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Optical remote sensing of lakes: an overview on Lake Maggiore

Claudia Giardino, Mariano Bresciani, Daniela Stroppiana, Alessandro Oggioni, Giuseppe Morabito
2013 Journal of Limnology  
Optical satellite remote sensing represents an opportunity to integrate traditional methods for assessing water quality of lakes: strengths of remote sensing methods are the good spatial and temporal coverage, the possibility to monitor many lakes simultaneously and the reduced costs. In this work we present an overview of optical remote sensing techniques applied to lake water monitoring. Then, examples of applications focused on Lake Maggiore, the second largest lake in Italy are discussed by
more » ... presenting the temporal trend of chlorophyll-a (chl-a), suspended particulate matter (SPM), coloured dissolved organic matter (CDOM) and the z90 signal depth (the latter indicating the water depth from which 90% of the reflected light comes from) as estimated from the images acquired by the Medium Resolution Imaging Spectrometer (MERIS) in the pelagic area of the lake from 2003 to 2011. Concerning the chl-a trend, the results are in agreement with the concentration values measured during field surveys, confirming the good status of Lake Maggiore, although occasional events of water deterioration were observed (e.g., an average increase of chl-a concentration, with a decrease of transparency, as a consequence of an anomalous phytoplankton occurred in summer 2011). A series of MERIS-derived maps (summer period 2011) of the z90 signal are also analysed in order to show the spatial variability of lake waters, which on average were clearer in the central pelagic zones. We expect that the recently launched (e.g., and the future satellite missions (e.g., Sentinel-3) carrying sensors with improved spectral and spatial resolution are going to lead to a larger use of remote sensing for the assessment and monitoring of water quality parameters, by also allowing further applications (e.g., classification of phytoplankton functional types) to be developed.
doi:10.4081/jlimnol.2014.817 fatcat:unogad6xtfeopdqqc4zdnqokmm

Satellite Remote Sensing of Chlorophyll-a inSubalpine Italian Lakes in the last 15 Years

Mariano Bresciani, Claudia Giardino, Daniela Stroppiana, Ilaria Cazzaniga
2018 Zenodo  
Poster at ells-iaglr-2018 Subalpine Lakes constitute an environment for ecosystem (flora and fauna),tourism,bathing, intake of drinking water, and agriculture. Due to the increasing demand for freshwater,the effect of climate change and the anthropogenic pressure on natural resources, their water quality is in danger. Therefore, there is an increasing need for regular long term monitoring of lake waters as well as to support national and international directives and conventions such as the
more » ... ean Water Framework Directive (WFD).Within this study, Earth Observation (EO) data are used for acquiring timely, frequent synoptic information of main Subalpine lakes in norther Italy with a focus on chlorophyll-a(Chl-a).
doi:10.5281/zenodo.3601107 fatcat:fn65fnbldjapxnuhwg3rn6u2ce

A Scalable Synthesis of Multiple Models of Geo Big Data Interpretation

Alessia Goffi, Gloria Bordogna, Daniela Stroppiana, Mirco Boschetti, Pietro Alessandro Brivio
2020 Journal of Software Engineering and Applications  
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
more » ... rning 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.
doi:10.4236/jsea.2020.136008 fatcat:au5jn7uwp5cgxojmrcbkrtyjiu

Integration of Optical and SAR Data for Burned Area Mapping in Mediterranean Regions

Daniela Stroppiana, Ramin Azar, Fabiana Calò, Antonio Pepe, Pasquale Imperatore, Mirco Boschetti, João Silva, Pietro Brivio, Riccardo Lanari
2015 Remote Sensing  
Author Contributions Daniela Stroppiana supervised research and algorithm development, carried out the analyses, and prepared the manuscript.  ...  As for the spectral indices in Stroppiana et al.  ... 
doi:10.3390/rs70201320 fatcat:yk3sftl2u5hrtp7mkim5j4rsmi

A Weekly Indicator of Surface Moisture Status from Satellite Data for Operational Monitoring of Crop Conditions

Francesco Nutini, Daniela Stroppiana, Lorenzo Busetto, Dario Bellingeri, Chiara Corbari, Marco Mancini, Enrico Zini, Pietro Brivio, Mirco Boschetti
2017 Sensors  
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
more » ... ity of satellite based EF estimations as well as the robustness of input data. Weekly syntheses of the daily EF indicator (EF w ) were then derived at regional scale for the years 2010, 2011 and 2012 as a proxy of overall surface moisture condition. EF w 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 EF w 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. 2 of 24 distributed information at province/regional scale can hardly be extrapolated due to heterogeneity of land surface and complexity of the hydrological processes [3]. Since Remote Sensing (RS) data can provide a large variety of information on crop status and surface hydrological conditions, which are of key importance to highlight potential criticalities to support water planning and provide quantitative data for management [4,5], RS techniques are recognized as the only viable means to map ET at regional scale in a consistent and economically feasible way [6]. Instantaneous values of ET at satellite overpass can be used as diagnostics for surface status [7], or as controls for hydrological models through assimilation schemes [8]. Several approaches have been developed to estimate ET and/or indicators of water stress, spanning from simple empirical methods to more complex energy balance models, as fully described in literature reviews [9][10] [11] [12] . The Triangle Method: A Short Review on Past Applications and Recent Improvements Among the available RS-based approaches for ET analysis, the triangle method, based on the work of Price in the early 1990s [13] and later elaborated by Jiang and Islam [14] [15] [16] , has been widely exploited to estimate the Evaporative Fraction (EF), which is the ratio between the latent heat flux and the total available energy at canopy surface [5, 17] . EF is both a key parameter to estimate ET as well as a direct indicator of surface moisture conditions [18] and water stress itself [19, 20] . This approach, regarded as a simplification of more complex models such as the Surface Energy Balance Algorithm for Land (SEBAL) [21] , is suitable for large area monitoring of surface moisture conditions [14, 22] . Among its advantages are the simple parameterization/calibration and its reliance on operational satellite data [6, 20, 23] , which allow to spatially explicitly estimate EF in near real-time over large areas. In its original formulation, the triangle method builds on the triangular shape of the scatter plot of remotely sensed surface/canopy temperature (T s ) versus a Vegetation Index (VI) such as the Normalized Difference Vegetation Index (NDVI) [10, 24] . This scatterplot is used to compute the so-called wet and dry edges: the wet edge corresponds to areas where EF is at maximum (i.e., maximum evapotranspiration) whereas the dry edge corresponds to areas where the EF is close to zero (i.e., limited water availability) [23] . Given two co-registered raster images containing temperature and VI value, EF can then be estimated for each pixel based on its relative position with respect to the two edges (see Section 3.1 for details). Alternative formulations of the method were also proposed in later studies. In particular, using the difference between air surface and air temperature (∆T = T s − T a ) instead than simply T s to build the aforementioned scatterplot was proposed by Moran et al. [25] and Jiang and Islam [16] , and successively adopted by numerous studies (e.g., [26] [27] [28] [29] ). Additionally, other RS-derived indicators of vegetation characteristics (e.g., fractional cover, albedo, etc.) were proposed as an alternative to NDVI for computing the dry and wet edges [6, 30] . The triangle method has been exploited starting from data acquired by several satellite platforms/sensors, among which NOAA-Advanced Very High Resolution Radiometer (AVHRR) [31], Meteosat Second Generation-Spinning Enhanced Visible and Infrared Imager (MSG SEVIRI) [32], NASA Moderate Resolution Imaging Spectroradiometer (MODIS) [2], Envisat-Advanced Along-Track Scanning Radiometer (AATSR) and Medium Resolution Imaging Spectrometer (MERIS) [23], Landsat Thematic Mapper/Enhanced Thematic Mapper (TM/ETM+) [23] as well as airborne sensors [28]. One of its main advantages is its suitability for hydrological studies where field data availability is limited or missing. Thanks to this, it was implemented in operational monitoring systems such as the EVapotranspiration Assessment from SPAce (EVASPA) tool [33], exploited to derive the Temperature Vegetation Dryness Index (TVDI) [25] used for vegetation water stress detection [34-37] and soil moisture estimation [38,39] in semi-arid, temperate and tropical areas [18] and used to improve the performances of hydrological models [40]. Nevertheless, it is important to remind that several issues should be addressed for a full exploitation of satellite based EF/ET estimations. Sensors 2017, 17, 1338 3 of 24 A first assumption of the method is that incoming energy, aerodynamic properties and atmospheric conditions have to be reasonably uniform over the area at the time of satellite overpass [2,23,32] and the AOI should be as flat as possible in order to identify a proper triangular shape in the pixel distribution [24] . Tave et al. [41] recently tried to solve this requirement by improving the triangle method to compute EF for different elevation zones rather than keeping the estimation of flat area only. Second, a key issue for applying the triangle method is the proper computation of the wet and dry edges, which influences the accuracy of EF estimation [6]. The method can therefore be applied only under specific conditions, in relation to study area characteristics and EO data exploited, and a proper data handling is needed to retrieve reliable estimates. In particular: (i) a large range of combined soil moisture status and vegetation characteristics must be present in the study area to represent a wide range of evapotranspiration conditions [37, 42] , and (ii) this variability must be properly captured by the RS data used. These conditions influence the required extension of the area of interest (AOI) analyzed, which should be sufficiently large [43] to allow the construction of reliable scatterplots as a function of the spatial resolution of the EO data used. According to previous studies, the triangular shape depends more on the number of pixels, rather than on the spatial resolution [24, 44] . Hence, for small AOIs (e.g.,~4000 km 2 ) HR satellite data may allow obtaining more accurate EF estimations. On the other hand, while working with satellite data at coarse resolution the AOI should be bigger since a larger area is needed for an appropriate definition of the triangle shape [23] . If these conditions are not completely fulfilled, the shape of the temperature vs. VI scatterplot may be "flawed" (i.e., not triangular at all), leading the estimated dry and wet edges to be far from the theoretical ones, and therefore to large inaccuracies in EF estimation [32] . To deal with the aforementioned problem, many authors proposed modifications and advancements to the "standard" method allowing a better and more stable computation of the edges. Tang et al. [6] proposed a specific pre-processing technique for identifying outliers in the temperature vs. VI scatterplot space and removing them before the computation of the edges. De Tomas et al. [23] demonstrated how this approach can improve EF estimates when HR data (e.g., Landsat like) are used. Maltese et al. [28] compared scatterplots derived from different dates suggesting that, to cope with the uncertainness in the dry and wet edges identification, a multi-temporal analysis should be exploited to include outer extremes in soil water content. Minacapilli et al. [2] further developed this idea building the temperature vs. VI scatterplot in the temporal domains for each pixel of the AOI using all available dates; application of this approach resulted in an accurate estimation of ET at regional scale. Other recent contributions determined the theoretical edges on the base of the surface energy balance principle rather than by identifying the boundaries empirically in the data space; EF is then estimated for each pixel using the observed temperature vs. VI scatterplot [22, 32] . Comparison with in situ data of soil moisture and EF revealed that this approach could increase the accuracy of the triangle method. However, its implementation requires a more complex parameterization and requires input field data not be always available over the AOI. Another important issue discussed in literature is whether the output of the triangle method can be considered representative of the daily condition or the instantaneous momentum. Most approaches based on satellite data assumed EF to be relatively constant in daytime ("self-preservation of EF" [23]), so that satellite-based instantaneous EF estimates can be representative of daily conditions [30] (see Section 3.1 for details). Nevertheless, for some scientific applications it is necessary to more rigorously upscale the instantaneous estimation to daily conditions taking into account the diurnal cycle using exogenous information such as meteo data [45] . To perform this daily upscaling, Trezza [46] proposed to multiply the instantaneous EF by the daily reference ET computed with FAO approach [47] while Ryu et al. [48] exploited the variations of the daily extraterrestrial solar radiation. Finally, Tang et al. [49] recently presented a promising approach for upscaling, based on half-hourly meteo data (i.e., air temperature, Sensors 2017, 17, 1338 4 of 24
doi:10.3390/s17061338 pmid:28598399 pmcid:PMC5492004 fatcat:k73upfpdsndmzdwme7msjhrcsq

A Burned Area Mapping Algorithm for Sentinel-2 Data Based on Approximate Reasoning and Region Growing

Matteo Sali, Erika Piaser, Mirco Boschetti, Pietro Alessandro Brivio, Giovanna Sona, Gloria Bordogna, Daniela Stroppiana
2021 Remote Sensing  
This confirms previous findings obtained with Landsat images by Stroppiana et al. (2012) [26] .  ...  The algorithm inherits from the conceptual framework of the multi-criteria soft aggregation approach of burn evidence proposed by Stroppiana et al. (2012) [26] for burned area mapping and also applied  ... 
doi:10.3390/rs13112214 fatcat:mbvogwp5b5htvjgpedmupumr3m

Early season weed mapping in rice crops using multi-spectral UAV data

Daniela Stroppiana, Paolo Villa, Giovanna Sona, Giulia Ronchetti, Gabriele Candiani, Monica Pepe, Lorenzo Busetto, Mauro Migliazzi, Mirco Boschetti
2018 International Journal of Remote Sensing  
CONTACT Daniela Stroppiana Institute for Electromagnetic Sensing of the Environment (IREA), Consiglio Nazionale delle Ricerche (CNR), Via E.  ...  Stroppiana et al. 2015) .  ... 
doi:10.1080/01431161.2018.1441569 fatcat:kki2ehdx2nfzdnsr6ckszqfqcu

Advanced methods of plant disease detection. A review

Federico Martinelli, Riccardo Scalenghe, Salvatore Davino, Stefano Panno, Giuseppe Scuderi, Paolo Ruisi, Paolo Villa, Daniela Stroppiana, Mirco Boschetti, Luiz R. Goulart, Cristina E. Davis, Abhaya M. Dandekar
2014 Agronomy for Sustainable Development  
et al.. Advanced methods of plant disease detection. A review. Agronomy for Sustainable Development, Springer Verlag/EDP Sciences/INRA, 2015, 35 (1), pp. Abstract 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,
more » ... n 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 syste mic 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.
doi:10.1007/s13593-014-0246-1 fatcat:sdvsemwsire5lftrc242mzxyai

Assessing remotely sensed chlorophyll-a for the implementation of the Water Framework Directive in European perialpine lakes

Mariano Bresciani, Daniela Stroppiana, Daniel Odermatt, Giuseppe Morabito, Claudia Giardino
2011 Science of the Total Environment  
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
more » ... 200 MERIS (MEdium Resolution Imaging Spectrometer) satellite images for comparing water quality of 12 perialpine lakes in the period [2003][2004][2005][2006][2007][2008][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.
doi:10.1016/j.scitotenv.2011.05.001 pmid:21632091 fatcat:pcflrlxmmjajbeim2bi7urpdsa

In-Season Mapping of Crop Type with Optical and X-Band SAR Data: A Classification Tree Approach Using Synoptic Seasonal Features

Paolo Villa, Daniela Stroppiana, Giacomo Fontanelli, Ramin Azar, Pietro Brivio
2015 Remote Sensing  
Paolo Villa and Daniela Stroppiana prepared and wrote the manuscript, and all the authors revised it.  ...  Paolo Villa, Daniela Stroppiana, Giacomo Fontanelli, and Pietro Alessandro Brivio carried out the analysis of results.  ... 
doi:10.3390/rs71012859 fatcat:p65v7sbyobfb7gbvox3apqjc64

A Fully Automatic, Interpretable and Adaptive Machine Learning Approach to Map Burned Area from Remote Sensing

Daniela Stroppiana, Gloria Bordogna, Matteo Sali, Mirco Boschetti, Giovanna Sona, Pietro Alessandro Brivio
2021 ISPRS International Journal of Geo-Information  
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
more » ... s (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.
doi:10.3390/ijgi10080546 fatcat:d4qgbgwfgbcmnlzvs6vjmgt6w4

A Spatial Data Infrastructure Integrating Multisource Heterogeneous Geospatial Data and Time Series: A Study Case in Agriculture

Gloria Bordogna, Tomáš Kliment, Luca Frigerio, Pietro Brivio, Alberto Crema, Daniela Stroppiana, Mirco Boschetti, Simone Sterlacchini
2016 ISPRS International Journal of Geo-Information  
Pietro Alessandro Brivio was the leader of the S4A project, and contributed together with Daniela Stroppiana and Mirco Boschetti to define the use cases of both the S4A Mobile App and S4A geoportal, while  ... 
doi:10.3390/ijgi5050073 fatcat:fxecdixot5ddhgfjtbxutpixji

Exploitation of SAR and Optical Sentinel Data to Detect Rice Crop and Estimate Seasonal Dynamics of Leaf Area Index

Manuel Campos-Taberner, Francisco García-Haro, Gustau Camps-Valls, Gonçal Grau-Muedra, Francesco Nutini, Lorenzo Busetto, Dimitrios Katsantonis, Dimitris Stavrakoudis, Chara Minakou, Luca Gatti, Massimo Barbieri, Francesco Holecz (+2 others)
2017 Remote Sensing  
This paper presents and evaluates multitemporal LAI estimates derived from Sentinel-2A data on rice cultivated area identified using time series of Sentinel-1A images over the main European rice districts for the 2016 crop season. This study combines the information conveyed by Sentinel-1A and Sentinel-2A into a high-resolution LAI retrieval chain. Rice crop was detected using an operational multi-temporal rule-based algorithm, and LAI estimates were obtained by inverting the PROSAIL radiative
more » ... ransfer model with Gaussian process regression. Direct validation was performed with in situ LAI measurements acquired in coordinated field campaigns in three countries (Italy, Spain and Greece). Results showed high consistency between estimates and ground measurements, revealing high correlations (R 2 > 0.93) and good accuracies (RMSE < 0.83, rRMSE m < 23.6% and rRMSE r < 16.6%) in all cases. Sentinel-2A estimates were compared with Landsat-8 showing high spatial consistency between estimates over the three areas. The possibility to exploit seasonally-updated crop mask exploiting Sentinel-1A data and the temporal consistency between Sentinel-2A and Landsat-7/8 LAI time series demonstrates the feasibility of deriving operationally high spatial-temporal decametric multi-sensor LAI time series useful for crop monitoring. System (GTOS) and the Global Climate Observing System (GCOS) [1] . Defined as half of the total green leaf area per unit ground surface area [2], LAI plays a key role in land surface models [3] and can be used to address various agricultural issues, such as rice crop monitoring, yield forecasting and crop management [4] . Crop monitoring is necessary to identify the onset of stress conditions, which require actions for reducing their impact on crop yield. Anomaly drops in canopy LAI are indicators of plant stress conditions, which could lead to a decrease of plant production and to an increased senescence rate [5] . Rice yield forecasting is a crucial task for management and planning, and it can be addressed with both statistical and mathematical modeling approaches. Statistical approaches directly link crop biophysical variables, such as LAI, to crop yield. LAI is indeed recognized as the major morphological parameter for determining crop growth, and it is strongly correlated with crop productivity [6] . Crop models are able to simulate rice growing and are used to provide indications on crop status and to predict yields over large areas [7, 8] . However, crop models require information on soil, meteorological variable, crop parameters and management practices, which are not always available or practical to be obtained in a spatially distributed way and during the season [9] . Hence, the best way to simulate the real spatial-temporal differences in crop development of fields sowed the same day with the same variety is to assimilate exogenous observation, such as LAI maps, in the modeling solution [10]. Accurate estimation of LAI has been shown to improve the accuracy of grain yield estimates [11] , and an operational application of this workflow for rice was successfully demonstrated in Asia in the framework of the RIICE (Remote sensing-based Information and Insurance for Crops in Emerging economies) project ( where rice yield is estimated from the Oryza2000 model by assimilating LAI maps derived from synthetic aperture radar (SAR) images [12] . As far as precise crop management is concerned, LAI data have been found to be useful in new approaches for the determination of nitrogen concentration dilution curves, which are traditionally based on plant dry matter (PDM) estimation [13, 14] . Remotely-sensed data at decametric resolution (e.g., Sentinel-2A) are the sole source of information available to provide high-resolution (HR) LAI estimation on wide areas to exploit the nitrogen concentration dilution curve approach for optimal crop fertilization. In the last few years, the scientific community has made big efforts with the goal of providing reliable and accurate LAI estimates at local scales taking advantage of unmanned aerial vehicles and high-resolution sensors, such as Landsat [15] and SPOT5 [16] [17] [18] . The recently launched Sentinel-2A satellite [19] provides well-suited spectral and temporal data for LAI retrieval at high-resolution in near real time, useful for assessing crop status and providing support in agro-practices at the parcel level. Many methods have been proposed and implemented in retrieval chains from Earth observation (EO) data to derive LAI estimates [20] . Empirical parametric algorithms have been developed based on calibrated relationships between vegetation indices and canopy biophysical variables [21] . On the other hand, non-parametric algorithms estimate biophysical variables using a training database containing pairs of the biophysical parameter and the associated spectral data [22] . In statistical approaches, concomitant in situ measurements of the biophysical parameter of interest and the associated spectral data from remote sensing platforms are used as a training database, whereas the hybrid approaches rely on a database generated by radiative transfer models (RTMs). RTMs describe the interactions between the incoming radiation, canopy elements and the background soil surface. The PROSAIL RTM has become one of the most popular and widely-used RTMs due to its consistency and robustness [23] . Hybrid approaches retrieve LAI by inverting RTM through machine learning techniques with a large number of methods [24, 25] . Among them, neural networks (NN) [26] and kernel-based methods [27, 28] are the most popular and used regression methods in remote sensing. State-of-the-art kernel-based methods, such as Gaussian process regression (GPR) [29] , provided encouraging results in the framework of biophysical parameter estimation outperforming other kernel methods and NN [18, 30] .
doi:10.3390/rs9030248 fatcat:2v6g3wfey5dhveeuuc7wv67dxy

A Global Inventory of Burned Areas at 1 Km Resolution for the Year 2000 Derived from Spot Vegetation Data

Kevin Tansey, Jean-Marie Gr�goire, Elisabetta Binaghi, Luigi Boschetti, Pietro Alessandro Brivio, Dmitry Ershov, St�phane Flasse, Robert Fraser, Dean Graetz, Marta Maggi, Pascal Peduzzi, JOs� Pereira (+3 others)
2004 Climatic Change  
Australia was processed with an algorithm developed by GVM (developed by Stroppiana) .  ...  One region of Australia: 11-21 • S; 125-135 • E EC Joint Research Centre (JRC), Ispra (VA), Italy Stroppiana et al. (2002) Stroppiana et al. (2003) NRI A change detection algorithm using pre-and  ... 
doi:10.1007/s10584-004-2800-3 fatcat:xhy6goozsrhdfiti5zioyar3wu

Processing Optical and SAR data for burned forests mapping: An integrated framework

Daniela Stroppiana, Ramin Azar, Fabiana Calò, Antonio Pepe, Pasquale Imperatore, Mirco Boschetti, João M. N. Silva, Pietro A. Brivio, Riccardo Lanari
2015 Proceedings of Fringe 2015: Advances in the Science and Applications of SAR Interferometry and Sentinel-1 InSAR Workshop   unpublished
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
more » ... 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 ESA Sentinel (optical and SAR) data.
doi:10.5270/fringe2015.pp294 fatcat:ibgh3nvx4vfsrgszk54vdesaei
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