scholarly journals Improving the Classification Accuracy of Annual Crops Using Time Series of Temperature and Vegetation Indices

2020 ◽  
Vol 12 (19) ◽  
pp. 3202
Author(s):  
Xinran Chen ◽  
Yulin Zhan ◽  
Yan Liu ◽  
Xingfa Gu ◽  
Tao Yu ◽  
...  
Keyword(s):  
Time Series ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Classification Accuracy ◽  
Spectral Feature ◽  
Temperature Index ◽  
Long Time Series ◽  
Ndvi Time Series ◽  
Long Time

Accurate cropland classification is important for agricultural monitoring and related decision-making. The commonly used input spectral features for classification cannot be employed to effectively distinguish crops that have similar spectro-temporal features. This study attempted to improve the classification accuracy of crops using both the thermal feature, i.e., the land surface temperature (LST), and the spectral feature, i.e., the normalized difference vegetation index (NDVI), for classification. To amplify the temperature differences between the crops, a temperature index, namely, the modified land surface temperature index (mLSTI) was built using the LST. The mLSTI was calculated by subtracting the average LST of an image from the LST of each pixel. To study the adaptability of the proposed method to different areas, three study areas were selected. A comparison of the classification results obtained using the NDVI time series and NDVI + mLSTI time series showed that for long time series from June to November, the classification accuracy when using the mLSTI and NDVI time series was higher (85.6% for study area 1 in California, 96.3% for area 2 in Kansas, and 91.2% for area 3 in Texas) than that when using the NDVI time series alone (82.0% for area 1, 94.7% for area 2, and 90.9% for area 3); the same was true in most of the cases when using the shorter time series. With the addition of the mLSTI time series, the shorter time series achieved higher classification accuracy, which is beneficial for timely crop identification. The sorghum and soybean crops, which exhibit similar NDVI feature curves in this study, could be better distinguished by adding the mLSTI time series. The results demonstrated that the classification accuracy of crops can be improved by adding mLSTI long time series, particularly for distinguishing crops with similar NDVI characteristics in a given study area.

scholarly journals Evaluation of Landsat 8-Like Land Surface Temperature by Fusing Landsat 8 and MODIS Land Surface Temperature Product

Processes ◽  
2021 ◽  
Vol 9 (12) ◽  
pp. 2262
Author(s):  
Shenglin Li ◽  
Jinglei Wang ◽  
Dacheng Li ◽  
Zhongxin Ran ◽  
Bo Yang
Keyword(s):  
Time Series ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Landsat 8 ◽  
Spatiotemporal Resolution ◽  
Long Time Series ◽  
Long Time ◽  
Fusion Images ◽  
Better Than

High-spatiotemporal-resolution land surface temperature (LST) is a crucial parameter in various environmental monitoring. However, due to the limitation of sensor trade-off between the spatial and temporal resolutions, such data are still unavailable. Therefore, the generation and verification of such data are of great value. The spatiotemporal fusion algorithm, which can be used to improve the spatiotemporal resolution, is widely used in Landsat and MODIS data to generate Landsat-like images, but there is less exploration of combining long-time series MODIS LST and Landsat 8 LST product to generate Landsat 8-like LST. The purpose of this study is to evaluate the accuracy of the long-time series Landsat 8 LST product and the Landsat 8-like LST generated by spatiotemporal fusion. In this study, based on the Landsat 8 LST product and MODIS LST product, Landsat 8-like LST is generated using Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM), Enhanced STARFM (ESTARFM), and the Flexible Spatiotemporal DAta Fusion (FSDAF) algorithm, and tested and verified in the research area located in Gansu Province, China. In this process, Landsat 8 LST product was verified based on ground measurements, and the fusion results were comprehensively evaluated based on ground measurements and actual Landsat 8 LST images. Ground measurements verification indicated that Landsat 8 LST product was highly consistent with ground measurements. The Root Mean Square Error (RMSE) was 2.862 K, and the coefficient of determination R2 was 0.952 at All stations. Good fusion results can be obtained for the three spatiotemporal algorithms, and the ground measurements verified at All stations show that R2 was more significant than 0.911. ESTARFM had the best fusion result (R2 = 0.915, RMSE = 3.661 K), which was better than STARFM (R2 = 0.911, RMSE = 3.746 K) and FSDAF (R2 = 0.912, RMSE = 3.786 K). Based on the actual Landsat 8 LST images verification, the fusion images were highly consistent with actual Landsat 8 LST images. The average RMSE of fusion images about STARFM, ESTARFM, and FSDAF were 2.608 K, 2.245 K, and 2.565 K, respectively, and ESTARFM is better than STARFM and FSDAF in most cases. Combining the above verification, the fusion results of the three algorithms were reliable and ESTARFM had the highest fusion accuracy.

scholarly journals Exploring the Variation Trend of Urban Expansion, Land Surface Temperature, and Ecological Quality and Their Interrelationships in Guangzhou, China, from 1987 to 2019

2021 ◽  
Vol 13 (5) ◽  
pp. 1019
Author(s):  
Jianhui Xu ◽  
Yi Zhao ◽  
Caige Sun ◽  
Hanbin Liang ◽  
Ji Yang ◽  
...  
Keyword(s):  
Time Series ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Urban Expansion ◽  
Impervious Surface ◽  
Ecological Quality ◽  
Urban Center ◽  
Urban Centers ◽  
Rapid Urbanization

This study explored the model of urban impervious surface (IS) density, land surface temperature (LST), and comprehensive ecological evaluation index (CEEI) from urban centers to suburbs. The interrelationships between these parameters in Guangzhou from 1987 to 2019 were analyzed using time-series Landsat-5 TM (Thematic Mapper), Landsat-8 OLI (Operational Land Imager), and TIRS (Thermal Infrared Sensor) images. The urban IS densities were calculated in concentric rings using time-series IS fractions, which were used to construct an inverse S-shaped urban IS density function to depict changes in urban form and the spatio-temporal dynamics of urban expansion from the urban center to the suburbs. The results indicated that Guangzhou experienced expansive urban growth, with the patterns of urban spatial structure changing from a single-center to a multi-center structure over the past 32 years. Next, the normalized LST and CEEI in each concentric ring were calculated, and their variation trends from the urban center to the suburbs were modeled using linear and nonlinear functions, respectively. The results showed that the normalized LST had a gradual decreasing trend from the urban center to the suburbs, while the CEEI showed a significant increasing trend. During the 32-year rapid urban development, the normalized LST difference between the urban center and suburbs increased gradually with time, and the CEEI significantly decreased. This indicated that rapid urbanization significantly expanded the impervious surface areas in Guangzhou, leading to an increase in the LST difference between urban centers and suburbs and a deterioration in ecological quality. Finally, the potential interrelationships among urban IS density, normalized LST, and CEEI were also explored using different models. This study revealed that rapid urbanization has produced geographical convergence between several ISs, which may increase the risk of the urban heat island effect and degradation of ecological quality.

scholarly journals Relating Spatiotemporal Patterns of Forest Fires Burned Area and Duration to Diurnal Land Surface Temperature Anomalies

2018 ◽  
Vol 10 (11) ◽  
pp. 1777 ◽  
Author(s):  
Carmine Maffei ◽  
Silvia Alfieri ◽  
Massimo Menenti
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Forest Fires ◽  
Land Surface ◽  
Fire Danger ◽  
Parametric Models ◽  
Burned Area ◽  
Informative Content

Forest fires are a major source of ecosystem disturbance. Vegetation reacts to meteorological factors contributing to fire danger by reducing stomatal conductance, thus leading to an increase of canopy temperature. The latter can be detected by remote sensing measurements in the thermal infrared as a deviation of observed land surface temperature (LST) from climatological values, that is as an LST anomaly. A relationship is thus expected between LST anomalies and forest fires burned area and duration. These two characteristics are indeed controlled by a large variety of both static and dynamic factors related to topography, land cover, climate, weather (including those affecting LST) and anthropic activity. To investigate the predicting capability of remote sensing measurements, rather than constructing a comprehensive model, it would be relevant to determine whether anomalies of LST affect the probability distributions of burned area and fire duration. This research approached the outlined knowledge gap through the analysis of a dataset of forest fires in Campania (Italy) covering years 2003–2011 against estimates of LST anomaly. An LST climatology was first computed from time series of daily Aqua-MODIS LST data (product MYD11A1, collection 6) over the longest available sequence of complete annual datasets (2003–2017), through the Harmonic Analysis of Time Series (HANTS) algorithm. HANTS was also used to create individual annual models of LST data, to minimize the effect of varying observation geometry and cloud contamination on LST estimates while retaining its seasonal variation. LST anomalies where thus quantified as the difference between LST annual models and LST climatology. Fire data were intersected with LST anomaly maps to associate each fire with the LST anomaly value observed at its position on the day previous to the event. Further to this step, the closest probability distribution function describing burned area and fire duration were identified against a selection of parametric models through the maximization of the Anderson-Darling goodness-of-fit. Parameters of the identified distributions conditional to LST anomaly where then determined along their confidence intervals. Results show that in the study area log-transformed burned area is described by a normal distribution, whereas log-transformed fire duration is closer to a generalized extreme value (GEV) distribution. The parameters of these distributions conditional to LST anomaly show clear trends with increasing LST anomaly; significance of this observation was verified through a likelihood ratio test. This confirmed that LST anomaly is a covariate of both burned area and fire duration. As a consequence, it was observed that conditional probabilities of extreme events appear to increase with increasing positive deviations of LST from its climatology values. This confirms the stated hypothesis that LST anomalies affect forest fires burned area and duration and highlights the informative content of time series of LST with respect to fire danger.

scholarly journals Evaluation of Seven Atmospheric Profiles from Reanalysis and Satellite-Derived Products: Implication for Single-Channel Land Surface Temperature Retrieval

2020 ◽  
Vol 12 (5) ◽  
pp. 791 ◽  
Author(s):  
Jingjing Yang ◽  
Si-Bo Duan ◽  
Xiaoyu Zhang ◽  
Penghai Wu ◽  
Cheng Huang ◽  
...  
Keyword(s):  
Time Series ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Single Channel ◽  
Landsat 8 ◽  
Atmospheric Profiles ◽  
Atmospheric Profile

Land surface temperature (LST) is vital for studies of hydrology, ecology, climatology, and environmental monitoring. The radiative-transfer-equation-based single-channel algorithm, in conjunction with the atmospheric profile, is regarded as the most suitable one with which to produce long-term time series LST products from Landsat thermal infrared (TIR) data. In this study, the performances of seven atmospheric profiles from different sources (the MODerate-resolution Imaging Spectroradiomete atmospheric profile product (MYD07), the Atmospheric Infrared Sounder atmospheric profile product (AIRS), the European Centre for Medium-range Weather Forecasts (ECMWF), the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA2), the National Centers for Environmental Prediction (NCEP)/Global Forecasting System (GFS), NCEP/Final Operational Global Analysis (FNL), and NCEP/Department of Energy (DOE)) were comprehensively evaluated in the single-channel algorithm for LST retrieval from Landsat 8 TIR data. Results showed that when compared with the radio sounding profile downloaded from the University of Wyoming (UWYO), the worst accuracies of atmospheric parameters were obtained for the MYD07 profile. Furthermore, the root-mean-square error (RMSE) values (approximately 0.5 K) of the retrieved LST when using the ECMWF, MERRA2, NCEP/GFS, and NCEP/FNL profiles were smaller than those but greater than 0.8 K when the MYD07, AIRS, and NCEP/DOE profiles were used. Compared with the in situ LST measurements that were collected at the Hailar, Urad Front Banner, and Wuhai sites, the RMSE values of the LST that were retrieved by using the ECMWF, MERRA2, NCEP/GFS, and NCEP/FNL profiles were approximately 1.0 K. The largest discrepancy between the retrieved and in situ LST was obtained for the NCEP/DOE profile, with an RMSE value of approximately 1.5 K. The results reveal that the ECMWF, MERRA2, NCEP/GFS, and NCEP/FNL profiles have great potential to perform accurate atmospheric correction and generate long-term time series LST products from Landsat TIR data by using a single-channel algorithm.

Long-Time-Series Global Land Surface Satellite Leaf Area Index Product Derived From MODIS and AVHRR Surface Reflectance

2016 ◽  
Vol 54 (9) ◽  
pp. 5301-5318 ◽  
Author(s):  
Zhiqiang Xiao ◽  
Shunlin Liang ◽  
Jindi Wang ◽  
Yang Xiang ◽  
Xiang Zhao ◽  
...  
Keyword(s):  
Time Series ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Land Surface ◽  
Surface Reflectance ◽  
Area Index ◽  
Long Time Series ◽  
Global Land ◽  
Long Time
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