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BIBLIOGRAPHY
ARTICLES FOR READING AND DISCUSSION (OTHERS MAY BE INCLUDED):
ALBUQUERQUE, A.M, DEBIASI, P., LIMA, T.V.L, HIGA, G.T.H, PISTORI, H., SCOLFORO, H.F., SILVA, T.C.F., PORTO, J.V.P., STAPE, J.L. (2024), Qualitative Forest Inventory in Eucalyptus Plantations Using Unmanned Aerial Vehicles, Multispectral Sensors, and Deep Learning. IEEE Geoscience and Remote Sensing Letters, v. 21, p. 1-5. http://dx.doi.org/10.1109/lgrs.2024.3465892
BARBOSA, C.C.F.; NOVO, E.M.L.M.; MARTINS, V.S. (2019), Introdução ao Sensoriamento Remoto de Sistemas Aquáticos: princípios e aplicações. 1ª edição. Instituto Nacional de Pesquisas Espaciais. São José dos Campos. 161p. 2019.
BHARADIYA , J.P.; TZENIOS, N.T.; REDDY , M. (2023), Predicting Crop Yield Using Deep Learning and Remote Sensing. Journal of Engineering Research and Reports, v. 24, n. 12, p. 29–44, 2023. http://dx.doi.org/10.9734/jerr/2023/v24i12858.
BONANSEA, M., LEDESMA, C., RODRÍGUEZ, C., PINOTTI, L., ANTUNES, M. (2015), Effects of atmospheric correction of Landsat imagery on lake water clarity assessment. Advances in Space Research, p. 2345-2355. http://dx.doi.org/10.1016/j.asr.2015.09.018
CARREIRAS, J.M.B., PEREIRA, J.M.C., CAMPAGNOLO, M.L., SHIMABUKURO, Y.E. (2006), Assessing the extent of agriculture/pasture and secondary succession forest in the Brazilian Legal Amazon using SPOT VEGETATION data, Remote Sensing of Environment, 101:283-298.
CHANDER, G., HEWISON, T.J., FOX, N., WU, X., XIONG, X., BLACKWELL, W. (2013), Overview of Intercalibration of Satellite Instruments. IEEE Transactions on Geoscience and Remote Sensing, v.51, n.3, p. 1056-1080. https://doi.org/10.1109/TGRS.2012.2228654
CHANDER, G., MARKHAM, B.L., HELDER, D.L. (2009), Summary of Current Radiometric Calibration Coefficients for Landsat MSS, TM, ETM+, and EO-1 ALI Sensors. Remote Sensing of Environment, vol. 113, no 5, p. 893–903. https://doi.org/10.1016/j.rse.2009.01.007
CHAVEZ, P.S. (1988), An improved dark-object subtraction technique for atmospheric scattering correction of multispectral data. Remote Sensing of Environment, 24(3), 459-479. https://doi.org/10.1016/0034-4257(88)90019-3
CHUVIECO, E. (2020), Fundamentals of Satellite Remote Sensing, An Environmental Approach, 3ª Ed., CRC Press. 432 p. https://doi.org/10.1201/9780429506482
COMBER, A. J. (2008). Land use or land cover? Journal of Land Use Science, 3(4), 199–201. https://doi.org/10.1080/17474230802465140.
FEIZIZADEH, B., DARABI, S., BLASCHKE, T., & LAKES, T. (2022). QADI as a New Method and Alternative to Kappa for Accuracy Assessment of Remote Sensing-Based Image Classification. Sensors, 22(12), 4506. https://doi.org/10.3390/s22124506.
FOODY, G. M. (2020), Explaining the unsuitability of the kappa coefficient in the assessment and comparison of the accuracy of thematic maps obtained by image classification, Remote Sensing of Environment, Vol. 239, 111630. https://doi.org/10.1016/j.rse.2019.111630.
FREIRE, A.A.R., ANTUNES, M.A.H., BARROS, M.M., SOUZA, W.D., SILVA, W.S., SOUZA, T.M. (2022), Similarity Analysis between Contour Lines by Remotely Piloted Aircraft and Topography Using Hausdorff Distance: Application on Contour Planting. Remote Sensing, v. 14, p. 3269-3290. http://dx.doi.org/10.3390/rs14143269
GELSLEICHTER, Y.A., COSTA, E.M., ANJOS, L.H.C., MARCONDES, R.A.T. (2023), Enhancing Soil Mapping with Hyperspectral Subsurface Images generated from soil lab Vis-SWIR spectra tested in southern Brazil. GEODERMA REGIONAL, v. 1, p. e00641. http://dx.doi.org/10.1016/j.geodrs.2023.e00641
HOTT, M.C., CARVALHO, L.M.T., ANTUNES, M.A.H., RESENDE, J.C., ROCHA, W.S.D. (2019), Analysis of Grassland Degradation in Zona da Mata, MG, Brazil, Based on NDVI Time Series Data with the Integration of Phenological Metrics. Remote Sensing, v. 11, p. 2956. http://dx.doi.org/10.3390/rs11242956
LIANG, S. LI, X, WANG, J. (2012), Advanced remote sensing: terrestrial information extraction and applications. Academic Press. 800 p.
LIU, C., CHEN, Z., SHAO, Y., CHEN, J., HASI, T., PAN, H. (2019), Research advances of SAR remote sensing for agriculture applications: A review. Journal of Integrative Agriculture, Vol. 18(3), p. 506-525. https://doi.org/10.1016/S2095-3119(18)62016-7.
MA, Y., CHEN, S., ERMON, S., LOBELL, D.B. (2024), Transfer learning in environmental remote sensing, Remote Sensing of Environment, v. 301, p. 113924. https://doi.org/10.1016/j.rse.2023.113924.
MACEDO, P.S.M., OLIVEIRA, P.T.S., ANTUNES, M.A.H., DURIGON, V.L., FIDALGO, E.C.C., CARVALHO, D.F. (2020), New approach for obtaining the C-factor of RUSLE considering the seasonal effect of rainfalls on vegetation cover. INTERNATIONAL SOIL AND WATER CONSERVATION RESEARCH, v. 1, p. 1-10. http://dx.doi.org/10.1016/j.iswcr.2020.12.001
MARQUES, V., CEDDIA, M., ANTUNES, M., CARVALHO, D., ANACHE, J., RODRIGUES, D., OLIVEIRA, P. (2019), USLE K-Factor Method Selection for a Tropical Catchment. Sustainability, v. 11, p. 1840-1857. http://dx.doi.org/10.3390/su11071840
MONSERUD, R. A., LEEMANS, R. (1992), Comparing global vegetation maps with the Kappa statistic. Ecological Modelling, 62, p. 275-293.
MORAES, A.G.L., CARVALHO, D.F., ANTUNES, M.A.H., CEDDIA, M.B., FLANAGAN, D.C. (2020), Steady infiltration rate spatial modeling from remote sensing data and terrain attributes in southeast Brazil. GEODERMA REGIONAL, v. 20, p. e00242. http://dx.doi.org/10.1016/j.geodrs.2019.e00242
MORAES, A.G.L., CARVALHO, D.F., ANTUNES, M.A.H., CEDDIA, M.BA. (2018), Relationship between remote sensing data and field-observed interril erosion. PESQUISA AGROPECUÁRIA BRASILEIRA (ONLINE), v. 53, p. 332-341. http://dx.doi.org/10.1590/s0100-204x2018000300008
PÉREZ-RODRÍGUEZ, R, MARQUES, M.J., BIENES, R. (2007) Spatial variability of the soil erodibility parameters and their relation with the soil map at subgroup level, Science of the Total Environment 378:166–173.
PINHEIRO, H.S.K., BARBOSA, T.P.R., ANTUNES, M.A.H., CARVALHO, D.C., NUMMER, A.R., CARVALHO JUNIOR, W., CHAGAS, C.S., FERNANDES-FILHO, E.I., PEREIRA, M.G. (2019), Assessment of Phytoecological Variability by Red-Edge Spectral Indices and Soil-Landscape Relationships. Remote Sensing, v. 11, p. 2448. http://dx.doi.org/10.3390/rs11202448
PONZONI, F.J., Pinto, C.T., Lamparelli, R.A.C., Zullo Junior, J., Antunes, M.A.H.A. (2015), Calibração de Sensores Orbitais. São Paulo: Oficina de Textos, 2015. 96 p.
SCHLERF, M. AND ATZBERGER, C. (2006), Inversion of a forest reflectance model to estimate structural canopy variables from hyperspectral remote sensing data, Remote Sensing of Environment, 100:281-294.
SISHODIA, R. P., RAY, R. L., & SINGH, S. K. (2020). Applications of Remote Sensing in Precision Agriculture: A Review. Remote Sensing, 12(19), 3136. https://doi.org/10.3390/rs12193136
THEODORO, L.T.C., UBERTI, M.S., ANTUNES, M.A.H., DEBIASI, P. (2019), Avaliação em Massa de Imóveis Rurais Através da Regressão Clássica e da Geoestatística. RBC. REVISTA BRASILEIRA DE CARTOGRAFIA (ONLINE), v. 71, p. 459-485. http://dx.doi.org/10.14393/rbcv71n2-47458
UBERTI, M.S., ANTUNES, M.A.H., DEBIASI, P. (2021), Avaliação em massa de imóveis rurais utilizando regressão geograficamente ponderada. Boletim Goiano de Geografia, v. 41, p. 1-21. http://dx.doi.org/10.5216/BGG.v41.65227
UBERTI, MA.S., ANTUNES, M.A.H., DEBIASI, P., TASSINARI, W. (2018), Mass appraisal of farmland using classical econometrics and spatial modeling. LAND USE POLICY, v. 72, p. 161-170. http://dx.doi.org/10.1016/j.landusepol.2017.12.044
ZARCO-TEJADA, P.J., MILLER, J.R., MORALES, A., BERJÓN, A., AGÜERA, J. (2004), Hyperspectral indices and model simulation for chlorophyll estimation in open-canopy tree crops, Remote Sensing of Environment, 90:463-476.
SUGGESTION OF COMPLEMENTARY BIBLIOGRAPHY:
JENSEN, J.R. (2006), Remote Sensing of the Environment: An Earth Resource Perspective. 2. ed. Prentice Hall. 592 p.
MENESES, PR., ALMEIDA, T.A. (2012), Introdução ao Processamento de Imagens de Sensoriamento Remoto. Editora UNB. 266 p.
SABINS JR., F.F.; ELLIS, J.M. (2020), Remote Sensing: Principles, Interpretation, and Applications, 4th Ed. Waveland Press, Inc., 524 p.
SOOD, V., SRIVASTAV, A.L., KAUR, R., BHATI, N. (2026), Generative AI for Remote Sensing of the Environment: Algorithms and Applications, 1st Ed., CRC Press, 310 p.
SCHOWENGERDT, R.A. (2006), Remote Sensing, Models, and Methods for Image Processing, 3rd Edition, Academic Press, 560 p.
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