Hugo do Nascimento Bendini

@unesp.br

Department of Fisheries and Aquaculture / Faculty of Agricultural Sciences of Vale do Ribeira - Campus of Registro/SP
São Paulo State University (UNESP)

Hugo do Nascimento Bendini


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https://researchid.co/hbendini
Assistant Professor in Geosciences at FCAVR, Campus Registro

EDUCATION

Agronomist engineer graduated from UNESP in 2010. Master’s in Computer Science with emphasis on Digital Image and Signal Processing from UFSCar in partnership with Embrapa Instrumentation. PhD in Remote Sensing from INPE, with a sandwich doctorate at Humboldt University, Berlin, and postdoc at CESBIO, Université Toulouse III, France.

RESEARCH, TEACHING, or OTHER INTERESTS

Multidisciplinary, Agricultural and Biological Sciences, Computers in Earth Sciences, Earth and Planetary Sciences
30

Scopus Publications

598

Scholar Citations

12

Scholar h-index

13

Scholar i10-index

Scopus Publications

  • Mapping Irrigated Rice in Brazil Using Sentinel-2 Spectral–Temporal Metrics and Random Forest Algorithm
    Alexandre S. Fernandes Filho, Leila M. G. Fonseca, Hugo do N. Bendini
    Remote Sensing, 2024
    Brazil, a leading rice producer globally, faces challenges in systematically mapping its diverse rice fields due to varying cropping systems, climates, and planting calendars. Existing rice mapping methods often rely on complex techniques like deep learning or microwave imagery, posing limitations for large-scale mapping. This study proposes a novel approach utilizing Sentinel-2 spectral–temporal metrics (STMs) in conjunction with a random forest classifier for rice paddy mapping. By extracting diverse STMs and training both regional and global classifiers, we validated the method across independent areas. While regional models tended to overestimate rice areas, the global model effectively reduced discrepancies between our data and the reference maps, achieving an overall classifier accuracy exceeding 80%. Despite the need for further refinement to address confusion with other crops, STM exhibits promise for national-scale rice paddy mapping in Brazil.
  • Estimating Winter Cover Crop Biomass in France Using Optical Sentinel-2 Dense Image Time Series and Machine Learning
    Hugo do Nascimento Bendini, Rémy Fieuzal, Pierre Carrere, Harold Clenet, Aurelie Galvani, et al.
    Remote Sensing, 2024
    Cover crops play a pivotal role in mitigating climate change by bolstering carbon sequestration through biomass production and soil integration. However, current methods for quantifying cover crop biomass lack spatial precision and objectivity. Thus, our research aimed to devise a remote-sensing-based approach to estimate cover crop biomass across various species and mixtures during fallow periods in France. Leveraging Sentinel-2 optical data and machine learning algorithms, we modeled biomass across 50 fields representative of France’s diverse cropping practices and climate types. Initial tests using traditional empirical relationships between vegetation indices/spectral bands and dry biomass revealed challenges in accurately estimating biomass for mixed cover crop categories due to spectral interference from grasses and weeds, underscoring the complexity of modeling diverse agricultural conditions. To address this challenge, we compared several machine learning algorithms (Support Vector Machine, Random Forest, and eXtreme Gradient Boosting) using spectral bands and vegetation indices from the latest available image before sampling as input. Additionally, we developed an approach that incorporates dense optical time series of Sentinel-2 data, generated using a Radial Basis Function for interpolation. Our findings demonstrated that a Random Forest model trained with dense time series data during the cover crop development period yielded promising results, with an average R-squared (r2) value of 0.75 and root mean square error (RMSE) of 0.73 t·ha−1, surpassing results obtained from methods using single-image snapshots (r2 of 0.55). Moreover, our approach exhibited robustness in accounting for factors such as crop species diversity, varied climatic conditions, and the presence of weed vegetation—essential for approximating real-world conditions. Importantly, its applicability extends beyond France, holding potential for global scalability. The availability of data for model calibration across diverse regions and timeframes could facilitate broader application.
  • Estimating Crop Sowing and Harvesting Dates Using Satellite Vegetation Index: A Comparative Analysis
    Grazieli Rodigheri, Ieda Del’Arco Sanches, Jonathan Richetti, Rodrigo Yoiti Tsukahara, Roger Lawes, et al.
    Remote Sensing, 2023
    In the last decades, several methodologies for estimating crop phenology based on remote sensing data have been developed and used to create different algorithms. Although many studies have been conducted to evaluate the different methodologies, a comprehensive understanding of the potential of the different current algorithms to detect changes in the growing season is still lacking, especially in large regions and with more than one crop per season. Therefore, this work aimed to evaluate different phenological metrics extraction methodologies. Using data from over 1500 fields distributed across Brazil’s central area, six algorithms, including CropPhenology, Digital Earth Australia tools package (DEA), greenbrown, phenex, phenofit, and TIMESAT, to extract soybean crop phenology were applied. To understand how robust the algorithms are to different input sources, the NDVI and EVI2 time series derived from MODIS products (MOD13Q1 and MOD09Q1) and from Sentinel-2 satellites were used to estimate the sowing date (SD) and harvest date (HD) in each field. The algorithms produced significantly different phenological date estimates, with Spearman’s R ranging between 0.26 and 0.82 when comparing sowing and harvesting dates. The best estimates were obtained using TIMESAT and phenex for SD and HD, respectively, with R greater than 0.7 and RMSE of 16–17 days. The DEA tools and greenbrown packages showed higher sensitivity when using different data sources. Double cropping is an added challenge, with no method adequately identifying it.
  • IRRIGATED AGRICULTURE MAPPING IN A SEMI-ARID REGION IN BRAZIL BASED ON THE USE OF SENTINEL-2 DATA AND RANDOM FOREST ALGORITHM
    H. N. Bendini, L. M. G. Fonseca, C. A. Bertolini, R. F. Mariano, A. S. Fernandes Filho, et al.
    International Archives of the Photogrammetry Remote Sensing and Spatial Information Sciences ISPRS Archives, 2023
    Irrigation is important for agricultural production and is often decisive for this, especially in arid and semi-arid areas, where precipitation is insufficient. In Brazil, irrigated agriculture is responsible for 46% of withdrawals from water bodies and 67% of the consumption of the total volume of water collected, representing the highest consumptive use in the country. Remote sensing technologies have great potential for developing methods for monitoring irrigated areas. However, mapping irrigated areas is still a challenge, due to the complexity and diversity of irrigation methods and crops, especially in a country with continental dimensions like Brazil. Remote sensing techniques for mapping irrigated areas in Brazil have been applied mainly in areas with center pivot irrigation in the Cerrado, and with paddy rice in the south of Brazil. But few or no applications, involving mapping of crops irrigated by other irrigation methods, mainly in the semi-arid, have been carried out. The objective of this work was to investigate a method for classifying irrigated agriculture in a semiarid region of Brazil, based on the use of Sentinel 2 imagery and random forest algorithm. We proposed a novel and robust methodology showing with preliminary results that it's possible to identify irrigated agriculture in this region with a class-f1-score of 74% for complementary irrigation and 95% for center-pivots.
  • Mapping flooded rice in Brazil
    Proceedings of the Brazilian Symposium on Geoinformatics, 2023
  • Deforestation and agricultural fires in South-West Pará, Brazil, under political changes from 2014 to 2020
    Benjamin Jakimow, Matthias Baumann, Caroline Salomão, Hugo Bendini, Patrick Hostert
    Journal of Land Use Science, 2023
    The increasing deforestation and fires since 2019 raises concerns about the irreversible destruction of the Brazilian Amazon. Our goal was to better understand these changes in south-west Pará across different land-tenure and farm systems and between the terms of President Rousseff, Temer, and Bolsonaro. We reconstructed deforestation and fire history using all Landsat and Sentinel-2 observations from 2014 to 2020 and assessed, using quasi-experimental methods, the average treatment effects of each presidency on deforestation and fires across land-tenure and farm types. Deforestation nearly quadrupled to 1,201 km2, particularly during Bolsonaro in undesignated areas and conservation units and on medium-sized farms (p < 0.001). Burning increased to 4,805 km2 and in all tenure types (p < 0.001). The increase was strongest in agrarian settlements and conservation units and on medium and large farms. Our observations show the importance of clarifying land-tenure and re-strengthening disincentives of environmental infractions, which have been weakened specifically under President Bolsonaro.
  • Estimation of Water Use in Center Pivot Irrigation Using Evapotranspiration Time Series Derived by Landsat: A Study Case in a Southeastern Region of the Brazilian Savanna
    Marionei Fomaca de Sousa Junior, Leila Maria Garcia Fonseca, Hugo do Nascimento Bendini
    Remote Sensing, 2022
    In Brazil, irrigated agriculture is responsible for 46% of withdrawals of water bodies and 67% of use concerning the total water abstracted volume, representing the most significant consumptive use in the country. Understanding how different crops use water over time is essential for planning and managing water allocation, water rights, and farming production. In this work, we propose a methodology to estimate water used in agriculture irrigated by center pivots in the municipality of Itobi, São Paulo, in the Brazilian Savanna (known as Cerrado), which has strong potential for agricultural and livestock production. The methodology proposed for the water use estimate is based on mapping crops irrigated by center pivots for the 2015/2016 crop year and actual evapotranspiration (ETa). ETa is derived from the Operational Simplified Surface Energy Balance model (SSEBop) and parameterized for edaphoclimatic conditions in Brazil (SSEBop-Br). Three meteorological data sources (INMET, GLDAS, CFSv2) were tested for estimating ETa. The water use was estimated for each meteorological data source, relating the average irrigation balance and the total area for each crop identified in the map. We evaluated the models for each crop present in the center pivots through global accuracy and f1-score metrics, and f1-score was more significant than 0.9 for all crops. The potato was the crop that consumed the most water in irrigation, followed by soy crops, beans, carrots, and onions, considering the three meteorological data sources. The total water volume consumed by center pivots in the municipality of Itobi in the 2015/2016 agricultural year for each meteorological data source was 3.2 million m3 (INMET), 2.5 million m3; (GLDAS), and 1.8 million m3 (CFSv2).
  • EVALUATING THE SEPARABILITY BETWEEN DRY TROPICAL FORESTS AND SAVANNA WOODLANDS IN THE BRAZILIAN SAVANNA USING LANDSAT DENSE IMAGE TIME SERIES AND ARTIFICIAL INTELLIGENCE
    H. N. Bendini, L. M. G. Fonseca, B. M. Matosak, R. F. Mariano, R. F. Haidar, et al.
    International Archives of the Photogrammetry Remote Sensing and Spatial Information Sciences ISPRS Archives, 2022
    The Brazilian Savanna is the second largest biogeographical region in Brazil and present different vegetation types, consisting mostly of tropical savannas, grasslands, and forests. The forest types have different tree cover and floristic composition, which is associated to leaf deciduousness. Considering the importance of Cerrado to biodiversity conservation and the maintaining of environmental services, the development of methods to map the different forest types in Cerrado is important for conservation programmes, subsidize restauration plains, and to allow estimations of carbon sink and stock. Mapping heterogeneous tropical areas, such as the Brazilian Savanna, is very complex due to the natural factors and peculiarities of the vegetation types, and it's still particularly challenging to separate between different forest formations. In this study we tested machine learning approaches based on the use of dense image time series, in order to evaluate the separability Dry Tropical Forests and Savanna woodlands. We considered the Brazilian State of Tocantins as the study area, which is located in the Northern region of the country. RF classification of Landsat dense time series showed an overall accuracy of 0.85005, while the LSTM approach presented an overall accuracy of 0.88601, with the highest f1-score for the savanna woodlands class, suggesting the capability of the recurrent neural networks on handling complex long-term dependencies such as the EVI dense time series data. This study showed the potential for the development of a semi-automatic method for discriminating the different types of forest formations in the Brazilian Savanna, based on remote sensing.
  • Mapping Deforestation in Cerrado Based on Hybrid Deep Learning Architecture and Medium Spatial Resolution Satellite Time Series
    Bruno Menini Matosak, Leila Maria Garcia Fonseca, Evandro Carrijo Taquary, Raian Vargas Maretto, Hugo do Nascimento Bendini, et al.
    Remote Sensing, 2022
    Cerrado is the second largest biome in Brazil, covering about 2 million km2. This biome has experienced land use and land cover changes at high rates due to agricultural expansion so that more than 50% of its natural vegetation has already been removed. Therefore, it is crucial to provide technology capable of controlling and monitoring the Cerrado vegetation suppression in order to undertake the environmental conservation policies. Within this context, this work aims to develop a new methodology to detect deforestation in Cerrado through the combination of two Deep Learning (DL) architectures, Long Short-Term Memory (LSTM) and U-Net, and using Landsat and Sentinel image time series. In our proposed method, the LSTM evaluates the time series in relation to the time axis to create a deforestation probability map, which is spatially analyzed by the U-Net algorithm alongside the terrain slope to produce final deforestation maps. The method was applied in two different study areas, which better represent the main deforestation patterns present in Cerrado. The resultant deforestation maps based on cost-free Sentinel-2 images achieved high accuracy metrics, peaking at an overall accuracy of 99.81%±0.21 and F1-Score of 0.8795±0.1180. In addition, the proposed method showed strong potential to automate the PRODES project, which provides the official Cerrado yearly deforestation maps based on visual interpretation.
  • Pattern Recognition and Remote Sensing techniques applied to Land Use and Land Cover mapping in the Brazilian Savannah
    Leila M.G. Fonseca, Thales S. Körting, Hugo do N. Bendini, Cesare D. Girolamo-Neto, Alana K. Neves, et al.
    Pattern Recognition Letters, 2021
  • Spatio-Temporal Deep Learning Approach to Map Deforestation in Amazon Rainforest
    Raian V. Maretto, Leila M. G. Fonseca, Nathan Jacobs, Thales S. Korting, Hugo N. Bendini, et al.
    IEEE Geoscience and Remote Sensing Letters, 2021
  • DETECTING CLEARCUT DEFORESTATION EMPLOYING DEEP LEARNING METHODS AND SAR TIME SERIES
    Evandro C. Taquary, Leila G. M. Fonseca, Raian V. Maretto, Hugo N. Bendini, Bruno M. Matosak, et al.
    International Geoscience and Remote Sensing Symposium IGARSS, 2021
  • Characterization of Center Pivot Irrigation Systems in the Irecê-Bahia Agricultural Region Based On Random Forest Classification
    Proceedings of the Brazilian Symposium on Geoinformatics, 2021
  • EXPLORING A DEEP CONVOLUTIONAL NEURAL NETWORK AND GEOBIA FOR AUTOMATIC RECOGNITION OF BRAZILIAN PALM SWAMPS (VEREDAS) USING SENTINEL-2 OPTICAL DATA
    Hugo N. Bendini, Leila M. G. Fonseca, Raian V. Maretto, Bruno M. Matosak, Evandro C. Taquary, et al.
    International Geoscience and Remote Sensing Symposium IGARSS, 2021
  • Remote sensing image time series metrics for distinction between pasture and croplands using the random forest classifier
    M. A. A. Rodrigues, H. N. Bendini, A. R. Soares, T. S. Körting, L. M. G. Fonseca
    International Archives of the Photogrammetry Remote Sensing and Spatial Information Sciences ISPRS Archives, 2020
  • Assessing Differentiation between Pasture and Croplands Using Remote Sensing Image Time Series Metrics
    Marcos Antonio de Almeida Rodrigues, Hugo do Nascimento Bendini, Anderson Reis Soares, Thales Sehn Korting, Leila Maria Garcia Fonseca
    International Geoscience and Remote Sensing Symposium IGARSS, 2020
  • Stmetrics: A Python Package for Satellite Image Time-Series Feature Extraction
    Anderson R. Soares, Hugo N. Bendini, Daiane V. Vaz, Tatiana D. T. Uehara, Alana K. Neves, et al.
    International Geoscience and Remote Sensing Symposium IGARSS, 2020
  • Applying A Phenological Object-Based Image Analysis (Phenobia) for Agricultural Land Classification: A Study Case in the Brazilian Cerrado
    Hugo N. Bendini, Leila M. G. Fonseca, Anderson R. Soares, Philippe Rufin, Marcel Schwieder, et al.
    International Geoscience and Remote Sensing Symposium IGARSS, 2020
  • Combining environmental and landsat analysis ready data for vegetation mapping: A case study in the Brazilian Savanna Biome
    H. N. Bendini, L. M. G. Fonseca, M. Schwieder, P. Rufin, T. S. Korting, et al.
    International Archives of the Photogrammetry Remote Sensing and Spatial Information Sciences ISPRS Archives, 2020
  • Remote Sensing Image Time Series Metrics for Distinction between Pasture and Croplands Using the Random Forest Classifier
    M. A. A. Rodrigues, H. N. Bendini, A. R. Soares, T. S. Korting, L. M. G. Fonseca
    2020 IEEE Latin American Grss and ISPRS Remote Sensing Conference Lagirs 2020 Proceedings, 2020
  • A MULTI-SCALE SEGMENTATION APPROACH to FILLING GAPS in LANDSAT ETM+ SLC-OFF IMAGES through PIXEL WEIGHTING
    R. F. B. Marujo, L. M. G. Fonseca, T. S. Körting, H. N. Bendini
    International Archives of the Photogrammetry Remote Sensing and Spatial Information Sciences ISPRS Archives, 2020
  • Detailed agricultural land classification in the Brazilian cerrado based on phenological information from dense satellite image time series
    Hugo do Nascimento Bendini, Leila Maria Garcia Fonseca, Marcel Schwieder, Thales Sehn Körting, Philippe Rufin, et al.
    International Journal of Applied Earth Observation and Geoinformation, 2019
  • Spatiooral segmentation applied to optical remote sensing image time series
    Wanderson Santos Costa, Leila Maria Garcia Fonseca, Thales Sehn Korting, Hugo do Nascimento Bendini, Ricardo Cartaxo Modesto de Souza
    IEEE Geoscience and Remote Sensing Letters, 2018
  • Spectral normalization between landsat-8/OLI, Landsat-7/ETM+ and CBERS-4/MUX bands through linear regression and spectral unmixing
    Proceedings of the Brazilian Symposium on Geoinformatics, 2017
  • Segmentation of optical remote sensing images for detecting homogeneous regions in space and time
    Proceedings of the Brazilian Symposium on Geoinformatics, 2017
  • Assessment of a multi-sensor approach for noise removal on Landsat-8 OLI time series using CBERS-4 MUX data to improve crop classification based on phenological features
    Proceedings of the Brazilian Symposium on Geoinformatics, 2016
  • Combining time series features and data mining to detect land cover patterns: A case study in northern Mato grosso state, Brazil
    Proceedings of the Brazilian Symposium on Geoinformatics, 2015
  • Proposal for a monitoring system of black sigatoka based on environmental variables using the TerraMA2
    Proceedings of the Brazilian Symposium on Geoinformatics, 2014
  • Risk analysis of Black Sigatoka occurrence based on polynomial models: A case study
    Hugo do Nascimento Bendini, Wilson da S. Moraes, Silvia H.M.G. da Silva, Erika S. Tezuka, Paulo Estevão Cruvinel
    Tropical Plant Pathology, 2013
  • Estimating leaf area in anthurium with regression functions
    Silvia Helena Modenese-Gorla da Silva, Juliana Domingues Lima, Hugo do Nascimento Bendini, Edson Shigueaki Nomura, Wilson da Silva Moraes
    Ciencia Rural, 2008

RECENT SCHOLAR PUBLICATIONS

  • Efeito da mudança de uso e cobertura da terra na produtividade de açaí no estado do Pará
    TK Igawa, LMG Fonseca, H do Nascimento Bendini
    Anais do 21º Simpósio Brasileiro de Sensoriamento Remoto(pp. 247–250). Galoa … , 2025
    2025
    Citations: 1
  • Mapping irrigated rice in Brazil using Sentinel-2 spectral-temporal metrics and random forest algorithm.
    ASF Filho, LMG Fonseca, HN Bendini
    2024
  • Mapping irrigated rice in Brazil using Sentinel-2 spectral–temporal metrics and random forest algorithm
    AS Fernandes Filho, LMG Fonseca, HN Bendini
    Remote Sensing 16 (16), 2900 , 2024
    2024
    Citations: 9
  • Estimating winter cover crop biomass in france using optical sentinel-2 dense image time series and machine learning
    H do Nascimento Bendini, R Fieuzal, P Carrere, H Clenet, A Galvani, ...
    Remote Sensing 16 (5), 834 , 2024
    2024
    Citations: 17
  • Deforestation and agricultural fires in South-West Pará, Brazil, under political changes from 2014 to 2020
    B Jakimow, M Baumann, C Salomão, H Bendini, P Hostert
    Journal of Land Use Science 18 (1), 176-195 , 2023
    2023
    Citations: 17
  • Estimating crop sowing and harvesting dates using satellite vegetation index: A comparative analysis
    G Rodigheri, IDA Sanches, J Richetti, RY Tsukahara, R Lawes, ...
    Remote sensing 15 (22), 5366 , 2023
    2023
    Citations: 25
  • Irrigated agriculture mapping in a semi-arid region in Brazil based on the use of sentinel-2 data and random forest algorithm
    HN Bendini, LMG Fonseca, CA Bertolini, RF Mariano, AS Fernandes Filho, ...
    The International Archives of the Photogrammetry, Remote Sensing and Spatial … , 2023
    2023
    Citations: 7
  • Estimation of Water Use in Center Pivot Irrigation Using Evapotranspiration Time Series Derived by Landsat: A Study Case in a Southeastern Region of the Brazilian Savanna
    MF de Sousa Junior, LMG Fonseca, HN Bendini
    Remote Sensing 14 (23), 5929 , 2022
    2022
    Citations: 3
  • Evaluating the separability between dry tropical forests and savanna woodlands in the Brazilian savanna using Landsat dense image time series and artificial intelligence
    HN Bendini, LMG Fonseca, BM Matosak, RF Mariano, RF Haidar, ...
    The International Archives of the Photogrammetry, Remote Sensing and Spatial … , 2022
    2022
    Citations: 4
  • Mapping deforestation in cerrado based on hybrid deep learning architecture and medium spatial resolution satellite time series
    BM Matosak, LMG Fonseca, EC Taquary, RV Maretto, HN Bendini, ...
    Remote sensing 14 (1), 209 , 2022
    2022
    Citations: 48
  • Estimation of Water Use in Center Pivot Irrigation Using Evapotranspiration Time Series Derived by Landsat: A Study Case in a Southeastern Region of the Brazilian Savanna
    MFS Junior, LMG Fonseca, HN Bendini
    Remote Sensing (Web) 14 (23), 5929 , 2022
    2022
    Citations: 2
  • Deforestation and mining threaten an endangered and endemic bat species (Lonchophylla: Phyllostomidae) from the Brazilian Cerrado
    HFM de Oliveira, G Fandos, PL Zangrandi, HN Bendini, DC Silva, ...
    HYSTRIX-ITALIAN JOURNAL OF MAMMALOGY 33 (2) , 2022
    2022
  • Crops, caves, and bats: deforestation and mining threaten an endemic and endangered bat species (Lonchophylla: Phyllostomidae) in the Neotropical savannas
    HFM Oliveira, G Fandos, PL Zangrandi, HN Bendini, DC Silva, ...
    Hystrix: Italian journal of mammalogy , 2022
    2022
    Citations: 1
  • Land use and land cover databases for Mediterranean landscape analysis at the watershed scale
    J Pompeu, I Ruiz, A Ruano, H Bendini, MJ Sanz
    BC3 Working Paper Series 1 , 2021
    2021
    Citations: 6
  • Pattern recognition and remote sensing techniques applied to land use and land cover mapping in the Brazilian Savannah
    LMG Fonseca, TS Körting, HN Bendini, CD Girolamo-Neto, AK Neves, ...
    Pattern recognition letters 148, 54-60 , 2021
    2021
    Citations: 35
  • Exploring a deep convolutional neural network and geobia for automatic recognition of brazilian palm swamps (veredas) using Sentinel-2 optical data
    HN Bendini, LMG Fonseca, RV Maretto, BM Matosak, EC Taquary, ...
    2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, 5401 … , 2021
    2021
    Citations: 3
  • Detecting clearcut deforestation employing deep learning methods and SAR time series
    EC Taquary, LGM Fonseca, RV Maretto, HN Bendini, BM Matosak, ...
    2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, 4520 … , 2021
    2021
    Citations: 6
  • Characterization of Center Pivot Irrigation Systems in the Irecê-Bahia Agricultural Region Based On Random Forest Classification.
    PS Simões, MF de Sousa Junior, TB Hoffmann, LMG Fonseca, ...
    GEOINFO, 87-95 , 2021
    2021
  • Simple nonlinear iterative temporal clustering
    A Soares, T Körting, L Fonseca, H Bendini
    IEEE Transactions on Geoscience and Remote Sensing 59 (9), 7669-7679 , 2020
    2020
    Citations: 4
  • Applying a phenological object-based image analysis (Phenobia) for agricultural land classification: A study case in the Brazilian Cerrado
    HN Bendini, LMG Fonseca, AR Soares, P Rufin, M Schwieder, ...
    IGARSS 2020-2020 IEEE International Geoscience and Remote Sensing Symposium … , 2020
    2020
    Citations: 2

MOST CITED SCHOLAR PUBLICATIONS

  • Spatio-temporal deep learning approach to map deforestation in amazon rainforest
    RV Maretto, LMG Fonseca, N Jacobs, TS Körting, HN Bendini, LL Parente
    IEEE Geoscience and Remote Sensing Letters 18 (5), 771-775 , 2020
    2020
    Citations: 106
  • Detailed agricultural land classification in the Brazilian cerrado based on phenological information from dense satellite image time series
    H do Nascimento Bendini, LMG Fonseca, M Schwieder, TS Körting, ...
    International Journal of Applied Earth Observation and Geoinformation 82, 101872 , 2019
    2019
    Citations: 103
  • Mapping deforestation in cerrado based on hybrid deep learning architecture and medium spatial resolution satellite time series
    BM Matosak, LMG Fonseca, EC Taquary, RV Maretto, HN Bendini, ...
    Remote sensing 14 (1), 209 , 2022
    2022
    Citations: 48
  • Pattern recognition and remote sensing techniques applied to land use and land cover mapping in the Brazilian Savannah
    LMG Fonseca, TS Körting, HN Bendini, CD Girolamo-Neto, AK Neves, ...
    Pattern recognition letters 148, 54-60 , 2021
    2021
    Citations: 35
  • Estimativa da área foliar do antúrio com o uso de funções de regressão
    SHMG Silva, JD Lima, HN Bendini, ES Nomura, WS Moraes
    Ciência Rural 38 (1), 243-246 , 2007
    2007
    Citations: 30
  • Using Landsat 8 image time series for crop mapping in a region of Cerrado, Brazil
    H Bendini, ID Sanches, TS Körting, LMG Fonseca, AJB Luiz, ...
    The International Archives of the Photogrammetry, Remote Sensing and Spatial … , 2016
    2016
    Citations: 28
  • Estimating crop sowing and harvesting dates using satellite vegetation index: A comparative analysis
    G Rodigheri, IDA Sanches, J Richetti, RY Tsukahara, R Lawes, ...
    Remote sensing 15 (22), 5366 , 2023
    2023
    Citations: 25
  • Assessing rainfall erosivity with artificial neural networks for the Ribeira Valley, Brazil
    RB Silva, P Iori, C Armesto, HN Bendini
    International Journal of Agronomy 2010 (1), 365249 , 2010
    2010
    Citations: 23
  • Combining environmental and Landsat analysis ready data for vegetation mapping: a case study in the Brazilian savanna biome
    HN Bendini, LMG Fonseca, M Schwieder, P Rufin, TS Korting, ...
    The International Archives of the Photogrammetry, Remote Sensing and Spatial … , 2020
    2020
    Citations: 21
  • Spatio-temporal segmentation applied to optical remote sensing image time series
    WS Costa, LMG Fonseca, TS Körting, H do Nascimento Bendini, ...
    IEEE Geoscience and Remote Sensing Letters 15 (8), 1299-1303 , 2018
    2018
    Citations: 20
  • Estimating winter cover crop biomass in france using optical sentinel-2 dense image time series and machine learning
    H do Nascimento Bendini, R Fieuzal, P Carrere, H Clenet, A Galvani, ...
    Remote Sensing 16 (5), 834 , 2024
    2024
    Citations: 17
  • Deforestation and agricultural fires in South-West Pará, Brazil, under political changes from 2014 to 2020
    B Jakimow, M Baumann, C Salomão, H Bendini, P Hostert
    Journal of Land Use Science 18 (1), 176-195 , 2023
    2023
    Citations: 17
  • Combining Time Series Features and Data Mining to Detect Land Cover patterns: a Case Study in Northern Mato Grosso State, Brazil.
    AK Neves, HN Bendini, TS Körting, LMG Fonseca
    GeoInfo, 174-185 , 2015
    2015
    Citations: 12
  • Mapping irrigated rice in Brazil using Sentinel-2 spectral–temporal metrics and random forest algorithm
    AS Fernandes Filho, LMG Fonseca, HN Bendini
    Remote Sensing 16 (16), 2900 , 2024
    2024
    Citations: 9
  • Stmetrics: a python package for satellite image time-series feature extraction
    AR Soares, HN Bendini, DV Vaz, TDT Uehara, AK Neves, S Lechler, ...
    IGARSS 2020-2020 IEEE International Geoscience and Remote Sensing Symposium … , 2020
    2020
    Citations: 9
  • Segmentation of optical remote sensing images for detecting homogeneous regions in space and time
    WS Costa, LMG Fonseca, TS Körting, M Simões, HN Bendini, RCM Souza
    Revista Brasileira Cartografia 70 (5), 1779-1801 , 2018
    2018
    Citations: 8
  • Análise de risco da ocorrência de Sigatoka-negra baseada em modelos polinomiais: um estudo de caso
    HN Bendini, WS Moraes, SHMG Silva, ES Tezuka, PE Cruvinel
    Tropical Plant Pathology 38 (1), 35-43 , 2013
    2013
    Citations: 8
  • Irrigated agriculture mapping in a semi-arid region in Brazil based on the use of sentinel-2 data and random forest algorithm
    HN Bendini, LMG Fonseca, CA Bertolini, RF Mariano, AS Fernandes Filho, ...
    The International Archives of the Photogrammetry, Remote Sensing and Spatial … , 2023
    2023
    Citations: 7
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