Spatiotemporal dynamics of agricultural areas with central pivot using orbital remote sensing in the Brazilian semiarid

Silva, Jhon Lennon Bezerra da; Bezerra, Alan Cézar; Moura, Geber Barbosa de Albuquerque; Jardim, Alexandre Maniçoba da Rosa Ferraz; Batista, Pedro Henrique Dias; Jesus, Fernanda Lâmede Ferreira de; Sanches, Arthur Carniato; Silva, Marcos Vinícius da

doi:10.1016/j.atech.2022.100052

Cited by 6 publications

(8 citation statements)

References 40 publications

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“…Numerous studies have explored the utilization of remote sensing with satellites to monitor the health, yield, and growth of bean crops [15,34]. The NDVI, for instance, is commonly employed to assess crop health and vegetative density [2,83,84], while water indices such as NDMI_GAO and NDWI_GAO are more specific for evaluating water availability in plants, as highlighted in several studies [57,58,85].…”

Section: Satellite Data Analysis (Sentinel-2a/msi)mentioning

confidence: 99%

“…Research findings have demonstrated the effectiveness of the NDVI in monitoring the vegetative development and productivity of beans over time [86,87]. Increasing NDVI values signify a rise in vegetative cover and crop health, which can be particularly useful for identifying areas experiencing water stress or other growth-related issues [12,85]. Similarly, the NDMI_GAO and NDWI_GAO indices have been utilized to monitor productivity, soil moisture, and water availability for plants.…”

Section: Satellite Data Analysis (Sentinel-2a/msi)mentioning

confidence: 99%

“…Similarly, the NDMI_GAO and NDWI_GAO indices have been utilized to monitor productivity, soil moisture, and water availability for plants. Low NDWI_GAO values may indicate dry conditions or water stress, while higher values may suggest an adequate water supply [56,85].…”

Section: Satellite Data Analysis (Sentinel-2a/msi)mentioning

confidence: 99%

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Geotechnologies in Biophysical Analysis through the Applicability of the UAV and Sentinel-2A/MSI in Irrigated Area of Common Beans: Accuracy and Spatial Dynamics

de Oliveira,

de Castro,

Sousa

et al. 2024

Remote Sensing

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The applicability of remote sensing enables the prediction of nutritional value, phytosanitary conditions, and productivity of crops in a non-destructive manner, with greater efficiency than conventional techniques. By identifying problems early and providing specific management recommendations in bean cultivation, farmers can reduce crop losses, provide more accurate and adequate diagnoses, and increase the efficiency of agricultural resources. The aim was to analyze the efficiency of vegetation indices using remote sensing techniques from UAV multispectral images and Sentinel-2A/MSI to evaluate the spectral response of common bean (Phaseolus vulgaris L.) cultivation in different phenological stages (V4 = 32 DAS; R5 = 47 DAS; R6 = 60 DAS; R8 = 74 DAS; and R9 = 89 DAS, in 99 days after sowing—DAS) with the application of doses of magnesium (0, 250, 500, and 1000 g ha−1). The field characteristics analyzed were mainly chlorophyll content, productivity, and plant height in an experimental area by central pivot in the midwest region of Brazil. Data from UAV vegetation indices served as variables for the treatments implemented in the field and were statistically correlated with the crop’s biophysical parameters. The spectral response of the bean crop was also detected through spectral indices (NDVI, NDMI_GAO, and NDWI_GAO) from Sentinel-2A/MSI, with spectral resolutions of 10 and 20 m. The quantitative values of NDVI from UAV and Sentinel-2A/MSI were evaluated by multivariate statistical analysis, such as principal components (PC), and cophenetic correlation coefficient (CCC), in the different phenological stages. The NDVI and MCARI vegetation indices stood out for productivity prediction, with r = 0.82 and RMSE of 330 and 329 kg ha−1, respectively. The TGI had the best performance in terms of plant height (r = 0.73 and RMSE = 7.4 cm). The best index for detecting the relative chlorophyll SPAD content was MCARI (r = 0.81; R2 = 0.66 and RMSE = 10.14 SPAD), followed by NDVI (r = 0.81; R2 = 0.65 and RMSE = 10.19 SPAD). The phenological stage with the highest accuracy in estimating productive variables was R9 (Physiological maturation). GNDVI in stages R6 and R9 and VARI in stage R9 were significant at 5% for magnesium doses, with quadratic regression adjustments and a maximum point at 500 g ha−1. Vegetation indices based on multispectral bands of Sentinel-2A/MSI exhibited a spectral dynamic capable of aiding in the management of bean crops throughout their cycle. PCA (PC1 = 48.83% and PC2 = 39.25%) of the satellite multiple regression model from UAV vs. Sentinel-2A/MSI presented a good coefficient of determination (R2 = 0.667) and low RMSE = 0.12. UAV data for the NDVI showed that the Sentinel-2A/MSI samples were more homogeneous, while the UAV samples detected a more heterogeneous quantitative pattern, depending on the development of the crop and the application of doses of magnesium. Results shown denote the potential of using geotechnologies, especially the spectral response of vegetation indices in monitoring common bean crops. Although UAV and Sentinel-2A/MSI technologies are effective in evaluating standards of the common bean crop cycle, more studies are needed to better understand the relationship between field variables and spectral responses.

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Section: Satellite Data Analysis (Sentinel-2a/msi)mentioning

confidence: 99%

Section: Satellite Data Analysis (Sentinel-2a/msi)mentioning

confidence: 99%

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Geotechnologies in Biophysical Analysis through the Applicability of the UAV and Sentinel-2A/MSI in Irrigated Area of Common Beans: Accuracy and Spatial Dynamics

de Oliveira,

de Castro,

Sousa

et al. 2024

Remote Sensing

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“…These aspects are fundamental for adequate planning of water and soil conservation projects [11,14,15,26,28,51]. In this regard, research focused on the development and improvement of models of the evapotranspiration of native vegetation and agricultural areas stands out [12,49,[52][53][54]. It is essential for watershed management and widely necessary for climatological, meteorological, and hydrological modeling, as it has a direct influence on the hydrological cycle and water availability and is associated with land cover and land use (LULC) conditions and patterns.…”

Section: Introductionmentioning

confidence: 99%

“…Evapotranspiration is also strongly linked to irrigated agriculture, being essential for the management and adequate dimensioning of irrigation projects in a sector that demands and consumes high water capacity. For beyond this planning, there are more effective and sustainable management measures for water resources in arid and semiarid regions [11,23,50,[54][55][56][57].…”

Section: Introductionmentioning

confidence: 99%

Geospatial Insights into Aridity Conditions: MODIS Products and GIS Modeling in Northeast Brazil

Silva,

Jardim

et al. 2024

Hydrology

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Northeast Brazil (NEB), particularly its semiarid region, represents an area highly susceptible to the impacts of climate change, including severe droughts, and intense anthropogenic activities. These stresses may be accelerating environmental degradation and desertification of soil in NEB. The main aim of this study was to gain geospatial insights into the biophysical parameters of surface energy balance and actual evapotranspiration on a multi-temporal scale, aiming to detect and analyze the spectral behavioral patterns of areas vulnerable to degradation processes, based on thematic maps at the surface, for NEB and mainly the semiarid region of NEB from 2000 to 2019. Geospatial data from 8-day MODIS sensor products were used, such as surface reflectance (Terra/MOD09A1 and Aqua/MYD09A1), surface temperature (Terra/MOD11A2 and Aqua/MYD11A2), and actual evapotranspiration (Terra/MOD16A2 and Aqua/MYD16A2), version 6. Therefore, in this study, pixel-to-pixel values were processed by calculating the average pixel statistics for each year. From the reflectance product, digital processing of the surface albedo and spectral vegetation indices was also carried out, using computational programming scripts and machine learning algorithms developed via the Google Earth Engine (GEE) platform. The study also presents a seasonal analysis of these components and their relationships over 20 years. Through vegetation indices and statistical correlations, a new predictive model of actual evapotranspiration was developed. The quantitative and spatiotemporal spectral patterns of the parameters were assessed through descriptive statistics, measures of central tendency and dispersion, and statistical error analyses and correlation indices. Thematic maps highlighted the pixel-to-pixel results, with patterns of high temperature distribution mainly in the central and northeastern part of NEB and the semiarid region of NEB, highlighting the formation of persistent heat islands over time. Meanwhile, in these areas, the maps of actual evapotranspiration showed a drastic reduction due to the lesser availability of energy. Over time, the semiarid region of NEB presented areas with little and/or no vegetation cover, which were highly well-defined between the years 2012 and 2019, confirming that these areas are extremely vulnerable to degradation and desertification processes due to significant loss of vegetative and water resilience. The components of energy balance were highly interconnected to climatological and environmental conditions, showing the severe results of drought and accentuation of the water deficit in NEB, presenting a greater condition of aridity in the semiarid region of NEB over time.

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Environmental degradation of vegetation cover and water bodies in the semiarid region of the Brazilian Northeast via cloud geoprocessing techniques applied to orbital data

Silva

Moura

Silva

et al. 2023

Journal of South American Earth Sciences

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Spatiotemporal dynamics of agricultural areas with central pivot using orbital remote sensing in the Brazilian semiarid

Cited by 6 publications

References 40 publications

Geotechnologies in Biophysical Analysis through the Applicability of the UAV and Sentinel-2A/MSI in Irrigated Area of Common Beans: Accuracy and Spatial Dynamics

Geotechnologies in Biophysical Analysis through the Applicability of the UAV and Sentinel-2A/MSI in Irrigated Area of Common Beans: Accuracy and Spatial Dynamics

Geospatial Insights into Aridity Conditions: MODIS Products and GIS Modeling in Northeast Brazil

Environmental degradation of vegetation cover and water bodies in the semiarid region of the Brazilian Northeast via cloud geoprocessing techniques applied to orbital data

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