Estimativa de produtividade da cultura da soja baseada na estabilidade temporal de pixels utilizando dados MODIS/EVI

Orientadores: Jansle Vieira Rocha, Rubens Augusto Camargo LamparelliTese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia AgrícolaResumo: A soja é uma das principais commodities do mercado agrícola brasileiro, e está em constante especulação no mercado interno e externo. A estimativa da produtividade com precisão e antecedência utilizando o sensoriamento remoto representa um importante avanço na procura de formas objetivas para previsão de safras no Brasil, uma vez que pode auxiliar a avaliação de rendimento da cultura, servir de apoio à segurança alimentar, ao planejamento econômico e a gestão dos recursos naturais. No entanto, ainda não há no país um sistema operacional para estimar produtividade. O principal objetivo desse estudo foi propor uma metodologia para estimar, por município, a produtividade da soja, baseado em dados espectrais (EVI/MODIS) e dados históricos de rendimento durante os anos safra 2000/2001 a 2010/2011 no estado do Paraná. Esses dados foram utilizados para estabelecer a correlação entre EVI e produtividade da soja por pixel utilizando duas abordagens: por mês (outubro a abril) e por estágios fenológicos (emergência a maturação, emergência a floração, floração a maturação, floração ao enchimento dos grãos), criando-se então dois tipos de mapas de correlação. Com isso foi possível detectar pixels que tinham as melhores correlações ao longo do tempo e ainda encontrar o período mais adequado para estimar a produtividade. Os resultados mostraram que a maior correlação foi encontrada no período de pico vegetativo da cultura para ambas as abordagens. Em seguida comparou-se o desempenho dos mapas de correlação com máscaras de culturas especificas para estimar a produtividade. Os mapas de correlação apresentaram resultados mais significativos, com RMSE de 0.173 ton/ha, enquanto a máscara de cultura específica apresentou RMSE de 0.294 ton/ha. Em seguida selecionamos os pixels temporalmente estáveis dentro dos mapas de correlação por meio da técnica de estabilidade temporal, a fim de incluir somente pixels que apresentassem o mesmo padrão temporal de desenvolvimento durante a safra. A técnica apresentou-se eficiente, selecionando desde pixels puros a pixels com alguma porcentagem da cultura dentro dele, assim, estes pixels foram utilizados para estimar a produtividade da soja durante os onze anos de estudo, também utilizando as abordagens por mês e por fase fenológica. Para a primeira abordagem o período de pico vegetativo apresentou melhor resultado, sendo o mês de fevereiro o que apresentou valores mais próximos aos dados oficiais com RMSE de 0.187 ton/ha, na segunda abordagem o melhor desempenho foi para o período de floração a maturação com RMSE de 0.193 ton/ha e o índice de concordância de Willmott foi de 96% para fevereiro e 95.8% durante a floração e maturação. Esta metodologia mostrou ser eficiente para estimar a produtividade por mês, assim é possível utilizá-la como ferramenta auxiliar na previsão de produtividadeAbstract: Soybean is one of the main commodities of the Brazilian agricultural market, and is subject to constant speculation in internal and external markets. Timely and accurate yield estimation using remote sensing represents an important advance in the search for objective crop forecasting in Brazil, since it may help government to plan storage and/or acquisition of food, serving as support to food security, decision making and management of natural resources. However, an operating crop yield estimating system is not currently available in the country. The main goal of this study was to propose a methodology to estimate soybean yield at county level, based on spectral data (EVI/MODIS) and historical yield data during 2000/2001 to 2010/2011 cropping season, in Parana state. These data were used to establish the correlation between EVI and soybean yield at pixel level using two approaches: by month (October to April) and by phenological stages (emergence to maturity, emergence to flowering, flowering to maturity, flowering to grain filling), generating two types of correlation maps. It was possible to detect pixels that had the best correlation over the crop cycle and still find the most suitable period to estimate yield. The results showed that the highest correlation was found in the vegetative peak period of the crop for both approaches. Then I compared the performance of correlation maps against crop specific mask to estimate soybean yield. The correlation maps showed meaningful results with RMSE of 0.173 ton/ha while the crop specific mask showed RMSE of 0.294 ton/ha. Then I selected the temporally stable pixels within the correlation maps using the temporal stability technique in order to include only pixels that presented the same temporal development pattern during the crop cycle. The technique was efficient, once selected pure pixels or pixels with some percentage of the crop, so these pixels were used to estimate soybean yield during the eleven years of study; also using the approaches by month and by phenological stages. For the first approach the vegetative peak showed better results and February showed values closest to official data with RMSE of 0.187 ton/ ha, the best performance of the second approach was the period from flowering to maturity, with RMSE of 0.193 ton/ ha and Willmott agreement index of 96% for February and 95.8% for the flowering to maturity period. This methodology showed to be efficient to estimate yield monthly, thereby it is possible to use it as an auxiliary tool in yield forecastDoutoradoPlanejamento e Desenvolvimento Rural SustentávelDoutora em Engenharia Agrícol

Determining and mapping the beginning of summer crops cycle in the state of Parana through satellite imagery and rainfall data

Orientadores: Jansle Vieira Rocha, Rubens Augusto Camargo LamparelliDissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia AgricolaResumo: A agricultura está fortemente ligada às condições meteorológicas, tornando-se uma atividade de risco. Sendo assim, um sistema de monitoramento e previsão de safras é necessário para evitar grandes perdas. O monitoramento agrícola, aliado a estudos de varibilidade meteorológica, é de extrema importância, pois determina a melhor época em que deve ocorrer a semeadura, bem como os períodos de maior necessidade hídrica da planta. A utilização do sensoriamento remoto para monitoramento agrícola e sua associação a dados meteorológicos viabilizam o monitoramento de culturas, desde o plantio até a colheita. Desta forma, o objetivo deste trabalho foi mapear culturas de verão (soja e milho) no estado do Paraná por meio de composições decendiais NDVI de imagens do satélite SPOT Vegetation nas safras 2005/2006, 2006/2007 e 2007/2008, e associar a dados de precipitação para determinar e mapear épocas de semeadura. Para isso, foi utilizado o método de classificação digital supervisionada em composições multitemporais das imagens, de forma a gerar máscaras da cultura da soja para cada composição decendial. A verificação da acurácia das máscaras foi realizada utilizando o índice Kappa alcançando valores de 0,70, 0,75 e 0,77, Exatidão Global 0,91, 0,91 e 0,93 e Índice de concordância de Willmot (d) entre 0,93, 0,93 e 0,99. Os perfis temporais de NDVI possibilitaram a detecção do início do ciclo vegetativo das culturas. Foram utilizados dados de precipitação da rede de estações meteorológicas do SIMEPAR e do modelo atmosférico ECMWF. Os dados das estações meteorológicas foram compilados a partir de dados diários a dados decendiais, tendo em vista que as informações do modelo são decendiais. Em seguida foi necessário especializá-las e reamostrá-las para um grid de 1 km x 1 km. Essas duas informações foram interpoladas através do Inverso Quadrado da Distância a fim de obter o perfil de precipitação da região estudada durante a safra de verão. A interpolação de dados de precipitação mostrou-se expressiva, uma vez que os valores interpolados e os valores reais foram significativos quando avaliado pelo índice de concordância 'd' de Willmot, variando entre 0,74 a 0,99. Para as três safras em estudo, apenas o comportamento da última foi distinta das demais, onde foi possível detectar atraso da chuva de um mês em relação às outras safras. Com a espacialização da precipitação dentro do estado foi possível fazer o cruzamento entre as máscaras da cultura de verão e os mapas de precipitação decendial, de modo a descobrir o inicio do desenvolvimento do ciclo da cultura. Desta forma foi possível perceber que houve um retardamento no início do ciclo da cultura para regiões em que a semeadura ocorreu antes do período mais chuvoso. A associação entre o perfil temporal de NDVI e precipitação permitiu identificar e mapear diferenças entre épocas de início de ciclo dentro do estado do ParanáAbstract: Agriculture is highly dependent on meteorological conditions, making it a risky activity, so, in order to avoid large losses, it is necessary to devlop an appropriate crop yield forecasting system. Agricultural monitoring coupled with studies of weather variability is extremely important to determine the best time to sown as well as periods of higher water requirement by the plants. The use of remote sensing for agricultural monitoring in addition to meteorological data enables crop monitoring from planting to harvest. Thus, the goal of this study was to map summer crops in the state of Parana through dekadal composition of SPOT Vegetation NDVI imagery for the 2005/2006, 2006/2007 and 2007/2008 cropping seasons. Supervised classification of multitemporal image composites was used to generate cropland masks for each dekad. Accuracy assessment was performed using Kappa Index reaching values of 0.70, 0.75 and 0.77 and overall accuracy 0.91, 0.91 and 0.93. NDVI temporal profiles were used to detect the beginning of the crop vegetative cycles. Rainfall data from SIMEPAR network of meteorological ground stations and ECMWF atmospheric model were used. The data from the ground stations were compiled from daily to dekadal in order to comply with model data, which is a ten days compilation . Then it was necessary to spatialize and resample them to a 1 km x 1 km grid. These two data were interpolated using the inverse distance to square method to extract the profile of precipitation in the region during the summer harvesting season. The interpolation of rainfall generated good results, once the interpolated values as copared to actual values were significant when evaluated by the agreement index 'd' of Willmot, ranging between 0.74 to 0.99. For the three cropping seasons studied only the behavior of last season was distinct from the others , it was possible to detect a rain delay of one month in relation to other seasons. With the rainfall spacialization within the state it was possible to cross the masks of the summer crops with the maps of the decadal rainfall, in order to discover the beginning of the crop vegetative cycle. So it was possible to see that there was a delay at the beginning of the crop cycle in areas where the sowing occurred before the rainy season. The association between NDVI temporal profile and rainfall allowed to identify and to map the differences among starting of the crop vegetative cycle in the state of ParanaMestradoPlanejamento e Desenvolvimento Rural SustentávelMestre em Engenharia Agrícol

Correlation maps to assess soybean yield from EVI data in Paraná State, Brazil

Vegetation indices are widely used to monitor crop development and generally used as input data in models to forecast yield. The first step of this study consisted of using monthly Maximum Value Composites to create correlation maps using Enhanced Vegetation Index (EVI) from Moderate Resolution Imaging Spectroradiometer (MODIS) sensor mounted on Terra satellite and historical yield during the soybean crop cycle in Paraná State, Brazil, from 2000/2001 to 2010/2011. We compared the ability of forecasting crop yield based on correlation maps and crop specific masks. We ran a preliminary regression model to test its ability on yield estimation for four municipalities during the soybean growing season. A regression model was developed for both methodologies to forecast soybean crop yield using leave-one-out cross validation. The Root Mean Squared Error (RMSE) values in the implementation of the model ranged from 0.037 t ha−1 to 0.19 t ha−1 using correlation maps, while for crop specific masks, it varied from 0.21 t ha−1 to 0.35 t ha−1. The model was able to explain 96 % to 98 % of the variance in estimated yield from correlation maps, while it was able to explain only 2 % to 67 % for crop specific mask approach. The results showed that the correlation maps could be used to predict crop yield more effectively than crop specific masks. In addition, this method can provide an indication of soybean yield prior to harvesting

Correlation maps to assess soybean yield from EVI data in Paraná State, Brazil

Vegetation indices are widely used to monitor crop development and generally used as input data in models to forecast yield. The first step of this study consisted of using monthly Maximum Value Composites to create correlation maps using Enhanced Vegetation Index (EVI) from Moderate Resolution Imaging Spectroradiometer (MODIS) sensor mounted on Terra satellite and historical yield during the soybean crop cycle in Paraná State, Brazil, from 2000/2001 to 2010/2011. We compared the ability of forecasting crop yield based on correlation maps and crop specific masks. We ran a preliminary regression model to test its ability on yield estimation for four municipalities during the soybean growing season. A regression model was developed for both methodologies to forecast soybean crop yield using leave-one-out cross validation. The Root Mean Squared Error (RMSE) values in the implementation of the model ranged from 0.037 t ha−1 to 0.19 t ha−1 using correlation maps, while for crop specific masks, it varied from 0.21 t ha−1 to 0.35 t ha−1. The model was able to explain 96 % to 98 % of the variance in estimated yield from correlation maps, while it was able to explain only 2 % to 67 % for crop specific mask approach. The results showed that the correlation maps could be used to predict crop yield more effectively than crop specific masks. In addition, this method can provide an indication of soybean yield prior to harvesting.735462470COORDENAÇÃO DE APERFEIÇOAMENTO DE PESSOAL DE NÍVEL SUPERIOR - CAPESSem informaçã

Máscara da cultura de verão no estado do Paraná por meio de composições RGB

The search for less subjectivity in estimates has increased the use of remote sensing for agricultural monitoring and crop yield estimates, leading to more flexibility in data acquisition and less costs comparing to traditional methods such as census and surveys. Lower spatial resolution satellite images with higher frequency in image acquisition have shown to be adequate for generating cropland masks and monitoring in large areas. The main goal of this study was to map the summer cropland in the state of Paraná, Brazil, using dekadal composition of NDVI SPOT Vegetation data for 2005/2006, 2006/2007 and 2007/2008 cropping seasons. Supervised classification of multitemporal image composites was used to map summer cropland masks for each dekad. Accuracy assessment was performed using Kappa Index, Overall Accuracy and Willmott (d) agreement index. The results showed good accuracy and that the methodology used, with free low resolution data, allows generating summer cropland masks at state level.Pages: 313-31

Correlation maps to assess soybean yield from EVI data in Paraná State, Brazil

ABSTRACT Vegetation indices are widely used to monitor crop development and generally used as input data in models to forecast yield. The first step of this study consisted of using monthly Maximum Value Composites to create correlation maps using Enhanced Vegetation Index (EVI) from Moderate Resolution Imaging Spectroradiometer (MODIS) sensor mounted on Terra satellite and historical yield during the soybean crop cycle in Paraná State, Brazil, from 2000/2001 to 2010/2011. We compared the ability of forecasting crop yield based on correlation maps and crop specific masks. We ran a preliminary regression model to test its ability on yield estimation for four municipalities during the soybean growing season. A regression model was developed for both methodologies to forecast soybean crop yield using leave-one-out cross validation. The Root Mean Squared Error (RMSE) values in the implementation of the model ranged from 0.037 t ha−1 to 0.19 t ha−1 using correlation maps, while for crop specific masks, it varied from 0.21 t ha−1 to 0.35 t ha−1. The model was able to explain 96 % to 98 % of the variance in estimated yield from correlation maps, while it was able to explain only 2 % to 67 % for crop specific mask approach. The results showed that the correlation maps could be used to predict crop yield more effectively than crop specific masks. In addition, this method can provide an indication of soybean yield prior to harvesting

Classification of Crops, Pastures, and Tree Plantations along the Season with Multi-Sensor Image Time Series in a Subtropical Agricultural Region

Timely and efficient land-cover mapping is of high interest, especially in agricultural landscapes. Classification based on satellite images over the season, while important for cropland monitoring, remains challenging in subtropical agricultural areas due to the high diversity of management systems and seasonal cloud cover variations. This work presents supervised object-based classifications over the year at 2-month time-steps in a heterogeneous region of 12,000 km2 in the Sao Paulo region of Brazil. Different methods and remote-sensing datasets were tested with the random forest algorithm, including optical and radar data, time series of images, and cloud gap-filling methods. The final selected method demonstrated an overall accuracy of approximately 0.84, which was stable throughout the year, at the more detailed level of classification; confusion mainly occurred among annual crop classes and soil classes. We showed in this study that the use of time series was useful in this context, mainly by including a small number of highly discriminant images. Such important images were eventually distant in time from the prediction date, and they corresponded to a high-quality image with low cloud cover. Consequently, the final classification accuracy was not sensitive to the cloud gap-filling method, and simple median gap-filling or linear interpolations with time were sufficient. Sentinel-1 images did not improve the classification results in this context. For within-season dynamic classes, such as annual crops, which were more difficult to classify, field measurement efforts should be densified and planned during the most discriminant window, which may not occur during the crop vegetation peak

Assessing the capability of MODIS to monitor mixed pastures with high-intensity grazing at a fine-scale

MODIS time series carries valuable long-term data essential to support several studies such as biogeochemical modelling. However, there is a lack of validation studies applying MODIS data at a fine-scale to monitor pasture management practices. In this study, we assessed the potential of MODIS sensor in monitoring at a fine-scale four intensively managed mixed-pastures fields located in São Paulo State, Brazil. The MODIS spectral response was compared with Sentinel-2, and the ability of the two sensors in predicting aboveground biomass (AGB) and canopy height (CH) was assessed using the Random Forest algorithm. EVI images from MODIS and Sentinel-2 were correlated with field measurements of AGB and CH. The prediction performance of AGB (R2: Sentinel-2 = 38%; MODIS = 42%) and CH (R2: Sentinel-2 = 69%; MODIS = 85%) models was superior using EVI data from MODIS than Sentinel-2, highlighting MODIS ability to monitor small and intensively managed pasture fields