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Lakes have been observed as sentinels of climate change. In the last decades, global warming and increasing aridity has led to an increase in both the number and severity of wildfires. This has a negative impact on lake catchments by reducing forest cover and triggering cascading effects in freshwater ecosystems. In this work we used satellite remote sensing to analyse potential fire effects on lake water quality of Lake Baikal (Russia), considering the role of runoff and sediment transport, a less studied pathway compared to fire emissions transport. The main objectives of this study were to analyse time series and investigate relationships among fires (i.e., burned area), meteo-climatic parameters and water quality variables (chlorophyll-a, turbidity) for the period 2003–2020. Because Lake Baikal is oligotrophic, we expected detectable changes in water quality variables at selected areas near the three mains tributaries (Upper Angara, Barguzin, Selenga) due to river transport of fire-derived burned material and nutrients. Time series analysis showed seasonal (from April to June) and inter-annual fire occurrence, precipitation patterns (high intensity in summer) and no significant temporal changes for water quality variables during the studied periods. The most severe wildfires occurred in 2003 with the highest burned area detected (36,767 km2). The three lake sub-basins investigated have shown to respond differently according to their morphology, land cover types and meteo-climatic conditions, indicating their importance in determining the response of water variables to the impact of fires. Overall, our finding suggests that Lake Baikal shows resilience in the medium-long term to potential effects of fires and climate change in the region.

Land surface temperature (LST) is a manifestation of the surface thermal environment (LSTE) and an important driver of physical processes of surface land energy balance at local to global scales. Tenerife is one of the most heterogeneous islands among the Canaries from a climatological and bio-geographical point of view. We study the surface thermal conditions of the volcanic island with remote sensing techniques. In particular, we consider a time series of Landsat 8 (L8) level 2A images for the period 2013 to 2019 to estimate LST from surface reflectance (SR) and brightness Temperature (BT) images. A total of 26 L8 dates were selected based on cloud cover information from metadata (land cloud cover < 10%) to estimate pixel-level LST with an algorithm based on Radiative Transfer Equations (RTE). The algorithm relies on the Normalized Difference Vegetation Index (NDVI) for estimating emissivity pixel by pixel. We apply the Independent Component Analysis (ICA) that revealed to be a powerful tool for data mining and, in particular, to separate multivariate LST dataset into a finite number of components, which have the maximum relative statistical independence. The ICA allowed separating the land surface temperature time series of Tenerife into 11 components that can be associated with geographic and bioclimatic zones of the island. The first ten components are related to physical factors, the 11th component, on the contrary, presented a more complex pattern resulting possibly from its small amplitude and the combination of various factors into a single component. The signal components recognized with the ICA technique, especially in areas of active volcanism, could be the basis for the space-time monitoring of the endogenous component of the LST due to surface hydrothermal and/or geothermal activity. Results are encouraging, although the 16-day revisit frequency of Landsat reduces the frequency of observation that could be increased by applying techniques of data fusion of medium and coarse spatial resolution images. The use of such systems for automatic processing and analysis of thermal images may in the future be a fundamental tool for the surveillance of the background activity of active and dormant volcanoes worldwide.

Coarse resolution sensors are not very sensitive at detecting small fire patches, making current estimations of global burned areas (BA) very conservative. Using medium or high-resolution sensors to generate BA products becomes then a priority, particularly in areas where fires tend to be small and frequent. Building on previous work that developed a small fire dataset (SFD) for Sub-Saharan Africa for 2016, this paper presents a new version of the dataset for 2019 using the two Sentinel-2 satellites (A and B) and VIIRS active fires. Total estimated BA was 4.8 Mkm

In this work we present a procedure for building a dataset of fire reference perimeters over the African continent from Sentinel-2 (S2) time series. The strategy relies on a sampling scheme designed on the characteristics of the S2 tiling system to provide units suitable for statistical sampling of validation units. The S2 archive is searched to extract cloud free images for building time series that are classified with a Random Forest algorithm to provide fire reference perimeters. A test S2 tile over Tropical savanna (35 LMD) is used to assess the accuracy of S2 reference perimeters by comparison with polygons of burned areas extracted from high resolution Planetscope data.

The paper proposes a fully automatic algorithm approach to map burned areas from remote sensing characterized by human interpretable mapping criteria and explainable results. This approach is partially knowledge-driven and partially data-driven. It exploits active fire points to train the fusion function of factors deemed influential in determining the evidence of burned conditions from reflectance values of multispectral Sentinel-2 (S2) data. The fusion function is used to compute a map of seeds (burned pixels) that are adaptively expanded by applying a Region Growing (RG) algorithm to generate the final burned area map. The fusion function is an Ordered Weighted Averaging (OWA) operator, learnt through the application of a machine learning (ML) algorithm from a set of highly reliable fire points. Its semantics are characterized by two measures, the degrees of pessimism/optimism and democracy/monarchy. The former allows the prediction of the results of the fusion as affected by more false positives (commission errors) than false negatives (omission errors) in the case of pessimism, or vice versa, the latter foresees if there are only a few highly influential factors or many low influential ones that determine the result. The prediction on the degree of pessimism/optimism allows the expansion of the seeds to be appropriately tuned by selecting the most suited growing layer for the RG algorithm thus adapting the algorithm to the context. The paper illustrates the application of the automatic method in four study areas in southern Europe to map burned areas for the 2017 fire season. Thematic accuracy at each site was assessed by comparison to reference perimeters to prove the adaptability of the approach to the context, estimated average accuracy metrics are omission error = 0.057, commission error = 0.068, Dice coefficient = 0.94 and relative bias = 0.0046.

Sentinel-2 Multi-Spectral Instrument (MSI) (S-2) images have been used for mapping burned areas within the borders of the Vesuvio National park, Italy, severity affected by fires during summer 2017. A fuzzy algorithm, previously developed for Mediterranean ecosystems and Landsat data, have been adapted and applied to S-2 images. Major improvements with respect to the previous algorithm characteristics are i) the use of S-2 band reflectance in post-fire images and as temporal difference (delta pre- and post-fire) and ii) the definition of fuzzy membership function based on statistics (percentiles) of reflectance as derived from training areas.The following input bands were selected based on their ability to discriminate burned vs. unburned areas: post-fire NIR (Near Infrared, S-2 band 8), post-fire RE (Red Edge, S-2 bands 6 and 7) and temporal difference (delta post-pre fire) of the same bands and additionally of SWIR2 (ShortWave Infrared, S-2 band 12). For each input, a sigmoid function has been defined based on percentiles of the unburned and burned histogram distributions, respectively, derived from training data. In this way, and with respect to previous formulation of the algorithm, membership function can be defined in an automated way when ancillary layer are provided for extracting statistics of burned and unburned surfaces.Input membership degrees for the selected bands have been integrated to derived pixel-based synthetic scores of burned likelihood with Ordered Weighted Averaging (OWA) operators. Different operators were tested to represent different attitudes/needs of the stakeholders between pessimistic (the maximum extent of the phenomenon to minimise the chance of underestimating) and optimistic (minimise the chance of overestimating).Output score maps provided as continuous values in the [0,1] domain have been segmented to extract burned/unburned areas; the performance of the combined threshold and OWA operator has been evaluated by comparison with Copernicus fire damage layers from the Emergency Management Service (EMS) (https://emergency.copernicus.eu/). Error matrix, F-score and omission and commission error metrics have been analysed.Finally, the correlation between fuzzy score derived by applying OWA operators has been analysed by comparison with Copernicus EMS fire damage layers as well as fire severity computed as temporal difference of the NBR index. Results show satisfactory accuracy is achieved for the identification of the most severely affected areas while lower performance is observed for those areas identified as slightly damage and probably affected by fires of lower intensity. Moreover, some discrepancies have been observed between different layers of fire severity due to the non-unique definition of the criteria used for assessing the impact of fires on the vegetation layer.

The paper proposes a scalable fuzzy approach for mapping the status of the environment integrating several distinct models exploiting geo big data. The process is structured into two phases: the first one can exploit products yielded by distinct models of remote sensing image interpretation defined in the scientific literature, and knowledge of domain experts, possibly ill-defined, for computing partial evidence of a phenomenon. The second phase integrates the partial evidence maps through a learning mechanism exploiting ground truth to compute a synthetic Environmental Status Indicator (ESI) map. The proposal resembles an ensemble approach with the difference that the aggregation is not necessarily consensual but can model a distinct decision attitude in between pessimistic and optimistic. It is scalable and can be implemented in a distributed processing framework, so as to make feasible ESI mapping in near real time to support land monitoring. It is exemplified to map the presence of standing water areas, indicator of water resources, agro-practices or natural hazard from remote sensing by considering different models.

In this article, we propose an automatic procedure for classification of UAV imagery to map weed presence in rice paddies at early stages of the growing cycle. The objective was to produce a weed map (common weeds and cover crop remnants) to support variable rate technologies for site-specific weed management. A multi-spectral ortho-mosaic, derived from images acquired by a Parrot Sequoia sensor mounted on a quadcopter, was classified through an unsupervised clustering algorithm; cluster labelling into 'weed'/'no weed' classes was achieved using geo-referenced observations. We tested the best set of input features among spectral bands, spectral indices and textural metrics. Weed mapping performance was assessed by calculating overall accuracy (OA) and, for the weed class, omission (OE) and commission errors (CE). Classification results were assessed under an 'alarmist' approach in order to minimise the chance of overestimating weed coverage. Under this condition, we found that best results are provided by a set of spectral indices (OA = 96.5%, weed CE = 2.0%). The output weed map was aggregated to a grid layer of 5 × 5 m to simulate variable rate management units; a weed threshold was applied to identify the portion of the field to be subject to treatment with herbicides. Ancillary information on weed and crop conditions were derived over the grid cells to support precision agronomic management of rice crops at the early stage of growth.

Providing relatively fine spatial resolution multispectral data, Landsat-8, Landsat-7 (L8 and L7, respectively) and Sentinel-2 (S2) from 2013 to 2018 have been used in this study for enabling high-frequency monitoring of water quality of two small (the smaller with an area of 1.6 km2) freshwater dammed reservoirs. Located in Sardinia (Italy) and Crete (Greek), respectively, Mulargia and Aposelemis represent vital resources to supply drinking water in downstream valleys. A total of 400 cloud-free satellite images were turned into information on water quality by using an image processing chain implementing physically based methods for retrieving chlorophyll-a concentration (Chl-a), turbidity, Secchi disk depth (SDD) and surface water temperature. These estimates have been successfully validated (the lower Pearson correlation r was 0.88 for Chl-a) with 23 match-ups of in situ and satellite data. Results of the multi-temporal analyses showed a decrease of SDD due to the increase of Chl-a in Aposelemis or an increase of turbidity in Mulargia. For both freshwater reservoirs, the satellite-derived trophic state index assigned both lakes to mesotrophic conditions. The results finally suggested the effectiveness of S2 and Landsat in increasing, for the latest investigated years, the frequency of observations.

Il contributo, dopo aver analizzato i fattori che influenzano la capacità di affrontare e superare efficacemente situazioni di emergenza legate a rischi naturali e/o antropici, propone una soluzione implementata nell'ambito del progetto "SIMULATOR_ADS: un Sistema Integrato ModULAre per la gesTione e prevenziOne dei Rischi - Arricchito con Dati Satellitari", una piattaforma ICT originale, con un'architettura basata su servizi Web, a supporto degli operatori e delle autorità locali di Protezione Civile nelle fasi di preparazione e di gestione delle emergenze.

The paper proposes a transparent approach for mapping the status of environmental phenomena from multisource information based on both soft computing and machine learning. It is transparent, intended as human understandable as far as the employed criteria, and both knowledge and data-driven. It exploits remote sensing experts&rsquo, interpretations to define the contributing factors from which partial evidence of the environmental status are computed by processing multispectral images. Furthermore, it computes an environmental status indicator (ESI) map by aggregating the partial evidence degrees through a learning mechanism, exploiting volunteered geographic information (VGI). The approach is capable of capturing the specificities of local context, as well as to cope with the subjectivity of experts&rsquo, interpretations. The proposal is applied to map the status of standing water areas (i.e., water bodies and rivers and human-driven or natural hazard flooding) using multispectral optical images by ESA Sentinel-2 sources. VGI comprises georeferenced observations created both in situ by agronomists using a mobile application and by photointerpreters interacting with a geographic information system (GIS) using several information layers. Results of the validation experiments were performed in three areas of Northern Italy characterized by distinct ecosystems. The proposal showed better performances than traditional methods based on single spectral indexes.

In this work we propose an approach for mapping flooded areas from Sentinel-2 MSI (Multispectral Instrument) data based on soft fuzzy integration of evidence scores derived from both band combinations (i.e. Spectral Indices - SIs) and components of the Hue, Saturation and Value (HSV) colour transformation. Evidence scores are integrated with Ordered Weighted Averaging (OWA) operators, which model user’s decision attitude varying smoothly between optimistic and pessimistic approach. Output is a map of global evidence degree showing the plausibility of being flooded for each pixel of the input Sentinel-2 (S2) image. Algorithm set up and validation were carried out with data over three sites in Italy where water surfaces are extracted from stable water bodies (lakes and rivers), natural hazard flooding, and irrigated paddy rice fields. Validation showed more than satisfactory accuracy for the OR-like OWA operators (F-score > 0.90) with performance slightly decreased (F-score

The present short paper describes the functionalities of a desktop tool enabling non-expert users to develop downstream services for the periodic download and processing of data from Sentinel sources.

This paper aims at investigating the use of microwave and optical images for the detection and characterization of fire scars in vegetated areas. To cope with this issue, Sentinel-1 (S-1) C-band synthetic aperture radar (SAR), and multispectral Sentinel-2 (S-2) multispectral data are used. First, a consolidated fuzzy-based methodology, specifically designed for retrieving burned areas, is considered. Then, an investigation based on the analysis of the maps of backscattering coefficients, as obtained by processing stacks of SAR images, is performed. The outcomes obtained with SAR data are validated by comparison with the results obtained with the optical multispectral data. Presented experiments are related to the Soberanes fires, California, which ignited on July 22, 2016, and affected more than 400 square kilometers. In addition, we explore the potential use of stacks of spatial coherence maps generated from pairs of complex-valued SAR images that encompass a fire event to recover new, additional information on the vegetation state and extent of the fire scars. To the purpose, stacks of S-1 SAR images acquired at different polarizations over the Mt. Vesuvius, Italy, in the 2017 summer season has been used. Some preliminary results, which evidence how the variations of the spatial coherence values can be somehow related to a fire and used to recover additional pieces of information, are presented.

In this work a dataset of maps of Chl-a derived from different sensors (MERIS-OLI-MSI-OLCI) satellite images is used to evaluate water quality of the four most important Italian subalpine lakes (Garda, Maggiore, Iseo, Como) in the period 2003-2018. In order to produce Chl-a concentration maps, imagery needs to be processed along a processing chain, which includes radiometric correction (to convert digital numbers into at-satellite-atmospheric radiance), atmospheric correction to obtain Remote sensing reflectance (C2R for MERIS, 6SV for MSI and OLI, Polymer for OLCI), and finally the application of bio-optical models to retrieve chl-a concentration. The results presented in this study show: i) that the seasonal variability of chl-a concentration is particularly pronounced during spring and autumn; ii) Lake Iseo is the one that has a worse judgment than WFD; iii) 2005 and 2016 were quite critical years; iv) trends show a slight tendency to increase Chl-a concentrations during the period considered, particularly for Iseo and Maggiore lakes.

The aim of this paper is to investigate how optical and Synthetic Aperture Radar (SAR) data can be combined in an integrated multi-source framework to identify burned areas at the regional scale. The proposed approach is based on the use of fuzzy sets theory and a region-growing algorithm. Landsat TM and (C-band) ENVISAT Advanced Synthetic Aperture Radar (ASAR) images acquired for the year 2003 have been processed to extract burned area maps over Portugal. Pre-post fire SAR backscatter temporal difference has been integrated with optical spectral indices to the aim of reducing confusion between burned areas and low-albedo surfaces. The output fuzzy score maps have been compared with reference fire perimeters provided by the Fire Atlas of Portugal. Results show that commission and omission errors in the output burned area maps are a function of the threshold applied to the fuzzy score maps; between the two extremes of the greatest producer’s accuracy (omission error < 10%) and user’s accuracy (commission error < 5%), an intermediate threshold value provides errors of about 20% over the study area. The integration of SAR backscatter allowed reducing local commission errors from 65.4% (using optical data, only) to 11.4%, showing to significantly mitigate local errors due to the presence of cloud shadows and wetland areas. Overall, the proposed method is flexible and open to further developments; also in the perspective of the European Space Agency (ESA) Sentinel missions operationally providing SAR and optical datasets.

The triangle method has been applied to derive a weekly indicator of evaporative fraction on vegetated areas in a temperate region in Northern Italy. Daily MODIS Aqua Land Surface Temperature (MYD11A1) data has been combined with air temperature maps and 8-day composite MODIS NDVI (MOD13Q1/MYD13Q1) data to estimate the Evaporative Fraction (EF) at 1 km resolution, on a daily basis. Measurements at two eddy covariance towers located within the study area have been exploited to assess the reliability of satellite based EF estimations as well as the robustness of input data. Weekly syntheses of the daily EF indicator (EFw) were then derived at regional scale for the years 2010, 2011 and 2012 as a proxy of overall surface moisture condition. EFw showed a temporal behavior consistent with growing cycles and agro-practices of the main crops cultivated in the study area (rice, forages and corn). Comparison with official regional corn yield data showed that variations in EFw cumulated over summer are related with crop production shortages induced by water scarcity. These results suggest that weekly-averaged EF estimated from MODIS data is sensible to water stress conditions and can be used as an indicator of crops’ moisture conditions at agronomical district level. Advantages and disadvantages of the proposed approach to provide information useful to issue operational near real time bulletins on crop conditions at regional scale are discussed.

The ERMES agromonitoring system for rice cultivations integrates EO data at different resolutions, crop models, and user-provided in situ data in a unified system, which drives two operational downstream services for rice monitoring. The first is aimed at providing information concerning the behavior of the current season at regional/rice district scale, while the second is dedicated to provide farmers with field-scale data useful to support more efficient and environmentally friendly crop practices. In this contribution, we describe the main characteristics of the system, in terms of overall architecture, technological solutions adopted, characteristics of the developed products, and functionalities provided to end users. Peculiarities of the system reside in its ability to cope with the needs of different stakeholders within a common platform, and in a tight integration between EO data processing and information retrieval, crop modeling, in situ data collection, and information dissemination. The ERMES system has been operationally tested in three European rice-producing countries (Italy, Spain, and Greece) during growing seasons 2015 and 2016, providing a great amount of near-real-time information concerning rice crops. Highlights of significant results are provided, with particular focus on real-world applications of ERMES products and services. Although developed with focus on European rice cultivations, solutions implemented in the ERMES system can be, and are already being, adapted to other crops and/or areas of the world, thus making it a valuable testing bed for the development of advanced, integrated agricultural monitoring systems.

This paper aims at investigating the potential of Sentinel-1 C-band synthetic aperture radar (SAR) observations for detecting fire scars in vegetated areas at regional scale. A comprehensive analysis of the backscattering coefficients is carried out. The experimental analysis is conducted by analyzing the scenario of the Sardinia Island, which is one of the Italian regions most affected by fire events over the summer season. The detection capability of fire scars in such an environment is demonstrated by exploiting information extracted from dual-polarized SAR data. Our results reveal a significant decrease of the VH response over the fire-disturbed forests, thus, highlighting the effectiveness of such cross-polarized observations. In order to prove the validity of the proposed approach for the detection of fire scars in the vegetation layer, the results of the conducted experiments have been suitably compared with burned areas identified by using an existing fuzzy-based algorithm, which has been applied to multispectral Landsat-8 operational land imager data. This investigation opens the way to systematic methods for monitoring fire scars in heterogeneous environments, and in particular in fire-prone Mediterranean ecosystems.

Le risorse idriche sono limitate e si trovano ad affrontare problematiche dovute anche ai cambiamenti climatici, che potrebbero avere gravi conseguenze sulla qualità dell'acqua. Le tecniche di telerilevamento sono ampiamente utilizzate per lo studio della superficie terrestre. Hanno molte applicazioni in campo scientifico, tra cui la valutazione e la quantificazione dei cambiamenti nella qualità dell'acqua. Il Progetto SPACE-O (Space Assisted Water Quality Forecasting Platform for Optimized Decision Making in Water Supply Services) finanziato nel 2016 sul programma Horizon2020 sta catalizzando l'innovazione con una piattaforma operativa di servizio creata per facilitare l'interoperabilità tra dati di Osservazione della Terra, modelli ecologici ed idraulici ed i servizi implementati in un Sistema di supporto alle decisione di gestione (DSS).

Land Surface Temperature (LST) is a key variable in the interactions and energy fluxes between the Earth surface and the atmosphere. Satellite data provide consistent, continuous and spatially distributed information on the Earth's surface conditions among which LST. Ten years of NASA-MODIS day-time and night-time 1 km LST data over Southern Italy have been analyzed to quantify the influence of factors such as topography and the land cover on LST spatio-temporal variations. Results show that topography significantly influence LST variability as a function of the land cover and to a different extent for day-time and night-time data. Moreover, the relation between LST and the influential factors varies with the season during the year. This study contributes to a further understanding of the complex relationship between the spatio-temporal variability of the surface thermal conditions and its driving factors highlighting how these relationships might change within the year.

Studies of the impact of human activity on vegetation dynamics of the Sahelian belt of Africa have been recently re-invigorated by new scientific findings that highlighted the primary role of climate in the drought crises of the 1970s–1980s. Time series of satellite observations revealed a re-greening of the Sahelian belt that indicates no noteworthy human effect on vegetation dynamics at sub continental scale from the 1980s to late 1990s. However, several regional/local crises related to natural resources occurred in the last decades despite the re-greening thus underlying that more detailed studies are needed. In this study we used time-series (1998–2010) of SPOT–VGT NDVI and FEWS–RFE rainfall estimates to analyse vegetation – rainfall correlation and to map areas of local environmental anomalies where significant vegetation variations (increase/decrease) are not fully explained by seasonal changes of rainfall. Some of these anomalous zones (hot spots) were further analysed with higher resolution images Landsat TM/ETM+ to evaluate the reliability of the identified anomalous behaviour and to provide an interpretation of some example hot spots. The frequency distribution of the hot spots among the land cover classes of the GlobCover map shows that increase in vegetation greenness is mainly located in the more humid southern part and close to inland water bodies where it is likely to be related to the expansion/intensification of irrigated agricultural activities. On the contrary, a decrease in vegetation greenness occurs mainly in the northern part (12°–15°N) in correspondence with herbaceous vegetation covers where pastoral and cropping practices are often critical due to low and very unpredictable rainfall. The results of this study show that even if a general positive re-greening due to increased rainfall is evident for the entire Sahel, some local anomalous hot spots exist and can be explained by human factors such as population growth whose level reaches the ecosystem carrying capacity as well as population displacement leading to vegetation recovery.

Currently, the best practice to support land planning calls for the development of Spatial Data Infrastructures (SDI) capable of integrating both geospatial datasets and time series information from multiple sources, e.g., multitemporal satellite data and Volunteered Geographic Information (VGI). This paper describes an original OGC standard interoperable SDI architecture and a geospatial data and metadata workflow for creating and managing multisource heterogeneous geospatial datasets and time series, and discusses it in the framework of the Space4Agri project study case developed to support the agricultural sector in Lombardy region, Northern Italy. The main novel contributions go beyond the application domain for which the SDI has been developed and are the following: the ingestion within an a-centric SDI, potentially distributed in several nodes on the Internet to support scalability, of products derived by processing remote sensing images, authoritative data, georeferenced in-situ measurements and voluntary information (VGI) created by farmers and agronomists using an original Smart App; the workflow automation for publishing sets and time series of heterogeneous multisource geospatial data and relative web services; and, finally, the project geoportal, that can ease the analysis of the geospatial datasets and time series by providing complex intelligent spatio-temporal query and answering facilities.

This study investigated the feasibility of delivering a crop type map early during the growing season. Landsat 8 OLI multi-temporal data acquired in 2013 season were used to classify seven crop types in Northern Italy. The accuracy achieved with four supervised algorithms, fed with multi-temporal spectral indices (EVI, NDFI, RGRI), was assessed as a function of the crop map delivery time during the season. Overall accuracy (Kappa) exceeds 85% (0.83) starting from mid-July, five months before the end of the season, when maximum accuracy is reached (OA=92%, Kappa=0.91). Among crop types, rice is the most accurately classified, followed by forages, maize and arboriculture, while soybean or double crops can be confused with other classes.

Timely information about agricultural crops (e.g. typology, phenology, productivity, health) is crucial for proper agronomic planning and management by farmers and public administrations. SAR data at different frequencies have been used for retrieving biophysical crop parameters pertinent to the crops and for monitoring their intra-annual cycles, e.g. for the estimation of biomass and growth status. Here we present an experimental analysis based on the investigation of X-band backscattering derived from multitemporal TerraSAR-X (TSX) data over three different types of summer crops (rice, maize, soybean). The study area is located in south-eastern portion of Lombardy region, Northern Italy, framed within the Po river Plain and the Ticino river basin, and covers two farms located in Rosasco municipality (45°15'00" N, 8°35'00" E). In situ campaigns have been conducted along the summer crop season (May-September) on this area in order to measure biophysical parameters related to: i) agronomy (crop type and variety, agro-practices, seeding date, seeding scheme and density), ii) substrate (soil roughness, soil moisture, flooding conditions), iii) crop phenology (BBCH scale stage, context and detail photos), iv) morphology (plant height, number of leaves, leaves size), and v) biomass and density (biomass, plant water content, LAI). Field observations and measurements of crop parameters were carried out over 30 fields (14 for rice, 11 for maize, 5 for soybean), on 14 dates spanning over three years: 2014, 2015 and 2016. Contextually, the acquisition of TerraSAR-X Stripmap dual-pol HH/HV (2014-2015) and VV/HH (2016) scenes was planned and carried out in the context of TSX scientific proposals LAN2412 (2014), LAN2984 (2015) and LAN3228 (2016). TSX images acquired for HH, VV, and HV polarizations were radiometrically calibrated, including multilooking (7R x 4A) and terrain correction, to obtain sigma nought (?°) maps in different polarizations. The correlation of ?°HH, VV and HV with measured biophysical parameters was assessed using experimental data collected on test fields. Analysis of preliminary results led to some interesting remarks. Specifically, we observed a significant sensitivity of ?°HH to fresh and dry biomass, with different behaviour for the different crops. Rice ?°HH and VV derived from TSX data show a sensible decrement over the whole range of biomass values, while a different trend is observed for maize, with a first steep increment of ?°HH during the initial biomass growth phase, followed by a flat trend for values above 2.0 kg m-2. A reduced sensitivity to biomass changes has been observed for HV polarization case. According to these preliminary analyses, backscatter over soybean fields does not show a significant relationship with the measured parameters. A radiative transfer model (Paloscia et al., 2014) was used in order to evaluate the contribution of different components of the vegetation-soil system to total backscattering under different observation

Since fire is a major threat to forests and wooded areas in the Mediterranean environment of Southern Europe, systematic regional fire monitoring is a necessity. Satellite data constitute a unique cost-effective source of information on the occurrence of fire events and on the extent of the area burned. Our objective is to develop a (semi-)automated algorithm for mapping burned areas from medium spatial resolution (30 m) satellite data. In this article we present a multi-criteria approach based on Spectral Indices, soft computing techniques and a region growing algorithm; theoretically this approach relies on the convergence of partial evidence of burning provided by the indices. Our proposal features several innovative aspects: it is flexible in adapting to a variable number of indices and to missing data; it exploits positive and negative evidence (bipolar information) and it offers different criteria for aggregating partial evidence in order to derive the layers of candidate seeds and candidate region growing boundaries. The study was conducted on a set of Landsat TM images, acquired for the year 2003 over Southern Europe and pre-processed with the LEDAPS (Landsat Ecosystem Disturbance Adaptive Processing System) processing chain for deriving surface spectral reflectance ρ i in the TM bands. The proposed method was applied to show its flexibility and the sensitivity of the accuracy of the resulting burned area maps to different aggregation criteria and thresholds for seed selection. Validation performed over an entire independent Landsat TM image shows the commission and omission errors to be below 21% and 3%, respectively.

The lakes of the European perialpine region constitute a large water reservoir, which is threatened by the anthropogenic pressure altering water quality. The Water Framework Directive of the European Commission aims to protect water resources and monitoring is seen as an essential step for achieving this goal. Remote sensing can provide frequent data for large scale studies of water quality parameters such as chlorophyll-a (chl-a). In this work we use a dataset of maps of chl-a derived from over 200 MERIS (MEdium Resolution Imaging Spectrometer) satellite images for comparing water quality of 12 perialpine lakes in the period 2003-2009. Besides the different trophic levels of the lakes, results confirm that the seasonal variability of chl-a concentration is particularly pronounced during spring and autumn especially for the more eutrophic lakes. We show that relying on only one sample for the assessment of lake water quality during the season might lead to misleading results and erroneous assignments to quality classes. Time series MERIS data represents a suitable and cost-effective technology to fill this gap, depicting the dynamics of the surface waters of lakes in agreement with the evolution of natural phenomena.

Precise phenological calendars, for each cultivated species and variety, are necessary both to highlight anomalous agronomic situations and to feed crop models. This study, conducted in the Italian rice area, focuses on the evaluation of the contribution of remote sensing satellite data to providing phenological information on rice cropping systems. A time series of 5 years (2001–2005) of the Moderate Resolution Imaging Spectrometer (MODIS) Normalized Difference Vegetation Index (NDVI) 16-day composites was analysed with the TIMESAT program in order to automatically retrieve key phenological information such as the start of season (emergence), peak (heading) and end of season (maturity). The procedure involved two steps: (1) interpolation and smoothing of MODIS NDVI temporal profile and (2) the analysis of a temporal signal for the extraction of the phenological metrics. The remote sensing estimates were evaluated using information regarding cultivated variety, sowing dates, management and production directly acquired from rice farmers. A good correlation (R2 = 0.92, n = 24) has been observed between estimates derived from satellites and estimates produced with the traditional Growing Degree Days (GDD) method based on thermal unit accumulation. Improved estimates of the maturity stage were obtained using a procedure that integrates satellite and GDD methods; however its application requires spatially distributed information on the cultivated varieties. Satellite derived maps of the retrieved phenological parameters showed an intra-seasonal pattern related to different cultivated varieties. Inter-seasonal analysis allowed the anomalous behaviour of the year 2003 to be highlighted, characterized by rapid growth at the beginning of the spring and an early senescence. The results confirm the potential of remotely sensed data for the monitoring of crop status and for the forcing of crop models in a spatially distributed way.

Remotely sensed and field data have been used to evaluate and monitor reed vigour in three lakes (Garda, Mantova, Trasimeno) and one brackish wetland (Goro) in Italy.Results show that reed vegetation is highly productive (high NDVI) although local factors, such as lake water level (Trasimeno), the presence of brackish waters (Goro) or the presence of eutrophic waters (Mantova) can influence reed growth and vigour. In the case of Lake Garda we evaluated the effect of winter mowing and results confirmed that management had no detrimental effect on reed vigour although its effect is often inter-related with other local factors.

Remote sensing techniques can provide quantitative information on vegetation conditions such as Leaf Area Index and Nitrogen concentration. In this work we evaluate the potentiality of radiometric measurements for the prediction of rice Plant Nitrogen Concentration (PNC): we tested all possible wavelength combinations in the visible/Shortwave infrared region of the spectrum to derive a Normalized Difference Index (NDI) correlated to PNC. The NDI (λ 1 =503 nm, λ 2 =483 nm) was compared to traditional indices and found to perform better and to be least affected by structural parameters (LAI and biomass). An empirical NDIPNC model was calibrated and validated with independent data (R 2 =0.53).

UAVs platforms are promising for agricultural monitoring since they offer operating flexibility, very high spatial resolution and acquisition costs suitable for frequent on demand monitoring of crop field. In this work we carried out an experimental flight over a rice field in Lombardy region, northern Italy, to test the correlation between reflectance in the spectral channels and vegetation indices derived from imagery acquired with a multi-spectral sensor on board an UAV. Results show that UAV images can be used to map the within-field spatial variability and crop yield (R2~0.42-0.54 between NIR reflectance and/or spectral VIs and rice grain yield) and can successfully complement more traditional technologies for precision farming applications.

Crop monitoring services require the capability to investigate vegetation condition, especially in regions periodically affected by low or erratic rainfalls as recently happened in northern Italy. This paper define an operational methodology based on the "triangle method" to estimate Evaporative Fraction as an indicator of surface moisture condition. The approach, that relies on low resolution MODIS products and air temperature maps, it was applied to the 2010-2014 growing seasons deriving instantaneous maps to represent daily crop conditions. The qualitative assessment of the results shows the agronomical coherence of the estimated variable, and the preliminary multi-annual analyses indicates the approach as a promising tool to support near real time crop monitoring at regional scale.

International audience; Plant diseases are responsible for major economic losses in the agricultural industry worldwide. Monitoring plant health and detecting pathogen early are essential to reduce disease spread and facilitate effective management practices. DNA-based and serological methods now provide essential tools for accurate plant disease diagnosis, in addition to the traditional visual scouting for symptoms. Although DNA-based and serological methods have revolutionized plant disease detection, they are not very reliable at asymptomatic stage, especially in case of pathogen with systemic diffusion. They need at least 1–2 days for sample harvest, processing, and analysis. Here, we describe modern methods based on nucleic acid and protein analysis. Then, we review innovative approaches currently under development. Our main findings are the following: (1) novel sensors based on the analysis of host responses, e.g., differential mobility spectrometer and lateral flow devices, deliver instantaneous results and can effectively detect early infections directly in the field; (2) biosensors based on phage display and biophotonics can also detect instantaneously infections although they can be integrated with other systems; and (3) remote sensing techniques coupled with spectroscopy-based methods allow high spatialization of results, these techniques may be very useful as a rapid preliminary identification of primary infections. We explain how these tools will help plant disease management and complement serological and DNA-based methods. While serological and PCR-based methods are the most available and effective to confirm disease diagnosis, volatile and biophotonic sensors provide instantaneous results and may be used to identify infections at asymptomatic stages. Remote sensing technologies will be extremely helpful to greatly spatialize diagnostic results. These innovative techniques represent unprecedented tools to render agriculture more sustainable and safe, avoiding expensive use of pesticides in crop protection.

The application of an integrated monitoring tool to assess and understand the effects of annually occurring forest fires is presented, with special emphasis to Mediterranean and Temperate Continental zones of Europe. The distinctive features of the information conveyed by optical and microwave remote sensing data have been firstly investigated, and pertinent information have been subsequently combined to identify burned areas at the regional scale. We therefore propose a fuzzy-based multisource framework for burned area mapping, in order to overcome the limitations inherent to the use of only optical data (which can be severely affected by cloud cover or include low albedo surface targets). The relevant experimental validation has been carried out on an extensive area, thus quantitatively demonstrating how our approach successes in identifying areas affected by fires. Furthermore, the proposed methodological framework can also be profitably applied to Sentinel (optical and SAR) data.

Presentazione sull'utilizzo dei droni per il monitoraggio della vegetazione e delle colture agricole. La presentazione è stata inserita nella serie di interventi relativi a "Il monitoraggio ambientale mediante i droni".

The determination of sample size before collecting experimental data is fundamental to obtain reliable estimates of variables describing agroecosystem development. In order to analyze the influence of experimental factors (artificially-induced variability) on rice sample size, an experiment was carried out in 2004 in northern Italy. In particular, different sample size determinations were carried out for different fertilization levels, varieties (Indica and Japonica type), development stages, sowing techniques and typologies of the sampling unit. The obtained sample sizes were compared to investigate the influence of each factor, keeping the others constant (for example, we have compared the sample sizes computed for different fertilization levels within the same variety, the same phenological stage and the same sampling unit). Since original data were often not normally distributed and the variances of the original samples were not homogeneous, a new approach for sample size determination based on a visual evolution of the jackknife was preferred to classical techniques. Results (expressed as number of plants) showed that (i) sample sizes computed in an early phenological stage (between 21 and 27) are higher than those calculated for later stages (15–21); (ii) fertilization hides soil N content variability with the consequence that larger sample sizes are required for unfertilized plots (21–27) compared to fertilized plots (15–27) and (iii) for the early sampling, the Indica type variety required larger sample size (always 27) with respect to the Japonica type variety (21–24). For row-seeded rice, the number of plants instead of linear centimeters as the sampling unit led to lower sample sizes (18–27 versus 30–33). These results highlight the influence of experimental factors and development stage on within-plot variability, and therefore the importance of preliminary samplings for sample size determination.

An algorithm to map burnt areas has been developed for SPOT VEGETATION (VGT) data in Australian woodland savannas. A time series of daily VGT images (15 May to 15 July 1999) was composited into 10-day periods by applying a minimum value criterion to the near-infrared band (0.78-0.89 @m). The algorithm was developed using a classification tree methodology that was confirmed as a powerful means of image classification. This methodology allowed the identification of three classes of burnt surfaces that appear to be differentiated by the proportion of the pixel that is burnt, the intensity of the fire and the density of the tree layer. The performance of the algorithm was assessed by classification of one VGT composite image (31 May-9 June) using, as representative of the ground truth, burnt areas extracted from two Landsat TM scenes (9 June). We randomly extracted 30 windows (each of ∼14 km by 14 km) for which we compared the percentage of area burnt as derived from TM and VGT. The estimated mean absolute de...

The main objective of this study is to evaluate several algorithms to produce monthly composite images of the VEGETATION sensor onboard SPOT-4 over the Brazilian Amazon. The ability of the commonly used Normalized Difference Vegetation Index maximum value composite (MNDVI) and other compositing algorithms (i.e. one- and/or two-step algorithms), in terms of reducing the presence of clouds and cloud shadows and assessing spatial coherence of the composite images was tested. Among the one-step algorithms, the Soil Adjusted Vegetation Index maximum value composite (MSAVI), the minimum value composite of the red band (mRed) and the minimum value composite of the red band with an additional temporal persistence condition (mRedtp) were also analysed. The two-step algorithms included MNDVI or MSAVI followed by the minimum value of the viewing zenith angle, and mRed followed by MNDVI or by MSAVI. These eight compositing algorithms were used to produce monthly (August 2000) composite images of four regions (200 km ...

Radiometric analysis of SPOT-VEGETATION (VGT) images acquired over Australia was carried out as a basis for the development of an algorithm to map burnt areas in woodland savannas. We analysed the variability of daily ground reflectance and its relationship with illumination and viewing geometry. Finding that the geometrical effects can be parameterised by the phase angle (angle between the illumination and the viewing directions) and the viewing zenith angle (VZA), we fit a simple linear model to the observations. The results show that about 60–70% of the variability in the daily reflectance is caused by geometrical effects. The residual 30–40% of the variability is probably due to changes in vegetation condition, such as senescence, and residual atmospheric contamination. We tested temporal compositing as a practical method of reducing the variability in the reflectance whilst retaining the burnt area signal. We inspected the radiometric and geometrical effects of four different compositing criteria and showed that minimum near infrared (NIR) is the most appropriate for burnt area mapping over the study area. In order to analyse the sensitivity of the VGT spectral bands and derived indices to changes induced by fire, we extracted burnt area spectral signatures for different vegetation types. The persistence of the burnt signal, as observed with each band and index, was analysed. Among the bands, NIR is shown to be the most sensitive to fire occurrence. There is a clear drop in the reflectance immediately after the fire and it remains very low during subsequent weeks. On the other hand, the burnt signal in the short-wave infrared (SWIR) band is showed to be strongly dependent on the vegetation cover type and on the age of the burnt area. Among the indices, the Global Environment Monitoring Index (GEMI) is identified as the most suitable for detecting changes induced by fire on the vegetation cover.

This paper describes a mapping project carried out using both optical and SAR data on an agricultural area in northern Italy where the main crops are corn, rice and wheat. Temporal trends of backscatter and reflectance, given by the variations in vegetation growth, soil conditions and agricultural practices were analyzed and interpreted thanks to the ground-measured data. Information extracted from both optical and SAR data (vegetation indices, backscatter and texture features) were used to create training sets for implementing three different classification approaches. The work aimed at comparing early crop maps with maps derived at the end of the season. Results show that the classification accuracy obtained using only multispectral optical data is higher than the one reached using only SAR as input. Integrating both optical and SAR multitemporal features provides some advantages in terms of a more reliable crop map, especially during an early temporal stage scenario. Among the supervised algorithms tested, Maximum Likelihood shows the best overall accuracy performances at each thematic level, time step and using both optical and SAR input data.

Intervento al Convegno Internazionale "BERGAMO S-LOW AGRI AND GREEN SPACES IN THE CITY", tenutosi a Bergamo l'11 ottobre 2014, su "Tecnologie e Agricoltura: l'esperienza nel progetto Space4Agri applicata al caso di Bergamo". La presentazione è stata curata da Gloria Bordogna in collaborazione con Sandro Brivio, Mirco Boschetti, Paola Carrara, Alberto Crema, Luca Frigerio, Tomas Kliment, Alba L'Astorina, Alessandro Oggioni, Monica Pepe, Anna Rampini, Daniela Stroppiana, Irene Tomasoni, Paolo Villa.

Global active fire maps have been produced over a 21-month period from April 1992 to December 1993. A contextual active fire detection algorithm has been applied to the NOAA AVHRR (National Oceanic and Atmospheric Administration Advanced Very High Resolution Radiometer) 1.1 km images provided by the IGBP-DIS (International Geosphere-Biosphere Programme Data and Information System) 1 km AVHRR Global Land Project data set. The Global Fire Product (GFP) is composed of daily fire position tables, 10-day synthesis raster format maps containing fire density and cloud/no-data information; it is now available as the first global scale description of the spatial and temporal distribution of active vegetation fire. In answer to science community requirements the GFP supplies information which can be used to estimate fire impacts on atmospheric chemistry, climate, land use and land cover changes.

This paper presents the first published time series of burned area maps of Africa, covering an 8 year period, 1981–1983 and 1985–1991. These maps were derived from the analysis of the advanced very high resolution radiometer (AVHRR) global area coverage (GAC) images at 5 km resolution. The burned area maps for the period 1985–1991 were used with biomass density and burning efficiency figures, to estimate the quantity of burned biomass during this 6 year period. Emission factors were further used to estimate the trace gas and aerosol emissions produced by vegetation fires. Biomass density was estimated based on values found in the literature and on the accumulated normalized difference vegetation index (NDVI) as derived from the remote sensing images. Burning efficiency was assessed with a dryness index that was based on the relative greenness index (RGI), also derived from the NDVI. Average emission factors were retrieved from the literature. The uncertainties in the burned area, biomass density, combustion efficiency, and emission factors were considered, with a total error of 51% for the burned biomass and 58% for the emission estimates. The results obtained for the burned biomass in Africa were compared with other values found in the literature and showed values lower by a factor of 1.1–3.3. The annual burned biomass from vegetation fires in Africa on average was estimated between 704 and 2168 Tg . In the same way, the atmospheric emissions on average ranges are as follows: CO2 (990–3726 Tg), CO (40–151 Tg), CH4 (1.2–4.4 Tg), NOx (2.8–10.6 Tg), and PM (< 2.5 μm) (3.3–12.4Tg).

Fire activity in central Africa was monitored with NOAA advanced very high resolution radiometer (AVHRR) satellite imagery, acquired in situ during the 1996 dry season campaign of the Experiment for Regional Sources and Sinks of Oxidants (EXPRESSO). The extent of the area affected by fire was estimated with a contextual active fire detection algorithm, and with a burnt area mapping approach, based on multiple fuzzy thresholds. The latter was considered to produce more accurate results. In a study area of 2×106 km2, and during a 5-week period, the areas affected were estimated at 112,578 and 525,820 km2 by the active fires and burnt area algorithms, respectively. Biomass densities, combustion factors, and emissions factors for Sudanian savanna, Guinean savanna, and dense tropical forest vegetation types were obtained from the literature, and used to estimate biomass burnt (228–371 Tg), and pyrogenic emissions of aerosols (1.8–2.6 Tg) and of the trace gases CO2 (374–609 Tg), CO (29.2–39.0 Tg), CH4 (2.05–2.73 Tg), and NOx (1.1–1.4 Tg). Because of its high biomass density, the tropical forest was a major source of atmospheric emissions, in spite of the relatively small extent of area burnt in this ecosystem. This highlights the need for particularly accurate estimates of area burnt, biomass density, combustion factor, and emissions factor for the dense tropical forest, as well as the potential for a significant increase in regional pyrogenic emissions as a consequence of deforestation.

Mediterranean forests are every year affected by wildfires which have a significant effect on the ecosystem. Mapping burned areas is an important field of application for optical remote sensing techniques and several methodologies have been developed in order to improve mapping accuracy. We developed an automated procedure based on spectral indices and fuzzy theory for mapping burned areas from atmospherically corrected Landsat TM images. The algorithm proved to provide consistent accuracy over Mediterranean areas. We further tested algorithm's performance to assess the influence of the atmospheric correction on the accuracy of burned areas. In particular, we ran the Second Simulation of a Satellite Signal in the Solar Spectrum (6S) code with different Atmospheric Optical Thickness (AOT) levels and two aerosol models (continental and maritime) on one TM image acquired over Portugal (12/08/2003). Burned area maps derived from atmospherically corrected images and from the non corrected image (Top Of Atmosphere, TOA) have been analyzed. In the output burned areas maps the omission error varies in the range 4.6-6.5% and the commission error fluctuates between 11.9 and 22.2%; the highest omission (commission) errors occur with the continental (maritime) model. The accuracy of burned area maps derived from non corrected image is very low, with omission error greater than 90%. These results show that, although atmospheric correction is needed for the application of the algorithm, the AOT value does not significantly affect the performance.