Soil Moisture Content Retrieval from Remote Sensing Data by Artificial Neural Network Based on Sample Optimization

Liu, Qixin; Gu, Xingfa; Chen, Xinran; Mumtaz, Faisal; Yan, Lijun; Wang, Chunmei; Yu, Tao; Zhang, Yin; Wang, Dakang

doi:10.3390/s22041611

Cited by 7 publications

(3 citation statements)

References 72 publications

(82 reference statements)

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“…This can be achieved through methods such as remote sensing or the utilization of soil moisture probes. While existing techniques for soil moisture measurement primarily serve large‐scale hydrological and geoscience research (Liu et al ., 2022) or farming decision‐making (Maia et al ., 2022), developing more suitable approaches tailored for plant breeding is essential. The EM38, an electromagnetic induction instrument, offers a noninvasive and rapid approach for measuring soil moisture at multiple soil depths and soil electrical conductivity (Phathutshedzo‐Eugene et al ., 2023), making it promising for incorporation in plant breeding research trials.…”

Section: Identification and Selection Of Wild Candidate Accessionsmentioning

confidence: 99%

Back to the future for drought tolerance

Guadarrama‐Escobar,

Hunt,

Gurung

et al. 2024

New Phytologist

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SummaryGlobal agriculture faces increasing pressure to produce more food with fewer resources. Drought, exacerbated by climate change, is a major agricultural constraint costing the industry an estimated US$80 billion per year in lost production. Wild relatives of domesticated crops, including wheat (Triticum spp.) and barley (Hordeum vulgare L.), are an underutilized source of drought tolerance genes. However, managing their undesirable characteristics, assessing drought responses, and selecting lines with heritable traits remains a significant challenge. Here, we propose a novel strategy of using multi‐trait selection criteria based on high‐throughput spectral images to facilitate the assessment and selection challenge. The importance of measuring plant capacity for sustained carbon fixation under drought stress is explored, and an image‐based transpiration efficiency (iTE) index obtained via a combination of hyperspectral and thermal imaging, is proposed. Incorporating iTE along with other drought‐related variables in selection criteria will allow the identification of accessions with diverse tolerance mechanisms. A comprehensive approach that merges high‐throughput phenotyping and de novo domestication is proposed for developing drought‐tolerant prebreeding material and providing breeders with access to gene pools containing unexplored drought tolerance mechanisms.

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Section: Identification and Selection Of Wild Candidate Accessionsmentioning

confidence: 99%

Back to the future for drought tolerance

Guadarrama‐Escobar,

Hunt,

Gurung

et al. 2024

New Phytologist

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“…This paper presents the GMM into a Bayesian GMM through mean, coefficient mixture and covariance matrix taken as latent variable in the prior distribution as represented numerically in (12),…”

Section: Ifree-based Protection Level Estimation Using Gmm Overboundsmentioning

confidence: 99%

“…These covariances are utilized to define weight measurements during the position method and are employed to distinguish the error bound calculations [11]. By utilizing this error bound, the SBAS-based GNSS establishes whether or not to employ GNSS [12]. Further, for developing a scientific algorithm, a weighed mean model is employed to define output scores for positioning the SBAS.…”

Section: Introductionmentioning

confidence: 99%

Overbounding Ifree errors based on bayesian Gaussian mixture model for ground-based augmentation system

Banu,

Shanavas

2024

IJECE

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A dual-frequency measurement is employed in conjunction with an innovative Ifree filtering technique for mitigating the primary sources of Ifree influence on ground-based augmentation systems (GBAS) to safeguard the reliability of GBAS. The protective level achieved through the conventional Gaussian overbounding approach that are considered as much conventional technique. This adherence to tradition results in decreased reliability and a higher likelihood of false alarms. In contrast, the utilization of the Ifree algorithm contributes to reducing errors associated with dual-frequency measurements. This paper proposes the overbounding process according to Bayesian Gaussian mixture model (GMM) for maintaining Ifree-based GBAS range error. The Bayesian GMM is utilized for single-frequency model errors to examine the ambiguity estimations. The Monte Carlo (MC) simulation is established for defining estimated GMM assurance level accuracy which is attained through the general estimation method. Then, the last Bayesian GMM which is utilized for overbounding Ifree error distribution is investigated. According to the property of convolution invariance, the vertical protection in position field is determined without presenting difficult numerical calculations.

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Estimating soil moisture from environmental gamma radiation monitoring data

Akter,

Huisman,

Bogena

2024

Vadose Zone Journal

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Soil moisture (SM) information is invaluable for a wide range of applications, including weather forecasting, hydrological and land surface modeling, and agricultural production. However, there is still a lack of sensing information that adequately represents root‐zone SM for longer periods and larger spatial scales. One option for root‐zone SM observation is terrestrial gamma radiation (TGR), as it is inversely related to SM. Hence, the near real‐time data of more than 5000 environmental gamma radiation (EGR) monitoring stations archived by the EUropean Radiological Data Exchange Platform (EURDEP) is a potential source to develop a root‐zone SM product for Europe without extra investments in SM sensors. This study aims to investigate to what extent the EURDEP data can be used for SM estimation. For this, two EGR monitoring stations were equipped with in situ SM sensors to measure reference SM. The terrestrial component of EGR was extracted after eliminating the contributions of rain washout and secondary cosmic radiation, and used to obtain a functional relationship with SM. We predicted the weekly volumetric SM with a root mean square error of 7%–9% from TGR measurements. Nevertheless, we believe that this technique, due to its greater penetration depth and long data legacy, can provide useful data complementary to satellite‐based remote sensing techniques to estimate root‐zone SM at the continental scale.

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Soil Moisture Content Retrieval from Remote Sensing Data by Artificial Neural Network Based on Sample Optimization

Cited by 7 publications

References 72 publications

Back to the future for drought tolerance

Back to the future for drought tolerance

Overbounding Ifree errors based on bayesian Gaussian mixture model for ground-based augmentation system

Estimating soil moisture from environmental gamma radiation monitoring data

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