Impact of soil types on sugarcane development monitored over time by remote sensing

Amorim, Merilyn Taynara Accorsi; Silvero, Nélida Elizabet Quiñonez; Bellinaso, Henrique; Gomez, A.; Greschuk, Lucas T.; Campos, Lucas Rabelo; Demattê, José A. M.

doi:10.1007/s11119-022-09896-1

Cited by 6 publications

(3 citation statements)

References 53 publications

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“…Soil type and properties at different depths influence the development of sugarcane [61]. While a shallow groundwater table can impact soil moisture dynamics [62], this research was conducted in an area that has a water table that is from 5 to 50 m deep [18], which is associated with vertical, subsurface soil water movement [17].…”

Section: Sustainability Implicationsmentioning

confidence: 99%

Water Dynamics and Hydraulic Functions in Sandy Soils: Limitations to Sugarcane Cultivation in Southern Brazil

et al. 2023

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Crop cultivation on sandy soils is susceptible to water stress. Therefore, we determined the physical-hydric attributes of a Latossolo Vermelho distrófico (Oxisol) in northwestern Paraná state, Brazil. Soil samples were collected at depth ranges of 0 to 0.2 m, 0.2 to 0.4 m, and 0.4 to 0.6 m. We measured clay, silt, sand, fine and coarse sand contents, soil particle density, soil bulk density, total porosity, microporosity, and macroporosity. We also measured soil characteristics such as saturated and unsaturated soil hydraulic conductivities, pore distribution, water retention, available water capacity, and easily available water. We also estimated soil moisture, matric potential at field capacity, and time at field capacity. Validation of associations among these soil physical-hydric attributes was performed using principal component analysis. For the sandy soils analyzed, the distributions of coarse and fine sand fractions were measured for better evaluation of the soil’s physical and hydric attributes. Higher coarse sand contents increased soil hydraulic conductivities, maximum pore diameter, and macroporosity while reducing microporosity. Fine sand content reduced conductivity and increased soil water retention in subsurface layers. Simulated sugarcane yield increased with soil water storage. These results support improving crop simulation modeling of sugarcane to support sustainable intensification in regions with sandy soils.

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Section: Sustainability Implicationsmentioning

confidence: 99%

Water Dynamics and Hydraulic Functions in Sandy Soils: Limitations to Sugarcane Cultivation in Southern Brazil

et al. 2023

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“…Satellite remote sensing technology plays a crucial role in efficient land information acquisition. By employing appropriate backend data-processing techniques, it becomes possible to extract valuable agricultural information from satellite data [5][6][7][8][9]. This enables efficient and real-time crop identification [10][11][12][13][14][15][16][17][18][19][20].…”

Section: Introductionmentioning

confidence: 99%

Crop-Planting Area Prediction from Multi-Source Gaofen Satellite Images Using a Novel Deep Learning Model: A Case Study of Yangling District

Kuang,

Guo,

Bai

et al. 2023

Remote Sensing

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Neural network models play an important role in crop extraction based on remote sensing data. However, when dealing with high-dimensional remote sensing data, these models are susceptible to performance degradation. In order to address the challenges associated with multi-source Gaofen satellite data, a novel method is proposed for dimension reduction and crop classification. This method combines the benefits of the stacked autoencoder network for data dimensionality reduction, and the convolutional neural network for classification. By leveraging the advantages of multi-dimensional remote sensing information, and mitigating the impact of dimensionality on the classification accuracy, this method aims to improve the effectiveness of crop classification. The proposed method was applied to the extraction of crop-planting areas in the Yangling Agricultural Demonstration Zone, using multi-temporal spectral data collected from the Gaofen satellites. The results demonstrate that the fusion network, which extracts low-dimensional characteristics, offers advantages in classification accuracy. At the same time, the proposed model is compared with methods such as the decision tree (DT), random forest (RF), support vector machine (SVM), hyperspectral image classification based on a convolutional neural network (HICCNN), and a characteristic selection classification method based on a convolutional neural network (CSCNN). The overall accuracy of the proposed method can reach 98.57%, which is 7.95%, 4.69%, 5.68%, 1.21%, and 1.10% higher than the above methods, respectively. The effectiveness of the proposed model was verified through experiments. Additionally, the model demonstrates a strong robustness when classifying based on new data. When extracting the crop area of the entire Yangling District, the errors for wheat and corn are only 9.6% and 6.3%, respectively, and the extraction results accurately reflect the actual planting situation of crops.

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“…Monitoring this variability is crucial for farmers, landowners, and insurers to make informed decisions (Kayad et al, 2022;Oliveira et al, 2018). Soil, as a key factor in agricultural production, exhibits considerable variability in tropical regions, with diverse physical and chemical characteristics that influence crop growth and productivity (Amorim et al, 2022). Spatial and temporal variability of soil properties significantly impact crop yield, making it important to understand this variation and its relationship with crop performance for precision management practices (Kayode et al, 2022;Zhou et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Spatial and Temporal Variability of Vegetation Indices with Industrial Tomato Yield

O. Martins,

Reis,

L. Lopes

2023

JSD

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Vegetation indices indicate crop development and help identify areas with potential productivity reduction in the desired crop. Thus, this study aimed to determine the spatial and temporal influence of vegetation indices on industrial tomato (Solanum lycopersicum L.) yield. The research was conducted at Barcelos Farm in Anápolis, GO, covering a 55-hectare area. A sampling grid of 61-point pairs, spaced at 90 x 90 meters, was established using a GPS receiver. Vegetation indices were characterized by measuring the Soil Plant Analysis Development (SPAD) index, foliar nitrogen content through the Kjeldahl method, and Normalized Difference Vegetation Index (NDVI) using both a spectroradiometer and satellite imagery. Industrial tomato yield was assessed during harvest. Through the results, it was observed that temporal variability between vegetation indices and actual foliar nitrogen content exhibited a significant and positive correlation with industrial tomato productivity, particularly during the flowering stage. However, temporal variability between vegetation indices and foliar nitrogen content displayed low correlation across the mapped areas over time. Determining foliar nitrogen content and vegetation indices during the flowering stage is recommended for the industrial tomato crop. The relationship between ground-based remote sensing NDVI and orbital NDVI displayed a 55% positive correlation during the flowering stage.

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Impact of soil types on sugarcane development monitored over time by remote sensing

Cited by 6 publications

References 53 publications

Water Dynamics and Hydraulic Functions in Sandy Soils: Limitations to Sugarcane Cultivation in Southern Brazil

Water Dynamics and Hydraulic Functions in Sandy Soils: Limitations to Sugarcane Cultivation in Southern Brazil

Crop-Planting Area Prediction from Multi-Source Gaofen Satellite Images Using a Novel Deep Learning Model: A Case Study of Yangling District

Spatial and Temporal Variability of Vegetation Indices with Industrial Tomato Yield

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