Investigating the capability of the Harmonic Analysis of Time Series (HANTS) algorithm in reconstructing time series images of daytime and nighttime land surface temperature from the MODIS sensor

被引：5

作者：

Aliabad, Fahime Arabi ^{[1
]}

Shojaei, Saeed ^{[1
]}

Zare, Mohammad ^{[1
]}

Malamiri, Hamidreza Ghafarian ^{[2
]}

机构：

[1] Yazd Univ, Dept Arid Lands Management, Fac Nat Resources & Desert Studies, Yazd, Iran

[2] Yazd Univ, Dept Geog, Yazd, Iran

来源：

SPATIAL INFORMATION RESEARCH | 2024年 / 32卷 / 04期

关键词：

Gap filling; Harmonic analysis; Time series reconstruction; Thermal remote sensing; FOURIER-ANALYSIS; VEGETATION;

D O I：

10.1007/s41324-023-00569-3

中图分类号：

TP7 [遥感技术];

学科分类号：

081102 ; 0816 ; 081602 ; 083002 ; 1404 ;

摘要：

A continuous, high-resolution surface temperature time series is necessary for hydrology, meteorology, and ecology. However, challenges such as cloud cover, aerosols, and algorithmic disturbances in satellite-based temperature images, particularly from MODIS, result in irregular observations, data loss, noise, and spatial-temporal outliers. The effectiveness of the Harmonic Analysis of Time Series (HANTS) algorithm in reconstructed day and night temperature series from MODIS in desert regions are assessed in this study. Utilizing daily and nightly surface temperature data from 2014 to 2020 (4380 images), data gap analysis revealed peak loss during spring and winter, averaging 6.19% during the day and 8.20% at night over seven years. Because of temperature differences between day and night, the HANTS algorithm was unable to reconstruct the day-night sequence in an accurate way, highlighting the potential of the algorithm in addressing challenges associated with desert environments.

引用

页码：425 / 439

页数：15

共 36 条

[1] Mapping air temperature using time series analysis of LST: the SINTESI approach
Alfieri, S. M.
De Lorenzi, F.
Menenti, M.
[J]. NONLINEAR PROCESSES IN GEOPHYSICS, 2013, 20 (04) : 513 - 527
[2] Advances in Quantitative Earth Remote Sensing: Past, Present and Future
Asrar, Ghassem R.
[J]. SENSORS, 2019, 19 (24)
[3] Inter-comparison of four models for smoothing satellite sensor time-series data to estimate vegetation phenology
Atkinson, Peter M.
Jeganathan, C.
Dash, Jadu
Atzberger, Clement
[J]. REMOTE SENSING OF ENVIRONMENT, 2012, 123 : 400 - 417
[4] Mapping vegetation-soil-climate complexes in southern Africa using temporal Fourier analysis of NOAA-AVHRR NDVI data
Azzali, S
Menenti, M
[J]. INTERNATIONAL JOURNAL OF REMOTE SENSING, 2000, 21 (05) : 973 - 996
[5] MODIS Land Surface Temperature Product Reconstruction Based on the SSA-BiLSTM Model
Cui, Jianyong
Zhang, Manyu
Song, Dongmei
Shan, Xinjian
Wang, Bin
[J]. REMOTE SENSING, 2022, 14 (04)
[6] Analysis of monotonic greening and browning trends from global NDVI time-series
de Jong, Rogier
de Bruin, Sytze
de Wit, Allard
Schaepman, Michael E.
Dent, David L.
[J]. REMOTE SENSING OF ENVIRONMENT, 2011, 115 (02) : 692 - 702
[7] Efthimiou N., 2013, EARSEL EPROCEEDINGS, V12, P58
[8] Reconstruction of MODIS land-surface temperature in a flat terrain and fragmented landscape
Fan, Xiao-Mei
Liu, Hong-Guang
Liu, Gao-Huan
Li, Shou-Bo
[J]. INTERNATIONAL JOURNAL OF REMOTE SENSING, 2014, 35 (23) : 7857 - 7877
[9] MODIS Collection 5 global land cover: Algorithm refinements and characterization of new datasets
Friedl, Mark A.
Sulla-Menashe, Damien
Tan, Bin
Schneider, Annemarie
Ramankutty, Navin
Sibley, Adam
Huang, Xiaoman
[J]. REMOTE SENSING OF ENVIRONMENT, 2010, 114 (01) : 168 - 182
[10] An algorithm to classify and monitor seasonal variations in vegetation phenologies and their inter-annual change
Geerken, Roland A.
[J]. ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING, 2009, 64 (04) : 422 - 431

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