Moderate Resolution Imaging Spectroradiometer Data Research Articles

Characterization of dust aerosols from ALADIN and CALIOP measurements

Abstract. Atmospheric aerosols have pronounced effects on climate at both regional and global scales, but the magnitude of these effects is subject to considerable uncertainties. A major contributor to these uncertainties is an incomplete understanding of the vertical structure of aerosol, largely due to observational limitations. Spaceborne lidars can directly observe the vertical distribution of aerosols globally and are increasingly used in atmospheric aerosol remote sensing. As the first spaceborne high-spectral-resolution lidar (HSRL), the Atmospheric LAser Doppler INstrument (ALADIN) on board the Aeolus satellite was operational from 2018 to 2023. ALADIN data can be used to estimate aerosol extinction and co-polar backscatter coefficients separately without an assumption of the lidar ratio. This study assesses the performance of ALADIN's aerosol retrieval capabilities by comparing them with Cloud-Aerosol LIdar with Orthogonal Polarization (CALIOP) measurements. A statistical analysis of retrievals from both instruments during the June 2020 Saharan dust event indicates consistency between the observed backscatter and extinction coefficients. During this extreme dust event, CALIOP-derived aerosol optical depth (AOD) exhibited large discrepancies with Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua measurements. Using collocated ALADIN observations to revise the dust lidar ratio to 63.5 sr, AODs retrieved from CALIOP are increased by 46 %, improving the comparison with MODIS data. The combination of measurements from ALADIN and CALIOP can enhance the tracking of aerosols' vertical transport. This study demonstrates the potential for spaceborne HSRL to retrieve aerosol optical properties. It highlights the benefits of spaceborne HSRL in directly obtaining the lidar ratio, significantly reducing uncertainties in extinction retrievals.

Automatic identification and quantification of volcanic hotspots in Alaska using HotLINK: the hotspot learning and identification network

An increase in volcanic thermal emissions can indicate subsurface and surface processes that precede, or coincide with, volcanic eruptions. Space-borne infrared sensors can detect hotspots—defined here as localized volcanic thermal emissions—in near-real-time. However, automatic hotspot detection systems are needed to efficiently analyze the large quantities of data produced. While hotspots have been automatically detected for over 20 years with simple thresholding algorithms, new computer vision technologies, such as convolutional neural networks (CNNs), can enable improved detection capabilities. Here we introduce HotLINK: the Hotspot Learning and Identification Network, a CNN trained to detect hotspots with a dataset of −3,800 satellite-based, Visible Infrared Imaging Radiometer Suite (VIIRS) images from Mount Veniaminof and Mount Cleveland volcanoes, Alaska. We find that our model achieves an accuracy of 96% (F1-score 0.92) when evaluated on −1,700 unseen images from the same volcanoes, and 95% (F1-score 0.67) when evaluated on −3,000 images from six additional Alaska volcanoes (Augustine Volcano, Bogoslof Island, Okmok Caldera, Pavlof Volcano, Redoubt Volcano, Shishaldin Volcano). In comparison with an existing threshold-based hotspot detection algorithm, MIROVA (Coppola et al., Geological Society, London, Special Publications, 2016, 426, 181–205), our model detects 22% more hotspots and produces 12% fewer false positives. Additional testing on −700 labeled Moderate Resolution Imaging Spectroradiometer (MODIS) images from Mount Veniaminof demonstrates that our model is applicable to this sensor’s data as well, achieving an accuracy of 98% (F1-score 0.95). We apply HotLINK to 10 years of VIIRS data and 22 years of MODIS data for the eight aforementioned Alaska volcanoes and calculate the radiative power of detected hotspots. From these time series we find that HotLINK accurately characterizes background and eruptive periods, similar to MIROVA, but also detects more subtle warming signals, potentially related to volcanic unrest. We identify three advantages to our model over its predecessors: 1) the ability to detect more subtle volcanic hotspots and produce fewer false positives, especially in daytime images; 2) probabilistic predictions provide a measure of detection confidence; and 3) its transferability, i.e., the successful application to multiple sensors and multiple volcanoes without the need for threshold tuning, suggesting the potential for global application.

Combining Sentinel 1, Sentinel 2 and MODIS data for major winter crop type classification over the Murray Darling Basin in Australia

Crop type classification is an essential task in agriculture that has been studied widely since the emergence of remote sensing technologies. Accurate crop mapping can help assist decision-making related to storage, marketing, and other production tasks. This study has designed robust crop classification models to classify two major crop types (cereals and canola) in the Murray Darling Basin (MDB) in Australia. These models combined Sentinel 1 and 2 and Moderate Resolution Imaging Spectroradiometer (MODIS) data. Three methods were applied to test classification quality: the holdout method, leave-one-season-out cross-validation (LOSOCV), and leave-one-cluster-out-cross-validation (LOCOCV). The holdout method evaluated the model's performance on data representing the entire population. The LOSOCV method was used to test the model's ability to extrapolate over time (unseen data from new seasons), and the LOCOCV method was used to test the ability to extrapolate over space (data from a new site). Crop type labelled data (n = 193 for cereals and n = 113 for canola) were extracted from high-resolution yield maps derived from grain yield monitors mounted on harvesters, and this was used to train and validate the classification models. The results showed that the holdout validation approach achieved the highest accuracy (overall and Kappa scores >0.98), but this might be caused by spatial autocorrelation of the sampling strategy implemented, which leads to an overoptimistic scenario. The overall accuracies and Kappa scores achieved from the LOSOCV method varied depending upon the difference in crop phenology from season to season, with overall accuracy ranging from 0.70 to 0.92 and Kappa scores from 0.61 to 0.90. The overall accuracies and Kappa scores for the LOCOCV ranged from 0.87 to 0.99 and 0.84 to 0.98, respectively. The gross primary productivity (GPP) and the chlorophyll red edge index (CIr) were the most important features across the three models. The outputs of this research can potentially complement models that require crop type information, such as carbon cycling and crop yield models.

Analytical study of land surface temperature for evaluation of UHI and UHS in the city of Chandigarh India

The Land Surface Temperature (LST) plays a crucial role in the ecological well-being of cities worldwide, intricately tied to the presence of vegetation. This investigation aims to assess the relationship between seasonal LST fluctuations and the Normalized Difference Vegetation Index (NDVI), as well as the correlation between LST and the Normalized Difference Built-up Index (NDBI), utilizing Pearson's correlation coefficient technique with extensive Landsat-8 data spanning 2016 to 2020. Additionally, the study evaluates the ecological and thermal qualities of the city using the Urban Thermal Field Variance Index (UTFVI). The ecological evaluation based on UTFVI for 2020 indicates that 52.03% of the area falls under the excellent category and 31.82% under the worse category. Notably, there is a significant increase in summer mean LST from 32.42°C in 2016 to 38.27°C in 2020. Identified Urban Heat Islands (UHIs), indicative of the warmest areas within city limits due to human activities, show an increasing trend in the summer UHI temperature from 36.92°C to38.06°C between 2016 and 2020, respectively. The study reveals a robust inverse correlation between LST and NDVI, as well as a direct relationship between LST and NDBI across the entire city. The differentiation between UHI and non-UHI areas is achieved using the LST range. Furthermore, LST maps pinpoint UHI hotspots in Chandigarh, highlighting areas unsuitable for human habitation due to extreme heat. Validation of LST values employs Moderate Resolution Imaging Spectroradiometer (MODIS) data as a baseline, where MOD11A1 data's thermal infrared band offers a spatial resolution of 1 km, contrasting with Landsat 8 (Band-10) 100 m resolution. Remarkably, a highly positive correlation is observed between approximated LST values derived from Landsat 8 and MOD11A1 data, without necessitating upscaling or downscaling.

Spatiotemporal aerosol prediction model based on fusion of machine learning and spatial analysis

This study examined long-term aerosol optical thickness (AOT) data from the Moderate Resolution Imaging Spectroradiometer (MODIS) to quantify aerosol conditions on the Korean Peninsula. Time-series machine learning (ML) techniques and spatial interpolation methods were used to predict future aerosol trends. This investigation utilized AOT data from Terra MODIS and meteorological data from Automatic Weather System (AWS) in eight selected cities in Korea (Gangneung, Seoul, Busan, Wonju, Naju, Jeonju, Jeju, and Baengyeong) to assess atmospheric aerosols from 2000 to 2021. A machine-learning-based AOT prediction model was developed to forecast future AOT using long-term observations. The accuracy analysis of the AOT prediction results revealed mean absolute error of 0.152 ± 0.15, mean squared error of 0.048 ± 0.016, bias of 0.002 ± 0.011, and root mean squared error of 0.216 ± 0.038, which are deemed satisfactory. By employing spatial interpolation, gridded AOT values within the observation area were generated based on the ML prediction results. This study effectively integrated the ML model with point-measured data and spatial interpolation for an extensive analysis of regional AOT across the Korean Peninsula. These findings have substantial implications for regional air pollution policies because they provide spatiotemporal AOT predictions.

Combined use of HYSPLIT model and MODIS aerosols optical depth to study the spatiotemporal circulation patterns of Saharan dust events over Central Europe

Mineral dust released from the desert region and transported into the atmosphere has a crucial impact on the Earth's climate system's biogeochemical cycle. It has serious adverse effects on human health. The Sahara is one of the world's dustiest areas. This investigation intends to uncover the underlying reasons for atmospheric dust dispersion throughout the year by tracking the dusttransport and deposition in Central Europe, focusing on arid areas of North Africa. In this paper, we use the GDAS (Global Data Assimilation System) archival meteorological database to compute the analytical forward trajectories and configure the particle concentrations using the HYSPLIT (Hybrid Single-Particle Lagrangian Integrated Trajectory) model. Besides, we investigate the synoptic meteorological conditions of acute Saharan dust episodes to determine the dynamic atmospheric system during their onset. The forward trajectories reveal the seasonality of wind-blown dust throughout the year. Dust storms are typically more prevalent in the spring, with a second peak in the Summer. As a result, particle transport takes various paths as the seasons and climatic conditions change. The most dust-laden masses, which reach high altitudes from the source areas, are often transported to Central Europe, where their seasonal distribution is relatively similar to that of the studied African region. However, the intensity and frequency of Saharan dust events (SDEs) have significantly changed in the previous decades, with an increased number of intense winter storms. According to the synoptic analysis, this variability is strongly linked to two factors. (1) The intensity and lifetime variation of the Mediterranean cyclones and (2) Climate change triggered lee-side (Sharav) cyclogenesis modified by the topographic complexity of Atlas. This study also confirmed the effectiveness of the HYSPLIT model in simulating atmospheric dust after comparing it with annual aerosol optical depth measurements from MODIS (Moderate-Resolution Imaging Spectroradiometer) data.

Assessment of agricultural drought in Iraq employing Landsat and MODIS imagery

Abstract Climate change poses an urgent global challenge in water resource management, with drought emerging as a pervasive threat worldwide. Over the past two decades, Iraq has faced severe droughts, categorized into meteorological, agricultural, hydrological, and socioeconomic types. Agricultural drought, marked by prolonged soil moisture deficits due to insufficient rainfall, often leads to crop failures. This comprehensive study delves into the impact of drought on Iraq’s vegetation cover from 2000 to 2022, utilizing advanced tools like remote sensing (RS) and geographic information systems. The normalized difference vegetation index (NDVI) facilitated the creation of multitemporal drought maps. Employing Landsat satellite imagery and Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation indices, the study revealed 2000, 2008, 2009, 2010, 2012, and 2022 as the most drought-prone years. In specific years such as 2000, 2008, 2010, and 2009, Landsat imagery showcased the lowest vegetation cover percentages (3.356, 4.984, 5.112, and 5.409%, respectively), while MODIS data indicated the lowest percentages in 2008, 2000, 2009, 2012, and 2022 (11.122, 11.260, 12.580, 13.026, and 14.445%, respectively). This study underscores the pivotal role of RS, particularly NDVI, as a valuable tool for agricultural drought early warning systems. The findings highlight the effectiveness of NDVI as a simple and cost-efficient index for monitoring changes in vegetation conditions and assessing the impact of droughts on agriculture.

Evapotranspiration partitioning by integrating eddy covariance, micro-lysimeter and unmanned aerial vehicle observations: A case study in the North China Plain

Partitioning of evapotranspiration (ET) into soil evaporation (E) and plant transpiration (T) poses a significant challenge. This study proposed a novel approach that combines eddy covariance, micro-lysimeter, and high-resolution unmanned aerial vehicle (UAV) images within the flux footprint to optimize the canopy and soil resistances of the Shuttleworth-Wallace model (S-W) using a multi-objective optimization scheme. A two-year experiment was conducted at the Yucheng and Luancheng sites to investigate the effectiveness of the proposed method for the summer maize-wheat winter system in the North China Plain. Our results showed that the S-W model with multi-objective optimization by using high-resolution UAV images within the flux footprint significantly improved the estimation accuracy of ET components compared with the traditional one- and two-objective optimization schemes. The estimation of ET, T, and E of our method had an average root mean square error of 0.67 mm day−1, 0.66 mm day−1, and 0.28 mm day−1, respectively. The optimized S-W model using high-resolution UAV data within the flux footprint produced better ET partitioning than that optimized using Moderate Resolution Imaging Spectroradiometer (MODIS) data. We then applied the proposed method to estimate ET and ET components in the North China Plain. This study highlighted the importance of utilizing multi-source data (especially high-resolution UAV images) and multi-objective optimization for accurate ET partitioning.

A Bayesian model for predicting monthly fire frequency in Kenya.

This study presents a comprehensive analysis of historical fire and climatic data to estimate the monthly frequency of vegetation fires in Kenya. This work introduces a statistical model that captures the behavior of fire count data, incorporating temporal explanatory factors and emphasizing the predictive significance of maximum temperature and rainfall. By employing Bayesian approaches, the paper integrates literature information, simulation studies, and real-world data to enhance model performance and generate more precise prediction intervals that encompass actual fire counts. To forecast monthly fire occurrences aggregated from the Moderate Resolution Imaging Spectroradiometer (MODIS) data in Kenya (2000-2018), the study utilizes maximum temperature and rainfall values derived from global GeoTiff (.tif) files sourced from the WorldClim database. The evaluation of the widely used Negative Binomial (NB) model and the proposed Bayesian Negative Binomial (BNB) model reveals the superiority of the latter in accounting for seasonal patterns and long-term trends. The simulation results demonstrate that the BNB model outperforms the NB model in terms of Root Mean Square Error (RMSE), and Mean Absolute Scaled Error (MASE) on both training and testing datasets. Furthermore, when applied to real data, the Bayesian Negative Binomial model exhibits better performance on the test dataset, showcasing lower RMSE (163.22 vs. 166.67), lower MASE (1.12 vs. 1.15), and reduced bias (-2.52% vs. -2.62%) compared to the NB model. The Bayesian model also offers prediction intervals that closely align with actual predictions, indicating its flexibility in forecasting the frequency of monthly fires. These findings underscore the importance of leveraging past data to forecast the future behavior of the fire regime, thus providing valuable insights for fire control strategies in Kenya. By integrating climatic factors and employing Bayesian modeling techniques, the study contributes to the understanding and prediction of vegetation fires, ultimately supporting proactive measures in mitigating their impact.

Remote Sensing Active Fire Detection Tools Support Growth Reconstruction for Large Boreal Wildfires

Spatial and temporal estimates of burned areas are often used to model greenhouse gas and air pollutant emissions from fire events that occur in a region of interest and over specified time frames. However, fire behaviour, fuel consumption, fire severity, and ecological effects vary over both time and space when a fire grows across varying fuels and topography under different environmental conditions. We developed a method for estimating the progression of individual wildfires (i.e., day-of-burn) employing ordinary kriging of a combination of different satellite-based active fire detection data sources. We compared kriging results obtained using active fire detection products from the Moderate Resolution Imaging Spectroradiometer (MODIS), the Visible Infrared Imaging Radiometer Suite (VIIRS), and combined MODIS and VIIRS data to study how inferences about a wildfire’s evolution vary among data sources. A quasi-validation procedure using combined MODIS and VIIRS active fire detection products that we applied to an independent data set of 37 wildfires that occurred in the boreal forest region of the province of Ontario, Canada, resulted in nearly half of each fire’s burned area being accurately estimated to within one day of when it actually burned. Our results demonstrate the strengths and limitations of this geospatial interpolation approach to mapping the progression of individual wildfires in the boreal forest region of Canada. Our study findings highlight the need for future validations to account for the presence of spatial autocorrelation, a pervasive issue in ecology that is often neglected in day-of-burn analyses.

Two-decadal climate impacts on growth of major forest types of Eastern Himalaya

Forests affect regional climates, livelihoods and global cycles of water, carbon and nitrogen. Anthropogenic activities and climatic change affect forest health and national growth. Therefore, developing effective forest management plans requires understanding of the drivers of forest growth. The primary objective of this study was to understand the long-term effect of abiotic factors on the growth of forests in the region. This study used Moderate Resolution Imaging Spectroradiometer (MODIS) data for vegetation indices like NDVI and EVI and NASA's Land Assimilation datasets (wind speed, evapotranspiration, soil moisture and temperature) to understand their role on forest growth through statistical techniques such as Pearson's correlation and Multiple Linear Regression. The study examined the relationship between standard monthly vegetation indices and abiotic variables (i.e., moisture and temperature at different soil profiles up to 2 m depth, land surface temperature, evapotranspiration, relative humidity, wind velocity, and air temperature at 2 m height) in selected forests of the Eastern Himalayas for two decades (2001–2020, n = 240). Rainfall, temperature, and other associated factors significantly affected forest growth in the region. It was observed that rainfall alone affected forest growth in the region. However, its impact was maximum after two months of the rain events, reflecting a significant lag effect. Soil moisture at different depths affected vegetation growth in all forest types. Reduced soil moisture had a more significant effect on old-growth forests than younger forests. Multiple Linear Regression models developed with the abiotic factors explained higher variability in forest growth. In conclusion, this study reveals that rainfall, temperature, and their associated variables significantly affected forest growth in the study area. The study has significant implications for forest management in the region for formulating better strategies to mitigate climate change effects on forests in the region in future.

Effectiveness of Spatiotemporal Data Fusion in Fine-Scale Land Surface Phenology Monitoring: A Simulation Study

Spatiotemporal data fusion technologies have been widely used for land surface phenology (LSP) monitoring since it is a low-cost solution to obtain fine-resolution satellite time series. However, the reliability of fused images is largely affected by land surface heterogeneity and input data. It is unclear whether data fusion can really benefit LSP studies at fine scales. To explore this research question, this study designed a sophisticated simulation experiment to quantify effectiveness of 2 representative data fusion algorithms, namely, pair-based Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) and time series-based Spatiotemporal fusion method to Simultaneously generate Full-length normalized difference vegetation Index Time series (SSFIT) by fusing Landsat and Moderate Resolution Imaging Spectroradiometer (MODIS) data in extracting pixel-wise spring phenology (i.e., the start of the growing season, SOS) and its spatial gradient and temporal variation. Our results reveal that: (a) STARFM can improve the accuracy of pixel-wise SOS by up to 74.47% and temporal variation by up to 59.13%, respectively, compared with only using Landsat images, but it can hardly improve the retrieval of spatial gradient. For SSFIT, the accuracy of pixel-wise SOS, spatial gradient, and temporal variation can be improved by up to 139.20%, 26.36%, and 162.30%, respectively; (b) the accuracy improvement introduced by fusion algorithms decreases with the number of available Landsat images per year, and it has a large variation with the same number of available Landsat images, and (c) this large variation is highly related to the temporal distributions of available Landsat images, suggesting that fusion algorithms can improve SOS accuracy only when cloud-free Landsat images cannot capture key vegetation growth period. This study calls for caution with the use of data fusion in LSP studies at fine scales.

Spatial-temporal dynamics of paddy productivity on the north coast of Java Island, Indonesia based on the principal component analysis of MODIS NDVI anomaly data

Comprehending the dynamics of paddy productivity is imperative for enhancing the efficacy of agricultural land developments. This study provides the application of principal component analysis (PCA) as a method for visualizing the spatial-temporal changes in paddy productivity. The analysis is conducted using the 8-day NDVI (normalized difference vegetation index) anomaly data of MODIS (Moderate Resolution Imaging Spectroradiometer) data spanning the period from 2000 to 2020. The regencies of Karawang, Subang, and Indramayu on the north coast of Java island are chosen as the study area because of their top rice production areas in Indonesia. The results show that the first leading PCA of the NDVI anomaly is related to the interannual variability of paddy productivity with 3-4 year cycles. The spatial and temporal dynamics of the first mode of eigenvectors and principal component time series can generally be grouped into nine categories. Two important categories to note are category-1 (1 January – 19 March) and category-8 (12 September – 16 December). In category-1, the NDVI anomalies move from north to middle and middle to north areas in Karawang and Subang regencies, respectively. In Indramayu Regency, the NDVI anomalies relatively remain in almost all areas. In contrast, in category-8 the NDVI anomalies move from the middle to northern areas in Karawang, Subang, and Indramayu regencies.

Analysis of Spatiotemporal Evolution and Influencing Factors of Vegetation Net Primary Productivity in the Yellow River Basin from 2000 to 2022

The Yellow River Basin (YRB) plays a very important role in China’s economic and social development and ecological security, so studying the spatiotemporal variation characteristics of net primary productivity (NPP) and its influencing factors is of great significance for protecting the stable development of its ecological environment. This article takes the YRB as the research area, based on Moderate Resolution Imaging Spectroradiometer (MODIS) data, climate data, terrain data, land data, social data, and the gravity recovery and climate experiment (GRACE) data. The spatiotemporal evolution characteristics of vegetation NPP in the YRB from 2000 to 2022 were explored using methods such as trend analysis, correlation analysis, and geographic detectors, and the correlation characteristics of NPP with meteorological factors, social factors, and total water storage (TWS) were evaluated. The results indicate that the NPP of vegetation in the YRB showed an increasing trend (4.989 gC·m−2·a−1) from 2000 to 2022, with the most significant changes occurring in the middle reaches of the YRB. The correlation coefficient indicates that temperature and accumulated temperature have a significant positive impact on the change of NPP, while TWS has a significant negative impact. In the study of the factors affecting vegetation NPP in the YRB, the most influential factors are soil type (0.48), precipitation (0.46), and temperature (0.32). The strong correlation between TWS and vegetation NPP in the YRB is about 39%, with a contribution rate of about 0.12, which is a factor that cannot be ignored in studying vegetation NPP changes in the YRB.

Spatial, seasonal and climatic drivers of suspended sediment atop Great Bahama Bank

ABSTRACTSuspension is the key mechanism by which fine‐grained sediment (≤125 μm) is winnowed and transported across shallow‐water carbonate platforms into adjacent deep waters. Unlike sliding and saltation, which deliver sedimentary structures via bedload, the sedimentological signature of suspended sediment is more cryptic. This study focuses on suspended sediment, and its drivers – wind, waves and tides – to better constrain the processes responsible for the transport of fine‐grained sediments. By building forward from remote sensing algorithms developed for deep‐waters, sediment suspension in the shallow water column can be mapped from satellite. By applying machine learning to Moderate Resolution Imaging Spectroradiometer data for Great Bahama Bank, this study demonstrates how the drivers of sediment suspension change over 18 years across this 100 000 km2 carbonate platform. Through time, both seasonal patterns of suspension, as well as those induced by El Niño‐Southern Oscillation and, more subtly, the Atlantic Meridional Overturning Circulation were tracked. El Niño‐Southern Oscillation modulates wind‐induced currents, while Atlantic Meridional Overturning Circulation affects local sea level. Across space, this study shows how the eastern margin of Great Bahama Bank, which is traditionally considered to be wind‐dominated, primarily owes its suspended sediment to tidal currents focused between islands. Sediment suspension across the leeward margin of Great Bahama Bank, meanwhile, can be attributed to wind‐induced currents and waves. The authors consider this work a step towards delivering a quantitative, reproducible, process‐based understanding of sediment suspension atop carbonate platforms using Earth observation data.

GF-1 WFV Surface Reflectance Quality Evaluation in Countries along “the Belt and Road”

The GaoFen-1 wide field of view (GF-1 WFV) has produced level 1 digital number data globally; however, most applications have focused on China, and data quality outside China has not been validated. This study presents a preliminary assessment of the 2020 GF-1 WFV surface reflectance data for Nepal, Azerbaijan, Kenya, and Sri Lanka along “the Belt and Road” route using Sentinel-2 Multi-Spectral Instrument (MSI), Landsat-8 Operational Land Image (OLI), and Moderate Resolution Imaging Spectroradiometer (MODIS) data. A method for obtaining the GF-1 WFV surface reflectance data was also proposed, with steps including atmospheric correction, cross-radiation calibration, and bidirectional reflectance distribution function correction. The results showed that WFV surface reflectance data was not significantly different from MSI, OLI, and MODIS surface reflectance data. In the visible and near-infrared bands, for most landcover types, the bias was less than 0.02, and the precision and root mean square error were less than 0.04. When the landcover types were forest and water, the MSI, OLI, and MODIS surface reflectance data were higher than that of WFV in the near-infrared band. The results of this study provide a basis for assessing the global application potential of GF-1 WFV.

Study on the Impact of MODIS-derived NDVI and NDBI on Land Surface Temperature

With the development and expansion of cities, the increase in built-up area and the drop in vegetation coverage have significant influences on the climatic environment of urban areas. This study processed Moderate Resolution Imaging Spectroradiometer (MODIS) data from four months in 2001 to investigate the Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-Up Index (NDBI), and Land Surface Temperature (LST) in the Chengdu region. This paper explored the correlation between NDVI and LST, as well as the correlation between NDBI and LST. The findings revealed that areas with higher NDBI, indicating a greater extent of urban built-up areas, exhibited higher daytime and nighttime LST, indicating a more pronounced urban heat island effect. Conversely, an increase in vegetation cover was found to lead to a decrease in surface temperature and a certain degree of mitigation of the urban heat island effect. Furthermore, a good linear relationship was observed between NDBI and LST, with a stronger correlation during the daytime compared to the nighttime.

Temporal convolutional network based rice crop yield prediction using multispectral satellite data

Early prediction of crop yield has a significant role in ensuring food security. The crop yield depends on several parameters, such as vegetation parameters, climatic parameters, soil condition, etc. Spatial and temporal analysis of cropland is necessary for accurate prediction of yield. Usage of satellite images along with climatic data improves the prediction accuracy. This paper outlines a novel crop yield prediction model for the Paddy from Moderate Resolution Imaging Spectroradiometer (MODIS) data and climatic parameters. Various vegetation indices (VI) are collected from MODIS data for the crop’s entire life cycle. The proposed Temporal Convolutional network (TCN) with a specially designed dilated convolution module predicts the rice crop yield from vegetation indices and climatic parameters. The causal property of TCN and dilated convolution contribute to the multivariate time-based analysis of the crop and results in better performance.

Identifying Outliers of the MODIS Leaf Area Index Data by Including Temporal Patterns in Post-Processing

The moderate resolution imaging spectroradiometer (MODIS) calculates the leaf area index (LAI) for each pixel without incorporating the temporal correlation information, leading to a higher sensitivity for the LAI that produces uncertainties in observed reflectance. As a result, an increased noise level is observed in the timeseries, making the data discontinuous and inconsistent in space and time. Therefore, it is important to identify and handle the outliers during the post-processing of MODIS data. This study proposed a method to identify the MODIS LAI outliers based on the analyses of temporal patterns, including the interannual and seasonal changes in the LAI. The analysis was carried out utilizing the data from 278 global MODIS LAI sites and the results were verified against the measurement obtained from 52 ground stations. The results from the analyses detected 50 and 92 outliers based on 1.5σ and 1.0σ standard deviations, respectively, of the difference between the MODIS LAI and ground measurements; correspondingly, 46 and 65 outliers, respectively, were identified by incorporating temporal patterns during the post-processing of the data. The validation results exhibited improved values of the coefficient of determination (R2) after eliminating the MODIS LAI outliers identified through the interannual and seasonal patterns. Specifically, the R2 between the ground measurement LAI and MODIS LAI increased from 0.51 to 0.54, 0.88, and 0.90 after eliminating MODIS LAI outliers when considering the interannual patterns, seasonal patterns, and both the interannual and seasonal patterns, respectively. The results from the study provided valuable information and theoretical support to improve MODIS LAI post-processing.

Long-term study of desert dust deposition effects on phytoplankton biomass in the Persian Gulf using Google Earth Engine

Persian Gulf is surrounded by the most active dust source regions of the world. Every year, millions of tons of dust are deposited in this area causing significant adverse effects. This research aimed to fill the gap in the field of dust impact quantification regarding different and important regions in the Persian Gulf. We evaluated aerosol optical thickness (AOT) as dust, chlorophyll a (Chl a) as phytoplankton biomass, sea surface temperature (SST), and particulate organic carbon (POC) using Moderate Resolution Imaging Spectroradiometer (MODIS) data to quantify and analyze the impacts of dust. According to dust frequency maps over 21 years, 4 critical regions were selected including the Northern, Central, and Southern regions of the Persian Gulf and Strait of Hormuz. Slightly lower Chl a values were noted in the southern region and Strait of Hormuz compared to the Northern and Central regions. The Mann-Kendall (MK) test and Sen's slope estimator, which can determine trend parameters, were used to analyze 4 selected regions using 8 days, monthly, and yearly averaged data. The results of the MK test showed a significant positive trend for SST and a significant negative trend for POC. AOT and Chl a trends varied based on their locations. The cross-correlation test with a time lag showed maximum correlations between Chl a and dust (AOT) with a delay of 6–7 months. Also, to quantify the impacts of dust on phytoplankton biomass, R2 and analysis of variance (ANOVA) regression were used for data with a 6–7-month time lag. The results showed that the contribution of dust in the amount of Chl a variation was about 10 %–20 %. This study showed that the simultaneous use of remote sensing and statistical methods could provide necessary and timely warnings (regarding management) to prevent algal blooms and aquatic loss in this important and strategic area.