Hyperspectral reflectance‐based partial least squares regression models for predicting cotton leaf physiological traits Article Swipe
Ved Parkash
,
John L. Snider
,
Christopher M. Montes
,
Elizabeth A. Ainsworth
,
Álvaro Sanz‐Sáez
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1002/ppj2.70042
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1002/ppj2.70042
Alterations in the mechanistic drivers of photosynthesis have the potential to improve crop productivity, but their measurement is inherently time‐consuming using traditional methods. High‐throughput approaches to estimate photosynthesis using hyperspectral reflectance could be developed by leveraging variation in cotton ( Gossypium hirsutum L.) leaf traits generated through nitrogen (N) management, synthetic growth regulation strategies, and leaf position within the canopy. Currently, no such models exist for cotton, and interactions among the aforementioned factors are relatively unexplored for cotton leaf traits. This study aimed to (1) evaluate the effects of N application rate, mepiquat chloride (MC) management, and leaf position within the canopy on photosynthesis and its components, and (2) develop and validate hyperspectral reflectance‐based partial least squares regression (PLSR) models for predicting cotton leaf physiological traits. N rate and leaf position interacted to affect net photosynthetic rate ( A N ), electron transport rate, chlorophyll a, and chlorophyll b, while MC influenced only leaf pigments and specific leaf weight (SLW). A N reductions under N deficiency were driven by declines in the maximum rate of Rubisco carboxylation ( V c,max ) and ribulose‐1,5‐bisphosphate regeneration ( J max ), whereas high N had no effect on A N . PLSR models exhibited good to high predictive accuracy, with R 2 values ranging from 0.62 to 0.87 for most traits, except for SLW. These findings enhance our understanding of the physiological responses to N rate, MC strategy, and leaf position and highlight the potential of hyperspectral reflectance‐based PLSR as a high‐throughput tool for predicting leaf physiological traits to improve photosynthetic efficiency in cotton.
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Hyperspectral reflectance‐based partial least squares regression models for predicting cotton leaf physiological traitsWork title
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enPrimary language
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2025Year of publication
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2025-09-25Full publication date if available
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Ved Parkash, John L. Snider, Christopher M. Montes, Elizabeth A. Ainsworth, Álvaro Sanz‐SáezList of authors in order
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https://doi.org/10.1002/ppj2.70042Publisher landing page
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| abstract_inverted_index.leaf | 44, 56, 79, 98, 124, 130, 154, 158, 237, 253 |
| abstract_inverted_index.most | 217 |
| abstract_inverted_index.only | 153 |
| abstract_inverted_index.rate | 128, 137, 174 |
| abstract_inverted_index.such | 63 |
| abstract_inverted_index.tool | 250 |
| abstract_inverted_index.were | 167 |
| abstract_inverted_index.with | 207 |
| abstract_inverted_index.These | 222 |
| abstract_inverted_index.aimed | 83 |
| abstract_inverted_index.among | 70 |
| abstract_inverted_index.c,max | 180 |
| abstract_inverted_index.could | 32 |
| abstract_inverted_index.exist | 65 |
| abstract_inverted_index.least | 116 |
| abstract_inverted_index.rate, | 92, 144, 233 |
| abstract_inverted_index.study | 82 |
| abstract_inverted_index.their | 16 |
| abstract_inverted_index.under | 164 |
| abstract_inverted_index.using | 21, 29 |
| abstract_inverted_index.while | 150 |
| abstract_inverted_index.(PLSR) | 119 |
| abstract_inverted_index.(SLW). | 160 |
| abstract_inverted_index.affect | 134 |
| abstract_inverted_index.canopy | 102 |
| abstract_inverted_index.cotton | 39, 78, 123 |
| abstract_inverted_index.driven | 168 |
| abstract_inverted_index.effect | 194 |
| abstract_inverted_index.except | 219 |
| abstract_inverted_index.growth | 52 |
| abstract_inverted_index.models | 64, 120, 200 |
| abstract_inverted_index.traits | 45, 255 |
| abstract_inverted_index.values | 210 |
| abstract_inverted_index.weight | 159 |
| abstract_inverted_index.within | 58, 100 |
| abstract_inverted_index.Rubisco | 176 |
| abstract_inverted_index.canopy. | 60 |
| abstract_inverted_index.cotton, | 67 |
| abstract_inverted_index.cotton. | 261 |
| abstract_inverted_index.develop | 110 |
| abstract_inverted_index.drivers | 5 |
| abstract_inverted_index.effects | 88 |
| abstract_inverted_index.enhance | 224 |
| abstract_inverted_index.factors | 73 |
| abstract_inverted_index.improve | 12, 257 |
| abstract_inverted_index.maximum | 173 |
| abstract_inverted_index.partial | 115 |
| abstract_inverted_index.ranging | 211 |
| abstract_inverted_index.squares | 117 |
| abstract_inverted_index.through | 47 |
| abstract_inverted_index.traits, | 218 |
| abstract_inverted_index.traits. | 80, 126 |
| abstract_inverted_index.whereas | 189 |
| abstract_inverted_index.Abstract | 0 |
| abstract_inverted_index.chloride | 94 |
| abstract_inverted_index.declines | 170 |
| abstract_inverted_index.electron | 142 |
| abstract_inverted_index.estimate | 27 |
| abstract_inverted_index.evaluate | 86 |
| abstract_inverted_index.findings | 223 |
| abstract_inverted_index.hirsutum | 42 |
| abstract_inverted_index.mepiquat | 93 |
| abstract_inverted_index.methods. | 23 |
| abstract_inverted_index.nitrogen | 48 |
| abstract_inverted_index.pigments | 155 |
| abstract_inverted_index.position | 57, 99, 131, 238 |
| abstract_inverted_index.specific | 157 |
| abstract_inverted_index.validate | 112 |
| abstract_inverted_index.Gossypium | 41 |
| abstract_inverted_index.accuracy, | 206 |
| abstract_inverted_index.developed | 34 |
| abstract_inverted_index.exhibited | 201 |
| abstract_inverted_index.generated | 46 |
| abstract_inverted_index.highlight | 240 |
| abstract_inverted_index.potential | 10, 242 |
| abstract_inverted_index.responses | 230 |
| abstract_inverted_index.strategy, | 235 |
| abstract_inverted_index.synthetic | 51 |
| abstract_inverted_index.transport | 143 |
| abstract_inverted_index.variation | 37 |
| abstract_inverted_index.Currently, | 61 |
| abstract_inverted_index.approaches | 25 |
| abstract_inverted_index.deficiency | 166 |
| abstract_inverted_index.efficiency | 259 |
| abstract_inverted_index.influenced | 152 |
| abstract_inverted_index.inherently | 19 |
| abstract_inverted_index.interacted | 132 |
| abstract_inverted_index.leveraging | 36 |
| abstract_inverted_index.predicting | 122, 252 |
| abstract_inverted_index.predictive | 205 |
| abstract_inverted_index.reductions | 163 |
| abstract_inverted_index.regression | 118 |
| abstract_inverted_index.regulation | 53 |
| abstract_inverted_index.relatively | 75 |
| abstract_inverted_index.unexplored | 76 |
| abstract_inverted_index.Alterations | 1 |
| abstract_inverted_index.application | 91 |
| abstract_inverted_index.chlorophyll | 145, 148 |
| abstract_inverted_index.components, | 107 |
| abstract_inverted_index.management, | 50, 96 |
| abstract_inverted_index.measurement | 17 |
| abstract_inverted_index.mechanistic | 4 |
| abstract_inverted_index.reflectance | 31 |
| abstract_inverted_index.strategies, | 54 |
| abstract_inverted_index.traditional | 22 |
| abstract_inverted_index.interactions | 69 |
| abstract_inverted_index.regeneration | 184 |
| abstract_inverted_index.carboxylation | 177 |
| abstract_inverted_index.hyperspectral | 30, 113, 244 |
| abstract_inverted_index.physiological | 125, 229, 254 |
| abstract_inverted_index.productivity, | 14 |
| abstract_inverted_index.understanding | 226 |
| abstract_inverted_index.aforementioned | 72 |
| abstract_inverted_index.photosynthesis | 7, 28, 104 |
| abstract_inverted_index.photosynthetic | 136, 258 |
| abstract_inverted_index.time‐consuming | 20 |
| abstract_inverted_index.High‐throughput | 24 |
| abstract_inverted_index.high‐throughput | 249 |
| abstract_inverted_index.reflectance‐based | 114, 245 |
| abstract_inverted_index.ribulose‐1,5‐bisphosphate | 183 |
| cited_by_percentile_year | |
| corresponding_author_ids | https://openalex.org/A5046429573 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 5 |
| corresponding_institution_ids | https://openalex.org/I165733156, https://openalex.org/I4210126868 |
| citation_normalized_percentile.value | 0.50140123 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |