EVALUATION OF AGRICULTURAL AREAS AND IRRIGATION CAPACITY OF DAMS
Abstract
A obra Avaliação de Áreas Agrícolas e Capacidade de Irrigação de Barragens , de autoria de Alceu Ramos de Bittencourt Neto, Cassiane Jrayj de Melo e Nelson Mario Victoria Bariani, constitui um livro técnico-científico de natureza aplicado que investiga, de forma integrada, o uso de geotecnologias e sensoriamento remoto no monitoramento de áreas agrícolas e na perspectiva da capacidade hídrica de reservatórios destinados à privacidade, com foco em sistemas produtivos de arroz irrigado no sul do Brasil. A obra é resultado direto de dedicados projetos de pesquisa, ensino e extensão coordenados pela Profª Drª Cassiane Jrayj de Melo, no âmbito da UNIGAIA – Grupo de Ações Interdisciplinares Aplicadas da Universidade Federal do Pampa (UNIPAMPA ), grupo de pesquisa certificado pela instituição e cadastrado no Diretório dos Grupos de Pesquisa do Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq), o que sua inserção no sistema nacional de ciência e tecnologia, sua produção científica e sua atuação consolidada na formação de recursos humanos e no desenvolvimento de soluções aplicadas às ciências agrárias e ambientais. O contexto de produção da obra está diretamente relacionado às atividades de ensino e formação acadêmica desenvolvidas no âmbito do Laboratório Interdisciplinar Integrado (LABii) da UNIPAMPA, especialmente por meio de estágio supervisionado realizado durante o período acadêmico de 2020, descrito conforme a seção metodológica do livro. O trabalho foi desenvolvido de forma remota em função do contexto pandêmico, evidenciando a adaptação de metodologias de ensino e pesquisa ao uso de tecnologias digitais, bem como a integração entre disciplinas como cartografia, geoprocessamento, hidráulica e topografia, ministradas no curso de graduação. Todas as contribuições que compõem a obra foram submetidas à rigorosa análise e aprovação pelo conselho editorial do grupo de pesquisa UNIGAIA, constituído por doutores e mestres, garantindo excelência acadêmica, profundidade analítica e relevância científica dos conteúdos apresentados. Esse processo garante consistência metodológica, rigor técnico e qualidade científica de produção. O tema central da obra consiste na avaliação espacial e temporal de áreas agrícolas e na quantificação da capacidade de supervisão de barragens, utilizando técnicas de sensoriamento remoto e modelagem espacial. O estudo aborda problemas científicos e aplicados relacionados à gestão de recursos hídricos, planejamento agrícola e otimização do uso da água em sistemas produtivos intensivos, com destaque para a cultura do arroz irrigado. Do ponto de vista metodológico, a obra fundamenta-se na integração de imagens de satélite das missões Landsat 8/OLI e Sentinel-2/L2A, associadas ao uso de sistemas de informação geográfica e modelos digitais de emissões derivadas da missão SRTM. Conforme descrito no resumo e nos capítulos iniciais, foram encontradas evidências de séries temporais de imagens para construção de índices de cultivo (NDVI), permitindo o monitoramento do desenvolvimento fenológico das culturas ao longo do ciclo produtivo. As figuras apresentadas ao longo do capítulo de resultados evidenciam análises espectro-temporais que permitem identificar fases de crescimento, maturação e colheita, bem como variações espaciais no vigor vegetativo das lavouras. A metodologia também contempla a aplicação de técnicas de geoprocessamento para modelagem geomorfométrica de reservatórios, incluindo a geração de curvas de nível (isóbatas), visualizações tridimensionais e cálculos volumétricos a partir de graus numéricos. Conforme ilustrado nas figuras de batimetria e modelos 3D (páginas interativas do capítulo de resultados), foi possível estimar com precisão o volume de água armazenado em duas barragens, bem como sua capacidade potencial de segurança, integrando dados de campo e modelagem digital. Os resultados indicam volumes superiores a 800 mil m³ por reservatório, com capacidade de precisão suficiente para expandir significativamente a área cultivada, evidenciando o potencial produtivo da propriedade comprovada. A relevância científica, técnica e aplicada da obra é expressiva, uma vez que demonstra, de forma concreta, a aplicabilidade das geotecnologias no monitoramento agrícola e na gestão eficiente de recursos hídricos. Ao integrar análise espectral, modelagem espacial e validação com dados de campo, o livro contribui para o avanço do conhecimento em agricultura de precisão, hidrologia aplicada e planejamento territorial. No âmbito formativo, a obra evidencia de maneira clara a participação ativa de estudantes em processos de pesquisa aplicada, sob orientação docente, promovendo o desenvolvimento de competências técnicas em sensoriamento remoto, geoprocessamento e análise ambiental. A integração entre ensino, pesquisa e extensão é reforçada pelo ambiente do LABii, que, conforme descrito no texto, atua desde 2011 na formação interdisciplinar de estudantes e no desenvolvimento de soluções baseadas em dados geoespaciais. Em resumo, a obra configura-se como uma produção acadêmica robusta e estrategicamente relevante, evidenciando liderança acadêmica, cooperativa de projetos de pesquisa, inserção no sistema nacional de ciência e tecnologia por meio de grupo certificado no CNPq, internacionalização e formação de recursos humanos. Ao rigor metodológico articular, inovação tecnológica e aplicabilidade prática, o livro contribui de forma significativa para o avanço das geotecnologias aplicadas às ciências agrárias e para o fortalecimento institucional da UNIPAMPA no cenário científico nacional e internacional.
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1 BOOK EVALUATION OF AGRICULTURAL AREAS AND IRRIGATION CAPACITY OF DAMS PROJECT GEOMORPHOMETRY APPLIED TO DAMS FOR THE ASSESSMENT OF AGRICULTURAL PRODUCTION INTEGRATED WITH AQUACULTURE Cassiane Jrayj de Melo Alceu Ramos de Bittencourt Neto Nelson Mario Victoria Bariani
2 SUMMARY 1 INTRODUCTION 3 2 THEORETICAL FRAMEWORK 7 2.1 Remote Sensing 7 2.2 Sentinel Satellite2/L2A 8 2.3 Landsat8/OLI Satellite 10 2.4 Vegetation index - NDVI 10 2.5 Remote Sensing Techniques for Estimating Water Volume in Dams 11 2.6 Geoprocessing software - SPRING GIS 12 2.7 Geoprocessing in the Cloud - Land Viewer 13 3 MATERIAL AND METHODS 15 3.1 Internship location 16 3.2 Characterisation of the Study Area 18 3.3 Satellite images and processing performed 19 3.4 Field data: bathymetric survey 20 4 DISCUSSION OF THE INFORMATION OBTAINED 24 4.1 Area cultivated in the 19/20 harvest 25 4.2 Relief of the property 28 4.3 Productive potential 30 4.4 Irrigation capacity 32 5 FINAL CONSIDERATIONS 38
3 SUMMARY The Integrated Interdisciplinary Laboratory - LABii - of Unipampa Campus Itaqui has been contributing since 2011 to the gradual improvement of remote sensing (RS) techniques that can be applied to agricultural and environmental management in the Western Frontier of Rio Grande do Sul. In the present stage two plots with irrigated rice culture were followed during the 2019/20 harvest, recording the spatial-temporal distribution of the normalized difference vegetation index (NDVI), as well as, RGB (Red, Green, Blue) images. The irrigation capacity of the property's dam was also assessed by SR, and the results compared with information provided by the owners from previous surveys. Six images from the Landsat8/OLI satellite and eight images from the Sentinel2/L2A satellite were evaluated spatially and temporally by means of the Land Viewer processing platform. The topographic survey, as well as the bathymetric analysis of the dam were carried out using SRTM images processed in the SPRING-GIS software of the National Institute for Space Research. From the results, it was possible to identify the spatial and temporal variations of NDVI during the cycle of development of the rice crop and the volume of water available for irrigation. The activities proposed in the internship provided learning about the application of SR techniques that have the potential to contribute to the process of agricultural production, favoring the decision making more accurately and efficiently. Keywords: NDVI, SRTM, Sentinel 2, Landsat8, Land Viewer. 1 INTRODUCTION
4 According to Santi (2007) among the adaptations and skills for high production are the qualification, efficiency of application and absorption of fertilizers, in the quality of soil use and management, in irrigation and improvement in the management of agricultural resources, and by adopting the tools of precision agriculture (PA). All the mentioned aspects receive increasingly important contributions from the so-called remote sensing technologies, which consist in the acquisition of information of an object without the need of direct contact with it. For example, in agriculture, the use of sensors makes it possible to establish relationships between the spectral responses of soil, crop and development parameters, with the possibility of using the images to detect changes in vegetation cover over time (ENCINA, 2018). One of the main vegetation indices used is the Normalized Difference Vegetation Index (NDVI), proposed by Rouse et al., (1973), which can be employed for the identification of the spatial variability of the production of plant biomass through the reflectance of the canopy (BREDEMEIER et al., 2013). The NDVI index is based on the contrast between the absorption in the red band by the chlorophyll present in the plant and the reflectance in the near infrared, due to the cellular structure of the leaf (JENSEN, 2011). According to Ponzoni & Shimabukuro (2007), the NDVI value is normalized to the range of -1 to +1, which represents an indirect measure of the density of photosynthetically active leaf phytomass per unit area. And, therefore, successfully used for monitoring changes in vegetation (JUNGES & FONTANA, 2009). Therefore, NDVI presents correlation with physiological and biophysical characteristics of the vegetation, such as leaf area, phytomass, anomalies, evapotranspiration, productivity and hydric condition (CRUSIOL et al., 2012) and when one has several images throughout the
11 image to be generated showing relative biomass. The absorption of chlorophyll in the red band and high reflectance vegetation in the NIR are used to calculate the NDVI. According to Jensen, (2011) NDVI is important because: a) seasonal and interannual changes in vegetation development and activity can be monitored; b) the ratio reduces many forms of multiplicative noise (differences in solar illumination, cloud shadows, some atmospheric changes, some topographic variations) present in multiple image bands and multiple dates. These indices make it possible to analyze the spatial distribution of cultivated areas, map the differences in plant health, improve the direction of sampling and field observations, thus promoting better evaluation of the production potential (Machado, 2003). The monitoring of the crop cycle and its relationship with productivity has been widely studied for annual crops producing grains in Rio Grande do Sul (JUNGES et al., 2011). 2.5 Remote Sensing Techniques for Estimating Water Volume in Dams It is important to stress that the remote sensing techniques used in this work enable companies, owners and tenants to monitor their properties remotely, following the stages of production, verifying anomalies during the cycle and the health condition of the crops. In this way the remote sensing tool contributes to the verification and supervision of each stage of the productive process, facilitating the identification of weak points within the property, which enables efficient decision making, as it allows contemporary updated visualisation or also a survey of the property's historical information.
12 On the other hand, remote sensing tools and geographical information systems make it possible to estimate the volume of water in cubic metres in dams intended for the irrigation of agricultural crops, which allows the producer greater security when planning the size of the area to be planted, avoiding errors of appreciation or approximate calculation. This monitoring ensures the correct use of agricultural insurance, as well as protecting the good productivity of crops by reducing the risk of occurrence of water deficit in planted areas. The information obtained, due to being based on objective measurements and methodologies accepted or being developed in the technical-scientific environment, documented in national and international literature, can be verified by other professionals and specialists in remote sensing. It is important to emphasize that although remote sensing is based on solid methodologies derived from exact sciences, it is an applied science, and the final validation of observations must happen through comparison with experience and results observed in the field. Any discrepancies between the perception of what happened in the field and the perception by remote sensing can and should be resolved with additional observations and processing, which provide complementary elements for the analysis of the results. 2.6 Geoprocessing software - SPRING GIS The SPRING (Georeferenced Information Processing System) product is a geographic database based on an object-oriented data model, from which its menu interface and the spatial language LEGAL are derived. Innovative algorithms, such as those used for spatial indexing,
13 image segmentation and triangular grid generation, ensure adequate performance for the most varied applications. Designed for the RISC platform and standard OSF Motif graphical interface, SPRING presents a highly interactive and friendly interface, and online documentation, both written in Portuguese, greatly facilitating its use and support (LOPES, 2009). Based on these characteristics SPRING has proven to be a highly attractive option in the area of geoprocessing, because it is now considered a public domain software, which can be acquired over the Internet ("http://www.dpi.inpe.br/spring"), simply by registering on the INPE website. SPRING is a product developed with national technology, made by the National Institute for Space Research - INPE, in São José dos Campos/SP, a city that stands out on the national scene for the companies and institutes linked to the area of technology, especially in the aerospace sector (Ibidem). 2.7 Geoprocessing in the Cloud - Land Viewer Land Viewer is a satellite image catalogue and geoprocessing website that has a simple and intuitive web interface, developed by EOS (Earth Observing System), a European and North American based company. The Land Viewer allows the non-specialist user to select a geographical area and apply an analysis on the image in real time (EOS, 2020). The company's platform offers one year for free, for one area, by simply filling in a form (Figure 1), it also offers access to other packages and specialised services.
14 Figure 1Plan values for obtaining agricultural monitoring by remote sensing in the Land Viewer platform. The Land Viewer platform allows the organisation of the recording of field activities such as: scheduling an application or setting a deadline for its completion, as well as harvest dates and other activities that producers or technicians want to evaluate in the system (Figure 2).
15 Figure 2Description of the functionality of recording field activities.
16 3 MATERIAL AND METHODS In general, the sequences of procedures adopted during this work involve: 1choosing the areas to be analysed; 2obtaining and processing satellite and radar images for analysis; 3visual analysis and correlation with information obtained in the field; 4preparation of maps and conclusions. The most relevant aspects of each stage will be described below. 3.1 Internship location The supervised internship was carried out remotely, due to the coronavirus pandemic, in the Integrated Interdisciplinary Laboratory (LABII) of the Federal University of the Pampa (Figure 3), located on the campus of Itaqui-RS, the same being coordinated by Professor Nelson Mario Victoria Bariani and supervised by the Education Technician Roberto Felice and Professor Cassiane Jrayj de Melo Victoria Bariani, who are members of the LABii team and research group UNIGAIA.
17 Figure 3Logo of the Integrated Interdisciplinary Laboratory (LABii) and the UNIGAIA research group of UNIPAMPA-Itaqui. The LABii was founded in 2009 (receiving physical space in 2011) with the aim of training students in techniques for measuring environmental, agricultural, industrial or social variables, bringing as results to society studies, reports, articles or opinions on regions or processes based on data from satellite sensors and measurements of samples in the laboratory. In short, the laboratory carries out studies on the environment, basic sanitation, agriculture, food production, health, research on topics of social interest in the academic field provided by Unipampa. The internship was supervised by the technician Roberto Dutra de Felice and the teacher Cassiane Jrayj de Melo Victoria Bariani and with the guidance of the teacher Dr. Nelson Mario Victoria Bariani, covering the period from 13 October to 01 December 2020, totaling 180 hours. As an internship proposal, we chose to monitor the practical classes of the subjects of Elements of Cartography and Geoprocessing for the Agronomy course and Hydraulics and Topography for the Aquaculture Technology course, taught by Professor Cassiane Bariani in the semester
18 2020-1. Thus, it was possible to develop the work using geoprocessing tools, traditional and modern, applied to agricultural areas and dams intended for their irrigation. 3.2 Characterisation of the Study Area The study area is situated in the municipality of Maçambará (Figure 4), on the western border of the State of Rio Grande do Sul, located at the geographical coordinates 29° 8'31 "S and 56°07'56 "O, with a Cfa climate according to the Koppen-Geiger classification (KUINCHTNER & BURIOL, 2001). Figure 04. Map of Location of the Study Area, Brazil, Rio Grande do Sul, Maçambará, Fazenda Fundão. In the satellite image referring to the municipality of Maçambará on the date of 05/01/2018 (Figure 04), numerous agricultural polygons can be recognized. It is observed that the farm is inserted in a region of intense agricultural activity, mainly rice farming, being recognized among the
19 most productive regions of the planet, with results similar to those of rice farmers in the USA (California), Australia and Japan (IRRI, 2019). The area analyzed belongs to the Fundão Farm and received irrigated rice cultivation in the 2019/2020 crop year. The property has approximately 50 hectares of cultivated area, which is irrigated by flooding. 3.3 Satellite images and processing From the identification of the limits of the rural property with the Google Earth Pro tool, the analysis by images of the Fazenda Fundão began. The Spring software of the National Institute for Space Research was used for the geoprocessing of the 29S57_ZN image of the SRTM (Shuttle Radar Topography Mission) radar. The Land Viewer platform was used to search and process Sentinel2/L2A and LANDSAT8/OLI satellite scenes. The platform provides free of charge the scenes at 10-day intervals. Images without cloud cover during the development cycle of the rice crop (November 2019 to March 2020) were sought. The Land Viewer platform also provides free of charge the calculation of the NDVI of Sentinel 2, using for this the bands B8A and B04, through the formula NDVI: (B8A-B04) / (B8A+B04). Thus, six images from the Landsat8/OLI satellite and eight images from the Sintinel2/L2A satellite coinciding with the period of crop development in each plot were selected and the NDVI of each scene collected. For the topographic analysis of altimetry and slope, as well as irrigation capacity, products from the SRTM mission were used. In this way it was possible to calculate the contour lines and the depth of the dam
20 from the extrapolation of the topography of the micro-basin, subsequently adjusted by some previously determined control points in the field. Geoprocessing allowed the realization and adjustment of the bathymetric model of the dam and its limits. Subsequently, well-established algorithms were used in the technical and scientific environment to calculate the area and volume. Thus, the volume of the dam was determined in cubic meters, corresponding to the digital elevation model produced. The volumes added by the historical average precipitation of the region during the months of irrigation and by the estimated flow rate of the stream that feeds the dams were also considered, discounting the volume evaporated during the same period. Based on the total volume, the maximum irrigation capacity of the dams was predicted for rice farming in terms of potentially irrigated rice blocks. 3.4 Field data: bathymetric survey The field data survey was carried out in the year 2018 in conjunction with the technician who provided service to the property, and the photographic survey of the personal collection of the person responsible was made available, as well as the maps of the topographic survey, Figure 5, 6 and 7, for dam 01 and Figure 8, 9 and 10, for dam 02.
27 plants with yellowish leaves (straw). From 10/12/19 to 04/03/20 it is observed the presence of vegetation in the mapped area, from the date of 24/03/20 it can be observed that there is no more presence of vegetation, concluding that the harvest has already been done. In the images between 10/12/19 and 04/03/20 areas with different shades of green can be observed. These areas should be analysed in the field to determine which variable (fertilization, water deficit, phytosanitary agents) is interfering with the difference in reflectance. Figure 13. Space-time analysis of NDVI and RGB, extracted from the Sentinel2/L2A satellite, of plot 01 of the Fundão Farm during the rice crop cycle in the 19/20 harvest. Red shades indicate higher percentage of exposed soil; uniform dark green shades indicate higher vegetative growth of the crop. Figure 14. Space-time analysis of NDVI and RGB, extracted from the Sentinel2/L2A satellite, of plot 02 of the Fundão Farm during the rice crop cycle in the 19/20 harvest. Red shades indicate higher percentage of exposed soil; uniform dark green shades indicate higher vegetative growth of the crop.
28 4.2 Relief of the property Through the topographic altimetry map, obtained by digital elevation models, in relation to the WGS84 ellipsoid, it is possible to see that plot 01 has altitudes that vary between 81 and 90 meters (Figure 15), varying from lower levels to the south to higher levels to the north, showing the path that the water follows in the plot. Figure 15. Altimetric map of plot 01. The topographic altimetry map for plot 02 has altitudes varying between 90 and 104 meters (Figure 16), varying from lower levels in the southwest to higher levels in the northeast, indicating the path of the irrigation water.
29 Figure 16. Altimetric map of plot 02. In relation to the topographic slope map of plot 01 (Figure 17) it can be seen that the area has slopes that vary between 0 and 6%, with the southern region and the border being the steepest, and the southern region with slopes that do not exceed 6%. Figure 17. Slope map of plot 01. The topographic map of slope in plot 02 (Figure 18) shows that the area has slopes varying between 0 and 7%.
30 Figure 18. Slope map of plot 02. 4.3 Productive potential The productive potential was assessed by processing the normalised difference vegetation index, which is associated with the vigour and health of the crops. In Figures 19 and 20 it is possible to visualise the two plots where the rice crop was implemented in the 19/20 crop year, as well as the spectral behaviour of the NDVI for the year 2019 index that relates to the growth and development of the plants during the rice crop cycle in the different plots. Through the analysis of photointerpretation of NDVI images from both the LANDSAT8/OLI satellite and the satellite SENTINEL2/L2A and the processing of automatic mathematical models from the Land Viewer site it is possible to qualify the plots analysed as having good productivity (around 7200 kg ha-1 ), because the NDVI values are around 0.8 in the period January to March 2020, the period in which the crop is in the reproductive phase, occurring a decline in April, which was already
31 expected, because in this period occurs the senescence and then the harvest. In figure 19, it is observed in the graphic that the green line (NDVI) remains between 0.8 and 0.6 from January 1 to March 20, this means that during this period there was vegetation covering the ground. Figure 19. NDVI analysis via image and annual graph for plot 01. In figure 20, it is observed in the graph that the green line (NDVI) remains between 0.8 and 0.6 from January 1 to March 20, this means that in this period there was vegetation covering the ground, there were two falls in this period, which can be attributed to clouds that interfered with the capture of the image in these two moments, from March 20 the NDVI value begins to decrease, so it is estimated that the harvest has begun.
32 Figure 20. NDVI analysis via image and annual graph for plot 02. 4.4 Irrigation capacity In irrigated rice cultivation one should look at the water resources available to support the development of the crop. Through the irrigation capacity of the dam it is possible to estimate the amount of area that could be cultivated if all the agricultural potential of the property were used. To know the irrigation capacity of a dam it is necessary to determine its volume of water in cubic metres. Traditional techniques use echo sounders, sonar or manual procedures, and measurements are taken in the field, which takes months to complete, making it a costly service. The purpose of this technical report was to use innovative remote sensing technologies to estimate, with a certain degree of accuracy, the irrigation capacity of the dams located on the Fundão Farm. This work contributes to the process of validating these tools. The estimation of the water volume in cubic meters of the dams was obtained by means of geoprocessing, using global digital elevation models adjusted by field measurements carried out previously (Figure 5.8). The
33 resulting topographic model can be seen illustratively in Figures 21, 22, 23 and 24. First the result is provided by means of contour lines, which indicate the points with the same height within the dams (Figures 21 and 22) and then it is possible to visualise the bathymetry in 3D (Figures 23 and 24), showing the depths. In this way, detailed information containing the characteristics of the dams such as depth, volume by quotas, and other calculations that allow for better administration and control of the installed water capacity. This is possible by means of evaluations of the volume of water during different periods of the year, guaranteeing an accurate assessment of the availability of water for irrigation. Figure 21. Isobath curves of the area of dam 01 obtained by SRTM.
34 Figure 22. Isobath curves of the area of dam 02 obtained by SRTM. Figure 23. 3D view of the bathymetry in the area of dam 01 obtained by SRTM.
35 Figure 24. 3D view of the bathymetry in the area of dam 02 obtained by SRTM. It can be observed in Figures 23 and 24 that both have deeper points, which can generate problems in the water catchment when its level drops too low, so through this 3D visualization a correction of these unevennesses can be programmed when its level is low. The calculation of the volume by geoprocessing was carried out from the rectangular grids of the numerical model created. From the numerical grid of bathymetric values obtained for the dam, the volume was calculated by numerical methods, considering the areas and heights or depths represented by each grid cell and its adjacent cells. Finally, all the volumes of the analyzed cells were added and the total volume of the dams was obtained, which was approximately 893,069.81 m3 for dam 01 and 898,806.88 m3 for dam 02. These values were later increased by the volume added by precipitation and by the supply stream, discounting the volume lost by evaporation, during the rice irrigation cycle, reaching 1,071,683 m³ for dam 01 and 1,078,567 m³, available
36 under normal conditions, with the dams filled at the beginning of the rice cycle. These results are shown to be overestimated, around 84,209m³ for dam 01 and 162,968m³ for dam 02 at normal level and 7,915.8 m³ for dam 01 and 240,295m³ for dam 02 at maximum level, when compared to the data measured in the field, constituting a difference of less than 10% for dam 01 and around 20% for dam 02. The calculation of dam volume can be carried out at various levels of accuracy, depending on the degree of adjustment between the model and reality, which is achieved by using a greater number of data and greater processing time. For high degrees of accuracy, it is necessary to study the dam's volume history and precipitation records, which allow modelling the dam's behaviour over the years, which gives a more exact notion of its irrigation capacity. The hydraulic model of water use on the farm during the rice cycle can also be considered. But these studies extrapolated the objectives of the present work. Considering, in simplified form, an average irrigation of 8000 m3 /ha during the rice harvest, in years of normal rainfall, and an estimated feeder stream flow of 900 l/min, one obtains an irrigation potential, for dam 01, of around 112 ha, estimated by SR and 101 ha estimated in the field, i.e., there is evidence that the overestimation for calculating the irrigation potential of the dam is around 10 ha for irrigated rice culture. This does not compromise the area destined for irrigation, which is approximately 50 ha. Moreover, it is observed the possible increase of irrigated area in up to 50%, with only the use of dam 01. For dam 02 an irrigation potential was obtained and around 111 ha, estimated by SR and 91 ha estimated in the field, i.e., there is evidence that the overestimation
43 PONZONI F.J.; SHIMABUKURO Y. E. Sensoriamento Remoto no Estudo da Vegetação. São José dos Campos-SP: INPE-Editora Parêntese, 2007. ROUSE J. W.; HAAS JR, R. H.; SCHELL, J. A.; DEERING, D. W. Monitoring vegetation systems in the Great Plains with ERTS, Third ERTS Symposium, NASA, SP-351 I, 309-317, 1973. RUDORFF, B. F. T. Produtos De Sensoriamento Remoto. Remote Sensing Division. National Institute for Space Research. São José dos Campos-SP. Available at: http://www3.inpe.br/unidades/cep/atividadescep/educasere/apostila.htm# :~:text=Remote%20remote%20Sensing%20remoto%20C3%A9%20term ,of%20devices%20called%20sensors%20remotes. Accessed: 09 Dec. 2020. SANTI, A. L. Relations between soil quality indicators and crop productivity in areas with precision agriculture. Santa Maria, 2005, 175f. Tese (Doutorado) - Programa de Pós-Graduação em Ciência do Solo, Centro de Ciências Rurais, Universidade Federal de Santa Maria, 2007. SANTOS, C. L. M. DE O. et al. Classification of Crops, Pastures, and Tree Plantations along the Season with Multi-Sensor Image Time Series in a Subtropical Agricultural Region. Remote Sensing, v. 11, n. 3, p. 334, 2019. TAQUIA, A. A. L. (2015). Producto Sentinel-2. Available at: < https://es.slideshare.net/AlejandroLe%20sentinel2-espaol>. Accessed on: 22/11/2015. TEIXEIRA, A. M. A.; CANDEIAS, A. L. B.; TAVARES JÚNIOR, J. R. Comparison of vegetation indices for TM/Landsat 5 images with and without atmospheric correction. Brazilian Journal of Geomatics, 5, 524542. 2017 USGS. Landsat Missions: Using the USGS Landsat8 Product. U.S. Department of the Interior - U.S. Geological Survey - NASA. Website: http://landsat.usgs.gov/Landsat8_Using_Product.php. Accessed on: 22/11/2020.
44 VAN DER MEER, F. D.; VAN DER WERFF, H. M. A.; VAN RUITENBEEK, F. J. A. Potential of ESA's Sentinel-2 for geological applications. Remote Sensing of Environment, v. 148, p. 124-133, 2014. ZHANG, X.; LIU, L.; CHEN, X.; XIE, S.; GAO, Y. Fine land-cover mapping in China using Landsat datacube and an operational SPECLibBased approach. Remote Sens. v. 11, p. 1056, 2019.
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