Integrated Satellite, Unmanned Aerial Vehicle (UAV) and Ground Inversion of the SPAD of Winter Wheat in the Reviving Stage

Zhang, Suming; Zhao, Gengxing; Lang, Kun; Su, Bai-Horng; Chen, Xiaona; Xi, Xu Guang; Zhang, Huabin

doi:10.3390/s19071485

Cited by 69 publications

(48 citation statements)

References 61 publications

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“…Although a comparison with ground truths calculated with a spectroradiometer would have been more effective, a test on compatibility between Sequoia and S2 data is also of scientific relevance, given the growing interest in the integration of data acquired from satellite and UAV platforms [48] for environmental applications [49,50], including PA [51][52][53], both from research and applied points of view.…”

Section: Radiometric Consistency Assessmentmentioning

confidence: 99%

Geometric and Radiometric Consistency of Parrot Sequoia Multispectral Imagery for Precision Agriculture Applications

et al. 2019

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This paper is about the geometric and radiometric consistency of diverse and overlapping datasets acquired with the Parrot Sequoia camera. The multispectral imagery datasets were acquired above agricultural fields in Northern Italy and radiometric calibration images were taken before each flight. Processing was performed with the Pix4Dmapper suite following a single-block approach: images acquired in different flight missions were processed in as many projects, where different block orientation strategies were adopted and compared. Results were assessed in terms of geometric and radiometric consistency in the overlapping areas. The geometric consistency was evaluated in terms of point cloud distance using iterative closest point (ICP), while the radiometric consistency was analyzed by computing the differences between the reflectance maps and vegetation indices produced according to adopted processing strategies. For normalized difference vegetation index (NDVI), a comparison with Sentinel-2 was also made. This paper will present results obtained for two (out of several) overlapped blocks. The geometric consistency is good (root mean square error (RMSE) in the order of 0.1 m), except for when direct georeferencing is considered. Radiometric consistency instead presents larger problems, especially in some bands and in vegetation indices that have differences above 20%. The comparison with Sentinel-2 products shows a general overestimation of Sequoia data but with similar spatial variations (Pearson’s correlation coefficient of about 0.7, p-value < 2.2 × 10−16).

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Section: Radiometric Consistency Assessmentmentioning

confidence: 99%

Geometric and Radiometric Consistency of Parrot Sequoia Multispectral Imagery for Precision Agriculture Applications

et al. 2019

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“…Second, the centers of areas with relatively uniform vegetation coverage were selected as sampling points so that vegetation factors could be taken into account during the remote sensing inversion. Furthermore, the survey found that the vegetation in the study area differed significantly under different salinization levels; thus, the vegetation cover could also be used as an indirect indicator in soil salinity monitoring [44][45][46].…”

mentioning

confidence: 99%

A Harmonious Satellite-Unmanned Aerial Vehicle-Ground Measurement Inversion Method for Monitoring Salinity in Coastal Saline Soil

Zhang¹,

Zhao²

2019

Remote Sensing

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Soil salinization adversely impacts crop growth and production, especially in coastal areas which experience serious soil salinization. Therefore, rapid and accurate monitoring of the salinity and distribution of coastal saline soil is crucial. Representative areas of the Yellow River Delta (YRD)—the Hekou District (the core test area with 140 sampling points) and the Kenli District (the verification area with 69 sampling points)—were investigated. Ground measurement data, unmanned aerial vehicle (UAV) multispectral imagery and Sentinel-2A multispectral imagery were used as the data sources and a satellite-UAV-ground integrated inversion of the coastal soil salinity was performed. Correlation analyses and multiple regression methods were used to construct an accurate model. Then, a UAV-based inversion model was applied to the satellite imagery with reflectance normalization. Finally, the spatial and temporal universality of the UAV-based inversion model was verified and the soil salinity inversion results were obtained. The results showed that the green, red, red-edge and near-infrared bands were significantly correlated with soil salinity and the spectral parameters significantly improved this correlation; hence, the model is more effective upon combining spectral parameters with sensitive bands, with modeling precision and verification precision of the best model being 0.743 and 0.809, respectively. The reflectance normalization yielded good results. These findings proved that applying the UAV-based model to reflectance normalized Sentinel-2A images produces results that are consistent with the actual situation. Moreover, the inversion results effectively reflect the distributions characteristic of the soil salinity in the core test area and the study area. This study integrated the advantages of satellite, UAV and ground methods and then proposed a method for the inversion of the salinity of coastal saline soils at different scales, which is of great value for real-time, rapid and accurate soil salinity monitoring applications.

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“…For example, [69] illustrated the superiority of the RF algorithm compared to SVM and A-NNs for leaf area index retrieving. Similarly, several regression methods such as random forest (RF) [70], and support vector regression (SVR) [12] have been used on UAV imagery.…”

Section: Machine Learning and Statistical Modelsmentioning

confidence: 99%

“…As shown, most studies pertaining to UAV imagery for agro-environmental monitoring are mainly located in North America, Asia, and Europe. Countries with more than ten studies are China (45), United States (26), Canada (13), Italy (12), and Germany (11). In addition, Australia (9), Finland (9), Spain (6), Netherlands (5), Japan (5), South Africa (4), and Brazil 4 Figure 6 illustrates the worldwide distribution of the 39 countries represented.…”

Section: General Characteristics Of Studiesmentioning

confidence: 99%

“…As shown, Canon (23%, 30 studies) is the most frequently used sensor brand, followed by Sony (20%, 27), MicaSense (21%, 21), and Tetracam (12%, 16). The majority of the articles used visible sensors, with the most popular being the Canon Powershot (14) and Sony NEX (9) series, followed by multispectral cameras with the commercial brands of MicaSense Parrot Sequoia (12) and MicaSense Rededge (9), and LiDAR sensor technology with the Velodyne brand. Hyperspectral sensor brands, such as Headwall and Cubert, are in the minority of usage in this survey.…”

Section: Uav and Sensor Typesmentioning

confidence: 99%

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Meta-analysis of Unmanned Aerial Vehicle (UAV) Imagery for Agro-environmental Monitoring Using Machine Learning and Statistical Models

et al. 2020

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Unmanned Aerial Vehicle (UAV) imaging systems have recently gained significant attention from researchers and practitioners as a cost-effective means for agro-environmental applications. In particular, machine learning algorithms have been applied to UAV-based remote sensing data for enhancing the UAV capabilities of various applications. This systematic review was performed on studies through a statistical meta-analysis of UAV applications along with machine learning algorithms in agro-environmental monitoring. For this purpose, a total number of 163 peer-reviewed articles published in 13 high-impact remote sensing journals over the past 20 years were reviewed focusing on several features, including study area, application, sensor type, platform type, and spatial resolution. The meta-analysis revealed that 62% and 38% of the studies applied regression and classification models, respectively. Visible sensor technology was the most frequently used sensor with the highest overall accuracy among classification articles. Regarding regression models, linear regression and random forest were the most frequently applied models in UAV remote sensing imagery processing. Finally, the results of this study confirm that applying machine learning approaches on UAV imagery produces fast and reliable results. Agriculture, forestry, and grassland mapping were found as the top three UAV applications in this review, in 42%, 22%, and 8% of the studies, respectively.

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Integrated Satellite, Unmanned Aerial Vehicle (UAV) and Ground Inversion of the SPAD of Winter Wheat in the Reviving Stage

Cited by 69 publications

References 61 publications

Geometric and Radiometric Consistency of Parrot Sequoia Multispectral Imagery for Precision Agriculture Applications

Geometric and Radiometric Consistency of Parrot Sequoia Multispectral Imagery for Precision Agriculture Applications

A Harmonious Satellite-Unmanned Aerial Vehicle-Ground Measurement Inversion Method for Monitoring Salinity in Coastal Saline Soil

Meta-analysis of Unmanned Aerial Vehicle (UAV) Imagery for Agro-environmental Monitoring Using Machine Learning and Statistical Models

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