Continuous and dense time series of satellite remote sensing data are needed for several land monitoring applications, including vegetation phenology, in-season crop assessments, and improving land use and land cover classification. Supporting such applications at medium to high spatial resolution may be challenging with a single optical satellite sensor, as the frequency of good-quality observations can be low. To optimize good-quality data availability, some studies propose harmonized databases. This work aims at developing an ‘all-in-one’ Google Earth Engine (GEE) web-based workflow to produce harmonized surface reflectance data from Landsat-7 (L7) ETM+, Landsat-8 (L8) OLI, and Sentinel-2 (S2) MSI top of atmosphere (TOA) reflectance data. Six major processing steps to generate a new source of near-daily Harmonized Landsat and Sentinel (HLS) reflectance observations at 30 m spatial resolution are proposed and described: band adjustment, atmospheric correction, cloud and cloud shadow masking, view and illumination angle adjustment, co-registration, and reprojection and resampling. The HLS is applied to six equivalent spectral bands, resulting in a surface nadir BRDF-adjusted reflectance (NBAR) time series gridded to a common pixel resolution, map projection, and spatial extent. The spectrally corresponding bands and derived Normalized Difference Vegetation Index (NDVI) were compared, and their sensor differences were quantified by regression analyses. Examples of HLS time series are presented for two potential applications: agricultural and forest phenology. The HLS product is also validated against ground measurements of NDVI, achieving very similar temporal trajectories and magnitude of values (R² = 0.98). The workflow and script presented in this work may be useful for the scientific community aiming at taking advantage of multi-sensor harmonized time series of optical data.

A alta variabilidade interanual da produtividade de milho, no Rio Grande do Sul, é determinada, principalmente, pela variabilidade da precipitação pluvial, e esta, em grande parte está associada ao fenômeno El Niño Oscilação Sul (ENOS). A disponibilidade, hoje, de previsões sazonais do ENOS faz vislumbrar a possibilidade de uso dessas informações para minimizar prejuízos e maximizar a produtividade. Há necessidade, porém, de se avaliar a vulnerabilidade da produtividade do milho a esse fenômeno. O objetivo deste trabalho foi quantificar a associação entre a produtividade de milho e a variabilidade da precipitação pluvial, causada pelo ENOS. Para a análise, foram tomadas séries históricas de produtividade, de precipitação pluvial mensal, de ocorrência das fases do ENOS (El Niño e La Niña), de Temperatura da Superfície do Mar (TSM) no Pacífico equatorial, e do Índice de Oscilação Sul (IOS). Há forte tendência do El Niño em favorecer a cultura do milho, o que dá oportunidade à alta produtividade, ao passo que em anos de ocorrência de La Niña há alta freqüência de baixa produtividade. A precipitação pluvial mais associada à produtividade do milho é a integrada de outubro a março. Essas informações são úteis para decisões quanto a alternativas de manejo da cultura, frente a uma previsão de El Niño ou La Niña. The high interannual variability of corn yield in Rio Grande do Sul State is determined mostly by the variability of rainfall, which is mainly associated to El Niño Southern Oscillation (ENSO). Current availability of seasonal ENSO forecasts shows the possibility of using this information to minimize losses and maximize yield. However, it is necessary to assess the vulnerability of corn yield to this phenomenon. The objective of this work was to quantify the association between corn yield and rainfall variability due to ENSO. In order to perform the analysis, historical series of yields, monthly rainfalls, ENSO (El Niño and La Niña) phases, Sea Surface Temperature (SST) in equatorial Pacific, and the Southern Oscillation Index (SOI) were used. Results show that there is a strong trend for El Niño to favor corn crops, providing opportunities for high yields, while in years when La Niña occurs there is a high frequency of low yields. The rainfall most associated with corn yields is the integrated one from October to March. This information is useful for decision making as to crop management alternatives when there is a forecast of El Niño or La Niña

The objective of this work was to locate and quantify, through geotechnologies, areas that are currently used for annual agriculture in the Cerrado biome of Central Brazil, located within the boundaries defined by the homogeneous region of adaptation of wheat cultivars 4 and that present favorable conditions for rainfed wheat (Triticum aestivum) cultivation. The following information layers were crossed: use and coverage of the Cerrado biome, digital elevation model, and water requirement satisfaction index for wheat in the Cerrado biome. In addition, different levels of water stress (low, moderate, and high), risk levels (20, 30, and 40%), available soil water capacities (ASWCs) (35, 55, and 75 mm), cultivar cycles (105, 115, and 125 days), and sowing dates (in February and March) were also considered. A greater favorable area was observed for sowing in early February, and group I of cultivars (105 days) presented the greatest favorable area. Above 800 m altitude, 2.7 million hectares were classified as favorable for the best combination of factors, i.e., sowing on February 5, ASWC of 75 mm, 105-day cycle, 20% risk level, and low and moderate impacts.

In vineyards, the monitoring of leaf area is used to guide management practices in order to produce grapes with quality and enological potential. Remote sensing has been used to estimate leaf area by means of vegetation indices. This work aimed at determining the temporal evolution of the Normalized Difference Vegetation Index (NDVI) while associating it with leaf area of vineyards in the Serra Gaúcha region, Brazil. To this end, between 10 and 20 plants were evaluated monthly (September to May) in three vegetative seasons. NDVI data were obtained using the remote sensor Greenseeker while the following leaf area related parameters were obtained non-destructively: unit leaf area (ULA), average number of leaves per branch (NLB), plant leaf area (PLA), leaf area index (LAI) and chlorophyll index (CI). Data correlation was assessed by joint temporal analysis and Pearson correlations (p < 0.05). The results indicated that NDVI variation over time is coherent with the leaf area dynamics and expresses biomass variations due to phenology and management actions. The NDVI evolution pattern is described by a quadratic regression equation: the index increases rapidly at first (September to November), followed by a relative stabilization (December to February), and decreases in the final stage (March to May). The correlations were higher in the initial and final stages of the cycle, especially for NLB (r = 0.73 and 0.77), PLA (r = 0.60 and 0.61) and CI (r = 0.48 and 0.84), which are promising parameters for characterizing leaf area in vineyards by remote sensing.

Conduziu-se o presente estudo com o objetivo de se avaliar o risco de ocorrência de deficiência hídrica decendial da metade sul do estado do Rio Grande do Sul; para tanto, obtiveram-se dados diários de temperatura máxima e mínima, insolação e precipitação pluvial de nove localidades, distribuídas nas cinco regiões ecoclimáticas (Campanha, Depressão Central, Grandes Lagoas, Litoral e Serra do Sudeste) da metade sul do Estado, do período 1961-1990. Realizaram-se balanços hídricos decendiais seriados pelo método de Thornthwaite-Mather com capacidade de armazenamento de água no solo de 50 e 125 mm. A partir daí, obteve-se o índice hídrico decendial (IH = ETR/ETo). Para analisar o risco de ocorrência de deficiência hídrica adotou-se o limite 0,6 como valor crítico. Os resultados mostram que a região da Campanha é a de maior probabilidade de ocorrência de deficiência hídrica decendial, principalmente no período compreendido entre o 2º decêndio de dezembro e o 2º de janeiro.

O objetivo deste trabalho foi avaliar a redução do vigor vegetativo da cobertura vegetal do Pampa do Brasil e do Uruguai, por meio da identificação de tendências negativas em séries temporais de imagens. Utilizaram-se séries temporais de imagens de NDVI/EVI do sensor Modis, de 2000 a 2011; imagens de índices de umidade do solo do "climate forecast system reanalysis"; e dados de precipitação pluvial de estações meteorológicas. O estudo quantificou tendências lineares e não lineares nas séries de NDVI e EVI, em áreas de campos. Na tendência monotônica de Mann-Kendall, a 5% de probabilidade, 81,9% da área total estudada foi significativa com o NDVI, e 74,8%, com o EVI; no entanto, o EVI apresentou contraste superior na estimativa dos parâmetros. Os resultados mostraram maior sinal negativo a oeste, com valores médios de R²>0,15, r<-0,3 e τ <-0,15 na tendência dos índices de vegetação, e tendência decrescente para NDVI, EVI e precipitação pluvial, com menores valores médios de umidade do solo. A tendência negativa dos índices de vegetação, relacionada à combinação da ocorrência de deficit hídrico em solos rasos com o sobrepastoreio, indica alterações no padrão de cobertura vegetal do Pampa, com redução do vigor vegetativo.