Integração de imagens NOAA/AVHRR: rede de cooperação para monitoramento nacional da safra de soja

Abstract

An early assessment of national crop development conditions while the plants are still in the fields is highly needed to calculate correctly projections for decision-making and policies related to government planning and food security. The aim of this study was to evaluate the suitability of NOAA /AVHRR (National Oceanic and Atmospheric Administration / Advanced Very High Resolution Radiometer) to detect changes in vegetation conditions, due to water stress during soybean crop, by means a combination of Land Surface Temperature (LST) and Normalized Difference Vegetation Index (NDVI). Both LST and NDVI data were combined and compared in a pixel basis over a soybean crop area in Rio Grande do Sul State. The predicted theoretical inverse relationship for the combination of LST and NDVI was detected. An average increase of LST was observed in a normal crop cycle ( from 301.02 K to 308.36 K) compared to a crop cycle under water stress condition. An average reduction in NDVI was observed for normal crop cycle development (from 0.65 to 0.53) compared to a crop cycle under drought-induced effects. It was observed a higher correlation of municipality yield with LST (R2=0.78) than NDVI (R2=0.59). Results obtained indicate that the aggregation of AVHRR images, from Uma avaliação inicial das condições do desenvolvimento da safra nacional, enquanto as plantas ainda estão nos campos, é altamente necessária para o cálculo correto das projeções na tomada de decisão e políticas relacionadas com o planejamento governamental e segurança alimentar. O objetivo deste trabalho foi avaliar a adequação dos dados NOAA/AVHRR (National Oceanic and Atmospheric Administration / Advanced Very High Resolution Radiometer) em detectar mudanças nas condições da vegetação, devidas à ocorrência de estresse hídrico, na soja, por meio de uma combinação do índice NDVI (Normalized Difference Vegetation Index) e da LST (Land Surface Temperature). Os dados LST e NDVI foram combinados e comparados pixel a pixel, sobre uma área de cultivo de soja, no Rio Grande do Sul. A relação teórica inversa prevista na combinação de LST e NDVI foi detectada. Foi observado que ocorre um aumento médio na LST em uma safra de ciclo normal (de 301,02 K para 308,36 K), quando comparada a uma safra sob condição de estresse hídrico, no desenvolvimento da cultura. Uma redução média do NDVI foi observada no ciclo normal (de 0,65 para 0,53), comparada com uma safra sob efeitos ocasionados pela estiagem no desenvolvimento da cultura. Foi observado maior correlação da produtividade municipal com LST (R2=0,78) do que com o NDVI (R2 = 0,59). Os resultados obtidos indicam que a integração de imagens do sensor AVHRR, proveniente de diferentes instituições, proporciona a adequada combinação espacial e temporal dos dados LST e NDVI, a fim de detectar a ocorrência de estresse hídrico, bem como sua intensidade, caracterizando as condições do ciclo de desenvolvimento da soja.Palavras-chave: sensoriamento remoto, estiagem, temperatura da superfície terrestre, índice de vegetação.

Multi-Temporal Patterns of Urban Heat Island as Response to Economic Growth Management

“…This concept is based on the assumption that vegetation coverage mitigates high LST occurrence values. The relationship between thermal characteristics of surface and vegetation indices has been extensively documented in the literature [7,13,[48][49][50][51][52]. Many studies have observed a negative relationship between LST and vegetation indices.…”

“…Numerous factors need to be quantified to assess accurate LST retrieval from satellite thermal data, including sensor radiometric calibration [38], atmospheric correction [30,39], surface emissivity correction [40] and physically driven conditions of land coverage [7]. Next, LST is obtained by correcting the radiating surface temperature to the surface emissivity (ε), which is the ratio of the thermal energy radiated by the surface to the thermal energy radiated by a blackbody at the same temperature [40].…”

“…It is essential to have knowledge about UHI in earth sciences for urban climatology, global environmental change and management practices in the urban environment because Land Surface Temperature (LST) is closely related to the distribution of LULC characteristics [6,7], and the physical conditions and properties of the vegetation types and surrounding urban environment are key factors that determine LST distribution [8].…”

NDVI variation according to the time of measurement, sampling size, positioning of sensor and water regime in different soybean cultivars

“…NDVI is calculated through the difference between reflectance detected in red (0.58-0.68 lm) and near infrared (0.725-1.1 lm) bands shared by the sum of these quantities (Rouse et al 1974), resulting in values ranging from -1.0 to ?1.0 (Chen et al 2002). Under normal conditions, vigorous vegetation absorbs the visible radiation for photosynthesis and reflects the near-infrared radiation due to the light scattering through mesophyllic tissues and the leaf water content (Gusso 2013). Several studies report the relationship between NDVI and physiological and biophysical characteristics of plants, such as biomass (Marti et al 2007), soil cover (Mullan and Reynolds 2010), nitrogen content (Wright Jr et al 2005), productivity (Royo et al 2003) and water deficit (Yuhas and Scuderi 2009).…”

Canopy temperatures distribution over soybean crop fields using satellite data in the Amazon biome frontier

“…Depending on future scenario of Brazilian agriculture which remains largely undetermined (Lapola et al, 2013;Cohn, VanWey, Spera, & Mustard, 2016), extreme meteorological fluctuations are able to cause severe effects on agricultural yields (Batistti & Naylor, 2009), causing an increase in environmental costs of food production (Godfray et al, 2010). Such changes can inhibit and adversely affect the crop development, since canopy temperature fluctuations often exceed the optimum range (Lobell & Burke, 2008;Gusso, 2013). Although seasonal meteorological fluctuations, such as high temperature and water stress occurrences, have been not always problematic, heat stress is increasingly gaining interest in scientific publications (Gusso, 2013), due to the need for assessment of future international demands (Lapola et al, 2013;Anderson et al, 2016) and market fluctuations (Batistti & Naylor, 2009) but especially for soybeans and maize with potential worldwide economic impacts by 2050 (UNEP -United Nations Environment Programme.…”