This work aimed to assess the potential of unmanned aerial vehicle (UAV) multi- and hyper-spectral platforms to estimate chlorophyll-a (Chl-a) and cyanobacteria in experimental fishponds in Brazil. In addition to spectral resolutions, the tested platforms differ in the price, payload, imaging system, and processing. Hyperspectral airborne surveys were conducted using a push-broom system 276-band Headwall Nano-Hyperspec camera onboard a DJI Matrice 600 UAV. Multispectral airborne surveys were conducted using a global shutter-frame 4-band Parrot Sequoia camera onboard a DJI Phantom 4 UAV. Water quality field measurements were acquired using a portable fluorometer and laboratory analysis. The concentration ranged from 14.3 to 290.7 µg/L and from 0 to 112.5 µg/L for Chl-a and cyanobacteria, respectively. Forty-one Chl-a and cyanobacteria bio-optical retrieval models were tested. The UAV hyperspectral image achieved robust Chl-a and cyanobacteria assessments, with RMSE values of 32.8 and 12.1 µg/L, respectively. Multispectral images achieved Chl-a and cyanobacteria retrieval with RMSE values of 47.6 and 35.1 µg/L, respectively, efficiently mapping the broad Chl-a concentration classes. Hyperspectral platforms are ideal for the robust monitoring of Chl-a and CyanoHABs; however, the integrated platform has a high cost. More accessible multispectral platforms may represent a trade-off between the mapping efficiency and the deployment costs, provided that the multispectral cameras offer narrow spectral bands in the 660–690 nm and 700–730 nm ranges for Chl-a and in the 600–625 nm and 700–730 nm spectral ranges for cyanobacteria.

The ongoing advancement of unmanned aerial vehicles (UAVs) and the evolution of small-scale cameras have bridged the gap between traditional ground-based surveys and orbital sensors. However, these systems present challenges, including limited coverage area, image stabilization constraints, and complex image processing. In water quality monitoring, these difficulties are further compounded by sun glint effects, which hinder the construction of accurate orthomosaics in homogeneous water surfaces and affect radiometric accuracy. This study focuses on evaluating these challenges by comparing two distinct airborne imaging platforms with different spectral resolutions, emphasizing Total Suspended Solids (TSS) monitoring. Hyperspectral airborne surveys were undertaken utilizing a pushbroom system comprising 276 bands, whereas multispectral airborne surveys were conducted employing a global shutter frame with 4 bands. Fifteen aerial survey campaigns were carried out over water bodies from two biomes in Brazil (Amazon and Savanna), at varying concentrations of TSS (0.6–130.7 mg L⁻¹, N: 53). Empirical models using near-infrared channels were applied to accurately monitor TSS in all areas (Hyperspectral camera—RMSE = 3.6 mg L⁻¹, Multispectral camera—RMSE = 9.8 mg L⁻¹). Furthermore, a key contribution of this research is the development and application of Sun Glint mitigation techniques, which significantly improve the reliability of airborne reflectance measurements. By addressing these radiometric challenges, this study provides critical insights into the optimal UAV platform for TSS monitoring in inland waters, enhancing the accuracy and applicability of airborne remote sensing in aquatic environments.

Water and sediment discharges can change rapidly, and low-frequency measurement devices might not be sufficient to elucidate existing dynamics. As such, above-water radiometry might enhance monitoring of suspended particulate matter (SPM) dynamics in inland waters. However, it has been barely applied for continuous monitoring, especially under partially cloudy sky conditions. In this study, an in situ, high-frequency (30 s timestep), above-water radiometric dataset, collected over 18 days in a tropical reservoir, is analyzed for the purpose of continuous monitoring of SPM concentration. Different modalities to retrieve reflectance spectra, as well as SPM inversion algorithms, were applied and evaluated. We propose a sequence of processing that achieved an average unsigned percent difference (UPD) of 10.4% during cloudy conditions and 4.6% during clear-sky conditions for Rrs (665 nm), compared to the respective UPD values of 88.23% and 13.17% when using a simple calculation approach. SPM retrieval methods were also evaluated and, depending on the methods used, we show that the coefficient of variation (CV) of the SPM concentration varied from 69.5% down to 2.7% when using a semi-analytical approach. As such, the proposed processing approach is effective at reducing unwanted variability in the resulting SPM concentration assessed from above-water radiometry, and our work paves the way towards the use of this noninvasive technique for high-frequency monitoring of SPM concentrations in streams and lakes.

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.

Coffee production and trade are globally important, and Brazil is the world's largest producer and second-largest consumer. However, climate change poses a major challenge to coffee production because the crop is highly sensitive to irregular rainfall distribution and temperature fluctuations. We evaluated temperature and precipitation fluctuations from 1984 to 2023 at two coffee-producing units in southern Minas Gerais, Brazil, to characterize regional climate dynamics and their impacts on coffee yield and total production. We used the Mann–Kendall test and Sen’s slope estimator to identify trends and estimate rates of change in maximum, minimum, and mean air temperature and in total, dry-season, and wet-season precipitation. We used Pearson’s and Spearman’s correlation coefficients to assess relationships between climatic and agronomic variables, selecting the appropriate coefficient based on prior Shapiro–Wilk normality tests. All temperature variables showed warming trends in September, during the transition from the dry to the wet season. Cooling trends were also detected in April, May, and December. At one production unit, dry-season rainfall declined over the historical series. At the other, precipitation declined in May but showed no trend in either the dry or wet season. Relationships between climatic and agronomic variables differed between production units: correlations were positive at one unit and negative at the other. Regionally, these trends indicate vulnerability during critical phenological stages that coincide with these seasonal windows, resulting in pollen tube desiccation, greater consumption of photoassimilates during physiological rest, and poor bean development under reduced dry-season rainfall. The contrasting correlation patterns were attributed to regional climate fluctuations and to the microclimatic, land-use, and land-cover characteristics of each production unit. Under this scenario, mitigating these impacts is essential to minimize damage to coffee crops. Recommended strategies include the implementation of agroforestry systems, rigorous monitoring of pests favored by climate fluctuations, and the adoption of more climate-resilient cultivars. Ultimately, this study highlights that crop management and mitigation strategies designed to buffer these climate impacts must be tailored to the distinct conditions of each production unit.

Os reservatórios hídricos além de serem importantes para a produção de energia elétrica, são recursos para outras necessidades da população. Imagens de sensores orbitais são aplicadas para complementar o monitoramento desses ambientes e assim suprir a deficiência de cobertura espacial e temporal das técnicas tradicionais. No entanto estudos envolvendo análises volumétricas de corpos d’água ainda são um grande desafio devido ao baixo sinal proveniente do corpo d’água e a interferência de fatores externos (ou fatores ambientais). Procedimentos de correção/melhoramento das imagens são propostos com frequencia, principalmente para a redução da interferencia atmosférica. Nesse estudo foram avaliadas as melhores técnicas de correção atmosférica disponíveis comercialmente no intuito de indicar aquela técnica que mais se aproxima da resposta espectral de sensoriamento remoto obtida em campo (referência). No decorrer do estudo foram aplicados seis algoritmos de correção atmosférica (FLAASH, Second simulation of a Satellite Signal in the Solar Spectrum (6S), L8SR, Aquatic Reflectance (USGS), ACOLITE e Sen2Cor) que, a partir das análises estatísticas de análise discriminante e covariância apontaram os aplicativos 6S para imagens Landsat e Sentinel e o Acolite para imagens Landsat como os mais acurados. Embora o 6S tenha apresentado resposta próxima dos dados de referencia, observou-se baixa variabilidade na resposta espectral. Para séries temporais, o Acolite apresentou maior capacidade de correção dos dados. O tipo de aplicação também é um fator preponderante, pois ficou evidente que o uso de series temporais indicou uma técnica de correção atmosférica diferente quando comparado com a análise das cenas de forma individual.