Potential Bands of Sentinel-2A Satellite for Classification Problems in Precision Agriculture

Zhang, Tianxiang; Su, Jinya; Liu, Cunjia

doi:10.1007/s11633-018-1143-x

Cited by 55 publications

(47 citation statements)

References 26 publications

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“…In this work, the NDVI index allowed us to identify no-greenery vegetation pixels, with or without water pixels, and also low greenery pixels, representing the damages caused by Western Swamphen in rice crops. It has been previously found with images from Sentinel-2 [42] that the use of vegetation indices improves the results of crop classifications over the use of the respective individual bands.…”

Section: Uav Imagery Processingmentioning

confidence: 98%

Calibrating Sentinel-2 Imagery with Multispectral UAV Derived Information to Quantify Damages in Mediterranean Rice Crops Caused by Western Swamphen (Porphyrio porphyrio)

Pla

Bota

Duane

et al. 2019

Drones

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Making agricultural production compatible with the conservation of biological diversity is a priority in areas in which human–wildlife conflicts arise. The threatened Western Swamphen (Porphyrio porphyrio) feeds on rice, inducing crop damage and leading to decreases in rice production. Due to the Swamphen protection status, economic compensation policies have been put in place to compensate farmers for these damages, thus requiring an accurate, quantitative, and cost-effective evaluation of rice crop losses over large territories. We used information captured from a UAV (Unmanned Aerial Vehicle) equipped with a multispectral Parrot SEQUOIA camera as ground-truth information to calibrate Sentinel-2 imagery to quantify damages in the region of Ebro Delta, western Mediterranean. UAV vegetation index NDVI (Normalized Difference Vegetation Index) allowed estimation of damages in rice crops at 10 cm pixel resolution by discriminating no-green vegetation pixels. Once co-registered with Sentinel grid, we predicted the UAV damage proportion at a 10 m resolution as a function of Sentinel-2 NDVI, and then we extrapolated the fitted model to the whole Sentinel-2 Ebro Delta image. Finally, the damage predicted with Sentinel-2 data was quantified at the agricultural plot level and validated with field information compiled on the ground by Rangers Service. We found that Sentinel2-NDVI data explained up to 57% of damage reported with UAV. The final validation with Rangers Service data pointed out some limitations in our procedure that leads the way to improving future development. Sentinel2 imagery calibrated with UAV information proved to be a viable and cost-efficient alternative to quantify damages in rice crops at large scales.

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Section: Uav Imagery Processingmentioning

confidence: 98%

Calibrating Sentinel-2 Imagery with Multispectral UAV Derived Information to Quantify Damages in Mediterranean Rice Crops Caused by Western Swamphen (Porphyrio porphyrio)

Pla

Bota

Duane

et al. 2019

Drones

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“…5 Although suitable for large area application, it is also acknowledged that there are certain limitations for satellite based remote sensing. 6,7 First, the cost of satellite remote sensing is usually high. Secondly, the spatial resolution of satellite imagery is usually low, e.g.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning-Based Crop Drought Mapping System by UAV Remote Sensing RGB Imagery

Coombes

Liu

et al. 2020

Un. Sys.

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Water stress has adverse effects on crop growth and yield, where its monitoring plays a vital role in precision crop management. This paper aims at initially exploiting the potentials of UAV aerial RGB image in crop water stress assessment by developing a simple but effective supervised learning system. Various techniques are seamlessly integrated into the system including vegetation segmentation, feature engineering, Bayesian optimization and Support Vector Machine (SVM) classifier. In particular, wheat pixels are first segmented from soil background by using the classical vegetation index thresholding. Rather than performing pixel-wise classification, pixel squares of appropriate dimension are defined as samples, from which various features for pure vegetation pixels are extracted including spectral and color index (CI) features. SVM with Bayesian optimization is adopted as the classifier. To validate the developed system, a Unmanned Aerial Vehicle (UAV) survey is performed to collect high-resolution atop canopy RGB imageries by using DJI S1000 for the experimental wheat fields of Gucheng town, Heibei Province, China. Two levels of soil moisture were designed after seedling establishment for wheat plots by using intelligent irrigation and rain shelter, where field measurements were to obtain ground soil water ratio for each wheat plot. Comparative experiments by three-fold cross-validation demonstrate that pixel-wise classification, with a high computation load, can only achieve an accuracy of 82.8% with poor F1 score of 71.7%; however, the developed system can achieve an accuracy of 89.9% with F1 score of 87.7% by using only spectral intensities, and the accuracy can be further improved to 92.8% with F1 score of 91.5% by fusing both spectral intensities and CI features. Future work is focused on incorporating more spectral information and advanced feature extraction algorithms to further improve the performance.

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“…Technical advancements in spatial sciences havefavored the researches to utilize remotely sensed imageries for extraction of land cover information. Potential remote sensing methods are highly capable of providingdatasets with high spatial, spectral, and temporal resolutions that promote further analysis [1,2]. Multispectral and hyperspectral datasets obtained from spaceborne and airborne platforms yield possible results when used for numerous geospatial use cases.…”

Section: Introductionmentioning

confidence: 99%

Assessment on the Potential of Multispectral and Hyperspectral Datasets for Land Use/Land Cover Classification

Priyadarshini

Sivashankari

Shekhar

et al. 2019

The 2nd International Electronic Conference on Geosciences

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Land use/land cover (LULC) is a significant factor which plays a vital role in defining an urban ecosystem. Interpretations of LULC are eased in recent times by utilizing hyperspectral and multispectral datasets obtained from various platforms. An attempt is made to comparatively assess the potentiality of AVIRIS NG with Sentinel 2 data through applied classification techniques for Kalaburagi urban sphere. Spectral responses of both datasets were analyzed to derive reflectance spectra. A standard supervised classification algorithm associated with dimensionality reduction techniques is applied. For performance evaluation, results are validated to check which dataset outperforms well and provides better accuracy.

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Potential Bands of Sentinel-2A Satellite for Classification Problems in Precision Agriculture

Cited by 55 publications

References 26 publications

Calibrating Sentinel-2 Imagery with Multispectral UAV Derived Information to Quantify Damages in Mediterranean Rice Crops Caused by Western Swamphen (Porphyrio porphyrio)

Calibrating Sentinel-2 Imagery with Multispectral UAV Derived Information to Quantify Damages in Mediterranean Rice Crops Caused by Western Swamphen (Porphyrio porphyrio)

Machine Learning-Based Crop Drought Mapping System by UAV Remote Sensing RGB Imagery

Assessment on the Potential of Multispectral and Hyperspectral Datasets for Land Use/Land Cover Classification

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