Cloud Cover Research Articles

Cloud removal in landsat images based on spatial-spectral-temporal random forests

Abstract The Landsat-8 Operational Land Imager (OLI), together with the Landsat-9 OLI2 sensors, provides important optical data for various applications. Due to frequent cloud cover, the obtained Landsat images always contain different degrees of spatial information loss. Although great efforts have been made to remove clouds in remote sensing images in recent decades, there still exists a large challenge, that is, the land cover changes between the target cloudy and the auxiliary cloud-free images, which limits greatly the accuracy of cloud removal. To cope with this issue, a spatial-spectral-temporal random forest (SSTRF) algorithm was proposed in this paper. The SSTRF model aims at removing clouds in Landsat images with the assistance of a remote sensing image with spatially complete cover from another satellite, that is, the 8-day MODIS surface reflectance product (MOD09A1). By compositing the valid data from 8 separate days, the MOD09A1 product has a great possibility to provide cloud-free land cover change information for the cloud removal of Landsat images. Through experiments with simulated and real clouds, SSTRF was demonstrated to produce greater accuracy than two benchmark cloud removal methods and has great potential to be applied to practical use.

Investigating spatial-temporal trend of snow cover over the three provinces of Northeast China based on a cloud-free MODIS snow cover product

Snow serves as a pivotal water resource in the three provinces of Northeast China (TPNC), the investigation of spatial and temporal distribution changes of snow cover is essential for the region’s efficient water resource management. The Moderate Resolution Spectroradiometer (MODIS) snow cover product has proven effective in providing detailed spatial–temporal distribution and identification of snow cover. However, its accuracy and reliability are significantly hampered by cloud obscuration. To address this issue, we have developed a novel four-step cloud removal approach, which includes Terra and Aqua combination, 5-day temporal combination, snowline method (IMS) as well as 8-pixel and 24-pixel adjacent pixel method. Results indicate that the proposed approach successfully eliminates all cloud cover, yielding a highly accurate cloud-free snow cover product with validation accuracies of 92% and 97% when using in situ snow depth measurements and cloud masking method, respectively. We investigated the spatial and temporal variations of snow cover for the TPNC during 2003–2018 with snow coverage and snow cover days (SCD) and analyzed the correlation between snow cover and climate factors. The results showed that the annual basin-averaged snow coverage increased at a rate of 1.27% yr−1 before 2009 and decreased at a rate of 1.80% yr−1 after 2009. The Mann-Kendall tests indicated that about 72% of the total area showed an increasing trend of annual SCD during 2003–2018. Conversely, 94% of the total area exhibited a decreasing trend of annual SCD for the period of 2009–2018 due to the increasing trend of temperature and decreasing trend of precipitation. Our approach shows a promising application of generating accurate cloud-free products and its potential to examine spatial–temporal variations of snow cover under climate change for other snow-covered regions globally.

How Does Organized Convection Impact Explicitly Resolved Cloud Feedbacks in the Radiative‐Convective Equilibrium Model Intercomparison Project?

AbstractIn simulations of radiative‐convective equilibrium (RCE), and with sufficiently large domains, organized convection enhances top of atmosphere outgoing longwave radiation due to the reduced cloud coverage and drying of the mean climate state. As a consequence, estimates of climate sensitivity and cloud feedbacks may be affected. Here, we use a multi‐model ensemble configured in RCE to study the dependence of explicitly calculated cloud feedbacks on the existence of organized convection, the degree to which convection within a domain organizes, and the change in organized convection with warming sea surface temperature. We find that, when RCE simulations with organized convection are compared to RCE simulations without organized convection, the propensity for convection to organize in RCE causes cloud feedbacks to have larger magnitudes due to the inclusion of low clouds, accompanied by a much larger inter‐model spread. While we find no dependence of the cloud feedback on changes in organization with warming, models that are, on average, more organized have less positive, or even negative, cloud feedbacks. This is primarily due to changes in cloud optical depth in the shortwave, specifically high clouds thickening with warming in strongly organized domains. The shortwave cloud optical depth feedback also plays an important role in causing the tropical anvil cloud area feedback to be positive which is directly opposed to the expected negative or near zero cloud feedback found in prior work.

Intraday analyses on weather-induced sentiment and stock market behavior

We examine the intraday relationship between weather conditions and investor sentiment in the Korean equity market. By uncovering the intermediary role of weather-induced sentiment in affecting market variables, we suggest a potential link between weather conditions, sentiment, and market dynamics. High temperatures, humidity, and cloud coverage hurt investors’ moods, while strong winds and long sunshine durations have a positive impact. Though only cloud coverage marginally impacts stock returns, weather factors significantly affect market turnover, volatility, and illiquidity, especially when altering sentiment dynamics.

LIDAR3DNETV2: Smart Approach to Classify 3D Objects Based on Real Point Clouds

Point clouds generated in simulators avoid collection time costs and provide a high and organized amount of point clouds, an ideal scenario for deep learning networks. However, these networks have limitations when applied to real point clouds. This work proposes a multilayer perceptron-based method to classify 3D objects based on real point clouds obtained using LiDAR sensors. The method includes a pre-processing step that normalizes and adjusts the point clouds in the 3D Cartesian plane to overcome discrepancies in the point distribution. Furthermore, we created a dataset and used the ModelNet dataset for comparison purposes. The proposed neural network, Lidar3DNetV2, achieved 98.47% and 125 μs in accuracy and test time with real data, respectively. The pre-processing step provided a significant increase in the classifier’s performance. Finally, the proposed method performs better than other state-of-the-art networks considering real point clouds.

HIGH-RESOLUTION SATELLITE ESTIMATION OF SNOW COVER FOR FLOOD ANALYSIS IN EAST KAZAKHSTAN REGION

The increasing frequency of extreme weather events linked to climate change has made flood forecasting an important issue, particularly in mountainous regions where snowmelt is a major driver of seasonal flooding. This study explores the application of snow cover estimation techniques to assess snowmelt dynamics and their potential impact on flood risks in the Ulba and Uba basins in East Kazakhstan. To achieve this, high-resolution multispectral satellite imagery from the Sentinel-2 Surface Reflectance dataset is used, focusing on images collected between March and October for the years 2021 to 2024. The images are processed in Google Earth engine platform with strict filtering based on spatial intersection with the basins and cloud cover pixels percentage, ensuring high-quality data for snow cover analysis. The study utilizes multiple remote sensing indices for snow cover estimation. The normalized difference snow index is calculated using the green and shortwave infrared bands to detect snow-covered pixels. Fractional snow-covered area is derived from the NDSI using the 'FRA6T' relationship, offering a more nuanced estimate of snow distribution across the basins. Additionally, a near-infrared to shortwave infrared ratio threshold is employed to minimize confusion between snow and water, improving the detection of snow cover, particularly in regions near water bodies or during melt periods. The resulting snow cover maps and fSCA estimates provide a detailed picture of snow distribution and melt dynamics, contributing to the assessment of snowmelt’s role in flood risk development. The obtained insights can assist in refining flood forecasting models, improving early warning systems, and supporting informed water resource management in vulnerable regions.

Century‐Long Variations in Surface Incident Solar Radiation Over Japan——Characterized by Observations and Reanalyses

ABSTRACTSurface incident solar radiation (Rs) is of great importance in determining the energy balance in the Earth system. In this study, we use century‐long homogenized observations over Japan to constrain five 20th century reanalyses to explore their performance in reproducing Rs variation on different time scales (high‐frequency components [HFCs], for signals with cycles less than 10 years; low‐frequency components [LFCs], for signals with cycles more than 10 years) by ensemble empirical mode decomposition (EEMD) method and to quantify the impact factors on the Rs estimations by the sum of tree (SOT) model. It is found that ERA20C, ERA20CM and CERA20C overestimated Rs by 0.48–1.49 W/m−2, while 20CRv2c and 20CRv3 underestimated Rs by −0.63 and −1.18 W/m−2, respectively for 1931–2010. Poor correlation coefficient (R) was found to be 0.10 for ERA20CM and 0.22 for 20CRv2c. 20CRv2c failed for 1931–1960 but improved considerably for 1961–2010. 20CRv3 uses an upgraded model and assimilates more observations compared with its predecessor 20CRv2c; however, only the original components and HFCs in Rs were improved, with nearly no improvement in the LFCs. In general, CERA20C Rs, with small biases and higher R of 0.73 for original signals, 0.83 for HFCs and LFCs, is superior to other reanalyses. No obvious trend in clear sky Rs demonstrated that reanalysed Rs are insensitive to the aerosol forcings. Therefore, cloud cover and water vapour maybe the main factors influenced reanalysed Rs. Most of time, Rs is more sensitive to cloud cover than vapour pressure for all reanalyses except original signals (with contribution ratio of 0.29 for cloud cover and 0.71 for vapour pressure) and LFCs (with contribution ratio of 0.41 for cloud cover and 0.59 for vapour pressure) in CERA20C, and original signals (with contribution ratio of 0.37 for cloud cover and 0.63 for vapour pressure) in ERA20C. This work pointed out that aerosol related processes such as aerosol forcings or aerosol radiative effect in reanalyses should be improved in the future, which will ultimately improve the interaction between aerosol and cloud in Rs simulations.

Air pollutant prediction based on a attention mechanism model of the Yangtze River Delta region in frequent heatwaves

Heatwaves pose significant threats to urban environments, affecting both ecological systems and public health, primarily through the exacerbation of air pollution. Accurate prediction of air pollutant concentrations during heatwave periods is crucial for authorities to develop timely prevention and control strategies. Thus, we developed the 1D-CNN-BiLSTM-attention model, specifically designed to account for the unique data characteristics associated with heatwave conditions. Our model leverages an attention mechanism to enhance its ability to learn and predict air pollutant behavior during heatwaves. Across six scenario-based experiments, the model demonstrated high predictive accuracy, achieving a MAPE of 2.93 %. The model integrates meteorological indicators such as temperature, humidity, wind speed, cloud cover, and precipitation, extending its predictive capability across a spatial range of 150 km. In experiments testing the model's applicability to three typical city types in the Yangtze River Delta region, the results confirmed its effectiveness in predicting air pollutants. These findings highlight the model's usefulness for studying air pollution during urban heatwave periods on a regional scale, demonstrating its robustness and reliability under varying weather conditions.

Variability of solar UV radiation in the northern mountains of the Czech Republic, 2020–2021

Solar ultraviolet (UV) radiation has a crucial role in many atmospheric processes and a huge impact on living organisms. Its main positive effect is the synthesis of vitamin D, but it also causes problems such as sunburn, skin cancer or eye cataracts. In the mountains, high doses of UV frequently occur due to a specific combination of atmospheric and geographical factors such as a high ground reflection as a consequence of a large number of days with snow cover, or a lower concentration of atmospheric pollutants in comparison to lowland urban regions. This study used measurements of erythemal UV radiation from two high altitude areas: the Hrubý Jeseník Mountains (Vysoká hole meteorological station, 1 464 m a.s.l.) and the Giant Mountains (Luční bouda meteorological station, 1 413 m a.s.l.) in the Czech Republic, during 2020 and 2021. We evaluated the daily and monthly changes in erythemal dose and UV index. The maximum daily dose of 5.0 kJ.m-2 (8.9 of UV index) was measured on 28 June 2020 at Vysoká hole. The maximal UV index of 10.1 was observed at Luční bouda on 5 July 2020, while the maximum daily dose of 4.9 kJ.m-2 occurred on 14 June 2021. The main factors that caused changes in solar UV radiation were the amount of cloud cover as well as the total ozone column.

The impact of coupled 3D shortwave radiative transfer on surface radiation and cumulus clouds over land

Abstract. Radiative transfer is a 3D process, but most atmospheric models consider radiation only in the vertical direction for computational efficiency. This results in inaccurate surface radiation fields, as the horizontal transport of radiation is neglected. Previous work on 3D radiative effects mainly used 3D radiative transfer uncoupled from the flow solver. In contrast, our current work uses 3D radiative transfer coupled to the flow solver to study its impact on the development of clouds and the resulting impact on the domain-averaged surface solar irradiance. To this end, we performed a series of realistic large-eddy simulations with MicroHH. To improve the level of realism of our radiation, we first included the direct effect of aerosols using aerosol data from the Copernicus Atmosphere Monitoring Service (CAMS) global reanalysis. Next, we performed simulations with 1D radiative transfer and with a coupled ray tracer for 12 d on which shallow cumulus clouds formed over Cabauw, the Netherlands. In general, simulations with the coupled ray tracer have a higher domain-averaged liquid water path, larger clouds, and similar cloud cover compared to simulations with 1D radiative transfer. Furthermore, the domain-averaged direct radiation is decreased with 3D radiative transfer, and the diffuse radiation is increased. However, the average difference in global radiation is less than 1 W m−2, as the increase in global radiation from uncoupled 3D radiative transfer is counterbalanced by a decrease in global radiation caused by changes in cloud properties.

Ship detection in reefs and deep-sea with medium-high resolution images

ABSTRACT Accurate and efficient ship detection is crucial for ocean monitoring and management, especially in reefs and deep-sea, where fishing and illegal activities threaten sustainability of ecosystems. Obtaining the size of ships in reefs and deep-sea helps to identify ship types and to assess the impact of ships on marine ecosystems quantitatively. Ship detections were mainly applied at coast or river of the inland region using high spatial resolution remote sensing data due to its rich details and frequent coverage. However, the ships located in reefs and deep-sea regions were rarely studied because of price, and availability of high spatial resolution remote sensing data. Global covered medium-high resolution (10–30 m) remote sensing data (such as Sentinel-2) makes it possible to obtain the spatial-temporal distribution of ships, especially small ships. This study developed a deep learning ship detection algorithm – an enhanced Rotated-Ship Detector (RShipDet) to detect ships in reefs and deep-sea regions. RShipDet was applied to Nansha Islands based on two medium-high resolution ship detection datasets (Sentinel2-Ship and SDGSAT-Ship). These two datasets include various backgrounds of reefs and deep-sea, and complex scenarios such as cloud cover and parallel ships, adapting RShipDet to ship detection under complex circumstances in reefs and deep-sea. The results showed that: (1) Small gains: gains of 3.3% and 8.7% Average-Precision (AP) compared to Rotation-equivariant Detector (ReDet) and Faster-RCNN on Sentinel2-Ship dataset; (2) Strong generalization capabilities: 77.4% AP on SDGSAT-Ship dataset; (3) Better performance under complex conditions: RShipDet obtained more accurate ship detection results over regions with cloud cover, islands and reefs, deep-sea, and ports compared to other classical detectors. Our algorithm could be applied for better management of ocean resources and activities in reefs and deep-sea.

Statistics of precipitable water vapour, optical thickness and cloud cover within the Northern part of Eurasia

One of the most important tasks in astroclimatic studies of possible locations for the Eurasian Submillimeter Telescopes is estimating statistics of precipitable water vapour, optical thickness and cloud cover. In this paper, the statistics of precipitable water vapour and total cloud cover within Northern part of Eurasia are studied using ERA-5 reanalysis. Optical thickness statistics at a wavelength of 3 mm were obtained using the Liebe model from the ERA-5 reanalysis for the region where the BTA is located. The most favorable astroclimatic zones of Eurasia include Tibet and the Eastern Pamirs, certain regions of the Sayan Mountains, Altai and Mts within Dagestan. Also we verified the ERA-5 reanalysis data using radiosonde data, GNSS measurement data and radiometric measurements for 2021.

Spacecraft formation flying orbital control for earth observation mission

Synthetic Aperture Radar (SAR) sensors have gained much attention for multiple civil use cases, where NewSpace startups have launched and planning to launch sensors to orbit to harvest the unique capabilities of SAR for the Earth Observation (EO) market. Unlike optical, SAR can capture images in all lighting conditions and penetrate cloud cover, generating exceptional use cases, which makes SAR the most reliable sensor. For example, monitoring soil deformation with millimeter accuracy, oil spills, crop lodging, and typical EO use cases of optical sensors that are unable to penetrate the clouds or see at night. However, SAR is still limited in civil use cases because it is tough to interpret. In addition, unlike government users who can consume the SAR data directly, most civil end-users need analytics on top of the data. To unlock the potential civil use cases, many experts believe that optical EO data should be fused with SAR at the EO upstream, thus suggesting optical and SAR sensors fly in formation. Spacecraft Formation Flying (SFF) is one of the key technology enablers for space exploration and EO space missions. This paper presents an SFF EO mission (SAR and Optical) design considering orbit requirements to maximize user objectives inspired by OptiSAR™ Constellation.

Unravelling the performance of atmospheric radiative transfer schemes in the simulation of mean surface climate in Central Africa using the RegCM5 climate model

AbstractThe theory of radiative transfer in the atmosphere is crucial in the study of climate, because radiative exchanges are at the origin of the atmospheric dynamics. It is therefore important to evaluate this phenomenon in order to be able to take effective measures to tackle climate change. The objective of this work is to evaluate the capability of the RegCM5 climate model to reproduce radiative transfer over Central Africa. The analysis is carried out over a 10‐year period, from January 2002 to December 2011 preceded by 1 year as spin‐up. RegCM5 model were evaluated using the ERA5 dataset for the radiative transfer parameters (the shortwave radiation [SWR], longwave radiation [LWR], cloud cover [CLT], surface albedo [ALB] and surface temperature), as well as CHIRPS dataset for precipitation. Three subregions were identified for more specific analysis of the model, namely the Sahel, Congo basin and Cameroon highlands. Two radiative schemes were used: the radiative scheme of the community climate model (CCM) and Rapid Radiative Transfer Model (RRTM). The assessment of radiative transfer parameters was carried out by examining their seasonal variability and annual cycles using data from two RegCM5 experiments, RegCM5‐CCM3 and RegCM5‐RRTM. Before this assessment, a sensibility analysis to convective schemes carried out with the default RegCM5 radiative scheme (CCM3) shows that Grell scheme with Arakawa and Shulber closure is the best scheme to represent key radiation parameters (LWR and SWR). This convective scheme is therefore used for assessing the two Radiative transfer schemes. Results show that both RegCM5 experiments simulate relatively well the variables linked to radiative transfer for the four seasons of the year. However, RegCM5 with RRTM as radiative scheme depicts better performance over all subregions and seasons, suggesting that the choice of this scheme does not depend on land cover, topography and rainfall regimes in a complex region such as Central Africa.

The Influence of Anomalous Biomass Emissions on ENSO in CESM2

Abstract The influence of biomass burning (BB) aerosols arising from wildfires and agricultural fires on the transient coupled evolution of ENSO is explored in CESM2. For both El Niño and La Niña, two 20-member ensembles are generated from initial states that are predisposed to evolve into ENSO events. For each ENSO phase, one ensemble is forced with the observed BB emissions during satellite-era ENSO events while the other is forced with a climatological annual cycle, with the responses to anomalous BB emissions estimated from inter-ensemble differences. It is found that the regional responses to anomalous BB emissions occur mainly during boreal fall, which is also the time of the climatological seasonal maximum in emissions. Transient responses are identified in precipitation, clouds, and radiation in both the tropics and extratropics. At the onset of El Niño, these include an increase precipitation in the northern branch of the ITCZ and an enhancement of cloud albedo and amount across the Maritime Continent and eastern subtropical Pacific Ocean. Additional responses are identified through the course of El Niño and successive La Niña events, the net effect of which is to strengthen SST anomalies in the eastern Pacific Ocean during El Niño and warm the tropical Pacific Ocean during La Niña. These responses improve simulation of ENSO power, diversity, and asymmetry in CESM2.

Mapping Field-Level Maize Yields in Ethiopian Smallholder Systems Using Sentinel-2 Imagery

Remote sensing offers a low-cost method for estimating yields at large spatio-temporal scales. Here, we examined the ability of Sentinel-2 satellite imagery to map field-level maize yields across smallholder farms in two regions in Oromia district, Ethiopia. We evaluated how effectively different indices, the MTCI, GCVI, and NDVI, and different models, linear regression and random forest regression, can be used to map field-level yields. We also examined if models improved by adding weather and soil data and how generalizable our models were if trained in one region and applied to another region, where no data were used for model calibration. We found that random forest regression models that used monthly MTCI composites led to the highest yield prediction accuracies (R2 up to 0.63), particularly when using only localized data for training the model. These models were not very generalizable, especially when applied to regions that had significant haze remaining in the imagery. We also found that adding soil and weather data did little to improve model fit. Our results highlight the ability of Sentinel-2 imagery to map field-level yields in smallholder systems, though accuracies are limited in regions with high cloud cover and haze.

Combining visual intelligence and social-physical urban features facilitates fine-scale seasonality characterization of urban thermal environments

Traditional methods for assessing urban thermal environments (UTEs) often rely on GIS and remote sensing data, suffering from data limitations in coverage, accuracy, and availability. To address these challenges, we established a novel framework developing visual intelligence simulated indices (VISIs) for improved UTE characterization. This framework combines GIS spatial data and pre-trained large-scale and local-trained GeoAI models to generate visual intelligence, extracting GIS-like visual elements from remote sensing imagery. These GIS-like VISIs comprehensively represent land surface temperature (LST) and urban fabric for cost-effective UTE analysis. We applied our framework to Zhengzhou, China, a city with diverse landscapes. Comparative experiments among Random Forests (RF), Neural Networks (NN), and Convolutional Neural Networks (CNN) demonstrated that RF models exhibited superior performance in LST representation (0.7 test R2 and 0.9 training R2). Interestingly, CNNs performed better than RFs and NNs when less cloud cover (<6 %) was present, suggesting that data diversity is essential for CNN to learn generalizable features for LST representation. For urban fabric representation, feature importance analysis indicated that VISIs (37 %) derived from large-scale GeoAI models outperform spectral bands (20 %). Furthermore, both Pearson correlation and Shapley values showed that VISIs (e.g., building features) better characterize urban heat islands than traditional remote sensing ecological indices (RSEIs) such as NDBI, underscoring the cost-effectiveness of our visual intelligence approach. In summary, our visual intelligence method effectively consolidates UTE seasonality characterization into a predictable scenario even given the unpredictable availability of remote sensing and GIS data, revealing novel perspectives on sensing unseen details for UTEs.

Estimation of the crop evapotranspiration for Udham Singh Nagar district using modified Priestley-Taylor model and Landsat imagery

The main challenges for utilizing daily evapotranspiration (ET) estimation in the study area revolve around the need for accurate and reliable data inputs, as well as the interpretation of ET dynamics within the context of local agricultural practices and environmental conditions. Factors such as cloud cover, atmospheric aerosols, and variations in land cover pose challenges to the precise estimation of ET from remote sensing data. This research aimed to utilize Landsat 8 and 9 datasets from the 2022–23 period in the Udham Singh Nagar district to apply the modified Priestley-Taylor (MPT) model for estimating ET. An average ET was estimated 1.33, 1.57, 1.70, 2.99, and 3.20 mm day−1 with 0.29, 0.33, 0.41, 0.69, and 1.03 standard deviation for December, January, February, March, and April month, respectively. In the validation phase, a strong correlation was found between the evaporative fraction derived from MPT and that observed by lysimeter, with R2 = 0.71, mean biased error = 0.04 mm day−1, root mean squared error = 0.62 mm day-1 and agreement index of 0.914. These results collectively support the effectiveness of the MPT model in accurately estimating ET across Udham Singh Nagar district. In essence, this research not only confirms the MPT model’s capability in ET estimation but also offers detailed insights into the spatial and temporal fluctuations of energy fluxes and daily ET rates.

SSGAN: Cloud removal in satellite images using spatiospectral generative adversarial network

Satellite data’s reliability, uniformity, and global scanning capabilities have revolutionized agricultural monitoring and crop management. However, the presence of clouds in satellite images can obscure useful information, rendering them difficult to infer. Aiming at the problem of cloud cover, this study presents a SpatioSpectral Generative Adversarial Network (SSGAN) approach for effectively eliminating cloud cover from multispectral satellite images. It utilizes the Synthetic Aperture Radar (SAR) images as complementary information with the optical images from the Sentinel-2 satellite. The proposed model exploits feature extraction by sub-grouping the 13 channels of Sentinel-2 images based on their electromagnetic wavelength. Experimentally, we demonstrated that the proposed SSGAN model surpasses conventional and state-of-the-art (SOTA) methods and can reconstruct regions obscured by clouds. The subgrouping optimized the utilization of sensor information and improved the performance metrics for reconstructed images. Compared to the state-of-the-art (SOTA) approach, the SSGAN model demonstrates higher performance, achieving a mPSNR of 32.771, mSSIM of 0.880, and correlation coefficient (CC) of 0.889. The SSGAN model was further evaluated under varying conditions, including scenarios without the inclusion of SAR data, where it achieved a mPSNR of 26.825, mSSIM of 0.726, and CC of 0.615. Adding SAR images into the model significantly enhanced its performance, resulting in a mPSNR of 29.932, mSSIM of 0.857, and CC of 0.735. These results indicate that higher mPSNR, mSSIM, and CC values correspond to better image reconstruction quality. Our method enhances the usability of satellite data for crop mapping, crop health monitoring, and crop yield prediction.

Variations of summer extreme high temperatures over the Indochina Peninsula: Roles of oceanic systems

Utilizing the reanalysis data and model simulations, we explore the interannual variations of extreme high temperatures over the Indochina Peninsula during 1960–2022, as well as their responses to critical oceanic systems and corresponding mechanisms. Given the intricate interactions among oceanic regions, this study employs the Generalized Equilibrium Feedback Analysis method to extract the atmospheric responses to key ocean systems, including the tropical Indian Ocean (TIO), El Niño–Southern Oscillation (ENSO), and tropical Atlantic. Results highlight the significant contributions of TIO and ENSO. It is suggested that the anomalous anticyclonic circulation located over the Indochina Peninsula, as a response to the warm TIO and ENSO, favors the local anomalous downward motions, resulting in reduced cloud cover, diminished precipitation, increased net radiative energy to the surface and increased sensible and latent heat flux from the surface to the atmosphere, and finally inducing an increase in extreme high temperatures. These observed patterns are also well simulated by the Community Earth System Model tropical Indian Ocean and tropical Pacific Ocean pacemaker experiments, indicating that the warmer tropical Indian Ocean and ENSO could induce anomalous anticyclonic (cyclonic) patterns at the lower (upper) troposphere over the South China Sea, thereby promoting the subsidence and occurrence of extreme high temperatures over the Indochina Peninsula.