Moderate Resolution Imaging Spectrometer Research Articles

First retrieval of daily 160 m aerosol optical depth over urban areas using Gaofen-1/6 synergistic observations: Algorithm development and validation

The satellite-based aerosol optical depth (AOD), which can provide continuous spatial observations of aerosol loadings, is widely adopted to estimate atmospheric environmental quality and evaluate its risk for human health. However, current satellite-retrieved AOD products characterized by a comparatively coarse spatial resolution (≥1 km) can hardly analyze the structure of atmospheric pollution or its correlation with urban landscapes over populated urban areas. Existed studies have tried to address this deficiency by retrieving high-resolution AOD using Landsat images, whose long revisit period (16 days nominally), however, largely limits its applications related to urban air pollution research. To achieve both high spatial resolution and expected revisit period from satellite observation, in this study, a comprehensive AOD retrieval framework is developed for Wide-Field-of-View (WFV) satellite sensors to yield a daily AOD dataset over urban areas with 160 m spatial resolution, based on the Gaofen-1 and Gaofen-6 synergistic observations. To address the crucial challenge that the high spatial resolution and complex urban landscape both contribute a dramatic variation of land surface bidirectional reflectance, a Simulated-Annealing-coupled Semiempirical Modified Rahman-Pinty-Verstraete (SAS-MRPV) scheme is proposed to model and estimate the land surface reflectance (LSR) in the AOD retrieval framework, where the SAS-MRPV scheme is implemented using simulated annealing iteration initialized by the bidirectional reflectance distribution function (BRDF) products from the Moderate Resolution Imaging Spectrometer (MODIS) as a priori knowledge. The validation results demonstrate that the Gaofen WFV AOD retrievals exhibit good agreement with the ground-based AErosol RObotic NETwork (AERONET) and SONET (Sun-sky radiometer Observation NETwork) AOD, demonstrating the correlation coefficient and the expected error (EE)±(0.05 + 0.15AODAERONET/SONET) ratio respectively reaching up to 0.97 and over 80 %, and only slight bias fluctuations are found under different land cover types, aerosol loading, and seasonal conditions. Moreover, the validation results of Gaofen WFV AOD retrievals, with LSR estimated based on the SAS-MRPV scheme, the MODIS BRDF Products, and the Minimum Reflectance Technique (MRT) method, demonstrate better accuracy and completeness of AOD retrievals using the SAS-MPRV scheme over both natural and impervious land surfaces, indicating the stability and reliability of proposed SAS-MRPV LSR determination scheme in WFV AOD retrieval. In addition to that, the error analyses and quality control results demonstrate that the SAS-MRPV scheme is effective and necessary to guarantee the reliability and accuracy of land surface bidirectional reflectance estimation by identifying and excluding the land surface pixels unsuitable for LSR modeling, which mitigates the uncertainty and yield better accuracy in the final WFV AOD products. Furthermore, the inter-comparison between WFV-derived AOD against the operational MODIS Deep Blue (10 km), Dark Target (3 km), and Multi-Angle Implementation of Atmospheric Correction (MAIAC, 1 km) AOD products demonstrates that the 160 m WFV AOD retrievals, on the basis of obtaining similar aerosol overall descriptions as MODIS AOD retrievals, possess the capability to characterize the spatial distribution of atmospheric pollution at a finer scale with smoother variation under both clean and polluted conditions. With outstanding accuracy and reliable performance, the developed comprehensive high-resolution AOD retrieval framework exhibits substantial potential for operational AOD retrieval of Gaofen WFV satellite observations, which could be directly applied to other urban areas supporting further studies related to air pollution emission management and health risk assessment at extra-fine spatial scale.

Long-term vegetation trends and driving factors of NDVI change on the slopes of Mount Kilimanjaro

ABSTRACT This study is the first to examine vegetation change on Kilimanjaro from 2000 to 2022, using the Normalised Difference Vegetation Index (NDVI) at a spatial resolution of 250 m via the Moderate Resolution Imaging Spectrometer (MODIS). There is a long-term increase in vegetation on an annual basis, but with decreases in the long/short rains at low/high elevations. Temperature significantly influences NDVI across all elevations and seasons. Precipitation effects are more variable and often delayed, especially in the forest zone. The study explores how El Nino Southern Oscillation (ENSO) events affect NDVI differently at contrasting elevations, indicating the need for further elevation-specific remote sensing research and field data.

Time-Series-Based Spatiotemporal Fusion Network for Improving Crop Type Mapping

Crop mapping is vital in ensuring food production security and informing governmental decision-making. The satellite-normalized difference vegetation index (NDVI) obtained during periods of vigorous crop growth is important for crop species identification. Sentinel-2 images with spatial resolutions of 10, 20, and 60 m are widely used in crop mapping. However, the images obtained during periods of vigorous crop growth are often covered by clouds. In contrast, time-series moderate-resolution imaging spectrometer (MODIS) images can usually capture crop phenology but with coarse resolution. Therefore, a time-series-based spatiotemporal fusion network (TSSTFN) was designed to generate TSSTFN-NDVI during critical phenological periods for finer-scale crop mapping. This network leverages multi-temporal MODIS-Sentinel-2 NDVI pairs from previous years as a reference to enhance the precision of crop mapping. The long short-term memory module was used to acquire data about the time-series change pattern to achieve this. The UNet structure was employed to manage the spatial mapping relationship between MODIS and Sentinel-2 images. The time distribution of the image sequences in different years was inconsistent, and time alignment strategies were used to process the reference data. The results demonstrate that incorporating the predicted critical phenological period NDVI consistently yields better crop classification performance. Moreover, the predicted NDVI trained with time-consistent data achieved a higher classification accuracy than the predicted NDVI trained with the original NDVI.

Automated actual evapotranspiration estimation: Hybrid model of a novel attention based U-Net and metaheuristic optimization algorithms

Actual evapotranspiration (ETa) plays a crucial role in the water and energy cycles of the earth. An accurate estimate of the ETa is essential for management of the water resources, agriculture, and irrigation, as well as research on atmospheric variations. Despite the importance of accurate ETa values, estimating and mapping them remains challenging due to the physical and biological complexity of the ET process. As a novel approach for rapid and reliable estimation of ETa, the present study develops automated deep learning (AutoDL) models that incorporate a metaheuristic optimization algorithm for image processing, architectural design, and hyperparameter tuning. The proposed AutoDL models integrate three different spatial and channel attention mechanisms, including a novel activated spatial attention mechanism (ASPAM), with the U-Net architecture. Bypassing the need for meteorological inputs, the proposed framework uses Moderate Resolution Imaging Spectrometer (MODIS) products and Digital Elevation Model (DEM) data as inputs. To evaluate the performance of the models, they are applied to three study areas in the United States with various climatic characteristics. According to the results, during the spring and summer, when the target values have higher certainty, the estimations are highly promising, with R2 as high as 0.91 and MAPE as low as 6.40%. Furthermore, the proposed ASPAM module improves the accuracy of ETa estimations compared to attention gate (AG) and squeeze and excitation (SE) attention modules. The results also indicate that the MODIS raw products and derived vegetation and water indices can predict ETa within a reliable range of accuracy, with the addition of DEM data marginally enhancing the models' performance. The automatic workflow of this model makes it significantly easy to use, contributing to its applicability and generalizability for enhancing atmospheric research.

Sourcing albedo data for bifacial PV systems in complex landscapes

Bifacial photovoltaic (PV) systems harvest additional light reflected from the ground. The proportion of light reflected from the ground (albedo) is a critical parameter in estimating the expected performance of these systems. It is important that albedo data are available at the system design stage are accurate and relevant, and that the confidence in these data can be quantified. This work investigates the challenges of different albedo data sources and proposes solutions towards more reliable datasets. Satellite sources, such as the NASA Moderate Resolution Imaging Spectrometer (MODIS) products, provide data that can be used to model effective albedo anywhere in the world. Nevertheless, there are numerous sources of error that can add uncertainty in bifacial gain, reducing confidence and increasing financial risk. For smaller sites (<∼10 MW) located in varied landscapes, the scaling error caused by the low spatial resolution can be large. By analysing case studies in complex agricultural landscapes, we investigate the accuracy of different albedo measurement approaches. Monitoring stations with reference cells and pyranometers were deployed to validate satellite data and to realise a site measurement campaign for albedo measurements. Since these sites are much smaller than a MODIS pixel, we have evaluated alternative approaches, including “typical landscapes” with adjustments for local latitude and weather, and a novel method of combining MODIS data with higher resolution satellite data. The latter offers a practical solution with good agreement with the ground-based station.

Analysis of the causes of extreme precipitation in major cities of Peninsular India using remotely sensed data

The frequent occurrence of Extreme Precipitation Events (EPEs) over the last decade has really created havoc in Indian cities. Anthropogenic emissions are one of the components that can influence the climatology over large urban conglomerates and cause the occurrence of EPEs. Aerosols are one of the emissions-related elements that can change the characteristics, production, and lifetime of clouds, leading to changes in precipitation dynamics. The goal of the current study is to investigate the influence of aerosols in causing EPEs by analyzing remotely sensed data of aerosols, clouds, and atmospheric features (K-index) from MODIS (Moderate Resolution Imaging Spectrometer) and precipitation from GPM-IMERG for major cities in Peninsular India. The last 10 days of remotely sensed data before the occurrence of an EPE event are analyzed. Along with Aerosol Optical Depth (AOD), cloud properties such as Cloud Fraction (CF), Cloud Top Pressure (CTP), and Cloud Top Temperature (CTT) are used in the analysis. The factors influencing precipitation in each city are analyzed through multiple linear regression models. For the major cities, the parameters that together caused extreme events are examined. All the observed EPEs had intensified precipitation due to a combination of middle-level clouds (CTP in the range of 440 hPa to 680 hPa), low AOD (0.4), with many thunderstorm states (K > 35 °C), and CTT 0 °C. Other elements that contributed to the occurrence of EPEs included low-level clouds (CTP >680 hPa), an AOD in the range of 0.4–0.6, an isolated thunderstorm state (20 °C < K < 25 °C), and CTT >0 °C.

Evaluation and Comparison of Multi-Satellite Aerosol Optical Depth Products over East Asia Ocean.

The atmosphere over the ocean is an important research field that involves multiple aspects such as climate change, atmospheric pollution, weather forecasting, and marine ecosystems. It is of great significance for global sustainable development. Satellites provide a wide range of measurements of marine aerosol optical properties and are very important to the study of aerosol characteristics over the ocean. In this study, aerosol optical depth (AOD) data from seventeen AERONET (Aerosol Robotic Network) stations were used as benchmark data to comprehensively evaluate the data accuracy of six aerosol optical thickness products from 2013 to 2020, including MODIS (Moderate-resolution Imaging Spectrometer), VIIRS (Visible Infrared Imaging Radiometer Suite), MISR (Multi-Angle Imaging Spectrometer), OMAERO (OMI/Aura Multi-wavelength algorithm), OMAERUV (OMI/Aura Near UV algorithm), and CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation) in the East Asian Ocean. In the East Asia Sea, VIIRS AOD products generally have a higher correlation coefficient (R), expected error within ratio (EE within), lower root mean square error (RMSE), and median bias (MB) than MODIS AOD products. The retrieval accuracy of AOD data from VIIRS is the highest in spring. MISR showed a higher EE than other products in the East Asian Ocean but also exhibited systematic underestimation. In most cases, the OMAERUV AOD product data are of better quality than OMAERO, and OMAERO overestimates AOD throughout the year. The CALIPSO AOD product showed an apparent underestimation of the AOD in different seasons (EE Below = 58.98%), but when the AOD range is small (0 < AOD < 0.1), the CALIPSO data accuracy is higher compared with other satellite products under small AOD range. In the South China Sea, VIIRS has higher data accuracy than MISR, while in the Bohai-Yellow Sea, East China Sea, Sea of Japan, and the western Pacific Ocean, MISR has the best data accuracy. MODIS and VIIRS show similar trends in R, EE within, MB, and RMSE under the influence of AOD, Angstrom exponent (AE), and precipitable water. The study on the temporal and spatial distribution of AOD in the East Asian Ocean shows that the annual variation of AOD is different in different sea areas, and the ocean in the coastal area is greatly affected by land-based pollution. In contrast, the AOD values in the offshore areas are lower, and the aerosol type is mainly clean marine type aerosol. These findings can help researchers in the East Asian Ocean choose the most accurate and reliable satellite AOD data product to better study atmospheric aerosols' impact and trends.

Extracting and Evaluating Urban Entities in China from 2000 to 2020 Based on SNPP-VIIRS-like Data

It is crucial to evaluate the expansion of urban entities to implement sustainable urban planning strategies in China. Thus, this study attempted to extract and evaluate the growth of urban entities 270 prefecture cities in mainland China (2000–2020) using a novel approach based on consistent night light images. After the urban entities were extracted, a rationality assessment was carried out to compare the derived urban entities with the LandScan population product, Landsat, and road network results. Additionally, the results were compared with other physical extent products, such as the Moderate Resolution Imaging Spectrometer (MODIS) and urban built-up area products (HE) products. According to the findings, the urban entities were basically consistent with the LandScan, road network, and HE and MODIS products. However, the urban entities more accurately reflected the concentration of human activities than did the impervious extents of the MODIS and HE products. At the prefecture levels, the area of urban entities increased from 8082 km2 to 74,417 km2 between 2000 and 2020, showing an average growth rate of 10.8% over those twenty years. As a reliable supplementary resource and guide for urban mapping, this research will inform new research on the K-means algorithm and on variations in NTL data brightness threshold dynamics at regional and global scales.

Seasonal and spatial variability in bio-optical properties of the Persian Gulf: Implications for ocean color remote sensing

In this study, the absorption coefficients of phytoplankton community (aPh(λ)), Colored Dissolved Organic Matter (CDOM) [aCDOM(λ)], and Non-Algal Particles (NAP) [aNap(λ)] were analyzed in the northern Persian Gulf, a Mediterranean-type semi-enclosed marginal sea, through seven cruise expeditions conducted from May 2018 to February 2022. The highest and lowest aPh(443) values were observed in the shallow area (depth ≤20 m) during the wet season (November–April) (0.043 ± 0.004 m-1) and within the deep area (depth >20 m) through the dry season (July–September) (0.024 ± 0.003 m-1), respectively. The chlorophyll-specific absorption (a*Ph(λ)) values increased from the deep to shallow areas as well as the wet to dry seasons. A significant difference was further detected in a*Ph(λ) magnitude at the blue (443 nm) and red (675 nm) wavelengths, with the mean values of 0.0124/0.0223 m2 mg−1 and 0.0069/0.0135 m2 mg−1 in the wet/dry seasons, respectively. The mean values of aCDOM(443) in the shallow area during the wet season (0.10 ± 0.001 m-1) were significantly larger than those in the deep area during the dry season (0.06 ± 0.002 m-1). Similarly, aNAP(443) decreased from the shallow (0.056 ± 0.021 m-1) to deep areas (0.019 ± 0.011 m-1), and from the wet (0.035 ± 0.020 m-1) to dry (0.029 ± 0.015 m-1) seasons. The statistical analysis revealed that the differences in aCDOM(λ) and aNAP(λ) spectral distribution were not statistically significant for the duration of the wet and dry seasons, whereas significant differences were observed in the shallow and deep samples. During both wet and dry seasons, the magnitude of the blue-green absorption spectra was dominated by CDOM, but the red bands were associated with phytoplankton dominated samples. The ratio of aCDOM(λ)/a*Ph(λ) was utilized as an index for uncertainty assessment to estimate Chl-a concentrations by the National Aeronautics and Space Administration (NASA) standard Chl-a algorithms for the Moderate Resolution Imaging Spectrometer (MODIS; OC3M) and Sentinel-3 Ocean and Land Color Instrument (OLCI) (OLCI; OC4v6). The results additionally demonstrated that the satellite-derived Chl-a was overestimated due to higher aCDOM(λ) and underestimated owing to lower a*Ph(λ) at the low (<0.5 mg m−3) and high (>3 mg m−3) concentrations of Chl-a, respectively. The study findings established that the red bands should be considered for tuning or developing new Chl-a algorithms in the Persian Gulf.

A dataset of normalized difference vegetation index in the Three-river Headwaters during 2000-2018

Vegetation is an important component of the terrestrial ecosystem. The changes of vegetation are assumed to well indicate the dynamic changes of the ecosystem. However, the changing global climate and the intensifying human activities have a great effect on vegetation growth, which particularly highlights the implications to monitor and assess vegetation changes. Vegetation changes are usually measured by vegetation indexes. The normalized difference vegetation index (NDVI) based on remote sensing is widely used in the studies on vegetation changes and climate impact. In this study, we used the spectral reflectance data product (MOD09Q1) of the Moderate Resolution Imaging Spectrometer (MODIS) from 2000 to 2018 to calculate the NDVI (with a spatial resolution of 250m and temporal step of 8 days). The S-G filtering method of the TIMESAT3.2 software is applied to remove the noise in the NDVI time series for the reconstruction of time series. In this way, we finally obtained this dataset, which is open to the public for sharing and downloading. It is expected to support further studies on the dynamic changes of vegetation in the Three-river Headwaters.

Turbidity dynamics in Indian peninsular river mouths derived from Kd490 reveals key anthropogenic drivers

Large rivers, which act as natural integrators of surface processes, contribute massive volume of terrestrial materials to the coastal oceans. However, the accelerated climate warming and increasing anthropogenic activities recorded in recent years have been severely affecting the hydrologic and physical regimes of river systems. These changes have a direct impact on river discharge and runoff, some of which are occurred rapidly in the past two decades. Here, we present a quantitative analysis on the effects of changes in surface turbidity at coastal river mouths using diffuse attenuation coefficient at 490 nm (Kd490) as a proxy of turbidity for six major Indian peninsular rivers. The time series (2000−2022) trends of Kd490 obtained from Moderate Resolution Imaging Spectrometer (MODIS) images shows a significant decreasing trend in Kd values (p < 0.001) at the mouths of the Narmada, Tapti, Cauvery, Krishna, Godavari, and Mahanadi rivers. This is despite an increased rainfall trend observed for the six studied river basins which can likely intensifies the surface runoff and deliver more sediments, suggesting that other factors such as land use changes and increased number of dam constructions are primarily responsible for the decreased sediment load from rivers to coastal mouths.

Research on the establishment of NDVI long-term data set based on a novel method

This study compares the relationship between different NDVI (Normalized Difference Vegetation Index), the NDVI of AVHRR (Advanced Very High Resolution Radiometer) (NDVIa), the NDVI of MODIS (Moderate Resolution Imaging Spectrometer) (NDVIm), and the NDVI of VIRR (Visible and Infrared Radiometer) (NDVIv), and found that there is a significant correlation between the NDVIa and the NDVIm, and between the NDVIv and the NDVIa, the relationship between the three is NDVIv < NDVIa < NDVIm. Machine learning is an important method in artificial intelligence. It can solve some complex problems through algorithms. This research uses linear regression algorithm in machine learning to construct the Fengyun Satellite NDVI correction method. By constructing a linear regression model, the NDVI value of Fengyun Satellite VIRR is corrected to a level that is basically the same as NDVIm. The corrected correlation coefficients (R2) were significantly improved, and the corrected correlation coefficients were significantly improved, and the confidence levels were all significant correlations less than 0.01. It is proved that the corrected normalized vegetation index of Fengyun Satellite has significantly improved accuracy and product quality compared with the normalized vegetation index of MODIS.

Deriving the Vertical Variations in the Diffuse Attenuation Coefficient of Photosynthetically Available Radiation in the North Pacific Ocean from Remote Sensing

Diffuse attenuation coefficient of photosynthetically available radiation (PAR), KPAR, is a key product of ocean color remote sensing. Current ocean color algorithms generally detect only the average KPAR within one optical depth, KPARRS. Due to the marked vertical variations of KPAR, knowledge of KPARRS is insufficient to accurately evaluate the submarine light field. By using field in situ observations, a two-step approach, based on the development of an ocean color algorithm for KPARRS and the relationships between KPARRS and the average KPAR from the surface down to depth Z (K¯PARZ), was developed to remotely estimate the vertical variations in K¯PARZ in the North Pacific from the MODerate-resolution Imaging Spectrometer (MODIS) imagery. The root mean square difference of log(K¯PARZ) in depths within the euphotic zone was around ±0.059 (in unit of m−1 for K¯PARZ), which corresponded to a deviation of ±15% for the estimated K¯PARZ and the penetration depths of PAR. Our study may provide a promising approach to detect the vertical variations of K¯PARZ and underwater PAR distributions in the North Pacific Ocean.

Global climate regulates dimensions of terrestrial ecosystem stability

AbstractEcosystem stability represents the ability of the ecosystem to maintain and restore its functions. The indicators of ecosystem stability include temporal stability, resistance, and resilience, which are crucial parameters to predict the basic functions of the earth under global change. However, the correlation between the indicators of ecosystem stability and the dimensions of ecosystem stability at global scale remains unclear. Here, we quantified the global dimensions of ecosystem stability using moderate resolution imaging spectrometer (MODIS) enhanced vegetation index (EVI) data from 2001 to 2018 as well as climate variables and land standard species richness data. We revealed a strong positive correlation between temporal stability and resistance and no correlation between resistance and resilience at global scale. The dimension of ecosystem stability changed along the gradients of latitude or altitude. The dimensions of ecosystem stability were different among biomes, showing the lowest in the evergreen broad‐leaved forest (EBF) and the highest in the grassland (GRA). Climate factors were strongly associated with the changes in dimensions of ecosystem stability at global scale. These findings highlight the crucial role of dimensions in exploring ecosystem stability, potentially eliminating contradictions in the ecosystem service mechanism of the earth.

Potential improvement of XCO2 retrieval of the OCO-2 by having aerosol information from the A-train satellites

ABSTRACT Near-real time observations of aerosol properties could have a potential to improve the accuracy of XCO2 retrieval algorithm in operational satellite missions. In this study, we developed a retrieval algorithm of XCO2 (Yonsei Retrieval Algorithm; YCAR) based on the Optimal Estimation (OE) method that used aerosol information at the location of the Orbiting Carbon Observatory-2 (OCO-2) measurement from co-located measurement of the Afternoon constellation (A-train) such as the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) onboard the Cloud-Aerosol Lidar and Infrared Pathfinder Observation (CALIPSO) and the MODerate-resolution Imaging Spectrometer (MODIS) onboard the Aqua. Specifically, we used optical depth, vertical profile, and optical properties of aerosol from MODIS and CALIOP data. We validated retrieval results to the Total Carbon Column Observing Network (TCCON) ground-based measurements and found general consistency. The impact of observed aerosol information and its constraint was examined by retrieval tests using different settings. The effect of using additional aerosol information was analyzed in connection with the bias correction process of the operational retrieval algorithm. YCAR using a priori aerosol loading parameters from co-located satellite measurements and less constraint of aerosol optical properties made comparable results with operational data with the bias correction process in three of the four cases subject to this study. Our work provides evidence supporting the bias correction process of operational algorithms and quantitatively presents the effectiveness of synergic use of multiple satellites (e.g. A-train) and better treatment of aerosol information.

Validation of Simplified Level 2 Prototype Processor Sentinel-2 fraction of canopy cover, fraction of absorbed photosynthetically active radiation and leaf area index products over North American forests

Canopy biophysical variables such as the fraction of canopy cover (fCOVER), fraction of absorbed photosynthetically active radiation (fAPAR), and leaf area index (LAI) are widely used for ecosystem modelling and monitoring. The Sentinel-2 mission was designed for systematic global mapping of these variables at 20 m resolution using imagery from the MultiSpectral Instrument. The Simplified Level 2 Prototype Processor (SL2P) is available as a baseline mapping solution. Previous validation over limited sites indicates that SL2P generally satisfies user requirements for all three variables over crops, but underestimates LAI over forests. In this study, Sentinel-2 fAPAR, fCOVER, and LAI products, from SL2P, were validated over 281 sites representative of most North American forest ecozones and also compared to Moderate Resolution Imaging Spectrometer (MODIS) and Copernicus Global Land Service (CGLS) products. In addition to meeting the Committee on Earth Observation Satellites Stage 3 validation requirements for these areas, our study also explores the relationship between bias in SL2P products and canopy clumping and provides empirical bias correction functions for each variable.SL2P was implemented within the Landscape Evolution and Forecasting Toolbox in Google Earth Engine both for efficiency and due to bugs in the Sentinel Application Platform implementation. SL2P was found to underestimate LAI by 20% to 50% over forests with LAI > 2; in agreement with other studies and with comparisons to MODIS and CGLS products. SL2P bias for fCOVER and fAPAR transitions from ∼0.1 at low values to ∼ − 0.1 at high values. Precision error, at one standard deviation, was ∼0.5 for LAI and slightly less than ∼0.1 for fCOVER and fAPAR. Total uncertainty was dominated by bias for LAI and was slightly greater than precision error for fCOVER and fAPAR. Target user requirements were satisfied for 51% of LAI, 37% of fCOVER and 31% of fAPAR comparisons to in-situ measurements. For all variables, accuracy exhibited weak to moderate linear relationships to clumping (r2 ≤0.52), but scatter plots indicated larger negative LAI biases over northern latitude sites where canopies exhibited greater clumping. With the exception of evergreen broadleaf forests, empirical bias correction using in-situ data reduced accuracy error by 40% for fCOVER, 57% for fAPAR and, 92% for LAI and increased the agreement rate with uncertainty requirements by up to 8%. Users of SL2P LAI over forests are recommended to apply bias correction or consider recalibrating SL2P with spatially heterogenous radiative transfer model simulations.

Algal biomass mapping of eutrophic lakes using a machine learning approach with MODIS images

Algal blooms are a widespread issue in eutrophic lakes. Compared with the satellite-derived surface algal bloom area and chlorophyll-a (Chla) concentration, algae biomass is a more stable way to reflect water quality. Although satellite data have been adopted to observe the water column integrated algal biomass, the previous methods mostly are empirical algorithms, which are not stable enough for widespread use. This paper proposed a machine learning algorithm based on Moderate Resolution Imaging Spectrometer (MODIS) data to estimate the algal biomass, which was successfully applied to a eutrophic lake in China, Lake Taihu. This algorithm was developed by linking Rayleigh-corrected reflectance to in situ algae biomass data in Lake Taihu (n = 140), and the different mainstream machine learning (ML) methods were compared and validated. The partial least squares regression (PLSR) (R2 = 0.67, mean absolute percentage error (MAPE) = 38.88 %) and support vector machines (SVM) (R2 = 0.46, MAPE = 52.02 %) performed poor satisfactory. In contrast, random forest (RF) and extremely gradient boosting tree (XGBoost) algorithms had higher accuracy (RF: R2 = 0.85, MAPE = 22.68 %; XGBoost: R2 = 0.83, MAPE = 24.06 %), demonstrating greater application potential in algal biomass estimation. Field biomass data were further used to estimate the RF algorithm, which showed acceptable precision (R2 = 0.86, MAPE < 7 mg Chla). Subsequently, sensitivity analysis showed that the RF algorithm was not sensitive to high suspension and thickness of aerosols (rate of change <2 %), and inter-day and consecutive days verification showed stability (rate of change <5 %). The algorithm was also extended to Lake Chaohu (R2 = 0.93, MAPE = 18.42 %), demonstrating its potential in other eutrophic lakes. This study for algae biomass estimation provides technical means with higher accuracy and greater universality for the management of eutrophic lakes.

Effect of local climate zone (LCZ) and building category (BC) classification on the simulation of urban climate and air-conditioning load in Hong Kong

Reliable energy models are required to predict urban-scale air-conditioning (AC) load under extreme outdoor temperatures which is crucial to power security. In this study, we proposed a new landuse/landcover (LULC) scheme for these models by combining the local climate zones (LCZs) from World Urban Database Access Portal Tools (WUDAPT) and building category (BC) map. Apart from evaluating against the conventional LULC of Moderate-resolution Imaging Spectrometer (MODIS) and WUDAPT, the impact of urban canopy parameters and BC on the simulated meteorological conditions and AC load during the 2016 heatwave in Hong Kong was examined by the Weather Research and Forecasting (WRF) model. Incorporating LCZs (BC) reduced the bias of 2-m temperature (T2), 2-m relative humidity (RH2), and AC load intensity by 0.4 °C (2.0 °C), 1.98% (0.68%) and 6.78 W m−2 (13.09 W m−2), respectively. It captured the heterogeneous, intra-urban T2 and turbulence kinetic energy (TKE). Further sub-classifying the BC in LCZs led to WUDAPT/BC which performed even better and significantly improved WRF output. It simulated well the daytime wind speed, mitigated the overprediction (underprediction) of T2 (RH2), and rectified the underpredicted AC load in WUDAPT. Its benefit was prominent in daytime as well as in compact high-rise/mid-rise and open high-rise areas. (200 words).

A Priori Land Surface Reflectance Synergized With Multiscale Features Convolution Neural Network for MODIS Imagery Cloud Detection

Moderate Resolution Imaging Spectrometer (MODIS) images are widely used in land, ocean, and atmospheric monitoring, due to their wide spectral coverage, high temporal resolution, and convenient data acquisition. Accurate cloud detection is critical to the fine processing and application of MODIS images. Owing to spatial resolution limitations and the influence of mixed pixels, most MODIS cloud detection algorithms struggle to effectively recognize of clouds and ground objects. Here, we propose a novel cloud detection method based on land surface reflectance (LSR) and a multiscale feature convolutional neural network to achieve high-precision cloud detection, particularly for thin clouds and clouds over bright surface. A monthly surface reflectance dataset was constructed by MODIS products (MOD09A1) and employed to provide background information for cloud detection. Difference-based samples were obtained using surface reflectance as well MODIS images of different phases based on difference operations. The multiscale feature network (MFCD-Net) using an atrous spatial pyramid pooling (ASPP) and a channel and spatial attention module (CSAM) integrated low-level spatial features and high-level semantic information to capture multiscale features and generate a high-precision cloud mask. For cloud detection experiments and quantitative analysis, 61 MODIS images acquired at different times on various underlying surface types were used. Cloud detection results were compared to those of UNet, Deeplabv3+, UNet++, PSPNet, and top of atmosphere-based (MFCD-TOA) methods. The proposed method performed well, with the highest overall accuracy (96.55%), precision (92.13%), and recall (88.90%). It improved cloud detection accuracy in various scenarios, reducing thin cloud omission and bright surface misidentification.

WRF‐CHEM Simulations of Unhealthy PM10 Concentrations During Four Dust Events in Senegal

AbstractWinter season Saharan dust aerosols are transported into West Africa and pose as a natural hazard with individual dust events significantly reducing air quality. We simulate four multi‐day dust events using the Weather, Research and Forecasting Chemistry (WRF‐CHEM) in Senegal during December 2016, December 2017, February 2019, and March 2019. We use Geospatial Informational System mapping to estimate PM10 concentrations for the 14 administrative districts within Senegal. Saharan dust events are evaluated using surface PM10 concentrations at Dakar, Senegal, Moderate Resolution Imaging Spectrometer Aerosol Optical Depth, Aerosol Robotic Network, and the WRF‐CHEM model to evaluate dust concentrations across Senegal. We show that a north to south pressure gradient associated with the southward movement of the Azores high into North Africa are responsible for dust generation and transport into Senegal during 2016 and 2017. Conversely, Saharan depressions moving across the Sahara Desert are responsible for the two high‐impact dust events in February and March of 2019. Based on our analysis, the most severe of the four dust events impacting Senegal occurred in December 2017, with hazardous PM10 concentrations simulated for 7 days affecting an estimated 86% of Senegal's population. Furthermore, the February 2019 dust event occurred during the presidential elections, potentially exposing many voters to poor air quality. A network of surface dust observations and dust forecasts are critical for protecting the Sahelian public from dangerous dust events. Together, they allow for preventative measures to minimize harmful PM10 concentration exposure, especially for children under 5, the elderly, and those with existing respiratory and cardiovascular diseases.