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Special Issue "Lessons Learned from the SPOT4 (Take5): Experiment in Preparation for Sentinel-2"

A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: closed (31 May 2015)

Special Issue Editors

Guest Editor
Mr. Olivier Hagolle

CESBIO/CNES, 18 avenue E. Belin, 31401 Toulouse Cedex 9, France
Website | E-Mail
Interests: calibration; atmospheric corrections; time series
Guest Editor
Dr. Benjamin Koetz

Directorate of Earth Observation Programmes, European Space Agency, Via Galileo Galilei, 00044 Frascati, Italy
E-Mail
Fax: +39 06 941 80 552
Interests: remote sensing of ecosystem structure and processes; multi-temporal optical remote sensing for vegetation monitoring; radiative transfer modeling; fusion of multi-source data
Guest Editor
Dr. Olivier Arino

Directorate of Earth Observation Programmes, European Space Agency, Via Galileo Galilei, 00044 Frascati, Italy
E-Mail
Interests: remote sensing of forest; agriculture and land cover
Guest Editor
Mrs. Sylvia Sylvander

CNES, 18 avenue E. Belin, 31401 Toulouse Cedex 9, France
E-Mail
Interests: time series of optical images; geometrical processing; optical satellite geometric performances

Special Issue Information

Dear Colleagues,

Starting in 2015, the European Sentinel-2 Mission, developed by ESA, will be the first satellite mission ever to provide image time series with the four following features:

- High resolution images (10 to 20 m)
- Worldwide systematic coverage of all lands
- Repetitive observations every 5th day with two satellites, under constant viewing angles
- Thirteen spectral bands from visible to short wave infrared (SWIR), providing continuity to SPOT and Landsat

On top of these features, the access to Sentinel-2 data will be free and open, and the mission foresees a series of satellites each with an expected lifetime of seven years over a time period of 20 years. The Sentinel-2 will certainly foster new applications, products and services, and will enhance the accuracy of existing ones. Sentinel-2 frequent revisits will ensure that any given surface will be observed at least once a month, except in the most cloudy periods and regions, which will enable the development of various operational applications.

In order to manage time series covering very large areas and to take full advantage of the Sentinel-2 repetitive observations, new processing methods must be implemented and tested. For instance, these methods need to be able to handle large data volumes and to cope with data gaps due to clouds that will be present on most Sentinel-2 300*300 km² images. Given the large number of images taken on each site, methods will need to be automatic and the large area covered will push users to develop methods that are robust enough to work over large territories with variations in climate and land cover.

To help users get ready for the arrival of Sentinel-2 data, the French Space Agency CNES (Centre National d'Etudes Spatiales), decided to implement the SPOT4 (Take5) experiment, which took place during the first half of 2013. This experiment was joined by ESA and USGS/NASA to provide complementing data sets from the RapidEye and Landsat missions. This experiment aimed at providing time series of optical images simulating the revisit frequency, resolution and the large area of Sentinel-2 images, in order to help users set up and test their applications and methods, before the mission is launched. CNES started this experiment on January 31, 2013, and it lasted until June 19, 2013. Time series of SPOT4 images were acquired on every 5th day, over 45 sites scattered on nearly all continents, with a total of 28 acquisitions above each of the 45 sites. Over a subset of 14 sites, RapidEye acquisitions were collected at the same frequency of five days and Landsat-8 acquisitions complemented the data set starting in April with a repeat cycle of 16 days. The time series have been used for very diverse applications (land cover and land use, agriculture, phenology, hydrology, snow monitoring, coastal and lake monitoring, habitats characterization and biodiversity) and the full Take5 data set can be downloaded free of charge (SPOT4 (Take5) data, Rapid Eye (Take5) data).

This Special Issue calls for submissions that report on the lessons learned from this experiment, in terms of:

- pre-processing methods (ortho-rectification, atmospheric correction, cloud detection, monthly syntheses)

- calibration and validation of reflectance and bio-physical variables

- suitability of Sentinel-2 time series as a function of applications

- new processing methods for applications of time series of high resolution images, such as

  • bio-physical variable estimates,
  • land cover classification
  • crop monitoring
  • crop water status monitoring
  • biodiversity
  • estimation of vegetation biomass and yield
  • production of phenology indicators
  • coastal or inland water monitoring
  • forest monitoring

 

Authors are required to check and follow specific Instructions to Authors, see https://dl.dropboxusercontent.com/u/165068305/Remote_Sensing-Additional_Instructions.pdf.

Mr. Olivier Hagolle
Dr. Benjamin Koetz
Dr. Olivier Arino
Mrs. Sylvia Sylvander
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (14 papers)

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Open AccessArticle On the Importance of High-Resolution Time Series of Optical Imagery for Quantifying the Effects of Snow Cover Duration on Alpine Plant Habitat
Remote Sens. 2016, 8(6), 481; https://doi.org/10.3390/rs8060481
Received: 11 March 2016 / Revised: 13 May 2016 / Accepted: 2 June 2016 / Published: 7 June 2016
Cited by 8 | PDF Full-text (10559 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
We investigated snow cover dynamics using time series of moderate (MODIS) to high (SPOT-4/5, Landsat-8) spatial resolution satellite imagery in a 3700 km2 region of the southwestern French Alps. Our study was carried out in the context of the SPOT (Take 5)
[...] Read more.
We investigated snow cover dynamics using time series of moderate (MODIS) to high (SPOT-4/5, Landsat-8) spatial resolution satellite imagery in a 3700 km2 region of the southwestern French Alps. Our study was carried out in the context of the SPOT (Take 5) Experiment initiated by the Centre National d’Etudes Spatiales (CNES), with the aim of exploring the utility of high spatial and temporal resolution multispectral satellite imagery for snow cover mapping and applications in alpine ecology. Our three objectives were: (i) to validate remote sensing observations of first snow free day derived from the Normalized Difference Snow Index (NDSI) relative to ground-based measurements; (ii) to generate regional-scale maps of first snow free day and peak standing biomass derived from the Normalized Difference Vegetation Index (NDVI); and (iii) to examine the usefulness of these maps for habitat mapping of herbaceous vegetation communities above the tree line. Imagery showed strong agreement with ground-based measurements of snow melt-out date, although R2 was higher for SPOT and Landsat time series (0.92) than for MODIS (0.79). Uncertainty surrounding estimates of first snow free day was lower in the case of MODIS, however (±3 days as compared to ±9 days for SPOT and Landsat), emphasizing the importance of high temporal as well as high spatial resolution for capturing local differences in snow cover duration. The main floristic differences between plant communities were clearly visible in a two-dimensional habitat template defined by the first snow free day and NDVI at peak standing biomass, and these differences were accentuated when axes were derived from high spatial resolution imagery. Our work demonstrates the enhanced potential of high spatial and temporal resolution multispectral imagery for quantifying snow cover duration and plant phenology in temperate mountain regions, and opens new avenues to examine to what extent plant community diversity and functioning are controlled by snow cover duration. Full article
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Open AccessArticle Production of a Dynamic Cropland Mask by Processing Remote Sensing Image Series at High Temporal and Spatial Resolutions
Remote Sens. 2016, 8(1), 55; https://doi.org/10.3390/rs8010055
Received: 3 June 2015 / Revised: 8 December 2015 / Accepted: 16 December 2015 / Published: 11 January 2016
Cited by 19 | PDF Full-text (14015 KB) | HTML Full-text | XML Full-text
Abstract
The exploitation of new high revisit frequency satellite observations is an important opportunity for agricultural applications. The Sentinel-2 for Agriculture project S2Agri (http://www.esa-sen2agri.org/SitePages/Home.aspx) is designed to develop, demonstrate and facilitate the Sentinel-2 time series contribution to the satellite EO component of agriculture monitoring
[...] Read more.
The exploitation of new high revisit frequency satellite observations is an important opportunity for agricultural applications. The Sentinel-2 for Agriculture project S2Agri (http://www.esa-sen2agri.org/SitePages/Home.aspx) is designed to develop, demonstrate and facilitate the Sentinel-2 time series contribution to the satellite EO component of agriculture monitoring for many agricultural systems across the globe. In the framework of this project, this article studies the construction of a dynamic cropland mask. This mask consists of a binary “annual-cropland/no-annual-cropland” map produced several times during the season to serve as a mask for monitoring crop growing conditions over the growing season. The construction of the mask relies on two classical pattern recognition techniques: feature extraction and classification. One pixel- and two object-based strategies are proposed and compared. A set of 12 test sites are used to benchmark the methods and algorithms with regard to the diversity of the agro-ecological context, landscape patterns, agricultural practices and actual satellite observation conditions. The classification results yield promising accuracies of around 90% at the end of the agricultural season. Efforts will be made to transition this research into operational products once Sentinel-2 data become available. Full article
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Open AccessArticle A Generic Algorithm to Estimate LAI, FAPAR and FCOVER Variables from SPOT4_HRVIR and Landsat Sensors: Evaluation of the Consistency and Comparison with Ground Measurements
Remote Sens. 2015, 7(11), 15494-15516; https://doi.org/10.3390/rs71115494
Received: 31 August 2015 / Revised: 23 October 2015 / Accepted: 9 November 2015 / Published: 18 November 2015
Cited by 15 | PDF Full-text (2563 KB) | HTML Full-text | XML Full-text
Abstract
The leaf area index (LAI) and the fraction of photosynthetically active radiation absorbed by green vegetation (FAPAR) are essential climatic variables in surface process models. FCOVER is also important to separate vegetation and soil for energy balance processes. Currently, several LAI, FAPAR and
[...] Read more.
The leaf area index (LAI) and the fraction of photosynthetically active radiation absorbed by green vegetation (FAPAR) are essential climatic variables in surface process models. FCOVER is also important to separate vegetation and soil for energy balance processes. Currently, several LAI, FAPAR and FCOVER satellite products are derived moderate to coarse spatial resolution. The launch of Sentinel-2 in 2015 will provide data at decametric resolution with a high revisit frequency to allow quantifying the canopy functioning at the local to regional scales. The aim of this study is thus to evaluate the performances of a neural network based algorithm to derive LAI, FAPAR and FCOVER products at decametric spatial resolution and high temporal sampling. The algorithm is generic, i.e., it is applied without any knowledge of the landcover. A time series of high spatial resolution SPOT4_HRVIR (16 scenes) and Landsat 8 (18 scenes) images acquired in 2013 over the France southwestern site were used to generate the LAI, FAPAR and FCOVER products. For each sensor and each biophysical variable, a neural network was first trained over PROSPECT+SAIL radiative transfer model simulations of top of canopy reflectance data for green, red, near-infra red and short wave infra-red bands. Our results show a good spatial and temporal consistency between the variables derived from both sensors: almost half the pixels show an absolute difference between SPOT and LANDSAT estimates of lower that 0.5 unit for LAI, and 0.05 unit for FAPAR and FCOVER. Finally, downward-looking digital hemispherical cameras were completed over the main land cover types to validate the accuracy of the products. Results show that the derived products are strongly correlated with the field measurements (R2 > 0.79), corresponding to a RMSE = 0.49 for LAI, RMSE = 0.10 (RMSE = 0.12) for black-sky (white sky) FAPAR and RMSE = 0.15 for FCOVER. It is concluded that the proposed generic algorithm provides a good basis to monitor the seasonal variation of the vegetation biophysical variables for important crops at decametric resolution. Full article
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Open AccessArticle A Spectral Unmixing Model for the Integration of Multi-Sensor Imagery: A Tool to Generate Consistent Time Series Data
Remote Sens. 2015, 7(10), 14000-14018; https://doi.org/10.3390/rs71014000
Received: 29 May 2015 / Revised: 16 October 2015 / Accepted: 20 October 2015 / Published: 23 October 2015
Cited by 9 | PDF Full-text (3874 KB) | HTML Full-text | XML Full-text
Abstract
The Sentinel missions have been designed to support the operational services of the Copernicus program, ensuring long-term availability of data for a wide range of spectral, spatial and temporal resolutions. In particular, Sentinel-2 (S-2) data with improved high spatial resolution and higher revisit
[...] Read more.
The Sentinel missions have been designed to support the operational services of the Copernicus program, ensuring long-term availability of data for a wide range of spectral, spatial and temporal resolutions. In particular, Sentinel-2 (S-2) data with improved high spatial resolution and higher revisit frequency (five days with the pair of satellites in operation) will play a fundamental role in recording land cover types and monitoring land cover changes at regular intervals. Nevertheless, cloud coverage usually hinders the time series availability and consequently the continuous land surface monitoring. In an attempt to alleviate this limitation, the synergistic use of instruments with different features is investigated, aiming at the future synergy of the S-2 MultiSpectral Instrument (MSI) and Sentinel-3 (S-3) Ocean and Land Colour Instrument (OLCI). To that end, an unmixing model is proposed with the intention of integrating the benefits of the two Sentinel missions, when both in orbit, in one composite image. The main goal is to fill the data gaps in the S-2 record, based on the more frequent information of the S-3 time series. The proposed fusion model has been applied on MODIS (MOD09GA L2G) and SPOT4 (Take 5) data and the experimental results have demonstrated that the approach has high potential. However, the different acquisition characteristics of the sensors, i.e. illumination and viewing geometry, should be taken into consideration and bidirectional effects correction has to be performed in order to reduce noise in the reflectance time series. Full article
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Open AccessArticle Application of High Resolution Satellite Imagery to Characterize Individual-Based Environmental Heterogeneity in a Wild Blue Tit Population
Remote Sens. 2015, 7(10), 13319-13336; https://doi.org/10.3390/rs71013319
Received: 25 May 2015 / Revised: 15 September 2015 / Accepted: 29 September 2015 / Published: 12 October 2015
Cited by 5 | PDF Full-text (959 KB) | HTML Full-text | XML Full-text
Abstract
Environmental heterogeneity in space and time plays a key role in influencing trait variability in animals, and can be particularly relevant to animal phenology. Until recently, the use of remotely sensed imagery in understanding animal variation was limited to analyses at the population
[...] Read more.
Environmental heterogeneity in space and time plays a key role in influencing trait variability in animals, and can be particularly relevant to animal phenology. Until recently, the use of remotely sensed imagery in understanding animal variation was limited to analyses at the population level, largely because of a lack of high-resolution data that would allow inference at the individual level. We evaluated the potential of SPOT 4 (Take 5) satellite imagery data (with observations every fifth day at 20 m resolution and equivalent to acquisition parameters of Sentinel-2) in animal ecology research. We focused on blue tit Cyanistes caeruleus reproduction in a study site containing 227 nestboxes scattered in a Mediterranean forest dominated by deciduous downy oaks Quercus pubescens with a secondary cover of evergreen holm oaks Quercus ilex. We observed high congruence between ground data collected in a 50 m radius around each nestbox and NDVI values averaged across a 5 by 5 pixel grid centered around each nestbox of the study site. The number of deciduous and evergreen oaks around nestboxes explained up to 66% of variance in nestbox-centered, SPOT-derived NDVI values. We also found highly equivalent patterns of spatial autocorrelation for both ground- and satellite-derived indexes of environmental heterogeneity. For deciduous and evergreen oaks, the derived NDVI signal was highly distinctive in winter and early spring. June NDVI values for deciduous and evergreen oaks were higher by 58% and 8% relative to February values, respectively. The number of evergreen oaks was positively associated with later timing of breeding in blue tits. SPOT-derived, Sentinel-2 like imagery thus provided highly reliable, ground-validated information on habitat heterogeneity of direct relevance to a long-term field study of a free-living passerine bird. Given that the logistical demands of gathering ground data often limit our understanding of variation in animal reproductive traits across time and space, there appears to be great promise in applying fine-resolution satellite data in evolutionary ecology research. Full article
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Open AccessArticle An Automated Method for Annual Cropland Mapping along the Season for Various Globally-Distributed Agrosystems Using High Spatial and Temporal Resolution Time Series
Remote Sens. 2015, 7(10), 13208-13232; https://doi.org/10.3390/rs71013208
Received: 1 June 2015 / Revised: 15 September 2015 / Accepted: 17 September 2015 / Published: 6 October 2015
Cited by 31 | PDF Full-text (5904 KB) | HTML Full-text | XML Full-text
Abstract
Cropland mapping relies heavily on field data for algorithm calibration, making it, in many cases, applicable only at the field campaign scale. While the recently launched Sentinel-2 satellite will be able to deliver time series over large regions, it will not really be
[...] Read more.
Cropland mapping relies heavily on field data for algorithm calibration, making it, in many cases, applicable only at the field campaign scale. While the recently launched Sentinel-2 satellite will be able to deliver time series over large regions, it will not really be compatible with the current mapping approach or the available in situ data. This research introduces a generic methodology for mapping annual cropland along the season at high spatial resolution with the use of globally available baseline land cover and no need for field data. The methodology is based on cropland-specific temporal features, which are able to cope with the diversity of agricultural systems, prior information from which mislabeled pixels have been removed and a cost-effective classifier. Thanks to the JECAM network, eight sites across the world were selected for global cropland mapping benchmarking. Accurate cropland maps were produced at the end of the season, showing an overall accuracy of more than 85%. Early cropland maps were also obtained at three-month intervals after the beginning of the growing season, and these showed reasonable accuracy at the three-month stage (>70% overall accuracy) and progressive improvement along the season. The trimming-based method was found to be key for using spatially coarse baseline land cover information and, thus, avoiding costly field campaigns for prior information retrieval. The accuracy and timeliness of the proposed approach shows that it has substantial potential for operational agriculture monitoring programs. Full article
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Open AccessArticle Monitoring Irrigation Consumption Using High Resolution NDVI Image Time Series: Calibration and Validation in the Kairouan Plain (Tunisia)
Remote Sens. 2015, 7(10), 13005-13028; https://doi.org/10.3390/rs71013005
Received: 1 June 2015 / Revised: 20 August 2015 / Accepted: 9 September 2015 / Published: 1 October 2015
Cited by 9 | PDF Full-text (6742 KB) | HTML Full-text | XML Full-text
Abstract
Water scarcity is one of the main factors limiting agricultural development in semi-arid areas. Remote sensing has long been used as an input for crop water balance monitoring. The increasing availability of high resolution high repetitivity remote sensing (forthcoming Sentinel-2 mission) offers an
[...] Read more.
Water scarcity is one of the main factors limiting agricultural development in semi-arid areas. Remote sensing has long been used as an input for crop water balance monitoring. The increasing availability of high resolution high repetitivity remote sensing (forthcoming Sentinel-2 mission) offers an unprecedented opportunity to improve this monitoring. In this study, regional crop water consumption was estimated with the SAMIR software (SAtellite Monitoring of IRrigation) using the FAO-56 dual crop coefficient water balance model fed with high resolution NDVI image time series providing estimates of both the actual basal crop coefficient and the vegetation fraction cover. Three time series of SPOT5 images have been acquired over an irrigated area in central Tunisia along with a SPOT4 time series acquired in the frame of the SPOT4-Take5 experiment, which occurred during the first half of 2013. Using invariant objects located in the scene, normalization of the SPOT5 time series was realized based on the SPOT4-Take5 time series. Hence, a NDVI time profile was generated for each pixel. The operationality and accuracy of the SAMIR tool was assessed at both plot scale (calibration based on evapotranspiration ground measurements) and perimeter scale (irrigation volumes) when several land use types, irrigation and agricultural practices are intertwined in a given landscape. Results at plot scale gave after calibration an average Nash efficiency of 0.57 between observed and modeled evapotranspiration for two plots (barley and wheat). When aggregated for the whole season, modeled irrigation volumes at perimeter scale for all campaigns were close to observed ones (resp. 135 and 121 mm, overestimation of 11.5%). However, spatialized evapotranspiration and irrigation volumes need to be improved at finer timescales. Full article
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Open AccessArticle Assessment of an Operational System for Crop Type Map Production Using High Temporal and Spatial Resolution Satellite Optical Imagery
Remote Sens. 2015, 7(9), 12356-12379; https://doi.org/10.3390/rs70912356
Received: 28 May 2015 / Revised: 4 September 2015 / Accepted: 8 September 2015 / Published: 22 September 2015
Cited by 48 | PDF Full-text (1967 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Crop area extent estimates and crop type maps provide crucial information for agricultural monitoring and management. Remote sensing imagery in general and, more specifically, high temporal and high spatial resolution data as the ones which will be available with upcoming systems, such as
[...] Read more.
Crop area extent estimates and crop type maps provide crucial information for agricultural monitoring and management. Remote sensing imagery in general and, more specifically, high temporal and high spatial resolution data as the ones which will be available with upcoming systems, such as Sentinel-2, constitute a major asset for this kind of application. The goal of this paper is to assess to what extent state-of-the-art supervised classification methods can be applied to high resolution multi-temporal optical imagery to produce accurate crop type maps at the global scale. Five concurrent strategies for automatic crop type map production have been selected and benchmarked using SPOT4 (Take5) and Landsat 8 data over 12 test sites spread all over the globe (four in Europe, four in Africa, two in America and two in Asia). This variety of tests sites allows one to draw conclusions applicable to a wide variety of landscapes and crop systems. The results show that a random forest classifier operating on linearly temporally gap-filled images can achieve overall accuracies above 80% for most sites. Only two sites showed low performances: Madagascar due to the presence of fields smaller than the pixel size and Burkina Faso due to a mix of trees and crops in the fields. The approach is based on supervised machine learning techniques, which need in situ data collection for the training step, but the map production is fully automatic. Full article
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Open AccessArticle SPOT-4 (Take 5): Simulation of Sentinel-2 Time Series on 45 Large Sites
Remote Sens. 2015, 7(9), 12242-12264; https://doi.org/10.3390/rs70912242
Received: 25 May 2015 / Revised: 31 August 2015 / Accepted: 10 September 2015 / Published: 21 September 2015
Cited by 33 | PDF Full-text (6228 KB) | HTML Full-text | XML Full-text
Abstract
This paper presents the SPOT-4 (Take 5) experiment, aimed at providing time series of optical images simulating the repetitivity, the resolution and the large swath of Sentinel-2 images. The aim was to help users set up and test their applications and methods, before
[...] Read more.
This paper presents the SPOT-4 (Take 5) experiment, aimed at providing time series of optical images simulating the repetitivity, the resolution and the large swath of Sentinel-2 images. The aim was to help users set up and test their applications and methods, before Sentinel-2 mission data become available. In 2016, when both Sentinel-2 satellites are operational, and for at least fifteen years, users will have access to high resolution time series of images systematically acquired every five days, over the whole Earth land surfaces. Thanks to Sentinel-2’s high revisit frequency, a given surface should be observed without clouds at least once a month, except in the most cloudy periods and regions. In 2013, the Centre National d’Etudes Spatiales (CNES) lowered the orbit altitude of SPOT-4, to place it on a five-day repeat cycle orbit for a duration of five months. This experiment started on 31 January 2013 and lasted until 19 June 2013. SPOT-4 images were acquired every fifth day, over 45 sites scattered in nearly all continents and covering very diverse biomes for various applications. Two ortho-rectified products were delivered for each acquired image that was not fully cloudy, expressed either as top of atmosphere reflectance (Level 1C) or as surface reflectance (Level 2A). An extensive validation campaign was held to check the performances of these products with regard to the multi-temporal registration, the quality of cloud masks, the accuracy of aerosol optical thickness estimates and the quality of surface reflectances. Despite high a priori geo-location errors, it was possible to register the images with an accuracy better than 0.5 pixels in the large majority of cases. Despite the lack of a blue band on the SPOT-4 satellite, the cloud and shadow detection yielded good results, while the aerosol optical thickness was measured with a root mean square error better than 0.06. The surface reflectances after atmospheric correction were compared with in situ data and other satellite data showing little bias and the standard deviation of surface reflectance errors in the range (0.01–0.02). The Take 5 experiment is being repeated in 2015 with the SPOT-5 satellite with an enhanced resolution. Full article
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Open AccessArticle Evaluation of Medium Spatial Resolution BRDF-Adjustment Techniques Using Multi-Angular SPOT4 (Take5) Acquisitions
Remote Sens. 2015, 7(9), 12057-12075; https://doi.org/10.3390/rs70912057
Received: 1 June 2015 / Revised: 9 September 2015 / Accepted: 14 September 2015 / Published: 18 September 2015
Cited by 6 | PDF Full-text (3070 KB) | HTML Full-text | XML Full-text
Abstract
High-resolution sensor Surface Reflectance (SR) data are affected by surface anisotropy but are difficult to adjust because of the low temporal frequency of the acquisitions and the low angular sampling. This paper evaluates five high spatial resolution Bidirectional Reflectance Distribution Function (BRDF) adjustment
[...] Read more.
High-resolution sensor Surface Reflectance (SR) data are affected by surface anisotropy but are difficult to adjust because of the low temporal frequency of the acquisitions and the low angular sampling. This paper evaluates five high spatial resolution Bidirectional Reflectance Distribution Function (BRDF) adjustment techniques. The evaluation is based on the noise level of the SR Time Series (TS) corrected to a normalized geometry (nadir view, 45° sun zenith angle) extracted from the multi-angular acquisitions of SPOT4 over three study areas (one in Arizona, two in France) during the five-month SPOT4 (Take5) experiment. Two uniform techniques (Cst, for Constant, and Av, for Average), relying on the Vermote–Justice–Bréon (VJB) BRDF method, assume no variation in space of the BRDF shape. Two methods (VI-dis, for NDVI-based disaggregation and LC-dis, for Land-Cover based disaggregation) are based on disaggregation of the MODIS-derived BRDF VJB parameters using vegetation index and land cover, respectively. The last technique (LUM, for Look-Up Map) relies on the MCD43 MODIS BRDF products and a crop type data layer. The VI-dis technique produced the lowest level of noise corresponding to the most effective adjustment: reduction from directional to normalized SR TS noises by 40% and 50% on average, for red and near-infrared bands, respectively. The uniform techniques displayed very good results, suggesting that a simple and uniform BRDF-shape assumption is good enough to adjust the BRDF in such geometric configuration (the view zenith angle varies from nadir to 25°). The most complex techniques relying on land cover (LC-dis and LUM) displayed contrasting results depending on the land cover. Full article
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Open AccessArticle Land Cover and Crop Type Classification along the Season Based on Biophysical Variables Retrieved from Multi-Sensor High-Resolution Time Series
Remote Sens. 2015, 7(8), 10400-10424; https://doi.org/10.3390/rs70810400
Received: 31 May 2015 / Revised: 30 July 2015 / Accepted: 4 August 2015 / Published: 13 August 2015
Cited by 17 | PDF Full-text (1785 KB) | HTML Full-text | XML Full-text
Abstract
With the ever-increasing number of satellites and the availability of data free of charge, the integration of multi-sensor images in coherent time series offers new opportunities for land cover and crop type classification. This article investigates the potential of structural biophysical variables as
[...] Read more.
With the ever-increasing number of satellites and the availability of data free of charge, the integration of multi-sensor images in coherent time series offers new opportunities for land cover and crop type classification. This article investigates the potential of structural biophysical variables as common parameters to consistently combine multi-sensor time series and to exploit them for land/crop cover classification. Artificial neural networks were trained based on a radiative transfer model in order to retrieve high resolution LAI, FAPAR and FCOVER from Landsat-8 and SPOT-4. The correlation coefficients between field measurements and the retrieved biophysical variables were 0.83, 0.85 and 0.79 for LAI, FAPAR and FCOVER, respectively. The retrieved biophysical variables’ time series displayed consistent average temporal trajectories, even though the class variability and signal-to-noise ratio increased compared to NDVI. Six random forest classifiers were trained and applied along the season with different inputs: spectral bands, NDVI, as well as FAPAR, LAI and FCOVER, separately and jointly. Classifications with structural biophysical variables reached end-of-season overall accuracies ranging from 73%–76% when used alone and 77% when used jointly. This corresponds to 90% and 95% of the accuracy level achieved with the spectral bands and NDVI. FCOVER appears to be the most promising biophysical variable for classification. When assuming that the cropland extent is known, crop type classification reaches 89% with spectral information, 87% with the NDVI and 81%–84% with biophysical variables. Full article
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Open AccessArticle Toward Sentinel-2 High Resolution Remote Sensing of Suspended Particulate Matter in Very Turbid Waters: SPOT4 (Take5) Experiment in the Loire and Gironde Estuaries
Remote Sens. 2015, 7(8), 9507-9528; https://doi.org/10.3390/rs70809507
Received: 28 May 2015 / Revised: 30 June 2015 / Accepted: 20 July 2015 / Published: 24 July 2015
Cited by 12 | PDF Full-text (24857 KB) | HTML Full-text | XML Full-text
Abstract
At the end of the SPOT4 mission, a four-month experiment was conducted in 2013 to acquire high spatial (20 m) and high temporal (5 days) resolution satellite data. In addition to the SPOT4 (Take5) dataset, we used several Landsat5, 7, 8 images to
[...] Read more.
At the end of the SPOT4 mission, a four-month experiment was conducted in 2013 to acquire high spatial (20 m) and high temporal (5 days) resolution satellite data. In addition to the SPOT4 (Take5) dataset, we used several Landsat5, 7, 8 images to document the variations in suspended particulate matter (SPM) concentration in the turbid Gironde and Loire estuaries (France). Satellite-derived SPM concentration was validated using automated in situ turbidity measurements from two monitoring networks. The combination of a multi-temporal atmospheric correction method with a near-infrared to visible reflectance band ratio made it possible to quantify SPM surface concentration in moderately to extremely turbid waters (38–4320 g·m−3), at an accuracy sufficient to detect the maximum turbidity zone (MTZ) in both estuaries. Such a multi-sensor approach can be applied to high spatial resolution satellite archives and to the new ESA Sentinel-2 mission. It offers a promising framework to study the response of estuarine ecosystems to global changes at unprecedented spatio-temporal resolution. Full article
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Open AccessTechnical Note Building a Data Set over 12 Globally Distributed Sites to Support the Development of Agriculture Monitoring Applications with Sentinel-2
Remote Sens. 2015, 7(12), 16062-16090; https://doi.org/10.3390/rs71215815
Received: 31 May 2015 / Revised: 10 November 2015 / Accepted: 16 November 2015 / Published: 2 December 2015
Cited by 13 | PDF Full-text (4225 KB) | HTML Full-text | XML Full-text
Abstract
Developing better agricultural monitoring capabilities based on Earth Observation data is critical for strengthening food production information and market transparency. The Sentinel-2 mission has the optimal capacity for regional to global agriculture monitoring in terms of resolution (10–20 meter), revisit frequency (five days)
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Developing better agricultural monitoring capabilities based on Earth Observation data is critical for strengthening food production information and market transparency. The Sentinel-2 mission has the optimal capacity for regional to global agriculture monitoring in terms of resolution (10–20 meter), revisit frequency (five days) and coverage (global). In this context, the European Space Agency launched in 2014 the “Sentinel­2 for Agriculture” project, which aims to prepare the exploitation of Sentinel-2 data for agriculture monitoring through the development of open source processing chains for relevant products. The project generated an unprecedented data set, made of “Sentinel-2 like” time series and in situ data acquired in 2013 over 12 globally distributed sites. Earth Observation time series were mostly built on the SPOT4 (Take 5) data set, which was specifically designed to simulate Sentinel-2. They also included Landsat 8 and RapidEye imagery as complementary data sources. Images were pre-processed to Level 2A and the quality of the resulting time series was assessed. In situ data about cropland, crop type and biophysical variables were shared by site managers, most of them belonging to the “Joint Experiment for Crop Assessment and Monitoring” network. This data set allowed testing and comparing across sites the methodologies that will be at the core of the future “Sentinel­2 for Agriculture” system. Full article
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Open AccessTechnical Note Validation of a Forage Production Index (FPI) Derived from MODIS fCover Time-Series Using High-Resolution Satellite Imagery: Methodology, Results and Opportunities
Remote Sens. 2015, 7(9), 11525-11550; https://doi.org/10.3390/rs70911525
Received: 4 June 2015 / Revised: 13 August 2015 / Accepted: 31 August 2015 / Published: 9 September 2015
Cited by 4 | PDF Full-text (1681 KB) | HTML Full-text | XML Full-text
Abstract
An index-based insurance solution was developed to estimate and monitor near real-time forage production using the indicator Forage Production Index (FPI) as a surrogate of the grassland production. The FPI corresponds to the integral of the fraction of green vegetation cover derived from
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An index-based insurance solution was developed to estimate and monitor near real-time forage production using the indicator Forage Production Index (FPI) as a surrogate of the grassland production. The FPI corresponds to the integral of the fraction of green vegetation cover derived from moderate spatial resolution time series images and was calculated at the 6 km × 6 km scale. An upscaled approach based on direct validation was used that compared FPI with field-collected biomass data and high spatial resolution (HR) time series images. The experimental site was located in the Lot and Aveyron departments of southwestern France. Data collected included biomass ground measurements from grassland plots at 28 farms for the years 2012, 2013 and 2014 and HR images covering the Lot department in 2013 (n = 26) and 2014 (n = 22). Direct comparison with ground-measured yield led to good accuracy (R2 = 0.71 and RMSE = 14.5%). With indirect comparison, the relationship was still strong (R2 ranging from 0.78 to 0.93) and informative. These results highlight the effect of disaggregation, the grassland sampling rate, and irregularity of image acquisition in the HR time series. In advance of Sentinel-2, this study provides valuable information on the strengths and weaknesses of a potential index-based insurance product from HR time series images. Full article
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