Predicting sandy soil moisture content with hyperspectral imaging

Haijun, Qi; Jin, Xin; Liu, Zhao; Maxime, DEDO Irene; Li, Shaowen

doi:10.25165/j.ijabe.20171006.2614

Cited by 15 publications

(7 citation statements)

References 17 publications

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“…By comparing the correlation between the spectral reflectance and SMC after treatment with different pretreatment methods, we found that the proper application of pretreatment methods can improve the correlation between the spectral reflectance and SMC, and at the same time, the sensitive band of SMC can be more obviously highlighted. This conclusion was confirmed in previous studies (Qi et al., 2017). For example, Jia et al.…”

Section: Discussionsupporting

confidence: 91%

Hyperspectral response and monitoring study of soil moisture content based on the optimized spectral index

Yan

Qiao

Yang

et al. 2023

Soil Science Soc of Amer J

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Soil moisture controls the exchange of energy between the land surface and the atmosphere and significantly affects plant growth and productivity. Hyperspectral (350–2500 nm) monitoring of soil moisture content (SMC) could provide a theoretical basis for the real‐time estimation of spatial and temporal variations in soil moisture. During this study, we have developed SMC prediction models in different soil moisture ranges by using the combination of spectral preprocessing methods and optimized spectral indices. Results indicated that first‐order derivative (FD) preprocessing method can highlight the effective information of the spectra and improve the correlation between spectral reflectance and SMC. The spectral reflectance exhibited a strong correlation with SMC near 1400, 1700, 1900, and 2200 nm. The FD‐NDSI‐W0‐model had the best SMC prediction and applicability (R2 = 0.977, root mean square error = 3.413%, relative percent deviation = 6.171) to predict SMCs. Overall, pretreatment methods, combined with the two‐band random combination to optimize the spectral index to build a prediction model of SMC, could be applied for accurate monitoring of SMC.

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Section: Discussionsupporting

confidence: 91%

Hyperspectral response and monitoring study of soil moisture content based on the optimized spectral index

Yan

Qiao

Yang

et al. 2023

Soil Science Soc of Amer J

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“…Therefore, the original data must be processed to extract the spectral data. The entire area of a single maize seed in the image is taken as the region of interest (ROI), and then the average reflectance of all pixels in the ROI is calculated as the spectral value of each maize seed ( Qi et al, 2017 ). Its value can be calculated using the following equation: m , which is the number of all pixels in the ROI area, and A ij is the spectral value of the i-th pixel in the j-th band.…”

Section: Methodsmentioning

confidence: 99%

Recognition of maize seed varieties based on hyperspectral imaging technology and integrated learning algorithms

Yang

Wang

Zhang

et al. 2023

PeerJ Computer Science

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Purity is an important factor of maize seed quality that affects yield, and traditional seed purity identification methods are costly or time-consuming. To achieve rapid and accurate detection of the purity of maize seeds, a method for identifying maize seed varieties, using random subspace integrated learning and hyperspectral imaging technology, was proposed. A hyperspectral image of the maize seed endosperm was collected to obtain a spectral image cube with a wavelength range of 400∼1,000 nm. Methods, including Standard Normal Variate (SNV), multiplicative Scatter Correction (MSC), and Savitzky–Golay First Derivative (SG1) were used to preprocess raw spectral data. Iteratively retains informative variables (IRIV) and competitive adaptive reweighted sampling (CARS) were used to reduce the dimensions of the spectral data. A recognition model of maize seed varieties was established using k-nearest neighbor (KNN), support vector machine (SVM), line discrimination analysis (LDA) and decision tree (DT). Among the preprocessing methods, MSC has the best effect. Among the dimensionality reduction methods, IRIV has the best performance. Among the base classifiers, LDA had the highest precision. To improve the precision in identifying maize seed varieties, LDA was used as the base classifier to establish a random subspace ensemble learning (RSEL) model. Using MSC-IRIV-RSEL, precision increased from 0.9333 to 0.9556, and the Kappa coefficient increased from 0.9174 to 0.9457. This study shows that the method based on hyperspectral imaging technology combined with subspace ensemble learning algorithm is a new method for maize seed purity recognition.

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“…Soil moisture (SM) is an important parameter in crop growth and produces a significant variation in soil spectral reflectance [11,12]. For wet soils, the difference between the refractive index of soil (nsoil, λ approximately 1.50) and water (nwater, λ approximately 1.33) is smaller than that of dry soils, where the particles are surrounded by air (nair, λ approximately 1.00).…”

Section: Introductionmentioning

confidence: 99%

Predicting Soil Organic Matter, Available Nitrogen, Available Phosphorus and Available Potassium in a Black Soil Using a Nearby Hyperspectral Sensor System

Wan,

Hou,

Zhao

et al. 2024

Sensors

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Black soils, which play an important role in agricultural production and food security, are well known for their relatively high content of soil organic matter (SOM). SOM has a significant impact on the sustainability of farmland and provides nutrients for plants. Hyperspectral imaging (HSI) in the visible and near-infrared region has shown the potential to detect soil nutrient levels in the laboratory. However, using portable spectrometers directly in the field remains challenging due to variations in soil moisture (SM). The current study used spectral data captured by a handheld spectrometer outdoors to predict SOM, available nitrogen (AN), available phosphorus (AP) and available potassium (AK) with different SM levels. Partial least squares regression (PLSR) models were established to compare the predictive performance of air-dried soil samples with SMs around 20%, 30% and 40%. The results showed that the model established using dry sample data had the best performance (RMSE = 4.47 g/kg) for the prediction of SOM, followed by AN (RMSE = 20.92 mg/kg) and AK (RMSE = 22.67 mg/kg). The AP was better predicted by the model based on 30% SM (RMSE = 8.04 mg/kg). In general, model performance deteriorated with an increase in SM, except for the case of AP. Feature wavelengths for predicting four kinds of soil properties were recommended based on variable importance in the projection (VIP), which offered useful guidance for the development of portable hyperspectral sensors based on discrete wavebands to reduce cost and save time for on-site data collection.

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Predicting sandy soil moisture content with hyperspectral imaging

Cited by 15 publications

References 17 publications

Hyperspectral response and monitoring study of soil moisture content based on the optimized spectral index

Hyperspectral response and monitoring study of soil moisture content based on the optimized spectral index

Recognition of maize seed varieties based on hyperspectral imaging technology and integrated learning algorithms

Predicting Soil Organic Matter, Available Nitrogen, Available Phosphorus and Available Potassium in a Black Soil Using a Nearby Hyperspectral Sensor System

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