Remote Sensing for Crop Management

Pinter, P. J.; Hatfield, Jerry L.; Schepers, James S.; Barnes, Edward M.; Moran, M. Susan; Daughtry, Craig S. T.; Upchurch, D. R.

doi:10.14358/pers.69.6.647

Cited by 491 publications

(295 citation statements)

References 221 publications

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“…Precision agriculture [14,[100][101][102][103] requires the development of methods of remote sensing of fields and fast analysis of the derived data. The prospective direction of field monitoring is in the application of spectral indices [41,104,105] due to their connection to physiological processes [15,61] and the damage caused by stressors and pathogens in plants [102,106,107].…”

Section: Discussionmentioning

confidence: 99%

Connection of the Photochemical Reflectance Index (PRI) with the Photosystem II Quantum Yield and Nonphotochemical Quenching Can Be Dependent on Variations of Photosynthetic Parameters among Investigated Plants: A Meta-Analysis

Sukhova

Sukhov

2018

Remote Sensing

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Abstract:The development of spectral methods of remote sensing, including measurement of a photochemical reflectance index (PRI), is a prospective trend in precision agriculture. There are many works which have investigated the connection between photosynthetic parameters and PRI; however, their results varied and were sometimes contradictory. For this paper, we performed a meta-analysis of works in this field. Here, only linear correlations of PRI with photosynthetic parameters-including quantum yield of photosystem II (∆F/Fm'), nonphotochemical quenching of chlorophyll fluorescence (NPQ), and light use efficiency (LUE)-were investigated. First, it was shown that the correlations were dependent on conditions of PRI measurements (leaf or canopy; artificial light or sunlight). Second, it was shown that a minimal level of the photosynthetic stress, and the variation of this level among investigated plants, can influence the linear correlation of PRI with ∆F/Fm' and NPQ; the effect was dependent on conditions of measurements. In contrast, the distribution of LUE among plants did not influence its correlation with PRI. Thus, the meta-analysis shows that the distribution of photosynthetic parameters among investigated plants can be an important factor that influences the efficiency of remote sensing on the basis of the PRI measurement.

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Section: Discussionmentioning

confidence: 99%

Connection of the Photochemical Reflectance Index (PRI) with the Photosystem II Quantum Yield and Nonphotochemical Quenching Can Be Dependent on Variations of Photosynthetic Parameters among Investigated Plants: A Meta-Analysis

Sukhova

Sukhov

2018

Remote Sensing

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“…Relative spectral indices of the N nutrition of wheat crops have been developed from comparisons with readings from well-fertilised strips (Raun et al 2001) or calibration stamps (Raun et al 2005). This avoids the need for calibrating the relationship between nitrogen content and crop reflectance, different growth stages or cultivars, and the need to convert data to surface reflectance factors (Pinter et al 2003).…”

Section: Introductionmentioning

confidence: 99%

Detection of nitrogen deficiency in wheat from spectral reflectance indices and basic crop eco-physiological concepts

Rodrı́guez¹,

Fitzgerald²,

Belford³

et al. 2006

Aust. J. Agric. Res.

130

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Abstract. We tested the capacity of several published multispectral indices to estimate the nitrogen nutrition of wheat canopies grown under different levels of water supply and plant density and derived a simple canopy reflectance index that is greatly independent of those factors. Planar domain geometry was used to account for mixed signals from the canopy and soil when the ground cover was low. A nitrogen stress index was developed, which adjusts shoot %N for plant biomass and area, thereby accounting for environmental conditions that affect growth, such as crop water status. The canopy chlorophyll content index (CCCi) and the modified spectral ratio planar index (mSR Pi ) could explain 68 and 69% of the observed variability in the nitrogen nutrition of the crop as early as Zadoks 33, irrespective of water status or ground cover. The CCCi was derived from the combination of 3 wavebands 670, 720 and 790 nm, and the mSR Pi from 445, 705 and 750 nm, together with broader bands in the NIR and RED. The potential for their spatial application over large fields/paddocks is discussed.Additional keywords: water stress, plant density, remote sensing, nitrogen stress index.

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“…Compared with hyperspectral systems, multispectral systems are much less expensive and are less data-intensive. Airborne multispectral systems are cost-effective and a good source of crop, soil, weed or ground cover information for agricultural application and production (Moran et al, 1997;Senay et al, 1998;GopalaPillai and Tian, 1999;Yang and Anderson, 1999;Yang and Everitt, 2002;Pinter et al, 2003;Dobermann and Ping, 2004;Huang et al, 2008;Inman et al, 2008;Yang et al, 2009;Lan et al, 2009;Zhang et al, 2009).…”

Section: Remote Sensingmentioning

confidence: 99%

Current status and future directions of precision aerial application for site-specific crop management in the USA

Lan

Thomson

Huang

et al. 2010

Computers and Electronics in Agriculture

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a b s t r a c tThe first variable-rate aerial application system was developed about a decade ago in the USA and since then, aerial application has benefitted from these technologies. Many areas of the United States rely on readily available agricultural airplanes or helicopters for pest management, and variable-rate aerial application provides a solution for applying field inputs such as cotton growth regulators, defoliants, and insecticides. In the context of aerial application, variable-rate control can simply mean terminating spray over field areas that do not require inputs, terminating spray near pre-defined buffer areas determined by Global Positioning, or applying multiple rates to meet the variable needs of the crop. Prescription maps for aerial application are developed using remote sensing, Global Positioning, and Geographic Information System technologies. Precision agriculture technology has the potential to benefit the agricultural aviation industry by saving operators and farmers time and money.Published by Elsevier B.V.

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Remote Sensing for Crop Management

Cited by 491 publications

References 221 publications

Connection of the Photochemical Reflectance Index (PRI) with the Photosystem II Quantum Yield and Nonphotochemical Quenching Can Be Dependent on Variations of Photosynthetic Parameters among Investigated Plants: A Meta-Analysis

Connection of the Photochemical Reflectance Index (PRI) with the Photosystem II Quantum Yield and Nonphotochemical Quenching Can Be Dependent on Variations of Photosynthetic Parameters among Investigated Plants: A Meta-Analysis

Detection of nitrogen deficiency in wheat from spectral reflectance indices and basic crop eco-physiological concepts

Current status and future directions of precision aerial application for site-specific crop management in the USA

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