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return kwic search for growth out of >500 occurrences
309169 occurrences (No.70 in the rank) during 5 years in the PubMed. [cache]

380) These physically meaningful metrics characterized crop inundation status independent of growth stage including under dense canopy cover.

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PMID:33791393 DOI:10.1029/2020EA001554

2021 Earth and space science (Hoboken, N.J.)

* Rice Inundation Assessment Using Polarimetric UAVSAR Data.

- Irrigated rice requires intense water management under typical agronomic practices. Cost effective tools to improve the efficiency and assessment of water use is a key need for industry and resource managers to scale ecosystem services. In this research we advance model-based decomposition and machine learning to map inundated rice using time-series polarimetric, L-band Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) observations. Simultaneous ground truth observations recorded water depth inundation during the 2019 crop season using instrumented fields across the study site in Arkansas, USA. A three-component model-based decomposition generated metrics representing surface-, double bounce-, and volume-scattering along with a shape factor, randomness factor, and the Radar Vegetation Index (RVI). These physically meaningful metrics characterized crop inundation status independent of growth stage including under dense canopy cover. Machine learning (ML) comparisons employed Random Forest (RF) using the UAVSAR derived parameters to identify cropland inundation status across the region. Outcomes show that RVI, proportion of the double-bounce within total scattering, and the relative comparison between the double-bounce and the volume scattering have moderate to strong mechanistic ability to identify rice inundation status with Overall Accuracy (OA) achieving 75%. The use of relative ratios further helped mitigate the impacts of far range incidence angles. The RF approach, which requires training data, achieved a higher OA and Kappa of 88% and 71%, respectively, when leveraging multiple SAR parameters. Thus, the combination of physical characterization and ML provides a powerful approach to retrieving cropland inundation under the canopy. The growth of polarimetric L-band availability should enhance cropland inundation metrics beyond open water that are required for tracking water quantity at field scale over large areas.

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(1)85 and	(12)4 performance	(25)2 as	(36)2 hormone
(2)71 factor	(13)4 phase	(26)2 compared	(37)2 lean
(3)51 of	(14)4 rate	(27)2 conditions	(38)2 on
(4)33 in	(15)4 restriction	(28)2 curve	(39)2 response
(5)26 null	(16)3 factor,	(29)2 curves	(40)2 stage
(6)12 inhibition	(19)3 failure	(30)2 defect	(41)2 the
(7)8 factors	(20)3 is	(31)2 during	(42)2 we
(8)7 plate	(21)3 rates,	(32)2 environmental	(43)2 were
(9)5 by	(22)3 was	(33)2 factor-beta
(10)4 factor-1	(23)3 will	(34)2 for
(11)4 mindset	(24)2 are	(35)2 functions

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--- WordNet output for growth --- =>茂み, 成長, 増加, 発展, 栽培, 腫よう, 成長物 Overview of noun growth The noun growth has 7 senses (first 5 from tagged texts) 1. (37) growth, growing, maturation, development, ontogeny, ontogenesis -- ((biology) the process of an individual organism growing organically; a purely biological unfolding of events involved in an organism changing gradually from a simple to a more complex level; "he proposed an indicator of osseous development in children") 2. (20) growth -- (a progression from simpler to more complex forms; "the growth of culture") 3. (3) increase, increment, growth -- (a process of becoming larger or longer or more numerous or more important; "the increase in unemployment"; "the growth of population") 4. (3) growth -- (vegetation that has grown; "a growth of trees"; "the only growth was some salt grass") 5. (1) emergence, outgrowth, growth -- (the gradual beginning or coming forth; "figurines presage the emergence of sculpture in Greece") 6. growth -- ((pathology) an abnormal proliferation of tissue (as in a tumor)) 7. growth -- (something grown or growing; "a growth of hair") --- WordNet end ---