scholarly journals An Improved Cloud Gap-Filling Method for Longwave Infrared Land Surface Temperatures through Introducing Passive Microwave Techniques

2021 ◽  
Vol 13 (17) ◽  
pp. 3522
Author(s):  
Thomas P. F. Dowling ◽  
Peilin Song ◽  
Mark C. De Jong ◽  
Lutz Merbold ◽  
Martin J. Wooster ◽  
...  
Keyword(s):  
Land Surface ◽  
Cloud Cover ◽  
Time Frame ◽  
Passive Microwave ◽  
Gap Filling ◽  
Coarse Spatial Resolution ◽  
Land Surface Temperatures ◽  
Filling Method ◽  
Microwave Techniques ◽  
Mean Square Errors

Satellite-derived land surface temperature (LST) data are most commonly observed in the longwave infrared (LWIR) spectral region. However, such data suffer frequent gaps in coverage caused by cloud cover. Filling these ‘cloud gaps’ usually relies on statistical re-constructions using proximal clear sky LST pixels, whilst this is often a poor surrogate for shadowed LSTs insulated under cloud. Another solution is to rely on passive microwave (PM) LST data that are largely unimpeded by cloud cover impacts, the quality of which, however, is limited by the very coarse spatial resolution typical of PM signals. Here, we combine aspects of these two approaches to fill cloud gaps in the LWIR-derived LST record, using Kenya (East Africa) as our study area. The proposed “cloud gap-filling” approach increases the coverage of daily Aqua MODIS LST data over Kenya from <50% to >90%. Evaluations were made against the in situ and SEVIRI-derived LST data respectively, revealing root mean square errors (RMSEs) of 2.6 K and 3.6 K for the proposed method by mid-day, compared with RMSEs of 4.3 K and 6.7 K for the conventional proximal-pixel-based statistical re-construction method. We also find that such accuracy improvements become increasingly apparent when the total cloud cover residence time increases in the morning-to-noon time frame. At mid-night, cloud gap-filling performance is also better for the proposed method, though the RMSE improvement is far smaller (<0.3 K) than in the mid-day period. The results indicate that our proposed two-step cloud gap-filling method can improve upon performances achieved by conventional methods for cloud gap-filling and has the potential to be scaled up to provide data at continental or global scales as it does not rely on locality-specific knowledge or datasets.

scholarly journals BVOC fluxes above mountain grassland

2010 ◽  
Vol 7 (5) ◽  
pp. 1413-1424 ◽  
Author(s):  
I. Bamberger ◽  
L. Hörtnagl ◽  
R. Schnitzhofer ◽  
M. Graus ◽  
T. M. Ruuskanen ◽  
...  
Keyword(s):  
Land Surface ◽  
Transfer Reaction ◽  
Growing Season ◽  
Proton Transfer Reaction ◽  
Gap Filling ◽  
Eddy Covariance Method ◽  
Mountain Grassland ◽  
Volatile Organic ◽  
Filling Method ◽  
Highly Correlated

Abstract. Grasslands comprise natural tropical savannah over managed temperate fields to tundra and cover one quarter of the Earth's land surface. Plant growth, maintenance and decay result in volatile organic compound (VOCs) emissions to the atmosphere. Furthermore, biogenic VOCs (BVOCs) are emitted as a consequence of various environmental stresses including cutting and drying during harvesting. Fluxes of BVOCs were measured with a proton-transfer-reaction-mass-spectrometer (PTR-MS) over temperate mountain grassland in Stubai Valley (Tyrol, Austria) over one growing season (2008). VOC fluxes were calculated from the disjunct PTR-MS data using the virtual disjunct eddy covariance method and the gap filling method. Methanol fluxes obtained with the two independent flux calculation methods were highly correlated (y = 0.95×−0.12, R2 = 0.92). Methanol showed strong daytime emissions throughout the growing season – with maximal values of 9.7 nmol m−2 s−1, methanol fluxes from the growing grassland were considerably higher at the beginning of the growing season in June compared to those measured during October (2.5 nmol m−2 s−1). Methanol was the only component that exhibited consistent fluxes during the entire growing periods of the grass. The cutting and drying of the grass increased the emissions of methanol to up to 78.4 nmol m−2 s−1. In addition, emissions of acetaldehyde (up to 11.0 nmol m−2 s−1), and hexenal (leaf aldehyde, up to 8.6 nmol m−2 s−1) were detected during/after harvesting.

scholarly journals Gap-Filling of 8-Day Terra MODIS Daytime Land Surface Temperature in High-Latitude Cold Region with Generalized Additive Models (GAM)

2021 ◽  
Vol 13 (18) ◽  
pp. 3667
Author(s):  
Dianfan Guo ◽  
Cuizhen Wang ◽  
Shuying Zang ◽  
Jinxi Hua ◽  
Zhenghan Lv ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
High Latitude ◽  
Land Surface ◽  
Growing Season ◽  
Additive Models ◽  
Permafrost Degradation ◽  
Cold Region ◽  
Gap Filling ◽  
Mean Square Errors

Land surface temperature (LST) is a crucial parameter driving the dynamics of the thermal state on land surface. In high-latitude cold region, a long-term, stable LST product is of great importance in examining the distribution and degradation of permafrost under pressure of global warming. In this study, a generalized additive model (GAM) approach was developed to fill the missing pixels of the MODIS/Terra 8-day Land Surface Temperature (MODIS LST) daytime products with the ERA5 Land Skin Temperature (ERA5ST) dataset in a high-latitude watershed in Eurasia. Comparison at valid pixels revealed that the MODIS LST was 4.8–13.0 °C higher than ERA5ST, which varies with land covers and seasons. The GAM models fairly explained the LST differences between the two products from multiple covariates including satellite-extracted environmental variables (i.e., normalized difference water index (NDWI), normalized difference vegetation index (NDVI), and normalized difference snow index (NDSI) as well as locational information. Considering the dramatic seasonal variation of vegetation and frequent snow in the cold region, the gap-filling was conducted in two seasons. The results revealed the root mean square errors (RMSE) of 2.7 °C and 3.4 °C between the valid MODIS LST and GAM-simulated LST data in the growing season and snowing season, respectively. By including the satellite-extracted land surface information in the GAM model, localized variations of land surface temperature that are often lost in the reanalysis data were effectively compensated. Specifically, land surface wetness (NDWI) was found to be the greatest contributor to explaining the differences between the two products. Vegetation (NDVI) was useful in the growing season and snow cover (NDSI) cannot be ignored in the snow season of the study region. The km-scale gap-filled MODIS LST products provide spatially and temporally continuous details that are useful for monitoring permafrost degradation in cold regions in scenarios of global climate change.

scholarly journals A baseline evaluation of atmospheric and river discharge conditions in the Hudson Bay Complex during 2016–2018

Elem Sci Anth ◽  
2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Jennifer V. Lukovich ◽  
Andrew Tefs ◽  
Shabnam Jafarikhasragh ◽  
Clark Pennelly ◽  
Paul G. Myers ◽  
...  
Keyword(s):  
Annual Cycle ◽  
Land Surface ◽  
River Discharge ◽  
Time Frame ◽  
Hudson Bay ◽  
Sea Level Pressure ◽  
Weather Forecasts ◽  
Baseline Evaluation ◽  
Land Surface Temperatures ◽  
High Precipitation

In this article, we examine atmospheric and river discharge conditions within the Hudson Bay Complex for the BaySys 2016–2018 field program time frame. Investigated in particular is a subset of European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis - Interim (ERA-Interim) atmospheric forcing variables, namely 2-m surface temperature, 10-m surface winds, precipitation, and sea-level pressure, in addition to river discharge. Results from this assessment show that 2016 was characterized by unusually warm conditions (terrestrial and marine) throughout the annual cycle; 2017 by strong cyclone activity in March and high precipitation in January, October, and November; and 2018 by cold and windy conditions throughout the annual cycle. Evaluation of terrestrial conditions showed higher than normal land surface temperatures (the Hudson Bay physical watershed) for all of the 2016–2018 period (excluding a colder than normal spell August–November 2018), particularly in January (2016 and 2017), higher than normal precipitation in October (2016 and 2017), and higher than normal terrestrial discharge to the Hudson Bay Complex in March (2016 and 2017), with drier than average June through October (2016–2018).

scholarly journals A Review of Reconstructing Remotely Sensed Land Surface Temperature under Cloudy Conditions

2021 ◽  
Vol 13 (14) ◽  
pp. 2838
Author(s):  
Yaping Mo ◽  
Yongming Xu ◽  
Huijuan Chen ◽  
Shanyou Zhu
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Spatial Scales ◽  
Remotely Sensed ◽  
Urban Heat Islands ◽  
Reconstruction Algorithms ◽  
Gap Filling ◽  
Practical Applications ◽  
Data Gaps

Land surface temperature (LST) is an important environmental parameter in climate change, urban heat islands, drought, public health, and other fields. Thermal infrared (TIR) remote sensing is the main method used to obtain LST information over large spatial scales. However, cloud cover results in many data gaps in remotely sensed LST datasets, greatly limiting their practical applications. Many studies have sought to fill these data gaps and reconstruct cloud-free LST datasets over the last few decades. This paper reviews the progress of LST reconstruction research. A bibliometric analysis is conducted to provide a brief overview of the papers published in this field. The existing reconstruction algorithms can be grouped into five categories: spatial gap-filling methods, temporal gap-filling methods, spatiotemporal gap-filling methods, multi-source fusion-based gap-filling methods, and surface energy balance-based gap-filling methods. The principles, advantages, and limitations of these methods are described and discussed. The applications of these methods are also outlined. In addition, the validation of filled LST values’ cloudy pixels is an important concern in LST reconstruction. The different validation methods applied for reconstructed LST datasets are also reviewed herein. Finally, prospects for future developments in LST reconstruction are provided.

scholarly journals Spatial-Temporal Distribution of the Freeze–Thaw Cycle of the Largest Lake (Qinghai Lake) in China Based on Machine Learning and MODIS from 2000 to 2020

2021 ◽  
Vol 13 (9) ◽  
pp. 1695
Author(s):  
Weixiao Han ◽  
Chunlin Huang ◽  
Juan Gu ◽  
Jinliang Hou ◽  
Ying Zhang
Keyword(s):  
Machine Learning ◽  
Land Surface ◽  
Temporal Dynamics ◽  
Temporal Distribution ◽  
Ice Cover ◽  
Qinghai Lake ◽  
Gap Filling ◽  
Thaw Cycle ◽  
Break Up ◽  
Ice Phenology

The lake ice phenology variations are vital for the land–surface–water cycle. Qinghai Lake is experiencing amplified warming under climate change. Based on the MODIS imagery, the spatio-temporal dynamics of the ice phenology of Qinghai Lake were analyzed using machine learning during the 2000/2001 to 2019/2020 ice season, and cloud gap-filling procedures were applied to reconstruct the result. The results showed that the overall accuracy of the water–ice classification by random forest and cloud gap-filling procedures was 98.36% and 92.56%, respectively. The annual spatial distribution of the freeze-up and break-up dates ranged primarily from DOY 330 to 397 and from DOY 70 to 116. Meanwhile, the decrease rates of freeze-up duration (DFU), full ice cover duration (DFI), and ice cover duration (DI) were 0.37, 0.34, and 0.13 days/yr., respectively, and the duration was shortened by 7.4, 6.8, and 2.6 days over the past 20 years. The increased rate of break-up duration (DBU) was 0.58 days/yr. and the duration was lengthened by 11.6 days. Furthermore, the increase in temperature resulted in an increase in precipitation after two years; the increase in precipitation resulted in the increase in DBU and decrease in DFU in corresponding years, and decreased DI and DFI after one year.

scholarly journals Optimization of the Local Split-Window Algorithm for FY-4A Land Surface Temperature Retrieval

2019 ◽  
Vol 11 (17) ◽  
pp. 2016
Author(s):  
Lijuan Wang ◽  
Ni Guo ◽  
Wei Wang ◽  
Hongchao Zuo
Keyword(s):  
Surface Temperature ◽  
Diurnal Variation ◽  
Land Surface Temperature ◽  
Satellite Data ◽  
Land Surface ◽  
Regional Scale ◽  
Pso Algorithm ◽  
Variation Characteristics ◽  
Split Window Algorithm ◽  
Mean Square Errors

FY-4A is a second generation of geostationary orbiting meteorological satellite, and the successful launch of FY-4A satellite provides a new opportunity to obtain diurnal variation of land surface temperature (LST). In this paper, different underlying surfaces-observed data were applied to evaluate the applicability of the local split-window algorithm for FY-4A, and the local split-window algorithm parameters were optimized by the artificial intelligent particle swarm optimization (PSO) algorithm to improve the accuracy of retrieved LST. Results show that the retrieved LST can efficiently reproduce the diurnal variation characteristics of LST. However, the estimated values deviate hugely from the observed values when the local split-window algorithms are directly used to process the FY-4A satellite data, and the root mean square errors (RMSEs) are approximately 6K. The accuracy of the retrieved LST cannot be effectively improved by merely modifying the emissivity-estimated model or optimizing the algorithm. Based on the measured emissivity, the RMSE of LST retrieved by the optimized local split-window algorithm is reduced to 3.45 K. The local split-window algorithm is a simple and easy retrieval approach that can quickly retrieve LST on a regional scale and promote the application of FY-4A satellite data in related fields.

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