Ensemble machine-learning-based framework for estimating total nitrogen concentration in water using drone-borne hyperspectral imagery of emergent plants: A case study in an arid oasis, NW China

Wang, Jingzhe; Shi, Tiezhu; Yu, Danlin; Teng, Dexiong; Ge, Xiangyu; Zhang, Zipeng; Yang, Xiaodong; Wang, Hanxi; Wu, Guofeng

doi:10.1016/j.envpol.2020.115412

Cited by 81 publications

(42 citation statements)

References 65 publications

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“…This may have been because the features of the red edge weaken as the order increases and inherent spectral noise strengthens. This result is consistent with those of other studies [49][50][51]100]. Additionally, studies have shown FOD pretreatment to be better than IOD pretreatment [100,101], because the IOD filters out large amounts of background information, highlighting the edge features and causing substantial information loss.…”

Section: Discussionsupporting

confidence: 92%

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Estimating Agricultural Soil Moisture Content through UAV-Based Hyperspectral Images in the Arid Region

Ding

Jin

et al. 2021

Remote Sensing

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Unmanned aerial vehicle (UAV)-based hyperspectral remote sensing is an important monitoring technology for the soil moisture content (SMC) of agroecological systems in arid regions. This technology develops precision farming and agricultural informatization. However, hyperspectral data are generally used in data mining. In this study, UAV-based hyperspectral imaging data with a resolution o 4 cm and totaling 70 soil samples (0–10 cm) were collected from farmland (2.5 × 104 m2) near Fukang City, Xinjiang Uygur Autonomous Region, China. Four estimation strategies were tested: the original image (strategy I), first- and second-order derivative methods (strategy II), the fractional-order derivative (FOD) technique (strategy III), and the optimal fractional order combined with the optimal multiband indices (strategy IV). These strategies were based on the eXtreme Gradient Boost (XGBoost) algorithm, with the aim of building the best estimation model for agricultural SMC in arid regions. The results demonstrated that FOD technology could effectively mine information (with an absolute maximum correlation coefficient of 0.768). By comparison, strategy IV yielded the best estimates out of the methods tested (R2val = 0.921, RMSEP = 1.943, and RPD = 2.736) for the SMC. The model derived from the order of 0.4 within strategy IV worked relatively well among the different derivative methods (strategy I, II, and III). In conclusion, the combination of FOD technology and the optimal multiband indices generated a highly accurate model within the XGBoost algorithm for SMC estimation. This research provided a promising data mining approach for UAV-based hyperspectral imaging data.

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Section: Discussionsupporting

confidence: 92%

“…The spectral derivative method was adopted to deal with the multicollinearity of the spectrum [49,50,100]. The results showed that the effects of FOD technology for hyperspectral data combining images with spectra were significant.…”

Section: Discussionmentioning

confidence: 99%

Estimating Agricultural Soil Moisture Content through UAV-Based Hyperspectral Images in the Arid Region

Ding

Jin

et al. 2021

Remote Sensing

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“…On the global scale, arid regions cover approximately one-third of the earth [49], and are one of the most sensitive regions to wind erosion phenomena [50]. In some recent research, a positive association was observed between the LST and wind erosion events [16,18].…”

Section: Harmful Effects Caused By Changes In Lst In Arid Regionsmentioning

confidence: 99%

Analysis on the Spatio-Temporal Changes of LST and Its Influencing Factors Based on VIC Model in the Arid Region from 1960 to 2017: An Example of the Ebinur Lake Watershed, Xinjiang, China

Nigenare

Ding

2021

Remote Sensing

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LST (Land surface temperature) is an important indicator for monitoring dynamic changes in the earth’s resources and environment. However, the complexity of obtaining long-term, continuous LST data hinders the development of research on LST responses to meteorological factors or LUCC in areas where data is lacking. The objective of this research was to use the VIC-3L (Variable Infiltration Capacity) based on multi-source remote sensing data to simulate and explore spatio-temporal changes in the LST, to analyze the relationship between the LST and meteorological elements by using cross-wavelet transform (XWT) and wavelet coherence (WTC), the relationship between the LST and LUCC by using three-phase remote sensing images of LUCC. The following results were obtained. The annual average LST of the study area is increasing at a rate of 0.027 °C per year. The annual average LST level is relatively high in the central and eastern regions. The average temperature has an important influence on LST, which is mainly reflected in the period scale of 1~4a in 1963–1972, 1980–1996, and 2004–2010. The sharp decline in open shrubs may have exacerbated the increase in LST in the study area. This study provides a scientific reference for studying LST in arid areas.

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“…However, this also augments the N losses to the environment via leaching or gaseous emissions. For example, fertilizer nitrate (NO 3 -) leaching pollutes the surface and ground water [13]. Ultimately, these NO 3 ends up in our diet.…”

Section: Introductionmentioning

confidence: 99%

“…RPAs are facilitating us in this domain too by furnishing the estimates of total N concentration in water, so that only the required amount of N fertilizer be applied avoiding the potential harmful impacts and saving the economic loss to farmers. One such practical example of using drone equipped with hyperspectral cameras to assess the N concentration in water has recently been reported [13]. Although the lower adoption rates of precision technologies than expected comprise of various factors including economical ones [15,16].…”

Section: Introductionmentioning

confidence: 99%

Remotely Piloted Aircraft (RPA) in Agriculture: A Pursuit of Sustainability

et al. 2020

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The current COVID-19 global pandemic has amplified the pressure on the agriculture sector, inciting the need for sustainable agriculture more than ever. Thus, in this review, a sustainable perspective of the use of remotely piloted aircraft (RPA) or drone technology in the agriculture sector is discussed. Similarly, the types of cameras (multispectral, thermal, and visible), sensors, software, and platforms frequently deployed for ensuring precision agriculture for crop monitoring, disease detection, or even yield estimation are briefly discoursed. In this regard, vegetation indices (VIs) embrace an imperative prominence as they provide vital information for crop monitoring and decision-making, thus a summary of most commonly used VIs is also furnished and serves as a guide while planning to collect specific crop data. Furthermore, the establishment of significant applications of RPAs in livestock, forestry, crop monitoring, disease surveillance, irrigation, soil analysis, fertilization, crop harvest, weed management, mechanical pollination, crop insurance and tree plantation are cited in the light of currently available literature in this domain. RPA technology efficiency, cost and limitations are also considered based on the previous studies that may help to devise policies, technology adoption, investment, and research activities in this sphere.

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Ensemble machine-learning-based framework for estimating total nitrogen concentration in water using drone-borne hyperspectral imagery of emergent plants: A case study in an arid oasis, NW China

Cited by 81 publications

References 65 publications

Estimating Agricultural Soil Moisture Content through UAV-Based Hyperspectral Images in the Arid Region

Estimating Agricultural Soil Moisture Content through UAV-Based Hyperspectral Images in the Arid Region

Analysis on the Spatio-Temporal Changes of LST and Its Influencing Factors Based on VIC Model in the Arid Region from 1960 to 2017: An Example of the Ebinur Lake Watershed, Xinjiang, China

Remotely Piloted Aircraft (RPA) in Agriculture: A Pursuit of Sustainability

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