Crop Monitoring Strategy Based on Remote Sensing Data (Sentinel-2 and Planet), Study Case in a Rice Field after Applying Glycinebetaine

Bautista, A. San; Fita, David; Franch, B.; Castiñeira-Ibáñez, Sergio; Arizo, Patricia; Sánchez-Torres, María José; Becker‐Reshef, Inbal; Michavila, Constanza Rubio

doi:10.3390/agronomy12030708

Cited by 15 publications

(8 citation statements)

References 60 publications

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“…While the issue of NDVI saturation has been widely documented in other studies and overcome by the application of alternative vegetation indices ( Rehman et al., 2019 ; Jiang et al., 2021 ; San Bautista et al., 2022 ), our research did not encounter this limitation due to the low-density cultivation conditions, low yields (below 8,000 kg ha -1 ), and sparse biomass coverage (considering the crop was dry seeded).…”

Section: Discussionmentioning

confidence: 88%

“…The Sentinel-2 satellite launched by the European Space Agency in 2016 is a multispectral tool for agricultural practices due to its relatively spatiotemporal high resolution (temporal resolution of five days and spatial resolution of 10m), wide coverage, and availability of 13 spectral bands ( ESA, 2023 ). This tool has also some limitations: i) although the spatial resolution of the Sentinel-2 data constitutes an improvement over other previously available satellites, it is still far from the spatial resolution provided by drone or proximal direct measurements ( Mileva et al., 2018 ; Salgueiro Romero et al., 2020 ); ii) NDVI saturates when canopies completely cover the soil (particularly under optimal growth conditions) since the red band remains unchanged for dense vegetation conditions, when the leaf area index became high ( Pettorelli et al, 2013 ; San Bautista et al., 2022 ). In this context, the research presented here explores the use of NDVI to detect deficiencies when values range below saturation observations.…”

Section: Introductionmentioning

confidence: 99%

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Agronomic performance and remote sensing assessment of organic and mineral fertilization in rice fields

Marti-Jerez,

Català-Forner,

Tomàs

et al. 2023

Front. Plant Sci.

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IntroductionRice heavily relies on nitrogen fertilizers, posing environmental, resource, and geopolitical challenges. This study explores sustainable alternatives like animal manure and remote sensing for resource-efficient rice cultivation. It aims to assess the long-term impact of organic fertilization and remote sensing monitoring on agronomic traits, yield, and nutrition.MethodsA six-year experiment in rice fields evaluated fertilization strategies, including pig slurry (PS) and chicken manure (CM) with mineral fertilizers (MIN), MIN-only, and zero-fertilization. Traits, yield, spectral responses, and nutrient content were measured. Sentinel-2 remote sensing tracked crop development.ResultsCost-effective organic fertilizers (PS and CM) caused a 13% and 15% yield reduction but still doubled zero-fertilization yield. PS reduced nitrogen leaching. Heavy metals in rice grains were present at safe amounts. Organic-fertilized crops showed nitrogen deficiency at the late vegetative stages, affecting yield. Sentinel-2 detected nutrient deficiencies through NDVI.DiscussionOrganic fertilizers, especially PS, reduce nitrogen loss, benefiting the environment. However, they come with yield trade-offs and nutrient management challenges that can be managed and balanced with reduced additional mineral applications. Sentinel-2 remote sensing helps manage nutrient deficiencies. In summary, this research favors cost-effective organic fertilizers with improved nutrient management for sustainable rice production.

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Section: Discussionmentioning

confidence: 88%

Section: Introductionmentioning

confidence: 99%

Agronomic performance and remote sensing assessment of organic and mineral fertilization in rice fields

Marti-Jerez,

Català-Forner,

Tomàs

et al. 2023

Front. Plant Sci.

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“…These studies underscore the utility of remote sensing with the Sentinel-2 satellite in providing valuable insights into the state of bean crops on a large scale and across different growth stages. This information can empower farmers to make informed decisions regarding crop management, optimize resource utilization, and enhance productivity [19,[88][89][90] In general, the PCA (PC1 = 48.83% and PC2 = 39.25%) reveals that the Sentinel-2A/MSI samples exhibit greater homogeneity, occupying a consistent quantitative behavior pattern in the biplot graph. In contrast, the UAV samples display a more heterogeneous quantitative pattern, indicating greater variability in the data depending on the crop's development stage and the application of MgSO 4 .…”

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

confidence: 99%

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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“…Remote sensing is one of the possible ways to produce deep analyses about the current state of a plant in a non-destructive way. A lot of spectral data are typically produced by satellite [1][2][3], UAV [4][5][6] or handheld spectrometers [7]. The measurement of reflectance using portable spectral measurement instruments is the most advantageous method of data acquisition [8].…”

Section: Introductionmentioning

confidence: 99%

Temporal Changes of Leaf Spectral Properties and Rapid Chlorophyll—A Fluorescence under Natural Cold Stress in Rice Seedlings

Székely

Szalóki

Jancsó

et al. 2023

Plants

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Nowadays, hyperspectral remote sensing data are widely used in nutrient management, crop yield forecasting and stress monitoring. These data can be acquired with satellites, drones and handheld spectrometers. In this research, handheld spectrometer data were validated by chlorophyll-a fluorescence measurements under natural cold stress. The performance of 16 rice cultivars with different origins and tolerances was monitored in the seedling stage. The studies were carried out under field conditions across two seasons to simulate different temperature regimes. Twenty-four spectral indices and eleven rapid chlorophyll-a fluorescence parameters were compared with albino plants. We identified which wavelengths are affected by low temperatures. Furthermore, the differences between genotypes were characterized by certain well-known and two newly developed (AAR and RAR) indices based on the spectral difference between the genotype and albino plant. The absorbance, reflectance and transmittance differences from the control are suitable for the discrimination of tolerant-sensitive varieties, especially based on their shape, peak and shifting distance. The following wavelengths are capable of determining the tolerant varieties, namely 548–553 nm, 667–670 nm, 687–688 nm and 800–950 nm in case of absorbance; above 700 nm for reflectance; and the whole spectrum (400–1100 nm) for transmittance.

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Crop Monitoring Strategy Based on Remote Sensing Data (Sentinel-2 and Planet), Study Case in a Rice Field after Applying Glycinebetaine

Cited by 15 publications

References 60 publications

Agronomic performance and remote sensing assessment of organic and mineral fertilization in rice fields

Agronomic performance and remote sensing assessment of organic and mineral fertilization in rice fields

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

Temporal Changes of Leaf Spectral Properties and Rapid Chlorophyll—A Fluorescence under Natural Cold Stress in Rice Seedlings

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