Journal ArticleDOI
A UAV-aided prediction system of soil moisture content relying on thermal infrared remote sensing
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This article is published in International Journal of Environmental Science and Technology.The article was published on 2022-02-09. It has received 14 citations till now. The article focuses on the topics: Water content & Principal component analysis.read more
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Journal ArticleDOI
A Method of Soil Moisture Content Estimation at Various Soil Organic Matter Conditions Based on Soil Reflectance
TL;DR: In this paper , the effect of the soil organic matter on its reflectance overlaps with the impact of soil moisture on its reflected spectrum, which can lead to the underestimation of the moisture content, with an MRE of 21.87%.
Journal ArticleDOI
Estimating soil moisture content under grassland with hyperspectral data using radiative transfer modelling and machine learning
Veronika Döpper,Alby Duarte Rocha,Katja Berger,Tobias Gränzig,Jochem Verrelst,Birgit Kleinschmit,Michael Förster +6 more
TL;DR: In this paper , a hybrid method targeting the soil brightness factor of the PROSAIL model using a variational heteroscedastic Gaussian Processes regression (VHGPR) algorithm was proposed for the retrieval of soil moisture content (SMC) over three grassland sites based on UAS-borne VIS-NIR hyperspectral data.
Journal ArticleDOI
Evaluating soil moisture content under maize coverage using UAV multimodal data by machine learning algorithms
TL;DR: In this paper , the authors used UAV-based multimodal data to quantify soil moisture content (SMC) in a maize field under various levels of irrigation over two years using three machine learning algorithms (MLA): partial least squares regression (PLSR), K nearest neighbor (KNN), and random forest regression (RFR).
Journal ArticleDOI
Near-Surface Soil Moisture Characterization in Mississippi's Highway Slopes Using Machine Learning Methods and UAV-Captured Infrared and Optical Images
TL;DR: In this article , the authors developed two methods to predict soil moisture content (θ) using UAV-captured optical and thermal combined with machine learning and statistical modeling, and they provided good prediction results.
References
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A Computational Approach to Edge Detection
TL;DR: There is a natural uncertainty principle between detection and localization performance, which are the two main goals, and with this principle a single operator shape is derived which is optimal at any scale.
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Canopy temperature as a crop water stress indicator
TL;DR: In this paper, a crop water stress index (CWSI) was calculated using infrared thermometry, along with wet and dry-bulb air temperatures and an estimate of net radiation.
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Normalizing the stress-degree-day parameter for environmental variability☆
TL;DR: In this paper, several experiments involving the measurement of foliage-air temperature differentials (TF-TA) and air vapor pressure deficits (VPD) were conducted on squash, alfalfa, and soybean crops at Tempe and Mesa, Arizona; Manhattan, Kansas; Lincoln, Nebraska; St Paul, Minnesota; and Fargo, North Dakota.
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Wheat canopy temperature: A practical tool for evaluating water requirements
TL;DR: In this paper, the authors used a sliding cubic smoothing technique to calculate daily water contents and thus water depletion rates for the entire growing season and used this to predict water use by wheat in six differentially irrigated plots.
Journal ArticleDOI
A review of imaging techniques for plant phenotyping.
Lei Li,Qin Zhang,Danfeng Huang +2 more
TL;DR: A brief review on a variety of imaging methodologies used to collect data for quantitative studies of complex traits related to the growth, yield and adaptation to biotic or abiotic stress in plant phenotyping.
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