This study assesses the performance of the new Global Precipitation Measurement (GPM)-based satellite precipitation estimates (SPEs) datasets in the Brazilian Central Plateau and compares it with the previous Tropical Rainfall Measurement Mission (TRMM)-era datasets. To do so, the Integrated Multi-satellitE Retrievals for GPM (IMERG)-v5 and the Global Satellite Mapping of Precipitation (GSMaP)-v7 were evaluated at their original 0.1° spatial resolution and for a 0.25° grid for comparison with TRMM Multi-satellite Precipitation Analysis (TMPA). The assessment was made on an annual, monthly, and daily basis for both wet and dry seasons. Overall, IMERG presents the best annual and monthly results. In both time steps, IMERG’s precipitation estimations present bias with lower magnitudes and smaller root-mean-square error. However, GSMaP performs slightly better for the daily time step based on categorical and quantitative statistical analysis. Both IMERG and GSMaP estimates are seasonally influenced, with the highest difficulty in estimating precipitation occurring during the dry season. Additionally, the study indicates that GPM-based SPEs products are capable of continuing TRMM-based precipitation monitoring with similar or even better accuracy than obtained previously with the widely used TMPA product.

Water erosion is one of the most important soil degradation processes and it can be intensified by land use and vegetal covering changes. Thus, water erosion modeling studies associated to multi temporal analyses of land use are effective in assessing how changes in land cover affects sediment yield. Therefore, considering the modifications in the land use from 1986 to 2011, the aim of this study ranged to estimate water erosion rates and compare them to the soil loss tolerance (SLT) limit in the Latosols (Oxisols) at Ribeirão Caçús sub-basin, in the South of Minas Gerais State, Southeast Brazil, by means of the Revised Universal Soil Loss Equation (RUSLE) in association with the geographic information system (GIS), and geostatistical techniques. So, for each year mapped, soil loss averages were compared by t test at 5% significance to assess the soil degradation stage. The results indicated that, in the period, the soil loss average rate was from 2.4 to 2.6 Mg ha -1 year -1 and the areas with soil loss above the limit of SLT were around 8.0%. The t test demonstrated there was no considerable difference among the soil loss averages (p = 0.18). In consequence, the area of degraded soils did not increase. Thus, the RUSLE model in GIS is a simple and useful tool to estimate the soil loss and help define soil conservation and recovery measures.

INTRODUÇÃOO uso de modelos para a avaliação e mitigação de impactos ambientais é imprescindível frente ao futuro crescimento da população e da demanda por commodities da agropecuária (UNFPA 2012), que deverão acarretar ainda maior pressão sobre os solos. Além disso, no Brasil, 79,6 % da energia elétrica ABSTRACT RESUMO

The recent and continuous development of unmanned aerial vehicles (UAV) and small cameras with different spectral resolutions and imaging systems promotes new remote sensing platforms that can supply ultra-high spatial and temporal resolution, filling the gap between ground-based surveys and orbital sensors. This work aimed to monitor siltation in two large rural and urban reservoirs by recording water color variations within a savanna biome in the central region of Brazil using a low cost and very light unmanned platform. Airborne surveys were conducted using a Parrot Sequoia camera (~0.15 kg) onboard a DJI Phantom 4 UAV (~1.4 kg) during dry and rainy seasons over inlet areas of both reservoirs. Field measurements of total suspended solids (TSS) and water clarity were made jointly with the airborne survey campaigns. Field hyperspectral radiometry data were also collected during two field surveys. Bio-optical models for TSS were tested for all spectral bands of the Sequoia camera. The near-infrared single band was found to perform the best (R2: 0.94; RMSE: 7.8 mg L−1) for a 0–180 mg L−1 TSS range and was used to produce time series of TSS concentration maps of the study areas. This flexible platform enabled monitoring of the increase of TSS concentration at a ~13 cm spatial resolution in urban and rural drainages in the rainy season. Aerial surveys allowed us to map TSS load fluctuations in a 1 week period during which no satellite images were available due to continuous cloud coverage in the rainy season. This work demonstrates that a low-cost configuration allows dense TSS monitoring at the inlet areas of reservoirs and thus enables mapping of the sources of sediment inputs, supporting the definition of mitigation plans to limit the siltation process.

No abstract

Cyanobacterial blooms pose a serious threat to the multiple uses of inland waters because of their adverse effects on the environment and human health. Monitoring cyanobacteria concentrations using traditional methods can be expensive and impractical. Recently, alternative efforts using remote sensing techniques have been successful. In particular, semi-analytical modelling approaches have been used to successfully predict chlorophyll (Chl)-a concentrations from remote sensing reflectance. The aims of this study were to test the performance of different semi-analytical algorithms in the estimation of Chl-a concentrations and the applicability of Sentinel-2 multispectral instrument (MSI) imagery, and its atmospheric correction algorithms, in the estimation of Chl-a concentrations. For our dataset, phycocyanin concentration was strongly correlated with Chl-a concentration and the inversion model of inland waters (IIMIW) semi-analytical algorithm was the best performing model, achieving a root mean square error of 4.6mgm–3 in the prediction of Chl-a. When applying the IIMIW model to MSI data, the use of top-of-atmosphere reflectance performed better than the atmospheric correction algorithm tested. Overall, the results were satisfactory, demonstrating that even without an adequate atmospheric correction pipeline, the monitoring of cyanobacteria can be successfully achieved by applying a semi-analytical bio-optical model to MSI data.

A modelagem do sistema solo a partir de técnicas geoestatísticas, sistemas de informação geográfica e sensoriamento remoto permite identificar, monitorar e utilizar de forma sustentável os recursos pedológicos. Assim, foi avaliada a interação entre os atributos da paisagem, ou variáveis independentes, sobre os atributos físicos e químicos ou variáveis dependentes de Latossolos Vermelhos distróficos do sul do Estado de Minas Gerais. Vinte e três amostras de solo foram correlacionadas por modelos de regressão lineares e não-lineares, com o cruzamento de variáveis da paisagem, como composição, configuração e relevo, com atributos físicos e químicos dos solos. Os modelos foram hierarquizados pelo critério de informação de Akaike, que indicam relações diretas entre os teores de matéria orgânica e a declividade média da paisagem; entre o diâmetro médio geométrico com o manejo adotado e a declividade. Os teores de argila, a porcentagem de mata nativa, a declividade média da paisagem e a soma de bases trocáveis foram condicionados, principalmente, pela declividade no local amostrado e forma dos fragmentos florestais. Os resultados apontam que os atributos são explicados principalmente pelo relevo, que restringe os usos e possibilita a preservação das matas, além dos tipos de manejo adotados nos diferentes usos e a abundância relativa de matas nativas. Portanto, o emprego de práticas conservacionistas e a melhoria das práticas de manejo assim como o respeito à legislação ambiental, tendem, em longo prazo, resultar em solos mais conservados e sustentáveis para as atividades agropecuárias, reduzindo os processos de morfogênese em relação aos de pedogênese.

Resumo O crescimento da área irrigada no Cerrado afeta o volume de água captado nas bacias hidrográficas, influenciando diretamente na disponibilidade e alocação dos recursos hídricos. Em bacias com predominância da agricultura irrigada, torna-se fundamental a compreensão da variação espaço-temporal da evapotranspiração real de uma cultura (ETR), para o mais assertivo planejamento e gerenciamento dos reservatórios. Considerando o potencial de cultivo do trigo no Cerrado, o estudo tem por objetivo estimar a demanda hídrica da cultura nas safras de 2018 e 2019, por meio dos modelos SEBAL e SSEBop. Em comparação com o método da razão de Bowen, o SEBAL apresentou variações de R2 entre 0,86 e 0,72, tendo seu desempenho classificado como satisfatório. O RMSE determinado foi de 0,50 mm d−1 em 2018 e de 0,42 mm d−1 em 2019. O modelo SSEBop expressou melhor desempenho nas duas safras, com variabilidade de R2 entre 0,95 e 0,78, representando de forma mais adequada a ETR com RMSE menor, de 0,25 mm d−1 e 0,41 mm d−1, respectivos aos ciclos de 2018 e 2019. A configuração simplificada do SSEBop e o bom desempenho nas condições verificadas, tornam o modelo uma ferramenta apropriada, podendo contribuir para um planejamento hídrico eficiente na região.