Two decades of distributed global radiation time series across a mountainous semiarid area (Sierra Nevada, Spain)

Abstract. The main drawback of the reconstruction of high-resolution distributed global radiation (Rg) time series in mountainous semiarid environments is the common lack of station-based solar radiation registers. This work presents 19 years (2000–2018) of high-spatial-resolution (30 m) daily, monthly, and annual global radiation maps derived using the GIS-based model proposed by Aguilar et al. (2010) in a mountainous area in southern Europe: Sierra Nevada (SN) mountain range (Spain). The model was driven by in situ daily global radiation measurements, from 16 weather stations with historical records in the area; a 30 m digital elevation model; and 240 cloud-free Landsat images. The applicability of the modeling scheme was validated against daily global radiation records at the weather stations. Mean RMSE values of 2.63 MJ m−2 d−1 and best estimations on clear-sky days were obtained. Daily Rg at weather stations revealed greater variations in the maximum values but no clear trends with altitude in any of the statistics. However, at the monthly and annual scales, there is an increase in the high extreme statistics with the altitude of the weather station, especially above 1500 m a.s.l. Monthly Rg maps showed significant spatial differences of up to 200 MJ m−2 per month that clearly followed the terrain configuration. July and December were clearly the months with the highest and lowest values of Rg received, and the highest scatter in the monthly Rg values was found in the spring and fall months. The monthly Rg distribution was highly variable along the study period (2000–2018). Such variability, especially in the wet season (October–May), determined the interannual differences of up to 800 MJ m−2 yr−1 in the incoming global radiation in SN. The time series of the surface global radiation datasets here provided can be used to analyze interannual and seasonal variation characteristics of the global radiation received in SN with high spatial detail (30 m). They can also be used as cross-validation reference data for other global radiation distributed datasets generated in SN with different spatiotemporal interpolation techniques. Daily, monthly, and annual datasets in this study are available at https://doi.org/10.1594/PANGAEA.921012 (Aguilar et al., 2021).

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Two decades of distributed global radiation time series across a mountainous semiarid area (Sierra Nevada, Spain)

10.5194/essd-2020-250 ◽

2020 ◽

Author(s):

Cristina Aguilar ◽

Rafael Pimentel ◽

María J. Polo

Keyword(s):

Time Series ◽

High Resolution ◽

Sierra Nevada ◽

Global Radiation ◽

Mountainous Area ◽

Mountain Range ◽

Wet Season ◽

Extreme Statistics ◽

Variation Characteristics ◽

Weather Stations

Abstract. The main drawback of the reconstruction of high resolution distributed global radiation (Rg) time series in mountainous semiarid environments is the common lack of station-based solar radiation registers. This work presents nineteen years (2000–2018) of high spatial resolution (30 m × 30 m) monthly and annual global radiation maps derived using the model proposed by Aguilar et al. (2010), driven by in situ daily global radiation measurements, from sixteen weather stations with historical records in the area, and a high resolution digital elevation model in a mountainous area in southern Europe: Sierra Nevada (SN) Mountain Range (Spain). The applicability of the modeling scheme was validated against daily global radiation registers at the weather stations with mean RMSE values of 2.63 MJ m−2 day−1 and best estimations on clear-sky days. Filled daily Rg at weather stations revealed quite stable minimum daily Rg values and greater variations in the maximum daily Rg, but no clear trends with altitude in any of the statistics unlike the analysis at the monthly and annual scale when there is an increase in the high extreme statistics with the altitude of the weather station, especially above 1500 m a.s.l. Monthly distributed Rg time series showed significant spatial differences of up to 200 MJ m−2 month−1 that clearly followed the terrain configuration. July and December were clearly the months with the highest and lowest values of Rg received and the highest dispersion in the monthly Rg values was found in the spring and fall months. The great heterogeneity found in the monthly distribution of Rg along the study period (2000–2018), especially at the wet season, finally determined the inter annual differences of up to 800 MJ m−2 year−1 in the incoming global radiation in SN. The time series of the surface global radiation datasets here provided can be used to analyze trends, inter-annual and seasonal variation characteristics of the global radiation received in SN with high spatial detail (30 m). Datasets are available at https://doi.pangaea.de/10.1594/PANGAEA.921012 (Aguilar et al., 2020).

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Review of the submitted article "Two decades of distributed global radiation time series across amountainous semiarid area (Sierra Nevada, Spain)" by Cristina Aguilar et al.

10.5194/essd-2020-250-rc2 ◽

2020 ◽

Author(s):

Anonymous

Keyword(s):

Time Series ◽

Sierra Nevada ◽

Global Radiation ◽

Semiarid Area ◽

Radiation Time

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Snow evolution in a semi-arid mountainous area combining snow modelling and Landsat spectral mixture analysis

Proceedings of the International Association of Hydrological Sciences ◽

10.5194/piahs-368-33-2015 ◽

2015 ◽

Vol 368 ◽

pp. 33-39

Author(s):

R. Pimentel ◽

J. Herrero ◽

M. J. Polo

Keyword(s):

Remote Sensing ◽

Sierra Nevada ◽

Integrated Management ◽

Spectral Mixture Analysis ◽

Mountainous Area ◽

Mixture Analysis ◽

Remote Sensing Images ◽

Landsat Images ◽

Mixed Pixels ◽

Spectral Mixture

Abstract. This study proposes the use of both physically-distributed hydrological modelling in combination with satellite remote sensing images, to study the evolution of the snowpack in the Sierra Nevada mountains, in southern Spain. The snowmelt-accumulation module inside WiMMed (Watershed Integrated Management in Mediterranean Environment) hydrological model was employed, which includes the use of depletion curves to expand the energy and water balance equations over a grid representation. Snow maps obtained from spectral mixture analysis of Landsat images were used to evaluate this model at the study site. The results show a significant agreement between observed and simulated snow pixels in the area, which allows production of sequences of snow maps with greater resolution than the remote sensing images employed. However, some mismatches do appear at the boundaries of the snow area, mainly related to: (a) the great number of mixed pixels; and (b) the influence of the snow transport by wind.

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Sensitivity Analysis of Sentinel-1 Backscatter to Oil Palm Plantations at Pluriannual Scale: A Case Study in Gabon, Africa

Remote Sensing ◽

10.3390/rs13112075 ◽

2021 ◽

Vol 13 (11) ◽

pp. 2075

Author(s):

J. David Ballester-Berman ◽

Maria Rastoll-Gimenez

Keyword(s):

Time Series ◽

Sensitivity Analysis ◽

Oil Palm ◽

Time Interval ◽

Potential Candidate ◽

Initial Growth ◽

Natural Forests ◽

Wet Season ◽

Radar Signatures ◽

The Times

The present paper focuses on a sensitivity analysis of Sentinel-1 backscattering signatures from oil palm canopies cultivated in Gabon, Africa. We employed one Sentinel-1 image per year during the 2015–2021 period creating two separated time series for both the wet and dry seasons. The first images were almost simultaneously acquired to the initial growth stage of oil palm plants. The VH and VV backscattering signatures were analysed in terms of their corresponding statistics for each date and compared to the ones corresponding to tropical forests. The times series for the wet season showed that, in a time interval of 2–3 years after oil palm plantation, the VV/VH ratio in oil palm parcels increases above the one for forests. Backscattering and VV/VH ratio time series for the dry season exhibit similar patterns as for the wet season but with a more stable behaviour. The separability of oil palm and forest classes was also quantitatively addressed by means of the Jeffries–Matusita distance, which seems to point to the C-band VV/VH ratio as a potential candidate for discrimination between oil palms and natural forests, although further analysis must still be carried out. In addition, issues related to the effect of the number of samples in this particular scenario were also analysed. Overall, the outcomes presented here can contribute to the understanding of the radar signatures from this scenario and to potentially improve the accuracy of mapping techniques for this type of ecosystems by using remote sensing. Nevertheless, further research is still to be done as no classification method was performed due to the lack of the required geocoded reference map. In particular, a statistical assessment of the radar signatures should be carried out to statistically characterise the observed trends.

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A Systematic Review of Landsat Data for Change Detection Applications: 50 Years of Monitoring the Earth

Remote Sensing ◽

10.3390/rs13152869 ◽

2021 ◽

Vol 13 (15) ◽

pp. 2869

Author(s):

MohammadAli Hemati ◽

Mahdi Hasanlou ◽

Masoud Mahdianpari ◽

Fariba Mohammadimanesh

Keyword(s):

Time Series ◽

Change Detection ◽

Policy Development ◽

Atmospheric Correction ◽

Open Data ◽

Detection Methods ◽

Landsat 8 ◽

Landsat Images ◽

Detection Analysis ◽

The Impact

With uninterrupted space-based data collection since 1972, Landsat plays a key role in systematic monitoring of the Earth’s surface, enabled by an extensive and free, radiometrically consistent, global archive of imagery. Governments and international organizations rely on Landsat time series for monitoring and deriving a systematic understanding of the dynamics of the Earth’s surface at a spatial scale relevant to management, scientific inquiry, and policy development. In this study, we identify trends in Landsat-informed change detection studies by surveying 50 years of published applications, processing, and change detection methods. Specifically, a representative database was created resulting in 490 relevant journal articles derived from the Web of Science and Scopus. From these articles, we provide a review of recent developments, opportunities, and trends in Landsat change detection studies. The impact of the Landsat free and open data policy in 2008 is evident in the literature as a turning point in the number and nature of change detection studies. Based upon the search terms used and articles included, average number of Landsat images used in studies increased from 10 images before 2008 to 100,000 images in 2020. The 2008 opening of the Landsat archive resulted in a marked increase in the number of images used per study, typically providing the basis for the other trends in evidence. These key trends include an increase in automated processing, use of analysis-ready data (especially those with atmospheric correction), and use of cloud computing platforms, all over increasing large areas. The nature of change methods has evolved from representative bi-temporal pairs to time series of images capturing dynamics and trends, capable of revealing both gradual and abrupt changes. The result also revealed a greater use of nonparametric classifiers for Landsat change detection analysis. Landsat-9, to be launched in September 2021, in combination with the continued operation of Landsat-8 and integration with Sentinel-2, enhances opportunities for improved monitoring of change over increasingly larger areas with greater intra- and interannual frequency.

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Detecting Drought-Induced Tree Mortality in Sierra Nevada Forests with Time Series of Satellite Data

Remote Sensing ◽

10.3390/rs9090929 ◽

2017 ◽

Vol 9 (9) ◽

pp. 929 ◽

Cited By ~ 17

Author(s):

◽

Keyword(s):

Time Series ◽

Sierra Nevada ◽

Satellite Data ◽

Tree Mortality

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Revisiting Climate Region Definitions via Clustering

Journal of Climate ◽

10.1175/2008jcli2455.1 ◽

2009 ◽

Vol 22 (7) ◽

pp. 1787-1800 ◽

Cited By ~ 22

Author(s):

Robert Lund ◽

Bo Li

Keyword(s):

Time Series ◽

Clustering Methods ◽

Distance Metric ◽

Climatic Zones ◽

Time Series Clustering ◽

Climate Region ◽

Covariate Effects ◽

Weather Stations ◽

Climatic Time Series ◽

Natural Way

Abstract This paper introduces a new distance metric that enables the clustering of general climatic time series. Clustering methods have been frequently used to partition a domain of interest into distinct climatic zones. However, previous techniques have neglected the time series (autocorrelation) component and have also handled seasonal features in a suboptimal way. The distance proposed here incorporates the seasonal mean and autocorrelation structures of the series in a natural way; moreover, trends and covariate effects can be considered. As an important by-product, the methods can be used to statistically assess whether two stations can serve as reference stations for one another. The methods are illustrated by partitioning 292 weather stations within the state of Colorado into six different zones.

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