Fast and accurate quantification of the available pasture biomass is essential to support grazing management decisions in intensively managed fields. The increasing temporal and spatial resolutions ...offered by the new generation of orbital platforms, such as Planet CubeSat satellites, have improved the capability of monitoring pasture biomass using remotely sensed data. Here, we assessed the feasibility of using spectral and textural information derived from PlanetScope imagery for estimating pasture aboveground biomass (AGB) and canopy height (CH) in intensively managed fields and the potential for enhanced accuracy by applying the extreme gradient boosting (XGBoost) algorithm. Our results demonstrated that the texture measures enhanced AGB and CH estimations compared to the performance obtained using only spectral bands or vegetation indices. The best results were found by employing the XGBoost models based only on texture measures. These models achieved moderately high accuracy to predict pasture AGB and CH, explaining 65% and 89% of AGB (root mean square error (RMSE) = 26.52%) and CH (RMSE = 20.94%) variability, respectively. This study demonstrated the potential of using texture measures to improve the prediction accuracy of AGB and CH models based on high spatiotemporal resolution PlanetScope data in intensively managed mixed pastures.
Regenerative agricultural practices are a suitable path to feed the global population. Integrated Crop–livestock systems (ICLSs) are key approaches once the area provides animal and crop production ...resources. In Brazil, the expectation is to increase the area of ICLS fields by 5 million hectares in the next five years. However, few methods have been tested regarding spatial and temporal scales to map and monitor ICLS fields, and none of these methods use SAR data. Therefore, in this work, we explored the potential of three machine and deep learning algorithms (random forest, long short-term memory, and transformer) to perform early-season (with three-time windows) mapping of ICLS fields. To explore the scalability of the proposed methods, we tested them in two regions with different latitudes, cloud cover rates, field sizes, landscapes, and crop types. Finally, the potential of SAR (Sentinel-1) and optical (Sentinel-2) data was tested. As a result, we found that all proposed algorithms and sensors could correctly map both study sites. For Study Site 1(SS1), we obtained an overall accuracy of 98% using the random forest classifier. For Study Site 2, we obtained an overall accuracy of 99% using the long short-term memory net and the random forest. Further, the early-season experiments were successful for both study sites (with an accuracy higher than 90% for all time windows), and no significant difference in accuracy was found among them. Thus, this study found that it is possible to map ICLSs in the early-season and in different latitudes by using diverse algorithms and sensors.
Integrated crop–livestock systems (ICLS) are among the main viable strategies for sustainable agricultural production. Mapping these systems is crucial for monitoring land use changes in Brazil, ...playing a significant role in promoting sustainable agricultural production. Due to the highly dynamic nature of ICLS management, mapping them is a challenging task. The main objective of this research was to develop a method for mapping ICLS using deep learning algorithms applied on Satellite Image Time Series (SITS) data cubes, which consist of Sentinel-2 (S2) and PlanetScope (PS) satellite images, as well as data fused (DF) from both sensors. This study focused on two Brazilian states with varying landscapes and field sizes. Targeting ICLS, field data were combined with S2 and PS data to build land use and land cover classification models for three sequential agricultural years (2018/2019, 2019/2020, and 2020/2021). We tested three experimental settings to assess the classification performance using S2, PS, and DF data cubes. The test classification algorithms included Random Forest (RF), Temporal Convolutional Neural Network (TempCNN), Residual Network (ResNet), and a Lightweight Temporal Attention Encoder (L-TAE), with the latter incorporating an attention-based model, fusing S2 and PS within the temporal encoders. Experimental results did not show statistically significant differences between the three data sources for both study areas. Nevertheless, the TempCNN outperformed the other classifiers with an overall accuracy above 90% and an F1-Score of 86.6% for the ICLS class. By selecting the best models, we generated annual ICLS maps, including their surrounding landscapes. This study demonstrated the potential of deep learning algorithms and SITS to successfully map dynamic agricultural systems.
In this review encouraged by original data, we first provided in vivo evidence that the kidney, comparative to the liver or brain, is an organ particularly rich in cysteine. In the kidney, the total ...availability of cysteine was higher in cortex tissue than in the medulla and distributed in free reduced, free oxidized and protein-bound fractions (in descending order). Next, we provided a comprehensive integrated review on the evidence that supports the reliance on cysteine of the kidney beyond cysteine antioxidant properties, highlighting the relevance of cysteine and its renal metabolism in the control of cysteine excess in the body as a pivotal source of metabolites to kidney biomass and bioenergetics and a promoter of adaptive responses to stressors. This view might translate into novel perspectives on the mechanisms of kidney function and blood pressure regulation and on clinical implications of the
as a tool in precision medicine.
The ‘gasotransmitters’ hydrogen sulfide (H2S), nitric oxide (NO), and carbon monoxide (CO) act as second messengers in human physiology, mediating signal transduction via interaction with or chemical ...modification of protein targets, thereby regulating processes such as neurotransmission, blood flow, immunomodulation, or energy metabolism. Due to their broad reactivity and potential toxicity, the biosynthesis and breakdown of H2S, NO, and CO are tightly regulated. Growing evidence highlights the active role of gasotransmitters in their mutual cross-regulation. In human physiology, the transsulfuration enzymes cystathionine β-synthase (CBS) and cystathionine γ-lyase (CSE) are prominent H2S enzymatic sources. While CBS is known to be inhibited by NO and CO, little is known about CSE regulation by gasotransmitters. Herein, we investigated the effect of s-nitrosation on CSE catalytic activity. H2S production by recombinant human CSE was found to be inhibited by the physiological nitrosating agent s-nitrosoglutathione (GSNO), while reduced glutathione had no effect. GSNO-induced inhibition was partially reverted by ascorbate and accompanied by the disappearance of one solvent accessible protein thiol. By combining differential derivatization procedures and mass spectrometry-based analysis with functional assays, seven out of the ten protein cysteine residues, namely Cys84, Cys109, Cys137, Cys172, Cys229, Cys307, and Cys310, were identified as targets of s-nitrosation. By generating conservative Cys-to-Ser variants of the identified s-nitrosated cysteines, Cys137 was identified as most significantly contributing to the GSNO-mediated CSE inhibition. These results highlight a new mechanism of crosstalk between gasotransmitters.
The recent advances in unmanned aerial vehicle (UAV)-based remote sensing systems have broadened the remote sensing applications for agriculture. Despite the great possibilities of using UAVs to ...monitor agricultural fields, specific problems related to missing parts in UAV orthomosaics due to drone flight restrictions are common in agricultural monitoring, especially in large areas. In this study, we propose a methodological framework to impute missing parts of UAV orthomosaics using PlanetScope (PS) and Sentinel-2 (S2) data and the random forest (RF) algorithm of an integrated crop–livestock system (ICLS) covered by grass at the time. We validated the proposed framework by simulating and imputing artificial missing parts in a UAV orthomosaic and then comparing the original data with the model predictions. Spectral bands and the normalized difference vegetation index (NDVI) derived from PS, as well as S2 images (separately and combined), were used as predictor variables of the UAV spectral bands and NDVI in developing the RF-based imputation models. The proposed framework produces highly accurate results (RMSE = 6.77–17.33%) with a computationally efficient and robust machine-learning algorithm that leverages the wealth of empirical information present in optical satellite imagery (PS and S2) to impute up to 50% of missing parts in a UAV orthomosaic.
ABSTRACT The knowledge on reliable estimates of areas under sugarcane cultivation is essential for the Brazilian agribusiness, since it helps in the development of public policies, in determining ...prices by sugar mills to producers and allows establishing the logistics of production disposal. The objective of this work was to develop a methodology for mapping the sugarcane crop area in the state of Paraná, Brazil, using images from the Landsat/TM/OLI and IRS/LISS-3 satellites, for the crop years from 2010/2011 to 2013/2014. The mappings were conducted through the supervised Maximum likelihood classification (Maxver) achieving, on average, an overall accuracy of 94.13% and kappa index of 0.82. The correlation with the official data of the IBGE ranged from moderate to strong (0.64 ≤ rs ≤ 0.80) with average agreement (dr) of 0.81. There was an increase of 2.73% (18,630 ha) in the area with sugarcane in Paraná between 2010/2011 and 2013/2014.
RESUMO O conhecimento de estimativas confiáveis de áreas cultivadas de cana-de-açúcar é imprescindível para o agronegócio brasileiro por auxiliar no desenvolvimento de políticas públicas, na determinação dos preços aos produtores pelas usinas e permitir estabelecer a logística de escoamento da produção. O objetivo deste trabalho foi realizar o mapeamento de área da cultura de cana-de-açúcar para o estado do Paraná a partir de imagens dos satélites Landsat/TM/OLI e IRS/LISS-3, para as safras de 2010/2011 a 2013/2014. Os mapeamentos foram realizados por meio da classificação supervisionada de Máxima verossimilhança (Maxver) obtendo-se, em média, uma exatidão global de 94,13% e índice kappa de 0,82. As correlações com os dados oficiais do IBGE variaram de moderada a forte (0,64 ≤ rs ≤ 0,80) com concordância (dr) média de 0,81. Houve aumento de 2,73% (18.630 ha) de área com cana-de-açúcar no Paraná entre 2010/2011 e 2013/2014.
ABSTRACT In the state of Paraná, Brazil, there are no major changes in areas cultivated with annual crops, mainly due to environmental laws that do not allow expansions to new areas. There is a great ...contribution of the annual crops to the domestic demand of food and economic demand in the exports. Thus, the area and distribution of annual crops are information of great importance. New methodologies, such as data mining, are being tested with the objective of analyzing and improving their potential use for classification of land use and land cover. This study used the classifiers decision tree and random forest with Normalized Difference Vegetation Index (NDVI) temporal metrics on images from Operational Land Imager (OLI)/Landsat-8. The results were compared with traditional methods spectral images and Maximum Likelihood Classifier (MLC). At first, seven classes were mapped (water bodies, sugarcane, urban area, annual crops, forest, pasture and reforestation areas); then, only two classes were considered (annual crops and other targets). When classifying the seven targets, both methods had corresponding results, showing global accuracy near 84%. NDVI temporal metrics showed producer’s and user’s accuracy for the annual crop class of 86 and 100%, respectively. However, if considering only two classes, the NDVI temporal metrics reached global accuracy of near 98% and producer’s and user’s accuracy above 94%.
RESUMO No Estado do Paraná, Brasil, não há grandes mudanças nas áreas cultivadas com culturas anuais, principalmente devido a leis ambientais que não permitem expansões para novas áreas. Há grande contribuição das culturas anuais para a demanda doméstica de alimentos e econômica nas exportações. Assim, a área e distribuição das culturas anuais são informações de grande importância. Novas metodologias, como data mining, estão sendo testadas com o objetivo de analisar e melhorar seu potencial de uso para classificação do uso e cobertura da terra. Neste estudo, foram utilizados os classificadores decision tree e random forest com métricas temporais de Normalized Difference Vegetation Index (NDVI) em imagens do Operational Land Imager (OLI)/ Landsat-8. Os resultados foram comparados com os métodos tradicionais (imagens espectrais e classificador Maximum Likelihood Classifier - MLC). Inicialmente, foram mapeadas sete classes (corpos d’água, cana-de-açúcar, área urbana, culturas anuais, floresta, pastagem e áreas de reflorestamento) e posteriormente apenas duas classes foram consideradas (culturas anuais e outras classes). Ao classificar os sete alvos, ambos os métodos tiveram resultados correspondentes, mostrando exatidão global próxima a 84%. As métricas temporais de NDVI mostraram a acurácia do produtor e do usuário para a classe de cultura de 86 e 100%, respectivamente. No entanto, considerando-se apenas duas classes, as métricas temporais do NDVI alcançaram exatidão global próxima a 98% e a acurácia do produtor e do usuário acima de 94%.
Accurate mapping of crops with high spatiotemporal resolution plays a critical role in achieving the Sustainable Development Goals (SDGs), especially in the context of integrated crop-livestock ...systems (ICLS). Stakeholders can make informed decisions and implement targeted strategies to achieve multiple SDGs related to agriculture, rural development, and sustainable livelihoods by understanding the spatial dynamics of these systems. Accurate information on the extent of ICLS derived from multitemporal remote sensing and emerging map techniques such as deep learning can help in the implementation of sustainable agricultural practices. However, far too little attention has been paid to ICLS map accuracy because it may not be at the forefront of research agendas compared to those of other agricultural practices. This paper aims to map ICLS using high spatiotemporal resolution imagery and deep learning neural network classifiers at two different sites located in Brazil. The pipeline involves four interpretation approaches based on the ICLS class: evaluating deep neural network classifiers with different image composition intervals, explaining commission and omission errors, evaluating the temporal transferability of the method, and evaluating the influence of variables. The study area consists of two locations in São Paulo (study site 1, SS1) and Mato Grosso state (study site 2, SS2), Brazil. We derived nine spectral variables from PlanetScope (PS) images and four metrics through object-based image analysis (OBIA) using two time intervals, 10 and 15 days, to generate the image compositions. These input variables were used in three deep neural network classifiers: convolutional neural network in one dimension (Conv1D), long short-term memory (LSTM), and LSTM with a fully convolutional network (LSTM-FCN). Our results showed that mapping dynamic land use such as ICLS is possible by using high-spatiotemporal-resolution imagery and deep neural network classifiers. The 15-day LSTM-FCN classifier returned the highest map accuracies for both sites, with the following class-level accuracies: producer accuracy (PA) = 97.0% and user accuracy (UA) = 97.0% for SS1 and PA = 82.0% and UA = 96.5% for SS2. Meanwhile, we found map uncertainties arising from the diverse crop calendars and spectro-temporal similarities between ICLS and other land use. The best approaches revealed that temporal generalization was suitable for mapping ICLS, but some classifiers could not generalize due to the inherent characteristics of the class. Most variables were considered efficient for predicting ICLS, although spectral indices revealed better functional relationships, while the PS bands had a lower influence on the predictions. The accuracies achieved with the proposed method represent promising opportunities for the sufficiently accurate mapping of ICLS and other complex crop activities.
•A novel approach is proposed for integrated-crop livestock system (ICLS) mapping.•PlanetScope time series and deep learning effectively map ICLS.•Vegetation indices are powerful predictors for ICLS mapping.•ICLS mapping models can be transferred across years to avoid sample collection.
The biodistribution profile of a new dextrin nanomagnetogel, which consists of γ-Fe2O3 superparamagnetic nanoparticles loaded within a polymeric matrix of modified dextrin, was studied in mice. The ...nanomagnetogel bear a monomodal size distribution profile (average diameter 110 nm) close to neutral surface charge and higher relaxivity (r 2 = 215–248 mM–1 s–1 and r 2/r 1 = 13–11) than those of commercial formulations (r 2 = 160–177 mM–1 s–1 and r 2/r 1 = 4–7). Also, the observed blood half-lifeapproximately 4 his superior to that of similar commercially available formulations, which remain for a few minutes in circulation. PEGylation resulted in 1.7- and 1.2-fold lower accumulation in the liver and spleen, respectively, within the first 24 h. Noteworthy, a good correlation was obtained between the amount of polymer (quantified by scintigraphy) in the spleen, 48 h after administration, and the amount of iron physically loaded through hydrophobic interactions (quantified by ICP) indicating the absence of iron leakage from the polymeric matrix. This study provides evidence of the in vivo stability of a self-assembled nanomagnetogel, a relevant feature which is seldom reported in the literature.
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