Article
Environmental Sciences
Hamid Dashti, William K. Smith, Xueli Huo, Andrew M. Fox, Mostafa Javadian, Charles J. Devine, Ali Behrangi, David J. P. Moore
Summary: The Arctic and Boreal Region (ABR) undergoes extensive land cover change (LCC) due to factors such as wildfire, permafrost thaw, and shrubification. These LCCs alter important biophysical variables including land surface temperature (LST), albedo, and evapotranspiration (ET), which have a significant impact on the warming trend over the ABR. The sensitivity of these variables to different types of LCC in heterogeneous systems like ABR remains uncertain.
ENVIRONMENTAL RESEARCH LETTERS
(2023)
Article
Environmental Sciences
Yang Li, Yanlan Liu, Gil Bohrer, Yongyang Cai, Aaron Wilson, Tongxi Hu, Zhihao Wang, Kaiguang Zhao
Summary: The study demonstrates that forest disturbances have biophysical effects on land surface temperature, with increased surface albedo, decreased evapotranspiration, and reduced leaf area index observed after forest loss. The magnitude of post-disturbance warming is related to precipitation, with greater warming in climate zones with higher precipitation levels.
SCIENCE OF THE TOTAL ENVIRONMENT
(2022)
Article
Biodiversity Conservation
Andrew F. Feldman, Daniel J. Short Gianotti, Jianzhi Dong, Isabel F. Trigo, Guido D. Salvucci, Dara Entekhabi
Summary: Vegetation cover has competing effects on land surface temperature, with dryland tropical vegetation having weaker cooling effects compared to previous studies suggested.
GLOBAL CHANGE BIOLOGY
(2023)
Article
Agronomy
Guanghui Yuan, Wenhui Tang, Erchen Li, Lei Zhang, Yubao Liu
Summary: To assess the impacts of afforestation on land surface temperature in China, this study compared adjacent forest and open land using ten years of satellite data. The results showed that afforestation leads to daytime cooling and nighttime warming in most regions, attributed to changes in evapotranspiration and albedo. The cooling effects of evapotranspiration dominate the daytime differences in temperature, while the nighttime warming effects are related to the release of stored energy in the soil. Evergreen broadleaf forest and deciduous broadleaf forest are recommended for afforestation in the south and north of the Yangtze River respectively to decrease land surface temperature.
AGRICULTURAL AND FOREST METEOROLOGY
(2022)
Article
Environmental Sciences
Yuxuan Wu, Yi Xi, Maoyuan Feng, Shushi Peng
Summary: Wetlands play a critical role in global hydrological and biogeochemical cycles and have different temperature regulation effects in various regions and seasons. Wetlands have a cooling effect in tropical regions and a warming effect in boreal regions. In addition to albedo and evapotranspiration, ground heat flux is also an important factor influencing wetland temperature.
Article
Meteorology & Atmospheric Sciences
Xing Luo, Jun Ge, Weidong Guo, Yipeng Cao, Yu Liu, Chaorong Chen, Limei Yang
Summary: This study comprehensively evaluates the performance of four Earth System Models (ESMs) in representing the effects of deforestation. The results show that while the models can capture the general temperature response, they tend to over- or underestimate the magnitude. Biases in the simulated responses of albedo and heat fluxes contribute to these discrepancies. The models consistently overestimate the albedo response under snow-covered conditions and fail to fully reproduce the observed responses of heat fluxes. Model biases in surface temperature responses mainly result from biases related to surface energy partitioning. These findings highlight the need for caution when interpreting simulated results using CMIP6 models and have implications for model improvement.
JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES
(2023)
Article
Meteorology & Atmospheric Sciences
Sunny Kant, Chandan Sarangi, Eric M. Wilcox
Summary: Although the impact of aerosol-cloud interactions on radiation/temperature in South Asia has been well observed, its influence on cloud occurrence trends and surface temperature is still unclear. This study presents evidence of aerosol-induced control on cloud occurrence trends over the Northern Bay of Bengal during the monsoon onset period. Increased aerosol emissions over North India have led to elevated aerosol loading at 1-3 km altitude, which has resulted in increased air temperature and atmospheric stability, leading to an increase in low-level cloud occurrences and potentially contributing to non-intuitive cooling trends in sea surface temperatures. These observations highlight the importance of improving aerosol representations in coupled ocean-atmosphere models for accurate climate change predictions in South Asia.
NPJ CLIMATE AND ATMOSPHERIC SCIENCE
(2023)
Article
Meteorology & Atmospheric Sciences
Jizeng Du, Shaojing Jiang, Baoshan Cui, Guocan Wu, Hongxi Liu
Summary: Terrestrial vegetation plays a crucial role in modulating land-atmosphere dynamics and can have both cooling and warming effects on land surface temperatures. Using satellite measurements, this study found that vegetation has a net cooling effect in China, reducing land surface temperatures by up to 2.18 degrees C. The spatial pattern of vegetation's net effect is primarily driven by evapotranspiration and albedo, and it is sensitive to changes in solar radiation and snow cover.
JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES
(2022)
Article
Engineering, Civil
Xiaoyang Li, Lei Zou, Jun Xia, Ming Dou, Hongwei Li, Zhihong Song
Summary: This study analyzed the interannual variation and spatial distribution characteristics of evapotranspiration (ET) in China from 2005 to 2020 using the Priestley-Taylor Jet Propulsion Laboratory (PT-JPL) model. The contributions of climate change and land use/land cover change (LUCC) to ET variation were quantified, and the results showed that climate change had a greater impact on ET than LUCC. Temperature and net radiation were identified as the most critical factors affecting ET on a national scale.
JOURNAL OF HYDROLOGY
(2022)
Article
Meteorology & Atmospheric Sciences
Yonghong Liu, Bing Dang, Yongming Xu, Fuzhong Weng
Summary: The study found that the Shangyi Wind Farm in Zhangjiakou has a significant impact on the local climate, especially with atmospheric warming effect at night. The annual mean wind speed decreased, mainly noted in spring and winter. However, the wind farm had minimal impact on precipitation and relative humidity.
ADVANCES IN ATMOSPHERIC SCIENCES
(2021)
Article
Agronomy
Fuxiao Jiang, Xianhong Xie, Shunlin Liang, Yibing Wang, Bowen Zhu, Xiaotong Zhang, Yuchao Chen
Summary: The implementation of ecological restoration programs in the Loess Plateau has resulted in changes in potential ET (PET) and actual ET (AET) due to changes in radiative forcing during the growing season. From 2003 to 2018, surface albedo reduction and increased solar radiation led to an increase in PET and AET, with the radiative forcing contributing to 8% and 5% of the total change in PET and AET. This highlights the importance of considering radiative forcing in understanding the impact of ecological restoration on water resources in the region.
AGRICULTURAL AND FOREST METEOROLOGY
(2021)
Article
Remote Sensing
Zengjing Song, Hong Yang, Xiaojuan Huang, Wenping Yu, Jing Huang, Mingguo Ma
Summary: This study analyzed the trends of land surface temperature (LST) using linear and nonlinear methods, identifying significant cooling trends during 2007 to 2011/2012. The warming effects in spring and winter played a crucial role in the interannual variations of LST, while air temperature and vegetation were identified as dominant factors influencing LST changes in most regions of China.
INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION
(2021)
Article
Meteorology & Atmospheric Sciences
Astrid Manciu, Anja Rammig, Andreas Krause, Benjamin Raphael Quesada
Summary: Colombia is vulnerable to climate change, especially due to regional deforestation. This study examines the impact of historical land cover changes and global warming on temperature and precipitation in Colombia. The results show that anthropogenic climate change has led to a temperature increase and increased precipitation, while land cover changes have reduced precipitation. La Nina events have a stronger impact in the Andes region compared to El Nino, but a weaker impact on the coast. Accurately accounting for both land cover changes and global warming is important for hydroclimatic assessments.
Editorial Material
Environmental Sciences
Anton Orlov, Kristin Aunan, Malcolm N. Mistry, Quentin Lejeune, Julia Pongratz, Wim Thiery, Antonio Gasparrini, Eilif Ursin Reed, Carl-Friedrich Schleussner
Summary: Climate change has a significant impact on temperature-related mortality and morbidity, particularly under high greenhouse gas emission pathways. Achieving the goals of the Paris Agreement requires not only drastic reductions in fossil fuel-based emissions, but also land-use and land-cover changes (LULCC), such as reforestation and afforestation. LULCC has been mainly analyzed in the context of land-based mitigation and food security, but there is growing scientific evidence that it can also substantially alter the climate through biogeophysical effects. The consequential impacts on human health are not well understood, and research on LULCC-related impacts should broaden its scope to include human health impacts. Collaboration across research communities and stronger stakeholder engagement are necessary to address this knowledge gap, as LULCC is relevant to several global agendas, such as the Sustainable Development Goals.
ENVIRONMENTAL RESEARCH LETTERS
(2023)
Article
Environmental Sciences
Meng Li, Ronghao Chu, Abu Reza Md Towfiqul Islam, Shuanghe Shen
Summary: The study found that ET in the HRB decreased significantly from 2001 to 2014 due to the impact of climate change and LUCC. The main land use type in the HRB is croplands, with a decreasing trend over time. This research provides insights into the impacts of climate change and LUCC on water resources in the area.
ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
(2021)
Article
Environmental Sciences
Yuzhen Zhang, Jindong Liu, Shunlin Liang, Manyao Li
Summary: This study proposes a novel spatiotemporal fusion method based on a depthwise separable convolutional neural network to generate Landsat-surface reflectance time series. The method shows stable and more accurate predictions in different regions, indicating its potential for global application.
Article
Environmental Sciences
Tianchen Liang, Shunlin Liang, Linqing Zou, Lin Sun, Bing Li, Hao Lin, Tao He, Feng Tian
Summary: This study investigates the possibility of using Landsat imagery and machine learning algorithms to develop high-resolution aerosol optical depth (AOD) estimations. Six machine learning algorithms were assessed, with the extremely randomized trees algorithm demonstrating the best performance. Comparison with AERONET observations confirms the accuracy of the method.
Article
Meteorology & Atmospheric Sciences
Aolin Jia, Dongdong Wang, Shunlin Liang, Jingjing Peng, Yunyue Yu
Summary: Land surface albedo plays a critical role in various aspects of Earth modeling and forecasting. This study introduces a new global land surface albedo climatology dataset that is more accurate and reliable than existing datasets, and is capable of capturing albedo variation over areas with distinct snow seasons.
JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES
(2022)
Review
Chemistry, Multidisciplinary
Yuzhen Zhang, Jingjing Liu, Wenjuan Shen
Summary: Machine learning algorithms are increasingly used in remote sensing applications for their ability to identify nonlinear correlations. This article provides an overview of three widely used ensemble techniques: bagging, boosting, and stacking. It summarizes the underlying principles of the algorithms and analyzes the current literature. The article also presents typical applications of ensemble algorithms in predicting crop yield, estimating forest structure parameters, mapping natural hazards, and spatial downscaling of climate parameters and land surface temperature.
APPLIED SCIENCES-BASEL
(2022)
Article
Environmental Sciences
Yuzhen Zhang, Jingjing Liu, Wenhao Li, Shunlin Liang
Summary: Feature selection can improve the accuracy of forest aboveground biomass (AGB) prediction and identify important predictors, but its role in AGB estimation has not received sufficient attention. This study quantified the benefits of feature selection in AGB prediction and proposed a stability-heterogeneity-correlation-based ensemble (SHCE) method that outperformed existing FS methods in terms of prediction accuracy and identification of important features.
Article
Environmental Sciences
Yu Bai, Shunlin Liang, Aolin Jia, Shenggong Li
Summary: Capturing the spatial and temporal dynamics of global GPP is crucial for understanding the carbon cycle and climate change. This study compared five GPP products to identify trends and sensitivities to environmental factors. Results showed inconsistent global trends and sensitivities among different products, highlighting the need for further investigation.
JOURNAL OF GEOPHYSICAL RESEARCH-BIOGEOSCIENCES
(2023)
Article
Environmental Sciences
Bo Jiang, Jiakun Han, Hui Liang, Shunlin Liang, Xiuwan Yin, Jianghai Peng, Tao He, Yichuan Ma
Summary: This study proposes two algorithms (DSR and TOA algorithms) to estimate the surface net radiation (Rn) using Landsat data. Validation results showed that the DSR algorithm outperformed the TOA algorithm in terms of accuracy. The Hi-GLASS product generated by the DSR algorithm captures more details of the variations in surface radiation.
SCIENCE OF REMOTE SENSING
(2023)
Article
Geosciences, Multidisciplinary
Yufang Zhang, Shunlin Liang, Han Ma, Tao He, Qian Wang, Bing Li, Jianglei Xu, Guodong Zhang, Xiaobang Liu, Changhao Xiong
Summary: Motivated by the lack of long-term global soil moisture products, a global 1 km daily spatiotemporally continuous soil moisture product (GLASS SM) was generated from 2000 to 2020 using an ensemble learning model. The model integrated multiple datasets and improved its performance by selecting representative soil moisture stations. The validation results and intercomparison with other soil moisture products demonstrated the accuracy and consistency of the GLASS SM product.
EARTH SYSTEM SCIENCE DATA
(2023)
Article
Geochemistry & Geophysics
Yingying Li, Shunlin Liang
Summary: Chlorophyll is of great physiological and ecological significance. Leaf chlorophyll content (LCC) and canopy chlorophyll content (CCC) can be retrieved from remotely sensed data based on vegetation indices (VIs). The impact of canopy structure and soil on VIs and the accuracy of different VIs for LCC and CCC estimation were investigated in this study.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2023)
Article
Geosciences, Multidisciplinary
Aolin Jia, Shunlin Liang, Dongdong Wang, Lei Ma, Zhihao Wang, Shuo Xu
Summary: This study improved the SEB-based cloudy-sky LST recovery method and generated a global hourly, 5 km resolution all-sky land surface temperature dataset. The dataset exhibited good spatiotemporal continuity and high accuracy compared to reference data, and it can be used for estimating global evapotranspiration, monitoring extreme weather, and improving meteorological forecasting models.
EARTH SYSTEM SCIENCE DATA
(2023)
Article
Geosciences, Multidisciplinary
Han Ma, Shunlin Liang, Changhao Xiong, Qian Wang, Aolin Jia, Bing Li
Summary: The fraction of absorbed photosynthetically active radiation (FAPAR) is a critical land surface variable for carbon cycle modeling and ecological monitoring. However, current FAPAR products suffer from spatiotemporal inconsistency. Deep learning approaches that utilize temporal information in satellite data can improve the spatiotemporal continuity and accuracy of FAPAR products.
EARTH SYSTEM SCIENCE DATA
(2022)
Article
Geochemistry & Geophysics
Yi Zhang, Shunlin Liang, Tao He
Summary: This study proposes an innovative deep learning method that combines radiative-transfer modeling with convolutional neural network learning for estimating surface downward shortwave radiation (DSR). The results show that the proposed algorithm outperforms traditional methods and the use of transfer learning further improves the estimation.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
Article
Geosciences, Multidisciplinary
Rui Ma, Jingfeng Xiao, Shunlin Liang, Han Ma, Tao He, Da Guo, Xiaobang Liu, Haibo Lu
Summary: This study proposes a hybrid modeling approach that combines traditional modeling techniques with data-driven machine learning methods to optimize parameters in complex terrestrial biosphere models. By spatially predicting optimized parameters for deciduous forests in the eastern United States, the study demonstrates significant spatial variability in parameters and the effectiveness of reducing simulation errors with optimized parameters.
GEOSCIENTIFIC MODEL DEVELOPMENT
(2022)
Article
Geosciences, Multidisciplinary
Jianglei Xu, Shunlin Liang, Bo Jiang
Summary: The surface radiation budget (R-n) is a crucial parameter for various land surface processes. This study proposes a new method to estimate R-n using satellite data and creates a global high-resolution long-term product. The method improves accuracy by incorporating spatially adjacent information and training the model with ground measurements. The results show that the product is highly accurate and outperforms other existing R-n products.
EARTH SYSTEM SCIENCE DATA
(2022)
Article
Geochemistry & Geophysics
Yan Liu, Xingfa Gu, Tianhai Cheng, Yulin Zhan, Hu Zhang, Juan Li, Xiangqin Wei, Min Gao, Qian Zhang, Yuzhen Zhang
Summary: A temporal shape-based fusion method is proposed to effectively incorporate fine- and coarse-resolution observations, providing high-accuracy fused images for various regions without cloud-free input requirements.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
