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1 SUMMARY Monitoring spectral characteristics over time using satellite images is scientifically recognised as providing information that can be associated with the type of cultivar or variety, vigour, density, health, nutrition and phenological stages, by conveniently using the electromagnetic information reflected by the earth's surface, using, for example, so-called vegetation indices. Knowledge of the spectral signature of different irrigated rice cultivars is an important tool for monitoring and inspecting agricultural crops, as it can facilitate the work of public certification bodies and grain receiving industries. This study monitored the cultivars IRGA 409, IRGA 424, IRGA 426, IRGA 428, IRGA 429, IRGA 430, PUITÁ and GURI. The spectral characteristics of the cultivars were described by associating field data and NDVI time profiles throughout the crop cycle. This was done using 11 images from the LANDSAT8/OLI satellite, orbits 224/80 and 225/80. The spectral curves throughout the cycle showed divergent spectral behaviour between the different cultivars in terms of the start of growth, rapid growth rate, time and value of maximum NDVI, senescence rate. The spectral behaviour of the different cultivars can be differentiated, but it can be seen that differentiation depends critically on the quality of the data and the processing. Keywords: Spectral signature, NDVI curve, satellite monitoring, agricultural surveillance.
2 SUMMARY CHAPTER 1 3 CHAPTER 2 5 CHAPTER 3 11 CHAPTER 4 31 CHAPTER 5 32
3 CHAPTER 1 INTRODUCTION The importance of rice both commercially and in terms of human nutrition has led to an increase in its production. It is the staple food of more than three billion people and the second most cultivated cereal in the world, second only to maize. Currently, rice is the crop with the greatest potential for increasing production and accounts for 20 per cent of the calories consumed in the world's diet (SOSBAI, 2016). Brazil ranks 9th in the world among countries with the highest rice production and 1st among Mercosur countries (CONAB, 2017). Rio Grande do Sul stands out as the largest national producer, accounting for 70% of the total produced in Brazil, thus guaranteeing the supply of this cereal to the Brazilian population (SOSBAI, 2016). Rice in Rio Grande do Sul is produced in 131 municipalities located in the southern half of the state, but it is on the western border that the highest yields are achieved, with the municipalities of Itaqui and Uruguaiana standing out with yields that exceed 7,000 kg per hectare (IRGA, 2015). The knowledge and entrepreneurial spirit of the rice grower in the use of more refined cultivation technologies and the availability by research in RS of cultivars with high production potential, with characteristics that meet the requirements of the cereal agro-industrial chain, have contributed significantly to this productivity and, of course, to Brazil's overall rice production (MAGALHÃES and FAGUNDES, 2015). The most widely used cultivars in RS, mainly due to their adaptability to the soil and climate and market preference, are IRGA 424, IRGA 429, IRGA 426, IRGA 430 and IRGA 409. The development cycle of the cultivars in RS can vary from super-early, with a cycle of less than 100 days, early, from 110 to 120 days, medium, from 121 to 130 days, and semi-early, greater than 130 days. There are four types of plant architecture: traditional (tall plants),
4 intermediate, semi-dwarf/filipin (modern/filipin) and semi-dwarf/American (modern/American). Distinguishing between plant groups helps growers as it facilitates decision-making regarding the management practices to be adopted, the diagnosis of biotic and abiotic stresses, and whether or not plants are susceptible to lodging (MAGALHÃES and FAGUNDES, 2015). In this context, remote sensing is a tool with a high potential to help monitor, manage or inspect crops. Remote monitoring with satellite images is scientifically recognised for providing information that can be associated with the vigour, density, health, nutrition and development of vegetation by conveniently using the electromagnetic information reflected by the earth's surface, using, for example, so-called vegetation indices (TASUMI and ALLEN, 2007; SAKAMOTO et al., 2010). Methodologies that use satellite remote sensing can help and facilitate the various agents involved in production, as well as various aspects such as crop management, monitoring and inspection. For the producer, the spatio-temporal distribution of information at the level of agricultural plots allows for remote monitoring of the behaviour of crops throughout their development cycle, resulting in lower travel costs when monitoring commercial crops, as it allows for better planning and use of field trips. For the industry, obtaining spectro-temporal curves for each cultivar facilitates its identification and traceability, which generates greater precision and accuracy when evaluating the grain, as each cultivar has an added value. For research institutes such as IRGA, for example, both space-time distribution and obtaining spectro-temporal curves help to monitor and validate crops for the purpose of seed certification. The aim of this research is to characterise the spectro-temporal curves of the IRGA 409, IRGA 424, IRGA 426, IRGA 429 and IRGA 430 cultivars in the municipality of Itaqui, RS, using remote sensing techniques.
11 CHAPTER 3 PRESENTATION OF THE RESEARCH AND ANALYSIS OF THE RESULTS Using NDVI extracted from 11 Landsat8/OLI images over the course of the irrigated rice development cycle, this research identified the spectral characteristics of five irrigated rice cultivars distributed over 81 agricultural plots in the municipality of Itaqui, RS. In this way it was possible to identify the characteristic curves, the average NDVI values and the period in days of each stage of development of the different cultivars. 3.2 Spatial configuration of NDVI Figures 4, 5 and 6 show the NDVI mapping for the different cultivars in each plot analysed. A three-colour gradient scale was used, where the overlapping area of two colours forms a smooth transition that represents different stages of vegetation or soil. - Blue indicates targets without vegetation and absorbers of infrared radiation, with negative NDVI values (-1 to -0.4), such as water from dams or deeper flooded areas; - Green indicates NDVI values around zero (-0.4 to 0.4), corresponding to targets with infrared reflectance similar to red, sometimes higher and sometimes lower, such as exposed soils or soils with little vegetation cover, shallow water mirrors or clouds and atmospheric aerosols; - Red indicates intermediate to high NDVI values, corresponding to vegetation of greater height and cover than grass, covering the ground to varying degrees, indicated by the intensity of the colour, with the tallest and densest vegetation corresponding to the most intense red.
12 Figure 4 - NDVI map for the 11 images analysed in each agricultural plot in the 2016/2017 harvest.
13 Figure 5 NDVI map for the 9 images analysed in each field in the 2016/2017 harvest for the Guri and Puitá cultivars.
14 Figure 6 - NDVI map for the 9 images analysed in each field in the 2016/2017 harvest for the IRGA 428 cultivar.
15 Figure 7 - NDVI map for the 9 images analysed in each field in the 2016/2017 harvest for the IRGA 424 cultivar.
16 Figure 8. NDVI map for the 9 images analysed in each field in the 2016/2017 harvest for the IRGA 409 cultivar.
17 Figure 9. NDVI map for the 9 images analysed in each field in the 2016/2017 harvest for the IRGA 424 cultivar.
18 In general, the figures show a progressive increase in vegetation cover over the course of the cycle, with a subsequent decrease in NDVI due to senescence and the advance of the harvest. Some images show the presence of clouds or aerosols, which appear as irregular shapes (clouds) with variable and diffuse tones, or as a uniform "veil" (fine to coarse aerosols), but these images have internal qualitative and quantitative comparative value after careful analyses and any corrections. Images with localised atmospheric interference areonly useful for the quantitative assessment of NDVI when local corrections are taken into account, with the necessary verification precautions, or by paying attention to the least affected pixels (use of the maximum). 3.3 Characteristics of the IRGA 409 cultivar A medium-cycle cultivar with panicle primordia appearing at 65 days, full bloom at 89 days and physiological maturity at 126 days (CERATTI, 2014). It was the first semi-dwarf cultivar of the modern/filipino plant type, launched in partnership by Embrapa and IRGA in 1979. It stands out for its excellent grain quality and high productivity. Its main limitations are susceptibility to brusone and iron toxicity. It is a cultivar that has high abrasiveness on the leaves and husk and has a variable size arista on some grains at the end of the panicle (MAGALHÃES and FAGUNDES, 2015). It is highly sought after in the industry because of its high grain quality. 47 agricultural plots and spectro-temporal curves of the IRGA 409 cultivar were analysed. The spectral signature of the average NDVI values can be seen
19 in Graph 1. Graph 1. Spectro-temporal curve of the mean NDVI values for 47 curves analysed for the IRGA 409 cultivar in 11 Landsat8/OLI satellite images.
20 Graph 2. Spectro-temporal curve of NDVI values for 3 plots analysed for the IRGA 409 cultivar in 11 Landsat8/OLI satellite images. 3.4 Characteristics of the IRGA 424 cultivar This medium-cycle cultivar can vary according to location, harvest and sowing time (NETO, MARCHESAN, et al., 2007). The first panicle appears at 62 days with full bloom at 96 days and physiological maturity at 132 days (CERATTI, 2014). It stands out for its high production potential and good industrial and cooking quality, but the price paid by the industry is around R$5.00 less per 50kg bag than the 409 cultivar. It is short, has hairy leaves, is tolerant to iron toxicity and resistant to brusone. It is especially suitable for cultivation in the South Zone and Campanha regions of RS due to its good adaptation to low average temperature conditions (MAGALHÃES and FAGUNDES, 2015).
27 industrial quality. It comes from a selection of crosses between Camba INTA PROARROZ x Puita INTA CL (CERATTI, 2014). Graph 10 - Spectro-temporal curve of the average NDVI values for the GURI INTA CL 11 cultivar from the Landsat8/OLI satellite images. 3.10 Characteristics of the cultivar PUITÁ INTA CL The PUITÁ INTA CL variety was selected for its resistance to herbicides belonging to the imidazolinone group in populations generated by inducing mutations. This gives it the particularity of not being a genetically modified variety. The main advantage of Clearfield® (CL) technology is the control of RED RICE, which enables optimisation of cultivation practices and the expression of the variety's genetic potential.
28 Graph 11 - Spectro-temporal curve of the average NDVI values for the cultivar PUITÁ INTA CL 11 images from the Landsat8/OLI satellite. 3.11 Analysing NDVI spectro-temporal curves As a first tool for quantitatively analysing the spectro-temporal graphs throughout the rice cycle, the methodology used by the USGS to assess changes in vegetation was tested, as explained in the methodology. It was possible to observe divergent behaviour between the different varieties under analysis. Table 1 shows the duration in days of each stage of rice development for the different cultivars analysed. Table 2 shows the NDVI values for the different periods of each cultivar's development cycle. Table 1. Duration in days of each development period analysed by the USGS (HARGROVE, SPRUCE, et al., 2010) and FAO (ALLEN, PEREIRA, et al., 1998) methods during the rice cycle for the different cultivars analysed.
29 USGS method IRGA 409 IRGA 424 IRGA 426 IRGA 429 IRGA 430 FAO method MIN 20% 45 45 59 58 46 Home Growth MIN 80% 29 34 13 18 31 Rapid Growth MAX 23 18 24 20 22 Ripening MAX 15 15 5 16 13 MIN 80% 20 19 28 17 17 Senescence MIN 20% 29 30 12 38 11 Table 2. Average NDVI values for each development period analysed by the USGS method during the rice cycle for the different cultivars analysed. USGS method IRGA 409 IRGA 424 IRGA 426 IRGA 429 IRGA 430 MIN 20% 0.39 0.38 0.39 0.37 0.36 MIN 80% 0.69 0.71 0.70 0.72 0.72 MAX 0.79 0.80 0.81 0.83 0.83 MAX 0.80 0.82 0.80 0.84 0.84 MIN 80% 0.71 0.73 0.74 0.78 0.75 MIN 20% 0.47 0.49 0.57 0.58 0.51 The initial 20% period, from sowing to the start of growth, lasted 45 and 46 days for the cultivars IRGA 409, 424, 430; and 58 and 59 days for the cultivars IRGA 429, 426. Lower NDVI values of between 0.36 and 0.39 were observed for all cultivars, due initially to the soil exposed at sowing and the application of herbicides (NOBRE, 2010) and later the entry of water. During this period, the first nitrogen application and water intake must have already taken place (SOSBAI, 2016). The water layer causes a decrease in NDVI values, while nitrogen fertilisation makes nitrogen available to the seedlings (more quickly due to solubilisation), leading to accelerated vegetative development marked by the start of rapid growth and consequently an increase in NDVI (WANG, HUANG, et al., 2015). At the 80 per cent or rapid growth stage, the increase in biomass was already expected, leading to an increase in NDVI values, which ranged from 0.69 to 0.72. This period occurred between two and four weeks after the start of growth (minimum 20%), as shown in Table 1. At this stage, there is total coverage of the soil/water blade by the vegetation (ALLEN and PEREIRA, 2009), and it is quite likely that the crop is entering the reproductive stage (R1), which is characterised by the differentiation of the floral primordium. At this stage, the second nitrogen fertilisation takes place and the number of spikelets
30 in each panicle is defined (SOSBAI, 2016). The maximum NDVI values occurred during the crop's ripening period (HARGROVE, SPRUCE, et al., 2010), when vegetative growth ceases and full flowering of the crop begins, at which point most of the plants (main stalks and tillers) have their panicles exposed and spikelets open (SOSBAI, 2016). NDVI values in this period ranged from 0.79 to 0.84 (Table 2). This moment is characterised by the greatest risk of yield loss, and the ambient temperature cannot reach 17°C or less (NOBRE, 2010). The duration of the ripening stage varied between 29 and 38 days depending on the cultivar (Table 1). After the NDVI values reach their maximum, they decline, which is characterised by the senescence or death of the crop (WANG, HUANG, et al., 2015). The beginning of this period indicates that the harvest is approaching, and all that is needed is the ideal humidity, close to 22% of grain moisture, for it to be carried out (SOSBAI, 2016). NDVI values fell to between 0.71 and 0.78. At harvest, the NDVI values return to lower levels, as shown in the graph by the area on the right (minimum 20%) with NDVI values between 0.47 and 0.58 (Table 2). From the above, there is evidence that the shape of the curve as well as the NDVI values and the duration of the different development periods can be a support tool for identifying cultivars, varieties and phenological stages of the rice crop planted in each plot or field analysed using Landsat images.
31 CHAPTER 4 FINAL CONSIDERATIONS Remote sensing techniques are proving to be valuable tools to support the monitoring, management and inspection of crops for public certification bodies, the grain receiving industry and financing agencies, as well as the producer and farm manager themselves. In this regard, the Central Bank's resolution No. 4,427 of 25 June 2015 authorised the use of remote sensing for the purposes of monitoring rural credit operations (BRASIL, 2015). This move signals new trends in the monitoring and management of agriculture in Brazil. This research contributes to identifying the spectro-temporal behaviour of NDVI in different plots of known characteristics containing irrigated rice. Field validation of the cultivars identified using this methodology is underway. The curves obtained constitute the start of a "spectral library" of the various crops used in the Itaqui, RS region. In order to use images from sensors on board satellites for agricultural monitoring purposes, it is necessary to obtain as many cloud-free images as possible during the harvest. In this study, the fact that the study area overlapped two orbits of the Landsat8 satellite made it easier to take scenes, totalling twenty images. It was possible to discard nine that had cloud interference and still have eleven images suitable for monitoring the harvest. Therefore, Landsat8/OLI satellite images, when in areas of overlapping orbits, become a suitable tool for agricultural monitoring. There is evidence that the temporal NDVI profile of irrigated rice crops can support the identification of different cultivars and varieties, and can be used by the industry, seed certification bodies and/or funding agencies as a tool to support their monitoring and mandatory inspection. However, further studies are needed to improve the USGS methodology for our region.
32 CHAPTER 5 REFERENCES ALLEN, R. G. et al. FAO Irrigation and drainage paper No. 56. In: FAO Food and Agriculture Organisation of the United Nations. Rome: [s.n.], 1998. p. 26-40. ALLEN, R. G.; PEREIRA, L. S. Estimating crop coefficients from fraction of ground cover and height. Irrig. Sci., 28, 2009. 17-34. BRAZIL, B. C. D. RESOLUTION NO. 4427, OF 25 JUNE 2015. BRASILIA, p. 4. 2015. CERATTI. Ceratti Seeds. Invest in Productivity, 2014. Available at: <http://sementesceratti.com.br/>. Accessed on: 04 July 2017. CHAVEZ, J. Image-based atmospheric corrections - revisited and improved. Protogrammetric Engineering and Remote Sensing, 62, n. 9, 1996. 10251036. CONAB, C. N. D. A. National Supply Company, 2017. Available at: <www.conab.gov.br>. Accessed on: 11 March 2017. HARGROVE, W. W. et al. Toward a National Early Warning System for Forest Disturbances Using Remotely Sensed Land Surface Phenology. USGS, 2010. Available at : <https://www.geobabble.org/~hnw/first/ncdc/slideshow.html>. Accessed on: 13 June 2017.
33 IRGA. 2014/2015 CROP REPORT. GOVERNMENT OF THE STATE OF RIO GRANDE DO SUL. ITAQUI, p. 1-3. 2015. JENSEN, J. R. Remote sensing of the environment: a perspective on terrestrial resources. [S.l.]: Parêntese, v. 2, 2011. 598 p. Authorised translation. MAGALHÃES, A. M. D.; FAGUNDES, P. R. Embrapa. Embrapa Technological Information Agency, 2015. Available at: <http://www.agencia.cnptia.embrapa.br/gestor/arroz/arvore/CONT000fojvoko c02wyiv80bhgp5povqqj3b.html>. Accessed on: 06 June 2017. NETO, F. P. M. et al. GENETIC GAIN IN PRODUCTIVE POTENTIAL OF IRRIGATED RICE IN RIO GRANDE DO SUL, AFTER THE LAUNCH OF CULTIVAR BR-IRGA 409. Brazilian Congress on Irrigated Rice. [S.l.]: [s.n.]. 2007. p. BRAZIL. NOBRE, F. L. D. L. SPECTROTEMPORAL CHARACTERISATION OF IRRIGATED RICE CROPS USING MODIS IMAGES. PELOTAS: UFPEL, 2010. ROUSE, J. W. et al. Monitoring Vegetation Systems in the Great Plains with ERTS. Third Earth Resources Technology Satellite-1 Symposium. Greenbelt: NASA. 1974. SAKAMOTO, T. et al. A Two-Step Filtering approach for detecting maize and soybean phenology with time-series MODIS data. Remote Sensing of
34 Environment, 114, 2010. 2146-2159. SOSBAI, R. T. D. C. D. A. I. Irrigated rice: technical research recommendations for southern Brazil. ISBN 978-85-69582-02-1. ed. Pelotas: [s.n.], 2016. 200 p. TASUMI, M.; ALLEN, R. G.; TREZZA, R. Estimation of at-surface reflectance and albedo from satellite for routine, operational calculation of land surface energy balance. Journal Hydrology Engineering, 2007. WANG, J. et al. Estimation of rice phenology date using integrated HJ-1 CCD and Landsat-8 OLI vegetation indices time-series images. Journal of Zhejiang University-SCIENCE B, v. 16, 14 October 2015. p. 832-844.
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