Soil moisture monitoring using hyper-spectral remote sensing technology

Yao, Yao; Wei, Na; Chen, Youqi; He, Yi; Tang, Pengqin

doi:10.1109/iita-grs.2010.5604219

Cited by 12 publications

(8 citation statements)

References 3 publications

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“…However, there is a lack of studies applying the laboratory-calibrated models to outdoor datasets. Indeed, this is a difficult task and more work is needed on spectral data comparison between field and laboratory to establish an accurate spectrum-soil moisture inversion model [20]. In addition, most of the results suggest the need to take into account the factors influencing surface reflectance, such as roughness and texture, organic matter content, vegetation cover and mineral composition [16,38] to improve the performance of the SMC retrieval [27].…”

Section: Introductionmentioning

confidence: 99%

“…Nevertheless, these data have two main limitations: their high sensitivity to surface roughness [27] for SAR (Synthetic Aperture Radar) sensor and their coarse spatial resolution. However, numerous spectroscopic studies have been conducted to characterize the SMC influence on the spectral reflectance mainly with laboratory and with few in-situ measurements [20,28,29]. Angström [30] demonstrated through laboratory measurements that soil moisture content had an impact on the behavior of soil spectral reflectances in the solar domain.…”

Section: Introductionmentioning

confidence: 99%

“…These missions will cover the reflective domain from 0.4 µm to 2.5 µm with a 10 nm spectral resolution. Specifically, the combination of high spectral resolution with a high spatial resolution, better than 10 m in the HYPXIM case, opens the way to the monitoring of the soil moisture dynamic for the development of precision agriculture [20].…”

Section: Introductionmentioning

confidence: 99%

“…Accurate estimation of SMC with a high spatial resolution would improve the characterization of soil texture like soil organic carbon or clay [10,11] but also the growth of the vegetation [12] and its biodiversity [13]. Timely characterization of soil moisture is widely used in drought analysis [14,15], crop yield estimation [16,17], irrigation management of precision agriculture [18,19], and plant health [20].…”

Section: Introductionmentioning

confidence: 99%

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Improvement of Soil Moisture Retrieval from Hyperspectral VNIR-SWIR Data Using Clay Content Information: From Laboratory to Field Experiments

et al. 2015

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Abstract:The aim of this work is to study the constraints and performance of SMC retrieval methodologies in the VNIR (Visible-Near InfraRed) and SWIR (ShortWave InfraRed) regions (from 0.4 to 2.5 µm) when passing from controlled laboratory conditions to field conditions. Five different approaches of signal processing found in literature were considered. Four local criteria are spectral indices (WISOIL, NSMI, NINSOL and NINSON). These indices are the ratios between the spectral reflectances acquired at two specific wavelengths to characterize moisture content in soil. The last criterion is based in the convex hull concept and it is a global method, which is based on the analysis of the full spectral signature of the soil. The database was composed of 464 and 9 spectra, respectively, measured over bare soils in laboratory and in-situ. For each measurement, SMC and texture were well-known and the database was divided in two parts dedicated to calibration and validation steps. The calibration part was used to define the empirical relation between SMC and SMC retrieval approaches, with coefficients of determination (R 2 ) between 0.72 and 0.92. A clay content (CC) dependence was detected for the NINSOL and NINSON indices. Consequently, two new criteria were proposed taking into account the CC contribution (NINSOLCC and NINSONCC). The well-marked regression between SMC and global/local indices, and the interest of using the CC, were confirmed during the validation step using laboratory data (R² in all cases) and using in-situ data, where WISOIL, NINSOLCC and NINSONCC criteria stand out among the NSMI and CH.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Improvement of Soil Moisture Retrieval from Hyperspectral VNIR-SWIR Data Using Clay Content Information: From Laboratory to Field Experiments

et al. 2015

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“…It has evolved as an important tool for deriving high spectral and spatial resolution information about soil and vegetation (Blackburn 2007, Yao et al 2010. The applications of hyperspectral data can be cited in various fields like agriculture, forestry and biodiversity, mineral and oil explorations as well as soil characterization.…”

Section: Introductionmentioning

confidence: 99%

Relating Hyperspectral Airborne Data to Ground Measurements in a Complex and Discontinuous Canopy

et al. 2015

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A b s t r a c tThe work described in this paper is aimed at validating hyperspectral airborne reflectance data collected during the Regional Experiments For Land-atmosphere EXchanges (REFLEX) campaign. Ground reflectance data measured in a vineyard were compared with airborne reflec- tance data. A sampling strategy and subsequent ground data processing had to be devised so as to capture a representative spectral sample of this complex crop. A linear model between airborne and ground data was tried and statistically tested. Results reveal a sound correspondence between ground and airborne reflectance data (R2 > 0.97), validating the atmospheric correction of the latter.

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Predicting soil moisture content over partially vegetation covered surfaces from hyperspectral data with deep learning

Zhang

Liu

et al. 2021

Soil Science Soc of Amer J

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Previous studies for retrieving soil moisture content (SMC) from visible and nearinfrared hyperspectral data over vegetation-covered surfaces using spectral unmixing, non-negative matrix factorization, and albedo/vegetation coverage in trapezoid spaces have required mass spectral preprocessing and offered only limited improvements in prediction accuracy. Recently, deep learning has triggered some improvements in soil properties prediction from hyperspectral data because of its automatic feature extraction and high accuracy. In this study, hyperspectral data in a simulation experiment with different vegetation coverages, SMCs, and soil types were acquired. Deep learning models, one-dimensional convolutional neural network (1D-CNN), and long short-term memory network (LSTM) are proposed to predict SMC. The results showed that two deep learning models achieved excellent predictions (residual prediction deviation [RPD] > 2.5) using the unpreprocessed mixed spectra and partial least squares regression (PLSR) had a good prediction (RPD = 1.88). The 1D-CNN (R 2 p = .91) and LSTM (R 2 p = .90) significantly outperformed PLSR (R 2 p = .72), which demonstrated that deep learning could improve SMC prediction over partially vegetation-covered surfaces. However, when only using bare soil spectra, the prediction accuracy was commensurate, whether through the 1D-CNN, LSTM, or PLSR models; additionally, 1D-CNN and LSTM had better performance on all mixed spectra than bare soil spectra. These results indicated that deep learning had no advantage on smaller datasets. We also found that SMC prediction with deep learning was affected by vegetation coverage and soil type but was still very good. The 1D-CNN and LSTM are effective models for predicting SMC with large hyperspectral datasets acquired from complex soil surface conditions.

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Soil moisture monitoring using hyper-spectral remote sensing technology

Cited by 12 publications

References 3 publications

Improvement of Soil Moisture Retrieval from Hyperspectral VNIR-SWIR Data Using Clay Content Information: From Laboratory to Field Experiments

Improvement of Soil Moisture Retrieval from Hyperspectral VNIR-SWIR Data Using Clay Content Information: From Laboratory to Field Experiments

Relating Hyperspectral Airborne Data to Ground Measurements in a Complex and Discontinuous Canopy

Predicting soil moisture content over partially vegetation covered surfaces from hyperspectral data with deep learning

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