Effects of saline reclaimed waters and deficit irrigation on Citrus physiology assessed by UAV remote sensing

Trigueros, Cristina Romero; Tortosa, Pedro Antonio Nortes; Alarcón, Juan José; Hunink, Johannes E.; Parra, María Dolores Serrano; Contreras, Sergio; Droogers, P.; Nicolás, Emilio

doi:10.1016/j.agwat.2016.09.014

Cited by 83 publications

(52 citation statements)

References 58 publications

(65 reference statements)

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“…Crops reflectance properties convey information about the crop, for instance, a healthier crop has higher reflectance in the NIR band. Most often, the bands are mathematically combined to form VIs, which provide information on the crop's health, growth stage, biophysical properties, leaf biochemistry, and water stress [29,[215][216][217][218]. Using multispectral or hyperspectral data, several Vis, such as green normalised difference vegetation index (GNDVI), renormalised difference vegetation index (RDVI), optimized soil-adjusted vegetation index (OSAVI), transformed chlorophyll absorption in reflectance index (TCARI), and TCARI/OSAVI, amongst others [34,79,82], can be calculated that correlate with the water stress of horticultural crops (see Table 2).…”

Section: Spectral Indicesmentioning

confidence: 99%

A Review of Current and Potential Applications of Remote Sensing to Study the Water Status of Horticultural Crops

Gautam

Pagay

2020

Agronomy

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With increasingly advanced remote sensing systems, more accurate retrievals of crop water status are being made at the individual crop level to aid in precision irrigation. This paper summarises the use of remote sensing for the estimation of water status in horticultural crops. The remote measurements of the water potential, soil moisture, evapotranspiration, canopy 3D structure, and vigour for water status estimation are presented in this comprehensive review. These parameters directly or indirectly provide estimates of crop water status, which is critically important for irrigation management in farms. The review is organised into four main sections: (i) remote sensing platforms; (ii) the remote sensor suite; (iii) techniques adopted for horticultural applications and indicators of water status; and, (iv) case studies of the use of remote sensing in horticultural crops. Finally, the authors' view is presented with regard to future prospects and research gaps in the estimation of the crop water status for precision irrigation.

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Section: Spectral Indicesmentioning

confidence: 99%

A Review of Current and Potential Applications of Remote Sensing to Study the Water Status of Horticultural Crops

Gautam

Pagay

2020

Agronomy

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“…Ivushkin et al [35] have tried combining a WIRIS thermal camera, a Rikola hyperspectral camera and a Riegl VUX-SYS LiDAR scanner to measure salt stress in quinoa plants, and they found UAV-borne remote sensing to be a useful technique for salt stress measurements. Romero-Trigueros et al [36] concluded that the red and near-infrared bands were critical to assess the saline stress Citrus suffered from. However, no existing literature has discussed the potential of synthesizing UAV-borne hyperspectral data and EMI measurements for soil salinity estimation.…”

Section: Introductionmentioning

confidence: 99%

Quantitative Estimation of Soil Salinity Using UAV-Borne Hyperspectral and Satellite Multispectral Images

Peng

Zhou

et al. 2019

Remote Sensing

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Soil salinization is a global issue resulting in soil degradation, arable land loss and ecological environmental deterioration. Over the decades, multispectral and hyperspectral remote sensing have enabled efficient and cost-effective monitoring of salt-affected soils. However, the potential of hyperspectral sensors installed on an unmanned aerial vehicle (UAV) to estimate and map soil salinity has not been thoroughly explored. This study quantitatively characterized and estimated field-scale soil salinity using an electromagnetic induction (EMI) equipment and a hyperspectral camera installed on a UAV platform. In addition, 30 soil samples (0~20 cm) were collected in each field for the lab measurements of electrical conductivity. First, the apparent electrical conductivity (ECa) values measured by EMI were calibrated using the lab measured electrical conductivity derived from soil samples based on empirical line method. Second, the soil salinity was quantitatively estimated using the random forest (RF) regression method based on the reflectance factors of UAV hyperspectral images and satellite multispectral data. The performance of models was assessed by Lin’s concordance coefficient (CC), ratio of performance to deviation (RPD), and root mean square error (RMSE). Finally, the soil salinity of three study fields with different land cover were mapped. The results showed that bare land (field A) exhibited the most severe salinity, followed by dense vegetation area (field C) and sparse vegetation area (field B). The predictive models using UAV data outperformed those derived from GF-2 data with lower RMSE, higher CC and RPD values, and the most accurate UAV-derived model was developed using 62 hyperspectral bands of the image of the field A with the RMSE, CC, and RPD values of 1.40 dS m−1, 0.94, and 2.98, respectively. Our results indicated that UAV-borne hyperspectral imager is a useful tool for field-scale soil salinity monitoring and mapping. With the help of the EMI technique, quantitative estimation of surface soil salinity is critical to decision-making in arid land management and saline soil reclamation.

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“…Hal ini dikarenakan pemanfaatan penginderaan jauh memberikan banyak keuntungan dan kemudahan untuk menganalisis berbagai hal. Pemanfaatan metode penginderaan jauh untuk bidang kebumian, semakin berkembang pesat dengan adanya teknologi Unmanned Aerial Vehicle (UAV) (Trigueros et al, 2017;Maulana dan Wulan, 2015a;Bemis et al, 2014).…”

Section: Pendahuluanunclassified

Pemetaan Cepat Kawasan Terdampak Bencana Longsor dan Banjir di Kabupaten Bangli, Provinsi Bali

Wulan

Ambarwulan

Putra

et al. 2017

MGI

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Abstrak Teknologi penginderaan jauh mengalami perkembangan yang sangat pesat. Salah satunya adalah teknologi akuisisi data dengan menggunakan UAV (Unmanned Aerial Vehicle). Teknologi UAV dapat dipergunakan dalam berbagai bidang, salah satunya adalah bidang kebencanaan. Tujuan penelitian ini adalah untuk melakukan pemetaan secara cepat kawasan terdampak bencana banjir dan longsor di Kabupaten Bangli, Bali dengan menggunakan teknologi UAV. Metode yang digunakan adalah pemotretan udara dengan UAV, survei lapangan dan analisis laboratorium. Pemotretan udara dilakukan satu hari pasca kejadian longsor dengan ketinggian jelajah pesawat antara 100-120 meter di atas permukaan tanah. Resolusi spasial yang dihasilkan antara 4,5 -6,5 cm. Wilayah yang berhasil dipetakan adalah wilayah yang terdampak banjir dan longsor di Desa Songan A serta Songan B, wilayah terdampak banjir bandang Yeh Mampeh di Desa Batur Selatan, serta wilayah terdampak longsor di Desa Sukawana dan Desa Awan. Berdasarkan hasil pemotretan udara, dapat diketahui luasan daerah terdampak longsor. Lebih lanjut, strategi rehabilitasi dan rekonstruksi dapat dilakukan dengan menggunakan hasil pemotretan udara. Kata kunci: UAV, Rapid Mapping, banjir, longsor, Bali Abstract Remote sensing technology is experiencing rapid developments. One of which is in the field of data acquisition that has currently adopted the use of Unmanned Aerial Vehicle (UAV). UAV technology is, for instance, employed in various studies related to disasters. This research aimed to perform a rapid mapping of flood-and landslide-affected areas in Bangli Regency

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Effects of saline reclaimed waters and deficit irrigation on Citrus physiology assessed by UAV remote sensing

Cited by 83 publications

References 58 publications

A Review of Current and Potential Applications of Remote Sensing to Study the Water Status of Horticultural Crops

A Review of Current and Potential Applications of Remote Sensing to Study the Water Status of Horticultural Crops

Quantitative Estimation of Soil Salinity Using UAV-Borne Hyperspectral and Satellite Multispectral Images

Pemetaan Cepat Kawasan Terdampak Bencana Longsor dan Banjir di Kabupaten Bangli, Provinsi Bali

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