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Satellite crop identification processes are increasingly being used on a large scale, both to verify the crop and to improve production. As it is necessary to study phenological data over a period of time across a large territory, a lot of storage space is needed to save the satellite images and a lot of calculation time to analyse all this information. Sensing periods are usually established based on subjective expert criteria or previous experience. However, this decision may cause several differences when discriminating crop patterns, besides not guaranteeing good precision. These processes would greatly improve if the appropriate time periods could be found systematically using the minimum number of satellite images in the shortest possible time. In this paper, we propose a new methodology to determine a suitable sensing period for crop identification using Sentinel-2 images, applying hill climbing algorithms to the training sets of neural network models. We have used the method successfully in the 2020 Common Agricultural Policy campaign in the Extremadura region, Spain. The article also describes the use of the method in a case on tobacco detection in this region. [ABSTRACT FROM AUTHOR]

This paper includes the mapping of cultivated areas (vineyards) in the experimental zone of the EFEDA project, where vineyard crop is the dominant one. The study area is located in central Spain with a surface area of about 10km × 10 km and showing important desertification hazards. The objective of the paper is to establish the mapping of vineyard areas by remote sensing techniques and to establish relationships between the spectral signature and agronomical parameters (biomass, percent of cover, etc.). For these objectives, several vegetation classes have been defined (vineyards, cereals, fallow, olives and forest) and multi-temporal Landsat-5 Thematic Mapper images were used with the maximum likelihood classification method and masking techniques. The results were checked by ground truth and by existing vegetation cover maps (1525000) and show in general a good correspondence between both information sources. Moreover, these results have permitted to define new vineyard categories like abandoned vineyards and young vineyards that explain the difference between the classification results for vineyards and the ground truth. The later stage of the study pays special attention to the correlation of the agronomical parameters, such as the height of the crop and biomass with the spectral signature of the vineyards represented by the normalized index NDVI, using multi-temporal Landsat images between May and July that comprehend the phenological evolution of the vineyards. Good correlation coefficients (r =0.86 and r =0.90) were obtained successively for the biomass, height and (r =0.82) for the vegetation cover surface. Finally, maps representing the spatial distribution of these parameters were generated for 14 July (period of time when the vineyard is most developed) using the adjustment previously obtained between the NDVI of the vineyard and these parameters. [ABSTRACT FROM AUTHOR]

A paper by Gonzalez-Alonso et al . (1997) presents a regression estimator with excellent relative efficiency values to estimate the area of barley in an intensive agricultural area in Lleida (Catalonia, Spain) using 1994 ground survey data and a 1993 Landsat TM classified image. The paper concludes that the regression estimator can be efficiently applied with ground data from the current year and a classified satellite image from a previous year. If these conclusions are applicable to large areas, using the same classified image for several years would greatly increase the cost-efficiency of the regression estimator. This Letter evaluates the suitability of the method in other areas of Spain. [ABSTRACT FROM AUTHOR]

The joint use of multiresolution sensors from different satellites offers many opportunities to describe vegetation and its dynamics. This paper introduces the concept of a virtual constellation (defined as an ensemble of all Earth Observation satellites in orbit that satisfy common requirements) for agricultural applications and contributes to providing the necessary inter-sensor calibration methodology for spectral reflectances and NDVI. For this purpose, we performed an observational study, comparing reflectances and the Normalized Difference Vegetation Index (NDVI), from near-synchronous image pairs of Landsat 7 Enhanced Thematic Mapper Plus (ETM+), as the reference sensor and Landsat 5 Thematic Mapper (TM), IRS 1C/D LISS-III (LISS), Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), QuickBird, and NOAA Advanced Very High-resolution Radiometer (AVHRR). Linear relationships were found for the intercalibration of reflectances and NDVI from one sensor to another, for all sensors, provided that some spatial aggregation was performed. The main source of data dispersion in our linear cross-sensor translation equations is the geolocation uncertainty inherent in the process of geometric correction. Consequently, spatial aggregation always needs to be performed if (different or the same) sensors are to be used to derive time-series of biogeophysical parameters over heterogeneous areas. The homogenous zone approach developed here is recommended as an excellent tool for deriving robust new cross-sensor relationships, provided that the selected homogeneous crops cover the full NDVI range. The linear cross-sensor relationships derived from one image pair were shown to be valid for the whole season and for all areas with similar vegetation and climate. [ABSTRACT FROM AUTHOR]

This paper presents an assessment of the performance of a hybrid method that allows a simultaneous retrieval of land-surface temperature (LST) and emissivity (LSE) from remotely-sensed data. The proposed method is based on a synergistic usage of the split-window (SW) algorithm and the two-temperature method (TTM) and combines the advantages of both procedures while mitigating their drawbacks. The method was implemented for thermal channels 76 (10.56 µm) and 78 (11.72 µm) of the Airborne Hyperspectral Scanner (AHS), which was flown over the Barrax test site (Albacete, Spain) in the second week of July 2005, within the framework of the Sentinel-2 and Fluorescence Experiment (SEN2FLEX) field campaign. A set of radiometric measurements was performed in the thermal infrared region in coincidence with aircraft overpasses for different surface types, e.g. bare soil, water body, corn, wheat, grass. The hybrid method was tested and compared with a standard SW algorithm and the results obtained show that the hybrid method is able to provide better estimates of LST, with values of bias (RMSE) of the order of 0.8 K (1.9 K), i.e. about one third (one half) of the corresponding values of 2.7 K (3.4 K) that were obtained for bias (RMSE) when using the SW algorithm. These figures provide a sound indication that the developed hybrid method is particularly useful for surface and atmospheric conditions where SW algorithms cannot be accurately applied. [ABSTRACT FROM AUTHOR]

This paper explores the potential of an artificial immune-based supervised classification algorithm for land-cover classification. This classifier is inspired by the human immune system and possesses properties similar to nonlinear classification, self/non-self identification, and negative selection. Landsat ETM+ data of an area lying in Eastern England near the town of Littleport are used to study the performance of the artificial immune-based classifier. A univariate decision tree and maximum likelihood classifier were used to compare its performance in terms of classification accuracy and computational cost. Results suggest that the artificial immune-based classifier works well in comparison with the maximum likelihood and the decision-tree classifiers in terms of classification accuracy. The computational cost using artificial immune based classifier is more than the decision tree but less than the maximum likelihood classifier. Another data set from an area in Spain is also used to compare the performance of immune based supervised classifier with maximum likelihood and decision-tree classification algorithms. Results suggest an improved performance with the immune-based classifier in terms of classification accuracy with this data set, too. The design of an artificial immune-based supervised classifier requires several user-defined parameters to be set, so this work is extended to study the effect of varying the values of six parameters on classification accuracy. Finally, a comparison with a backpropagation neural network suggests that the neural network classifier provides higher classification accuracies with both data sets, but the results are not statistically significant. [ABSTRACT FROM AUTHOR]

During the second half of the month of June 1997, a massive catch of red tuna (Thunnus thynnus) took place off the coast of Babarte (Spain), in contrast to the first half of that month when there was hardly any presence of this species. The aim of this paper was to examine the relation between the high fishing productivity and the meteorological conditions under which the oceanic events to which the tuna fisheries were attracted took place. This was carried out through the analysis of Advanced Very High Resolution Radiometer (AVHRR) sensor data and the data from the Tiros-N Operational Vertical Sounder (TOVS) probe of the NOAA-14 satellite from 10 to 24 June 1997. Results show that the formation of the fishing front was caused by an ocean-atmosphere energetic exchange, which was localized and described through the data transmitted from the NOAA satellites. [ABSTRACT FROM AUTHOR]

This paper presents preliminary results of updated land use-land cover (LULC) maps for two Spanish provinces using MERIS-FR images, data from the Third National Forest Inventory (NFI3) ground plots, and the European database CORINE Land Cover 2000 (CLC2000). Legend categories were defined using the FAO legend-based European Global Land Cover (GLC2000) product. The applied methodology is based on maximum likelihood supervised classification of MERIS-FR images. The delimitation of training areas was based on the NFI3 ground plots and CLC2000. High overall accuracies were obtained for Navarra (73.67%) and Murcia (73.70%). These preliminary results show that MERIS-FR data allow an operational development of LULC maps/products at medium scales. Results from the comparison between MERIS-based classifications and available thematic maps are also presented. [ABSTRACT FROM AUTHOR]

Classification accuracy depends on a number of factors, of which the nature of the training samples, the number of bands used, the number of classes to be identified relative to the spatial resolution of the image and the properties of the classifier are the most important. This paper evaluates the effects of these factors on classification accuracy using a test area in La Mancha, Spain. High spectral and spatial resolution DAIS data were used to compare the performance of four classification procedures (maximum likelihood, neural network, support vector machines and decision tree). There was no evidence to support the view that classification accuracy inevitably declines as the data dimensionality increases. The support vector machine classifier performed well with all test data sets. The use of the orthogonal MNF transform resulted in a decline in classification accuracy. However, the decision‐tree approach to feature selection worked well. Small increases in classifier accuracy may be obtained using more sophisticated techniques, but it is suggested here that greater attention should be given to the collection of training and test data that represent the range of land surface variability at the spatial scale of the image. [ABSTRACT FROM AUTHOR]

Space‐borne synthetic aperture radar has been proven to be a useful tool for ocean oil spill monitoring due to its large coverage, independence of the day–night cycle and all‐weather capability. In this paper, a method for oil spill detection based on a visual interpretation was applied to two consecutive Advanced Synthetic Aperture Radar (ASAR) images acquired during the Prestige oil spill off the Spanish coast. The obtained oil spill information was integrated into a Geographical Information System (GIS) database in order to study the spatial distribution and the evolution of the slicks between both days, in addition to carrying out a comparison with field observations. The results show the great capability of monitoring and forecasting marine oil spills caused by large oil tanker accidents by means of the use of radar imagery jointly with other information, such as wind data or in situ observations. [ABSTRACT FROM AUTHOR]

Support vector machines (SVM) represent a promising development in machine learning research that is not widely used within the remote sensing community. This paper reports the results of two experiments in which multi-class SVMs are compared with maximum likelihood (ML) and artificial neural network (ANN) methods in terms of classification accuracy. The two land cover classification experiments use multispectral (Landsat-7 ETM+) and hyperspectral (DAIS) data, respectively, for test areas in eastern England and central Spain. Our results show that the SVM achieves a higher level of classification accuracy than either the ML or the ANN classifier, and that the SVM can be used with small training datasets and high-dimensional data. [ABSTRACT FROM AUTHOR]

The high spectral resolution of Airborne Visible Infrared Imaging Spectrometer (AVIRIS; 224 channels from 400 to 2455 nm) and HyMap (127 channels between 437 and 2485 nm) images is necessary to conduct geological analysis with remote petrological determinations of rock types or soils, or to determine vegetation groups. When airborne images and field spectra are well adjusted between each other, and when the vegetation does not interfere in the analysis, the spectral shape analysis (SSA) method represents an easy treatment to reveal a large amount of geological information. The method presented in the paper takes into account both wells and peaks of spectra resulting from a combination of absorption features and continuum shapes. It was conducted on the Ronda peridotite, in the south-west of Spain, which was imaged by AVIRIS in 1991 and by HyMap in 2000, and which was partially sampled in the field using a GER 3700 spectrometer in 1997, 2000 and 2001. In this study, the AVIRIS and HyMap images are processed to infer geological features using first photo-interpretation of colour composite images and then using the SSA method. This allows us to distinguish easily the peridotite massif from its surrounding rocks, to identify petrological variations inside the peridotite, including different varieties of serpentines, but also inside the surrounding rocks, particularly the carbonate-bearing ones which comprise marbles and dolomites. [ABSTRACT FROM AUTHOR]

Traditionally, the estimation of fire danger is performed from meteorological danger indices that are computed for single locations, where the weather stations are located. Frequently, these locations are far from forested areas, and there is a need to spatially interpolate danger variables. Methods for spatial interpolation are always prone to error, especially for those variables that show a greater spatial variability (wind, mainly). Satellite images may be considered a good alternative for interpolation of danger values, since they perform a spatially exhaustive observation of the territory. This paper analyses the spatial distribution of the Canadian Drought Code (DC), part of the Canadian Forest Fire Weather Index System (CFFWIS), in the region of Andaluc}ía (south Spain) following two procedures. First, maps of DC values were obtained from spatial interpolation of a network of 30 weather stations using the squared inverse distance algorithm. These results were compared with interpolation based on linear regression analysis, using National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) derived bands as independent variables. The most significant variables found for these empirical fittings were relative greenness, the ratio of Normalized Difference Vegetation Index (NDVI) and surface temperature, and a temporal variable, which accounts for the variations in day length throughout the fire season. After several empirical fittings were obtained, the most precise estimation was found after adjusting the coefficients to the time period considered. [ABSTRACT FROM AUTHOR]

This paper presents a colour classification of coastal waters from features of spectral reflectance. Experimental data were collected in coastal waters of the Ebro river mouth area during two periods of non-bloom conditions and low suspended load. Examination of the reflectance spectra reveals a few significant shapes corresponding to different apparent colours and specific values of hydrological parameters. A colour classification is proposed. An empirical method and a semi-analytical method are presented in order to retrieve suspended particulate matter concentration, namely SPM, chlorophyll a concentration, chl, sediment refractive index, m[sub r], and absorption coefficient of yellow substance at 440 nm, a [sub y](440). The first method is a direct application of the colour classification. It is based on correlations found for each water type between spectral features of the first derivative of reflectance and SPM, chl, m[sub r], a[sub y](440) values. A second method uses the inversion of a three component reflectance model. Measured reflectance spectra and typical values of m[sub r], deduced from the colour classification, are assimilated in the model. As a result, it appears that estimates of SPM and chl are more accurate by calculating chl from the correlation technique and SPM along with a[sub y](440) from inversion of the reflectance model. [ABSTRACT FROM AUTHOR]

In this paper three methods for updating inventories of burned areas have been presented and examined. They include Multitemporal Principal Component Analysis (MPCA), Change Vector Analysis (CVA) and Multitemporal NDVI Classification (MNC). First, 11 Landsat-5 Thematic Mapper (TM) images of a forest area were radiometrically corrected to derive a multitemporal series of intercomparable images for each spring from 1984 to 1994. Then, in order to check the feasibility of the three approaches, they were used for mapping fire burns that occurred during 1992. The various procedures yielded different maps of burned areas; the MNC method seemed to be more reliable than the others, because it merges spectral data corresponding not only to 1992 (pre-fire) and 1993 (post-fire) but also to 1994 (the second year after the fires), which is key in the vegetation regeneration. Finally, this methodology was automated to yield an inventory of burned areas for each year during the period of study. [ABSTRACT FROM AUTHOR]

This paper describes how probabilistic methods provide a means to integrate analysis of remotely sensed imagery and geo-information processing. In a case study from southern Spain, geological map units were used to improve land-cover classification from Landsat TM imagery. Overall classification accurracy improved from 76% to 90% (1984) and from 64% to 69% (1995) when using stratification according to geology combined with iterative estimation of prior probabilities. Differences between the two years were mainly due to extremely dry conditions during the 1995 growing season. Per-pixel probabilities of class successions and entropy values calculated from the classification's posterior probability vectors served to quantify uncertainty in a post-classification comparison. It is concluded that iterative estimation of prior probabilities provides a practical approach to improve classification accuracy. Posterior probabilities of class membership provide useful information about the magnitude and spatial distribution of classification uncertainty. [ABSTRACT FROM AUTHOR]

The focus of the work reported in this paper is on the way in which land degradation processes affecting semi-arid Mediterranean environments are enhanced by the operation of external (human-induced) factors. A study of landscape change in the Los Monegros area of Aragon, north-east Spain, over the period 1984-1997 has been undertaken in order to evaluate the effects of the extension of irrigation on the expansion of arable agriculture, and to estimate the consequential effects on the landscape. Radiometrically-calibrated Landsat TM data were combined with ground-based observations (soil and geology maps, plus hydrogeological data) with the aim of analysing temporal change in land cover. A combination of remote sensing methods (linear spectral unmixing and principal components analysis) was used to determine the proportions of individual soil types. Change detection techniques were employed to pick out areas at risk from land degradation processes (increased soil erosion and soil salinization) and to explain the ways in which agricultural land-use practices interact with the geological and hydrogeological characteristics of the study area. [ABSTRACT FROM AUTHOR]