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Fast and accurate quantification of the available pasture biomass is essential to support grazing management decisions in intensively managed fields. The increasing temporal and spatial resolutions offered by the new generation of orbital platforms, such as Planet CubeSat satellites, have improved the capability of monitoring pasture biomass using remotely sensed data. Here, we assessed the feasibility of using spectral and textural information derived from PlanetScope imagery for estimating pasture aboveground biomass (AGB) and canopy height (CH) in intensively managed fields and the potential for enhanced accuracy by applying the extreme gradient boosting (XGBoost) algorithm. Our results demonstrated that the texture measures enhanced AGB and CH estimations compared to the performance obtained using only spectral bands or vegetation indices. The best results were found by employing the XGBoost models based only on texture measures. These models achieved moderately high accuracy to predict pasture AGB and CH, explaining 65% and 89% of AGB (root mean square error (RMSE) = 26.52%) and CH (RMSE = 20.94%) variability, respectively. This study demonstrated the potential of using texture measures to improve the prediction accuracy of AGB and CH models based on high spatiotemporal resolution PlanetScope data in intensively managed mixed pastures.

In Brazil, the State of Goiás is one of sugarcane expansion's frontiers to meet the growing demand for biofuels. The objective of this study was to identify the municipalities where there were replacement of annual crops (mainly grains) by sugarcane in the state of Goiás, as well as indicate correlations between the sugarcane expansion and the family farming production, in the period between 2005 and 2010. For this purpose, grains crop mask and sugarcane crop mask, obtained from satellite images, were intersected using geoprocessing techniques. It was also used IBGE data of sugarcane production and planted area, and data of family farming production linked with the National Food Acquisition Program (PAA), in relation to the number of cooperatives and family farmers. The crops masks and data tables of the National Food Acquisition Program were provided by National Food Supply Agency. There were 95 municipalities that had crops replacement, totaling 281,554 hectares of grains converted to sugarcane. We highlight the municipalities of Santa Isabel, Iaciara, Maurilândia, and Itapaci, where this change represented more than half of their agricultural areas. In relation to family farming, the sugarcane expansion in the state of Goiás has not affected their activities during the period studied.

Some models have been developed using agrometeorological and remote sensing data to estimate agriculture production. However, it is expected that the use of SAR images can improve their performance. The main objective of this study was to estimate the sugarcane production using a multiple linear regression model which considers agronomic data and ALOS/PALSAR images obtained from 2007/08, 2008/09 and 2009/10 cropping seasons. The performance of models was evaluated by coefficient of determination, t-test, Willmott agreement index (d), random error and standard error. The model was able to explain 79%, 12% and 74% of the variation in the observed productions of the 2007/08, 2008/09 and 2009/10 cropping seasons, respectively. Performance of the model for the 2008/09 cropping season was poor because of the occurrence of a long period of drought in that season. When the three seasons were considered all together, the model explained 66% of the variation. Results showed that SAR-based yield prediction models can contribute and assist sugar mill technicians to improve such estimates.

The climate variability between the growth and harvesting of sugar cane is very important because it directly affects yield. The MODIS sensor has characteristics like spatial and temporal resolution that can be applied to monitoring of vegetative vigor variability in the land surface and then, temporal profiles generation. Agro meteorological data from ECMWF model are free and easy to access and have a good representation of reality. In this study, we used the period between sugar cane growth and harvest in the state of Sao Paulo, Brazil, from temporal profiles selecting of NDVI behavior. For each period the precipitation, evapotranspiration, global radiation, length (days) and degree-days were accumulated. The periods were presented in a map format on MODIS spatial resolution of 250 meters. The results showed the spatial variability of climate variables and the relationship to the reality presented by official data.

Coffee production was closely linked to the economic development of Brazil and, even today, coffee is an important product of the national agriculture. The State of Minas Gerais currently accounts for 52% of the whole coffee area in Brazil. Remote sensing data can provide information for monitoring and mapping of coffee crops, faster and cheaper than conventional methods. In this context, the objective of this study was to assess the effectiveness of coffee crop mapping in Monte Santo de Minas municipality, Minas Gerais State, Brazil, from fraction images derived from MODIS data, in both dry and rainy seasons. The Spectral Linear Mixing Model was used to derive fraction images of soil, coffee, and water/shade. These fraction images served as input data for the supervised automatic classification using the SVM - Support Vector Machine approach. The best results concerning Overall Accuracy and Kappa Index were obtained in the classification of the dry season, with 67% and 0.41, respectively.

The aim of this study was to group temporal profiles of 10-day composites NDVI product by similarity, which was obtained by the SPOT Vegetation sensor, for municipalities with high soybean production in the state of Paraná, Brazil, in the 2005/2006 cropping season. Data mining is a valuable tool that allows extracting knowledge from a database, identifying valid, new, potentially useful and understandable patterns. Therefore, it was used the methods for clusters generation by means of the algorithms K-Means, MAXVER and DBSCAN, implemented in the WEKA software package. Clusters were created based on the average temporal profiles of NDVI of the 277 municipalities with high soybean production in the state and the best results were found with the K-Means algorithm, grouping the municipalities into six clusters, considering the period from the beginning of October until the end of March, which is equivalent to the crop vegetative cycle. Half of the generated clusters presented spectro-temporal pattern, a characteristic of soybeans and were mostly under the soybean belt in the state of Paraná, which shows good results that were obtained with the proposed methodology as for identification of homogeneous areas. These results will be useful for the creation of regional soybean "masks" to estimate the planted area for this crop.

This study aimed at identifying different conditions of coffee plants after harvesting period, using data mining and spectral behavior profiles from Hyperion/EO1 sensor. The Hyperion image, with spatial resolution of 30 m, was acquired in August 28th, 2008, at the end of the coffee harvest season in the studied area. For pre-processing imaging, atmospheric and signal/noise effect corrections were carried out using Flaash and MNF (Minimum Noise Fraction Transform) algorithms, respectively. Spectral behavior profiles (38) of different coffee varieties were generated from 150 Hyperion bands. The spectral behavior profiles were analyzed by Expectation-Maximization (EM) algorithm considering 2; 3; 4 and 5 clusters. T-test with 5% of significance was used to verify the similarity among the wavelength cluster means. The results demonstrated that it is possible to separate five different clusters, which were comprised by different coffee crop conditions making possible to improve future intervention actions.Este trabalho teve o objetivo de identificar diferentes condições na cultura do café, após a colheita, utilizando mineração de dados e curvas espectrais obtidas do sensor Hyperion/EO1.A imagem Hyperion, com resolução espacial de 30 m,foi obtida em 28 de agosto de 2008, fim de safra do café na área de estudo.Como pré-processamentos, foram realizadas a correção atmosférica através do algoritmo Flaash e a correção do sinal/ruído pelo algoritmo MNF (Minimum Noise Fraction Transform). Posteriormente, foram geradas curvas espectrais (38) de 150 comprimentos de onda, que foram analisadas através do algoritmo EM (Expectation-Maximization), considerando simulações de 2; 3; 4 e 5 clusters. Para verificar a igualdade das médias dos comprimentos de onda dos clusters, foi utilizado o teste t, com 5% de significância. Os resultados mostraram que foi possível separar os cafés em cinco diferentes grupos, segundo sua condição pós-colheita, possibilitando melhorar ações futuras de intervenção.

O objetivo deste trabalho foi avaliar as informações obtidas das imagens do satélite Landsat/TM5, utilizando técnicas de Análise por Componentes Principais (ACP) e Fator de Iluminação oriundo de um Modelo de Elevação do Terreno, calculado a partir de imagens ASTER, no mapeamento de áreas de café em terreno montanhoso. As imagens utilizadas (três) foram corrigidas para o efeito da atmosfera e cobriram, temporalmente, o ciclo da cultura. Foram calculadas as componentes principais e escolhidas as duas primeiras, as quais possuíam 94% das informações, para a definição das amostras. As amostras resultantes da ACP foram utilizadas na classificação supervisionada cujo resultado foi comparado com uma classificação convencional e uma classificação multitemporal convencional. A acurácia das classificações foi realizada por meio do cálculo da Exatidão Global e do Coeficiente Kappa, tendo como base uma máscara da área cafeeira da região. Os resultados mostraram que a técnica de ACP foi efetiva no estabelecimento de classes de iluminação, assim como na escolha das amostras, apesar de estas não terem representado a área efetivamente classificada. Em função disto, as classificações foram mais acuradas, principalmente aquela que considerou todos os pixels de cada imagem classificada individualmente pelo método da ACP, confirmando a importância do aspecto multitemporabilidade .The main goal of this study was to evaluate the information produced from Landsat/TM5 images using Principal Component Analysis (PCA) and Illumination Factor built from Digital Elevation Model from ASTER images for coffee areas mapping in complex relief. Three Landsat images were used to monitor the crop cycle. The Principal Component Analysis was applied to the Landsat images and the two first components were chosen, responsible for 94% of the initial information, and used as a sample set for the supervised classification of those images. That classification was compared with a conventional supervised classification (sampled from Landsat reflectance images) and multitemporal conventional supervised classification (using the three images). The accuracies of the classifications were calculated by Kappa index of agreement and Global Accuracy, using a coffee mask as reference. The results have shown that PCA was very efficient in illumination class definition as well as in sample choice, despite the samples had not represented the area classified. Due to that, the accuracy has increased, specially the one considering all the pixels classified as coffee in each image using PCA samples, demonstrating the importance of the multitemporal aspect.

The search for low subjectivity area estimates has increased the use of remote sensing for agricultural monitoring and crop yield prediction, leading to more flexibility in data acquisition and lower costs comparing to traditional methods such as census and surveys. Low spatial resolution satellite images with higher frequency in image acquisition have shown to be adequate for cropland mapping and monitoring in large areas. The main goal of this study was to map the Summer crops in the State of Paraná, Brazil, using 10-day composition of NDVI SPOT Vegetation data for 2005/2006, 2006/2007 and 2007/2008 cropping seasons. For this, a supervised digital classification method with Parallelepiped algorithm in multitemporal RGB image composites was used, in order to generate masks of Summer cultures for each 10-day composition. Accuracy assessment was performed using Kappa index, overall accuracy and Willmott's concordance index, resulting in good levels of accuracy. This methodology allowed the accomplishment, with free and low resolution data, of the mapping of Summer cultures at State level.A busca por menor subjetividade em estimativas de área tem aumentado a utilização do sensoriamento remoto para monitoramento agrícola e previsão de safras, pois proporciona maior agilidade na aquisição de dados e menor custo em relação a métodos tradicionais de censos e pesquisas. Imagens de satélite de baixa resolução espacial e alta periodicidade são adequadas para o mapeamento, acompanhamento e desenvolvimento de culturas em áreas extensas. O objetivo deste trabalho foi mapear culturas de verão no Estado do Paraná por meio de composições decendiais de imagens NDVI do satélite SPOT Vegetation para safras de 2005/2006, 2006/2007 e 2007/2008. Para isso, foi utilizado um método de classificação digital supervisionada com algoritmo Paralelepípedo em composições RGB multitemporais das imagens, de forma a gerar máscaras das culturas de verão para cada composição decendial. A verificação da acurácia das máscaras foi realizada utilizando índice Kappa, exatidão global e índice de concordância de Willmott, resultando em bons índices de acerto. Essa metodologia possibilitou realizar, com dados gratuitos e de baixa resolução espacial, o mapeamento de culturas de verão em nível estadual.

Este artigo descreve o desenvolvimento de um banco de dados relacional e de uma ferramenta para a visualização de perfis temporais do NDVI MODIS, a partir dos dados do produto MOD09Q1, referente ao fator de refletância bidirecional de superfície relativa ao comprimento de onda do vermelho e do infravermelho-próximo, composição temporal em mosaicos de 8 dias, e a banda de controle de qualidade, dos talhões de cana-de-açúcar no Estado de São Paulo, para analisar a maturação da cana-soca Tardia. Das fazendas de cana-de-açúcar são obtidos os dados de históricos sobre produtividade, solo, variedade, localização de cada pixel para cada microrregião monitorada. Todos os dados são integrados em um banco de dados desenvolvido em PostgreSQL. O aplicativo foi implementado usando a linguagem Java e permitiu uma forma rápida e automática para analisar padrões fenológicos na cana-de-açúcar. Concluiu-se que o perfil temporal do NDVI MODIS obtido a partir do produto MOD09Q1 é capaz de subsidiar o monitoramento das mudanças fenológicas na cultura da cana-de-açúcar.This paper describes the development of a relational database and a tool for viewing MODIS NDVI temporal profile, using data from MOD09Q1 product, specifically the surface bidirectional reflectance factor relative to the RED and NIR wavelength, mosaic of 8-day temporal composition, and the quality band, in sugarcane fields in the state of São Paulo, for analysis of the late stubble-cane maturation. From sugarcane farms were obtained the historical data about yield, soil, variety, location of the each pixel for each subregion monitored. All data were integrated in a database developed in PostgreSQL. The tool was implemented using Java language and allowed a fast and automatic way of analyzing sugarcane phenological patterns. It concluded that the MODIS NDVI temporal profile using data from MOD09Q1 product is able to subsidize the monitoring of phenological changes in the sugarcane.

O trabalho teve o objetivo de avaliar modelos lineares de regressão entre resposta espectral e produtividade em soja, na escala regional. Para isso, foram monitorados 36 municípios do oeste do Paraná, utilizando cinco imagens do satélite Landsat 5/TM da safra de 2004/2005. Foram realizados os procedimentos de transformação radiométrica e correção atmosférica nas imagens, determinando valores físicos das refletâncias aparente e de superfície. Posteriormente, foram calculados os índices de vegetação NDVI e GVI, os quais, por meio de regressões lineares simples e múltiplas, compararam-se com as produtividades oficiais dos municípios, obtidas das estatísticas IBGE. Aplicou-se também uma análise de diagnóstico, para detectar pontos influentes e de colinearidade. Os resultados mostraram que a média dos valores de NDVI e GVI de todas as imagens foi mais bem relacionada com a produtividade do que para cada data separadamente. O uso de regressões múltiplas com os dois índices, em todas as datas, propiciou melhores resultados de relação com a produtividade.The main objective of this work was to evaluate the linear regression between spectral response and soybean yield in regional scale. In this study were monitored 36 municipalities from the west region of the states of Parana using five images of Landsat 5/TM during 2004/05 season. The spectral response was converted in physical values, apparent and surface reflectances, by radiometric transformation and atmospheric corrections and both used to calculate NDVI and GVI vegetation indices. Those ones were compared by multiple and simple regression with government official yield values (IBGE). Diagnostic processing method to identify influents values or collinearity was applied to the data too. The results showed that the mean surface reflectance value from all images was more correlated with yield than individual dates. Further, the multiple regressions using all dates and both vegetation indices gave better results than simple regression.

O objetivo deste trabalho foi estudar as mudanças no comportamento espectral da cultura da soja, por meio dos perfis espectrais temporais dos índices de vegetação NDVI e GVI, expressos em diferentes valores físicos: fator de reflectância bidirecional (FRB) aparente, de superfície e normalizado derivados de imagens Landsat 5/TM. Foi monitorada área de cultura de soja localizada próxima ao município de Cascavel - PR, utilizando cinco imagens da safra de 2004/2005, sendo realizados nessas imagens os procedimentos de transformação radiométrica, correção atmosférica e normalização, determinando valores físicos dos fatores de reflectância bidirecional aparente, de superfície e normalizado, respectivamente. Com o intuito de caracterizar a resposta espectral da biomassa da soja, geraram-se imagens referentes aos índices de vegetação NDVI e GVI. Como resultado, a cultura mostrou-se diferente para os tratamentos dos fatores de reflectância bidirecional aparente, de superfície e de normalização. Por meio dos perfis médios espectrais do NDVI e GVI, foi possível acompanhar todo o ciclo da cultura da soja, caracterizando o seu desenvolvimento. Observou-se, ainda, que os dados provenientes do fator de reflectância bidirecional normalizado descaracterizaram a curva espectral da cultura da soja, principalmente em meio à fase de crescimento vegetativo, na data de 9-12-2004.The objective of this study was to analyze changes in the spectral behavior of the soybean crop through spectral profiles of the vegetation indexes NDVI and GVI, expressed by different physical values such as apparent bi-directional reflectance factor (BRF), surface BRF, and normalized BRF derived from images of the Landsat 5/TM. A soybean area located in Cascavel, Paraná, was monitored by using five images of Landsat 5/TM during the 2004/2005 harvesting season. The images were submitted to radiometric transformation, atmospheric correction and normalization, determining physical values of apparent BRF, surface BRF and normalized BRF. NDVI and GVI images were generated in order to distinguish the soybean biomass spectral response. The treatments showed different results for apparent, surface and normalized BRF. Through the profiles of average NDVI and GVI, it was possible to monitor the entire soybean cycle, characterizing its development. It was also observed that the data from normalized BRF negatively affected the spectral curve of soybean crop, mainly, during the phase of vegetative growth, in the 12-9-2004 image.

Monitoring agriculture cultures by aerial remote sensing present high potential of application. Despite of that potential, some problems still have been detected. One of them is the vignetting effect. This phenomenon introduces error in DN as far away as geometric image center the target is, according to the cos4Theta law. To study this effect it was adopted the procedure that computes Equation - Equação. If these values increase with the distances from images geometric center then the vignetting effect increases proportionally. The study was carried out analyzing the DN of white plate targets in aerial images in two dates 02/11/2001 and 11/04/2002. The white plate targets were distributed in the field and could be seen around the images geometric center, in different distances. In the aerial images the DN from the plates were extracted according to the cos4Theta law and compared to several distances in conformity to Equation - Equação. The results showed that the effect was observed in the first (02/11/2001) images, but not in later (11/04/2002) images. That difference can be explained by the different atmospheric haze and sensor-illumination source geometry. On the other hand when the experiment was performed at ground level the vignetting effect was identified. Therefore the effect exists and can be modeled.