Rodrigo Rizzi

Soybean crop area estimation by Modis/Evi data

The objective of this work was to develop a procedure to estimate soybean crop areas in Rio Grande do Sul state, Brazil. Estimations were made based on the temporal profiles of the enhanced vegetation index (Evi) calculated from moderate resolution imaging spectroradiometer (Modis) images. The methodology developed for soybean classification was named Modis crop detection algorithm (MCDA). The MCDA provides soybean area estimates in December (first forecast), using images from the sowing period, and March (second forecast), using images from the sowing and maximum crop development periods. The results obtained by the MCDA were compared with the official estimates on soybean area of the Instituto Brasileiro de Geografia e Estatistica. The coefficients of determination ranged from 0.91 to 0.95, indicating good agreement between the estimates. For the 2000/2001 crop year, the MCDA soybean crop map was evaluated using a soybean crop map derived from Landsat images, and the overall map accuracy was approximately 82%, with similar commission and omission errors. The MCDA was able to estimate soybean crop areas in Rio Grande do Sul State and to generate an annual thematic map with the geographic position of the soybean fields. The soybean crop area estimates by the MCDA are in good agreement with the official agricultural statistics.

Assessment of MODIS LAI retrievals over soybean crop in Southern Brazil

In the present paper we have looked into the excessive occurrence of 255 standard fill value retrievals in Collection 4 MODIS LAI product over soybean areas from crop year 2001/2002 to 2004/2005, in Southern Brazil. The 255 standard fill value indicates that no leaf area index (LAI) retrieval was possible for the considered pixel. Time series of eight‐day composite LAI images (MOD15A2) and 16‐day composite NDVI images (MOD13Q1) were both compared with a soybean reference map derived from multitemporal Landsat images. The Land Cover Type 3 product (MOD12Q1) was also analysed to verify if the occurrence of those retrievals was related to misclassification of the broadleaf crops biome. Results indicated that the 255 standard fill value retrievals in Collection 4 LAI product were mainly related to soybean areas during peak growing season and occurred in every crop year we have studied. Eventual misclassification in the biome map was not the cause of those retrievals in the Collection 4 MODIS LAI product.

Superfícies de resposta espectro-temporal de imagens do sensor MODIS para classificação de área de soja no Estado do Rio Grande do Sul

This paper was aimed at evaluating the potential and the limitations of MODIS images for soybean classification and area estimation through a Spectral-Temporal Response Surface (STRS) method. A soybean thematic map from Rio Grande do Sul State, Brazil, derived from Landsat images was used as reference data to assist both sample training and results comparison. Six 16-day composite MODIS images were classified through a supervised maximum likelihood algorithm (MAXVER) adapted to the STRS method. The results were evaluated using the Kappa coefficient for the entire study area and for one region dominated by large farms and another by small ones. The STRS method underestimated the soybean area by 6.6%, for the entire study area, with a Kappa coefficient of 0.503. For regions with large and small farms the soybean area was overestimated by 8% (Kappa=0.424) and underestimated by 43.4% (Kappa=0.358), respectively. Eventually, MODIS images, through the STRS method, demonstrated good potential to classify and estimate soybean area, mainly in regions with large farms. For regions with small farms the correct identification and classification of soybean areas showed to be less efficient due to the low spatial resolution of MODIS images.

Índices de vegetação Modis aplicados na discriminação de áreas de soja

The objective of this work was to evaluate the performance of the enhanced vegetation index (EVI) and the normalized difference vegetation index (NDVI) - both from the moderate resolution imaging spectroradiometer (Modis) sensor - to discriminate soybean cultivated areas from sugarcane, pasture, cerrado, and forest ones in the state of Mato Grosso, Brazil. Images acquired during two periods were used: off-season and maximum soybean crop development. For each analyzed class, 31 samples were selected from reference maps, and the differences in the values of each soybean vegetation index were evaluated against the other classes using the Tukey‑Kramer test. Afterwards, the differences between the vegetation indices were assessed using the Wilcoxon paired test. NDVI performed best in discriminating soybean areas during the off-season period, particularly when using images acquired from day of year (DOY) 161 to 273, whereas EVI performed best during maximum crop development, particularly when using images from DOY 353 to 33. Therefore, best classification results for soybean in the state of Mato Grosso can be achieved by coupling Modis NDVI images acquired during off-season period and EVI images acquired during the maximum crop development period.

Índice de vegetação do sensor MODIS na estimativa da produtividade agrícola da cana-de-açúcar

The contribution of sugarcane crop to provide raw material to produce sugar and also alcohol as an alternative energy source has been relevant to the economic growth of Brazil. Therefore, the availability of precise agricultural production information about this crop is important for planning and decision-making in the entire productive chain. The present work has the objective to estimate sugarcane yield in crop fields during the crop years 2004/2005 and 2005/2006, based on an agronomic model fit with orbital data. The innovation of this model consists in the use of the leaf area index (LAI) estimated from the NDVI (Normalized Difference Vegetation Index) produced by the MODIS sensor (Moderate Resolution Imaging Spectroradiometer) on board of the Terra satellite from NASA (National Aeronautics Space Administration). The agronomic model explained 31% and 25% of the yield variability among crop fields for the crop years 2004/2005 and 2005/2006, respectively, which is mainly attributed to use of NDVI images from MODIS. The model output should be useful to improve the precision of the crop yield estimation forecast performed in loco.

Estimativa da área de soja no Estado do Rio Grande do Sul por um método de amostragem

Este trabalho objetivou avaliar um metodo de amostragem por segmentos regulares na estimativa da area plantada com soja no Estado do Rio Grande do Sul. Um mapa tematico das areas com soja, oriundo da classificacao multitemporal de imagens do satelite Landsat, ano-safra 2000/01, foi utilizado como dado de referencia para comparacao dos resultados. A area de estudo foi dividida em segmentos regulares de 1 x 1km e estratificada em relacao ao percentual de soja cultivado no municipio, em tres extratos: a) 0-20; b) 20-40 e c) 40-67%. Um metodo probabilistico foi utilizado para definir quatro numeros amostrais, representando 0,06, 0,12, 0,24 e 0,48% da area de estudo, sendo cada um sorteado aleatoriamente cem vezes. A estimativa da area de soja para cada sorteio foi calculada analisando-se a area de cada segmento sorteado sobre o mapa tematico e entao comparada ao dado de referencia. Os melhores resultados foram obtidos para o maior numero amostral, o qual teve baixo Coeficiente de Variacao (5,2%), indicando que o metodo, alem de fornecer a area plantada com soja, em nivel estadual, pode ser usado para prever a area plantada no inicio da safra ou nos anos em que nao se dispoe de imagens de satelite livres de nuvens. Os tres melhores sorteios para o maior numero amostral tiveram sua area de soja tambem quantificada atraves do mapeamento de imagens adquiridas no ano-safra subsequente (2001/02). Neste caso, foi observado um incremento entre 11,4 e 12,5% em relacao ao ano-safra 2000/01, indicando que o incremento informado pelo IBGE (8,8%) esta subestimado.

Amostragem probabilística estratificada por pontos para estimar a área cultivada com soja

The objective of this work was to evaluate the performance of a probabilistic sampling model stratified by points and to define an appropriate sample size to estimate the cultivated soybean area in the state of Rio Grande do Sul, Brazil. The area was stratified according to the percentage of soybean cultivated in each state municipality: less than 20, from 20 to 40 and more than 40%. Estimates were evaluated based on six sample sizes, resulting from the combination of three significance levels (10, 5 and 1%) and two sampling errors (5 and 2,5%), choosing 400 random samples for each sample size. The estimates were compared to a reference soybean thematic map available for the crop year 2000/2001 that was derived from a careful automatic and visual classification of multitemporal TM/Landsat-5 and ETM+/Landsat-7 images. The soybean area in Rio Grande do Sul State can be estimated through a probabilistic sampling model stratified by points with best estimates obtained for the largest sample size (1,990 points), which differed -0.14% in relation to the estimate of the reference map and had a coefficient of variation of 6.98%.

Cloud Cover Assessment for Operational Crop Monitoring Systems in Tropical Areas

The potential of optical remote sensing data to identify, map and monitor croplands is well recognized. However, clouds strongly limit the usefulness of optical imagery for these applications. This paper aims at assessing cloud cover conditions over four states in the tropical and sub-tropical Center-South region of Brazil to guide the development of an appropriate agricultural monitoring system based on Landsat-like imagery. Cloudiness was assessed during overlapping four months periods to match the typical length of crop cycles in the study area. The percentage of clear sky occurrence was computed from the 1 km resolution MODIS Cloud Mask product (MOD35) considering 14 years of data between July 2000 and June 2014. Results showed high seasonality of cloud occurrence within the crop year with strong variations across the study area. The maximum seasonality was observed for the two states in the northern part of the study area (i.e., the ones closer to the Equator line), which also presented the lowest averaged values (15%) of clear sky occurrence during the main (summer) cropping period (November to February). In these locations, optical data faces severe constraints for mapping summer crops. On the other hand, relatively favorable conditions were found in the southern part of the study region. In the South, clear sky values of around 45% were found and no significant clear sky seasonality was observed. Results underpin the challenges to implement an operational crop monitoring system based solely on optical remote sensing imagery in tropical and sub-tropical regions, in particular if short-cycle crops have to be monitored during the cloudy summer months. To cope with cloudiness issues, we recommend the use of new systems with higher repetition rates such as Sentinel-2. For local studies, Unmanned Aircraft Vehicles (UAVs) might be used to augment the observing capability. Multi-sensor approaches combining optical and microwave data can be another option. In cases where wall-to-wall maps are not mandatory, statistical sampling approaches might also be a suitable alternative for obtaining useful crop area information.

Fusão de imagens por IHS para melhorar a identificação de uso do solo em elementos amostrais

Up-to-date metric aerial photographs covering specific areas are not always available. This is one of the main problems in Brazil when area sampling techniques for crop area estimation are used. This work aims to evaluate fused Landsat7-ETM+ images for ground survey data collection and to identify and map land elements in order to replace the use of aerial photographs. The spectral bands 3; 4; 5 and 7 of the ETM+ sensor were used in different RGB combinations merged with the panchromatic band 8 to produce images with better spatial resolution using IHS fusion technique. The resulting images were printed in a 1:25,000 scale and used during the ground survey campaign for 85 sampling units. Results pointed out that the fused images allowed identifying and mapping land elements in all the analyzed sampling units. Therefore, fused ETM+ images presented a great potential for ground survey data collection and to identify and map land elements for crop area estimation based on area sampling techniques.

Assessment of suitable observation conditions for a monthly operational remote sensing based crop monitoring system

Cloud cover is the main issue to consider when remote sensing images are used to identify, map and monitor croplands, especially over the summer season (October to March in Brazi). This paper aims at evaluating clear sky conditions over four Brazilian states (São Paulo, Paraná, Santa Catarina, and Rio Grande do Sul) to assess suitable observation conditions for a monthly basis operational crop monitoring system. Cloudiness was analyzed using MODIS Cloud Mask product (MOD35), which presents four labels for cloud cover status: cloudy, uncertainty, probably clear and confident clear. R software was used to compute average values of clear sky with a confidence interval of 95% for each month between July 1st, 2000 and June 30th, 2013. Results showed significant differences within and between the four tested states. Moreover, the period from November to March presented 50% less clear sky areas when compared to April to October.