OpenAlex Citation Counts

OpenAlex Citations Logo

OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

An Evaluation of Different NIR-Spectral Pre-Treatments to Derive the Soil Parameters C and N of a Humus-Clay-Rich Soil
Kurt Heil, Urs Schmidhalter
Sensors (2021) Vol. 21, Iss. 4, pp. 1423-1423
Open Access | Times Cited: 43

Showing 26-50 of 43 citing articles:

Predicting soil carbon in granitic soils using Fourier-transform mid-infrared (FT-MIR) spectroscopy: the value of database disaggregation
Kelebohile Rose Seboko, Johan van Tol, Elmarie Kotzé
South African Journal of Plant and Soil (2023) Vol. 40, Iss. 1, pp. 23-33
Open Access | Times Cited: 2

Estimation of Soil Characteristics from Multispectral Sentinel-3 Imagery and DEM Derivatives Using Machine Learning
Flavio Piccoli, Mirko Paolo Barbato, Marco Peracchi, et al.
Sensors (2023) Vol. 23, Iss. 18, pp. 7876-7876
Open Access | Times Cited: 2

A comparative study of acoustic and ultrasonic nondestructive testing for evaluating melon quality
Nafis Khuriyati, Avicenna Nur Kasih, Mastariyanto Perdana, et al.
IOP Conference Series Earth and Environmental Science (2024) Vol. 1377, Iss. 1, pp. 012006-012006
Open Access

Development of low-cost portable spectrometer equipped with 18-band spectral sensors using deep learning model for evaluating moisture content of rubber sheets
Amorndej Puttipipatkajorn, Amornrit Puttipipatkajorn
Smart Agricultural Technology (2024) Vol. 9, pp. 100562-100562
Open Access

Can soil organic carbon in long–term experiments be detected using Vis-NIR spectroscopy?
Roberto Barbetti, Francesco Palazzi, Pier Mario Chiarabaglio, et al.
(2023), pp. 154-159
Closed Access | Times Cited: 1

NIR spectroscopy: Developing predictive models for chemical attributes and in vitro dry matter digestibility of Megathyrsus maximus cv. Tanzania
Camila Cano Serafim, João Pedro Monteiro do Carmo, Erica Regina Rodrigues Franconere, et al.
Grassland Science (2024)
Closed Access

Development of a near‐infrared spectroscopy calibration for Hagberg falling number assessment of barley (Hordeum vulgare): A comparison of methods
Nils Gerisch, Ricardo Guerreiro, Franziska Wespel, et al.
Plant Breeding (2022) Vol. 141, Iss. 3, pp. 355-365
Open Access | Times Cited: 1

Proximal sensing approach for soil characterization and discrimination: a case of study in Brazil
Andrés Maurício Rico Gómez, Heidy Soledad Rodríguez Albarracín, Danilo César de Mello, et al.
Geocarto International (2022) Vol. 37, Iss. 27, pp. 15806-15822
Closed Access | Times Cited: 1

Estimating the accuracy of the NIRS prediction model based on soil t
Marina ILUȘCA
Akademos (2022), Iss. 2(56), pp. 93-98
Open Access | Times Cited: 1

The Use of Spatial Normalized Difference Vegetation Index for Determination of Humus Content in the Soils of Southern Ukraine
Pavlo Lykhovyd
Ecological Engineering & Environmental Technology (2023) Vol. 24, Iss. 4, pp. 223-228
Open Access

Estimating Soil Organic Matter (SOM) Using Proximal Remote Sensing: Performance Evaluation of Prediction Models Adjusted at Local Scale in the Brazilian Cerrado
Everson Cézar, Tatiane Amancio Alberton, Evandro Freire Lemos, et al.
Remote Sensing (2023) Vol. 15, Iss. 18, pp. 4397-4397
Open Access

Evaluation of Vis-Nir Pretreatments Combined with Pls Regression for Estimation SOC, Cec and Clay in Silty Soils from Eastern Croatia
Aleksandra Bensa, Boško Miloš, Božica Japundžić-Palenkić
SSRN Electronic Journal (2022)
Closed Access

Estimation of Soil Characteristics from Multispectral Sentinel-3 Imagery and Dem Derivatives Using Machine Learning
Flavio Piccoli, Marco Peracchi, Paolo Napoletano, et al.
SSRN Electronic Journal (2022)
Open Access

Previous Page - Page 2

Scroll to top