fusion in vineyards

Crop yield forecasting is essential for informed farm management decisions. However, most yield forecasting models have low spatial resolution, late-season predictions, and lack validation for unseen years or locations. These limitations likely stem from the scarcity of large-scale, high-resolution yield measurement data collected over multiple years, which is uncommon in commercial specialty crop operations. Additionally, these limitations raise concerns about the models’ utility and generalizability under new environmental and management conditions within or across farms. In this study, we develop a spatio-temporal deep learning model to forecast wine grape yield early in the season, utilizing a large dataset with high spatio-temporal resolution (i.e., yield data from ∼5 million grapevines of eight cultivars observed over four years). The model is trained on weekly 10 m RGB-NIR time-series satellite data from Sentinel 2A-B, fused with categorical and continuous management inputs, including cultivar type, trellis type, row spacing, and canopy spacing. We assess the model’s generalizability by examining its performance on data from unseen years and/or locations and at multiple spatial resolutions. Our results show that combining management data with satellite imagery significantly improves model performance on entirely unseen vineyard blocks at 10 m resolution, achieving an R2 of 0.76, a mean absolute error (MAE) of 4.21 tonnes/hectare, and a mean absolute percent error (MAPE) of 13%. We find that cultivars with considerable year-to-year yield variability tend to exhibit lower predictive performance and may benefit from longer time-series observations for model training to encompass a wide range of environmental and management conditions. We also observe improved estimations from the early season in April to the middle of the growing season in June. In conclusion, the yield forecasting and model validation framework established in this study lays the foundation for training spatio-temporally aware deep learning models on exceptionally large yield datasets with high spatio-temporal resolution. We anticipate that such models will become increasingly prevalent as yield monitors are more frequently deployed in specialty crop operations.

Long term of sea surface temperature prediction for Indonesia seas using multi time-series satellite data for upwelling dynamics projection

Global warming, which impacts global temperatures, has led to an increase in sea surface temperature (SST). This rise significantly affects marine ecological systems, especially in Indonesia. As a result, long-term forecasting of SST dynamics is essential for shaping various policies. However, most studies on SST forecasting have focused on short-term predictions in local or medium-sized areas and often overlook the dynamic upwelling that influences SST. In this paper, we propose a five-year SST prediction for the Indonesian seas, incorporating multi-time-series satellite data to project upwelling dynamics. We utilized two deep learning time series models to construct a predictive model that estimates monthly SST values. This model employs extensive multiple satellite data, encompassing 14,934 points (including SST with a resolution of 0.090, wind, ENSO, heat flux, and solar radiation) from 2003 to 2021. To enrich the training data and mitigate overfitting, we applied data augmentation for time series. Experimental results reveal that all satellite datasets correlate with SST over five years. The 1D-Convolution Neural Network outperformed the Long-Short Term Memory model, exhibiting the lowest mean absolute error of 0.39 °C compared to 0.45 °C. Our model detected a consistent upwelling dynamic over a five-year pattern in the Indonesian seas. These findings suggest that our proposed model offers accurate and efficient long-term monthly SST predictions, crucial for upwelling projections.

Quantifying forest resilience post forest fire disturbances using time-series satellite data.

Quantification of forest resilience will help us to manage the sustainability of the forest environment and the safety of biodiversity. Measuring forest resilience is crucial for ensuring long-term health of the forest ecosystem in the face of ongoing environmental changes and disturbances. This study focuses on providing a framework to estimate forest resilience scores to assess the vegetation condition after a disturbance. The resilience calculation framework provided uses number of recovery days, the phenological performance level of vegetation in the year when the disturbance took place, long-term mean phenological performance, and greenness levels in subsequent year to calculate the final resilience score at each pixel. Recovery of forests using Landsat data with the help of Normalized Difference Vegetation Index or Normalized Burn Ratio poses a challenge for continuous monitoring of forested landscapes due to cloud cover and availability of scenes at continuous intervals in Landsat datasets. In this regard, MODIS 16-day EVI products were used in this study (2001 to 2020) for monitoring vegetation health before, during, and after the disaster. Bandhavgarh National Park (BNP) located in Madhya Pradesh, India is considered for this study as it witnessed major forest fire breakouts in the second half of March 2018. The objectives of the study are the following: (1) to estimate post-fire recovery days and (2) to formulate new resilience index. The study revealed that the northern part of BNP is more vulnerable and shows slow recovery. The relationship between occupation of people living inside and in the neighboring area with forest resilience is also investigated in this study.

Coastal reclamation shaped narrower and steeper tidal flats in Fujian, China: Evidence from time-series satellite data

As one of the most ecologically and economically important ecosystems, tidal flats are under severe threat from extensive coastal reclamation. The lack of time-series topographic data limits the understanding of the evolution of tidal flats under stress. Existing approaches to tidal flat topographic mapping are complex and weak in their representation of micro-geomorphic features. In this study, we propose a pixel-by-pixel inundation-based topographic mapping algorithm (PITMA) to accurately reconstruct tidal flat topography from full-time-series satellite imagery and to assess multi-decadal changes in the tidal flats of Fujian Province, which has undergone intensive coastal reclamation. The results show that the proposed algorithm can generate accurate tidal flat topography with an RMSE of 0.30 m in Fujian province. The total areas of tidal flats were 1311.27 km2, 1136.33 km2, and 1048.18 km2 in 2000, 2010, and 2020, respectively. Coastal reclamation in Fujian Province has resulted in the loss of more than 20% of the tidal flats, but the average elevation of the tidal flats has increased by 40% with mean elevations of −0.68m, −0.82m, and −0.41m in 2000, 2010, and 2020, respectively. Meanwhile, the slope of tidal flats also increased in most tidal flats in bay areas. Topographic profiles in 2000, 2010, and 2020 showed that tidal flats in bay areas gained deposition at higher-elevation areas but suffered erosion at lower-elevation areas, due to insufficient sediment availability and extensive land reclamation, then the tidal flats have become narrower and steeper. In contrast, estuarine tidal flats exhibited seaward expansion with stable slopes because of available sediment provision from the upstream river. Such tidal flat morphology would be at increased risk of erosion as sea level rises and sediment supply decreases. This quantitative assessment of the impact of coastal reclamation on tidal flat evolution provides scientific guidance for sustainable coastal management.

Time Series Satellite and Observational Data for Assessment of Urban Air Pollution and Climate Dynamics Impacts on COVID-19 transmission in Bucharest

This study conducts a complex analysis to evaluate urban air pollution and climate dynamics impacts on COVID-19 viral infection incidence and mortality in Bucharest metropolitan city in Romania. It is motivated by the COVID-19 pandemic occurrence and environmental/health challenges caused by increasing urbanization in Bucharest. This paper presents the temporal patterns characteristics of the main air pollutants PM2.5 and PM10 (inhalable particulate matter with aerodynamic size less than or equal to 2.5 µm and 10 µm, respectively) as well as nitrogen dioxide-NO2, ozone-O3, sulfur dioxide-SO2, and carbon monoxide-CO during the period March 2020–March 2022 through the integration of time-series surface observation and satellite data. Through the employing of descriptive statistics and regression models for multiple datasets of air pollutants and climate-related parameters such as air temperature at 2m height (T), relative humidity (RH), wind speed intensity (w), and direction, Planetary Boundary Layer height-PBL, and surface solar irradiance-SI, this study found that seasonal variation of aerosol loading parameters (PM2.5 and PM10) over the investigated metropolitan city have a direct impact on COVID-19 spreading. Nevertheless, additional environmental and epidemiological investigations are required to test the causality of air pollution and climate seasonality impacts on COVID-19 seasonality and its severity.

Vegetation Trend Detection Using Time Series Satellite Data as Ecosystem Condition Indicators for Analysis in the Northwestern Highlands of Ethiopia

Vegetation is an essential component of the terrestrial ecosystem and has changed significantly over the last two decades in the Northwestern Highlands of Ethiopia. However, previous studies have focused on the detection of bitemporal change and lacked the incorporation of entire vegetation time series changes, which are considered significant indicators of ecosystem conditions. The Normalized Difference Vegetation Index (NDVI) time series dataset from the Moderate-Resolution Imaging Spectroradiometer (MODIS) is an efficient method for analyzing the dynamics of vegetation change over a lengthy period using remote sensing techniques. This study aimed to utilize time series satellite data to detect vegetation changes from 2000 to 2020 and investigate their links with ecosystem conditions. The time-series satellite processing package (TIMESAT) was used to estimate the seasonal parameter values of NDVI and their correlation across the seasons during the study period. Break Detection for Additive Season and Trend (BFAST) was applied to identify the year of breakpoints, the direction of magnitude, and the number of breakpoints. The results were reported, analyzed, and linked to ecosystem conditions. The overall trend in the study area increased from 0.58 (2000–2004) to 0.65 (2015–2020). As a result, ecosystem condition indicators such as peak value (PV), base value (BV), amplitude (Amp), and large integral (LI) exhibited significant positive trends, particularly for Acacia decurrens plantations, Eucalyptus plantations, and grasslands, but phenology indicator parameters such as start of season (SOS), end of season (EOS), and length of season (LOS) did not show significant trends for almost any vegetation type. The most abrupt changes were recorded in 2015 (24.7%), 2012 (18.6%), and 2014 (9.8%). Approximately 30% of the vegetation changes were positive in magnitude. The results of this study imply that there was an improvement in the ecosystem’s condition following the establishment of the Acacia decurrens plantation. The findings are considered relevant inputs for policymakers and serve as an initial stage for the assessment of the other environmental and climatic implications of Acacia decurrens plantations at the local scale.

Coral reef applications of Landsat-8: geomorphic zonation and benthic habitat mapping of Xisha Islands, China

ABSTRACT Being one of the most significant and valuable coral reef systems in the South China Sea, the Xisha Islands has undergone rapid transformation due to increasing stressors from human impacts and climate change in recent years. However, as indispensable information for coral reef monitoring and management, the detailed reef extent, geomorphic zonation, or benthic composition of the Xisha Islands is not well documented. Considering limited access to the Xisha Islands, the rapid development of optical remote sensing technology provides us with a feasible mean for coral reef observation. This study adopted a water depth substitution index – probabilistic inundation (PI) – combined with depth-invariant index (DII) to achieve reef extent exploration, geomorphologic and benthic habitat types classification with unsupervised classification algorithms based on Landsat-8 time-series satellite data. Compared with two open-access datasets, the extent of each independent reef extracted from PI exhibited higher similarity with the actual boundary conditions displayed in RGB (Red-Green-Blue) composite images from Landsat-8. Based on PI and derived slope, we obtained geomorphic zonation classification results, and similarly benthic compositions were retrieved based on PI, DII, and reflectance. The overall accuracy of geomorphic zonation and benthic habitat classification results were 72% and 86%, respectively. We also interestingly discovered that corals of the Xisha Islands may be capable of an ability to resist chronic heat stress as a growth trend of reef area after two successive stress events in 2014–2015 were observed at most reefs. The proposed mapping framework of this study provides a repeatable and flexible scheme in depicting the comprehensive situation of coral reefs at Xisha Islands based only on publicly available remote sensing data without complicated pre-set parameters, which could be easily extended to coral reef research around the world. Simultaneously, the findings also provide requisite information supporting the sustainable management and conservation of coral reef ecosystems in the Xisha Islands.

Thermal Infrared Anomalies Associated with Recent Crustal Earthquakes in Gorj County in Romania

Cumulative stress-energy in tectonic active regions associated with ongoing deformation (co-seismic stress) along the main active geologic faults manifests various earthquakes’ precursors. Macro-fracturing processes are preceded by micro-fracturing phenomena which amplify strain field, and geophysical fields’ disturbances like space-time anomalies of Earth’s fracto-emission signals (Thermal; Ionospheric; Electric Field; Acoustic; Radon in air, underground water, and soil; Neutron, etc.) registered months to days before the occurrence of earthquakes. Multispectral and multisensor time series satellite data and space-based geodetic (GPS and GNSS) measurements in synergy with ground-based geophysical/geochemical measurements bring a significant contribution to survey pre-earthquake signals in the tectonic active areas. This study aims to assess the role of seismo-induced thermal infrared (TIR) anomalies characterized by the ground-level air-AT and land surface temperature-LST changes as significant precursors of crustal moderate and strong earthquakes recorded in Gorj County, Oltenia region in Romania. North-West Targu Jiu area is an active tectonic area characterized by two strike-slip faults systems of North-West- South West and East-West orientation, where on 13 February 2023 was recorded a shallow strong earthquake of moment magnitude Mw= 5.2 and 17 km focal depth, followed by 14 February 2023 stronger earthquake of Mw = 5.7 and 6 km focal depth. Several months beyond have been recorded more than 1000 aftershocks of Mw ≤ 4.9. Based on MODIS Terra/Aqua and NOAA AVHRR time series data, some days to weeks before the seismic sequence has detected LST and AT anomalies.

Winter wheat yield prediction using integrated Landsat 8 and Sentinel-2 vegetation index time-series data and machine learning algorithms

Timely and accurate forecasting of winter wheat yield is important to crop management, food security, and sustainable agricultural development. Unfortunately, the process of predicting winter wheat yield using satellite time series data often fails to capture complete and critical information about the crop growth process, which can restrict the accuracy of crop yield predictions. To overcome this challenge, it is necessary to increase the frequency of monitoring crop growth dynamics, identify suitable vegetation index (VI), and determine the optimal prediction model for time-series remote sensing data. In this study, we propose proposes a novel method for predicting winter wheat yield using integrated Landsat 8 (L8) and Sentinel-2 (S2) vegetation index time-series data and machine learning algorithms. Firstly, the integrated L8 and S2 dataset was obtained through the steps of cloud masking, re-sampling, re-projection, BRDF correction, and band adjustment. Then, the optimal VI was determined based on the association between the growth characteristics of winter wheat and the time-series characteristic curves of each VI. Subsequently, we employed Bayesian optimized CatBoost (BO-CatBoost) regression model to predict winter wheat yield, and compared this method with three other data-driven methods, including least absolute shrinkage and selection operator (LASSO), support vector regression (SVM), and random forest (RF). Our results showed that the winter wheat yield prediction accuracies reached the best performance using integrated Landsat 8 and Sentinel-2 WDRVI (Wide Dynamic Range Vegetation Index) time-series data and BO-CatBoost model. The R2 values were 0.70, 0.63, and 0.68, and RMSE values were 0.62, 0.73, and 0.62 t/ha for the years 2019 to 2021, respectively. In addition, acceptable accuracy was obtained for yield prediction in 2021 based on the model trained with historical data from 2019 and 2020. Moreover, this study demonstrated the result that winter wheat yield could be predicted about 40 days earlier using the proposed method. Finally, results showed that the harmonized data improved the yield estimation accuracies by a factor of 1.52, 1.29, and 1.13 compared with single S2 dataset for years 2019–2021. Various experiments demonstrated that the proposed method could effectively estimate and predict winter wheat yield data with good accuracy and robustness. This study provides technical support for improving the accuracy of winter wheat yield prediction and has the potential to be extended to yield estimation for other crops.

Spatio-temporal dynamics of water use efficiency over forest ecosystems using time series satellite data and carbon flux measurements

The significance of forests in limiting the consequences of climate change and maintaining the biogeochemical cycle is immense. Gross primary productivity (GPP) and evapotranspiration (ET) are the two most important measures of carbon and water fluxes in the forest ecosystem. The ratio between these two parameters is termed water use efficiency (WUE). In this study, the WUE was estimated for two contrasting forest ecosystems: a moist deciduous forest at the Barkot Flux Site (BFS) and a young mixed dry deciduous forest plantation at the Haldwani Flux Site (HFS), Uttarakhand, India. The Vegetation Photosynthesis Model (VPM) was utilized to estimate GPP, whilst the Mapping Evapo-Transpiration at High Resolution with Internal Calibration (METRIC) approach was employed to evaluate ET using Landsat 8 OLI satellite data from 2016 to 2018. Concurrent measurements of WUE as the ratio of GPP to ET, obtained from eddy covariance (EC) towers, were used for validation purpose. For both the sites, the maximum and minimum GPP were reported in October and December-January, respectively, whereas, the months of April-May and December had the highest and lowest ET, respectively. WUE remained elevated during November-December and reached its lowest point in April-May at both sites. The METRIC model estimated ET and EC-based GPP had reasonably good agreement with R2 values of 0.76 and 0.71, respectively. Consequently, the simulated WUE also matched closely with the observed WUE (R2 = 0.62). Accurate quantification of spatial WUE using the GPP and ET obtained from remote sensing-based VPM and METRIC models provided insight into its variability in different vegetation compositions. Further, the availability of long-term satellite data may pave the way for an improved understanding of forest ecosystems' response to water limitation in changing climate.

Integrative assessment of urban dust polycyclic aromatic hydrocarbons using ground and satellite data in Iran.

Recently, for quick urbanization and industrialization, pollutants, especially urban dust, have posed many threats to the human environment. Polycyclic aromatic hydrocarbons (PAHs) are regarded as the main dangerous pollutants that are widespread, persistent, and carcinogenic. The present work aimed to investigate the contamination and sources of PAHs, as well as to assess the risk of cancer for 16 priority PAHs, in urban dust samples in Ahvaz, Isfahan, and Shiraz cities in Iran. We measured PAH concentrations by gas chromatography-mass spectrometry (GC-MS). The average concentrations of the 16 PAHs in Ahvaz, Isfahan, and Shiraz were 6215.11, 7611.03, and 7810.37 μg kg-1, respectively. The domination of low-molecular-weight (LMW) PAHs was observed in Ahvaz, while maximum contribution was observed for high-molecular-weight (HMW) PAHs in Esfahan and Shiraz. For PAHs' source identification, diagnostic ratio, correlation analysis, clustering, and positive matrix factorization (PMF) model were used. PAHs had a combustion (coal and wood, oil, fossil fuels) and gasoline/diesel engine emissions in all cities. Comparative studies suggest that the PAH compounds' level is higher in the research area than in other countries, except for China and India. Also, the pollution of urban dust PAHs has increased over time compared to previous studies in the same cities. The cancer risk from exposure to dust contaminated with PAHs was assessed using the Incremental Lifetime Cancer Risk (ILCR) model. According to the findings, a high risk of exposure to cancer was observed in Ahvaz, Isfahan, and Shiraz. However, compared to adults, children are at higher risk of cancer in their daily lives via dermal contact and unconscious ingestion. Based on the ILCR values, the risk of cancer is in the order of Shiraz > Isfahan > Ahvaz. To assess air pollutants and their effects on urban dust, TROPOMI onboard the Sentinel-5P data were used in the studied cities during 2018-2021. The results show that Ahvaz has different high levels of CO compared to the other 2 cities. Also, Isfahan has different high levels of NO2 compared to the other 2 cities, but Shiraz has different low levels of O3. According to satellite time series data, the trend of the Aerosol Absorbing Index (AAI) has been increasing, while there was a decreasing trend in AAI from the beginning of the COVID-19 pandemic until 12 months later. Therefore, the natural and anthropogenic sources of urban dust PAHs have been increasing in all studied cities. Our findings show that PAH compounds in urban dust pose a significant threat to human health. Therefore, strategic management and planning are vital in reducing urban dust pollution.