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•SMC retrieval using a physically constrained model based on Kubelka-Munk theory.•Good predictive accuracy was achieved with less than 1% of the initial bands.•Negative predictive ...value was completely avoided in the estimation of SMC.•The influence of SOM variation on SMC estimation was reduced.
Soil moisture plays an important role in the transpiration, evaporation and plant growth processes at the land surface-atmosphere interface. Optical remote sensing has great potential for the retrieval of surface soil moisture content (SMC), with many empirical data-driven models or physical models developed to address this issue. Nevertheless, most data-driven models face the challenge of poor interpretability, while the application of many existing physical models is limited by complicated calibration steps. The aim of this work is to validate the potential of a physically-based approach based on the Kubelka-Munk (KM) radiative transfer theory to strike a balance between physical significance and practical applicability in the optical estimation of SMC. Specifically, an adequate and heterogeneous soil dataset in Jianghan Plain, China was used to calibrate the model wavelength by wavelength under laboratory conditions. The performance of the approach (at the optimal band) was compared with several commonly used methods. The effect of soil organic matter (SOM) on the estimation of SMC was also investigated by validating the model transferability between subsets with different SOM levels. Results showed that there were two local optimal bands at around 1460 and 1940 nm in the full band analysis of the approach, and the performance at around 1940 nm is better or comparable to linear regression, logarithmic regression, and spectral index models. Although partial least squares regression (PLSR) could achieve higher prediction accuracy with the enrichment of band information, this approach stood out for its balance of model parsimony with single-band calibration, model interpretability with the incorporation of physical mechanisms and predictive capability. More importantly, we found that the approach could enhance the spectral sensitivity in the water absorption region, avoid negative predictions at low SMCs, and reduce the interference effect of SOM on the estimation of SMC, probably due to the physical constraints inside the approach. This paper demonstrates the parsimony, interpretability, and predictive capability of the physically-based approach in the optical estimation of SMC, and provides new insights into the application of this approach in the airborne/satellite imaginary spectroscopy sensing of SMC.
Recent advances in remote and proximal sensing technologies provide a valuable source of information for enriching our geo-datasets, which are necessary for soil management and the precision ...application of farming input resources ...
It is well-documented in the visible and near-infrared reflectance spectroscopy (VNIRS) studies that soil moisture content (SMC) negatively affects the prediction accuracy of soil attributes. This ...work was undertaken to remove the negative effect of SMC on the on-line prediction of soil organic carbon (SOC). A mobile VNIR spectrophotometer with a spectral range of 305–1700 nm and spectral resolution of 1 nm (CompactSpec, Tec5 Technology, Germany) was used for the spectral measurements at four farms in Flanders, Belgium. A total of 381 fresh soil samples were collected and divided into a calibration set (264) and a validation set (117). The validation samples were processed (air-dried and grind) and scanned with the same spectrophotometer in the laboratory. Three SMC correction methods, namely, external parameter orthogonalization (EPO), piecewise direct standardization (PDS), and orthogonal signal correction (OSC) were used to correct the on-line fresh spectra based-on its corresponding laboratory spectra. Then, the Cubist machine learning method was used to develop calibration models of SOC using the on-line spectra (after correction) of the calibration set. Results indicated that the EPO-Cubist outperformed the PDS-Cubist and the OSC-Cubist, with considerable improvements in the prediction results of SOC (coefficient of determination (R2) = 0.76, ratio of performance to deviation (RPD) = 2.08, and root mean square error of prediction (RMSEP) = 0.12%), compared with the corresponding uncorrected on-line spectra (R2 = 0.55, RPD = 1.24, and RMSEP = 0.20%). It can be concluded that SOC can be accurately predicted on-line using the Cubist machine learning method, after removing the negative effect of SMC with the EPO method.
Smart greenhouse farming has emerged as one of the solutions to global food security, where farming productivity can be managed and improved in an automated manner. While it is known that plant ...development is highly dependent on the quantity and quality of light exposure, the specific impact of the different light properties is yet to be fully understood. In this study, using the model plant Arabidopsis, we systematically investigate how six different light properties (i.e., photoperiod, light offset, intensity, phase of dawn, duration of twilight and period) would affect plant development i.e., flowering time and hypocotyl (seedling stem) elongation using an established mathematical model of the plant circadian system relating light input to flowering time and hypocotyl elongation outputs for smart greenhouse application. We vary each of the light properties individually and then collectively to understand their effect on plant development. Our analyses show in comparison to the nominal value, the photoperiod of 18 hours, period of 24 hours, no light offset, phase of dawn of 0 hour, duration of twilight of 0.05 hour and a reduced light intensity of 1% are able to improve by at least 30% in days to flower (from 32.52 days to 20.61 days) and hypocotyl length (from 1.90 mm to 1.19mm) with the added benefit of reducing energy consumption by at least 15% (from 4.27 MWh/year to 3.62 MWh/year). These findings could provide beneficial solutions to the smart greenhouse farming industries in terms of achieving enhanced productivity while consuming less energy.
Proper management of within-field variability is crucial for maximizing crop yield, production outcomes and resource use efficiency and reducing environmental impacts. This study evaluated the ...agroeconomic and environmental feasibilities of site-specific nitrogen fertilization (SNF) in opium poppy (Papaver somniferum L.). On-line visible and near-infrared reflectance spectroscopy was used to estimate soil pH, organic carbon (OC), soil organic matter (SOM), P, K, Mg, Ca, Na, moisture content (MC), Ca:Mg and K:Mg for one field in Spain. Normalized difference vegetation indexes of the previous crop were retrieved from Sentine-2 images. Rasterization of soil and crop data layers created a spatially homogenous dataset followed by delineation of a management zone (MZ) map using a k-means cluster analysis. MZ clusters were ranked relying on the within-cluster soil fertility attributes. A strip experiment was conducted by creating parallel stripes distributed over the MZ map, over which two SNF treatments (i.e., SNF-Kings approach KA and SNF-Robin Hood approach RHA) were compared against the uniform rate N (URN) control treatment. In SNF-KA, the highest and lowest N dose was applied in the most and least fertile MZ, respectively, whereas the opposite approach was adopted in the SNF-RHA treatment. Yield and cost–benefit analyses provided both SNF treatments to produce more yield (KA = 2.72 and RHA = 2.74 t ha−1) than the URN (2.64 t ha−1) treatment, leading to increasing gross margins by EUR 91 ha−1 (SNF–KA) and EUR 88.5 ha−1 (SNF–RHA). While SNF-KA reduced N input by 66.54 kg N ha−1, SNF–RHA applied more N by 17.90 kg N ha−1 than URN. Additionally, SNF–RHA attempted to equalize yield responses to N across MZ classes, with a small increase in N input. This study, therefore, suggests adopting SNF–RHA for increasing yield and gross margin and accurate distribution of N according to per MZ N response. Future studies, however, should address the limitations of the current study by delineating MZ maps with the incorporation of additional soil information (e.g., mineral N and clay) for optimizing N doses as well as evaluating agroeconomic performance across multiple sites and years using a full-budget analysis.
► The accuracy of on-line measurement of OC, TN and MC is affected by spiking strategy. ► Reducing number of samples used for spiking results in deteriorating prediction accuracy. ► About 1–2 samples ...per ha are recommended for spiking to establish models at farm scale.
A previously developed on-line visible and near infrared (vis–NIR) spectroscopy-based soil measurement system was implemented for the measurement of soil organic carbon (OC), total nitrogen (TN) and moisture content (MC) in three fields at three European farms. The on-line sensor platform was coupled with a mobile, fibre type, vis–NIR spectrophotometer (AgroSpec from tec5 Technology for Spectroscopy, Germany), with a measurement range of 305–2200nm, to acquire soil spectra in diffuse reflectance mode. A general calibration set of 425 soil samples, spiked with different number of spectra from the three validation fields were used to establish calibration models for the studied soil properties using partial least squares (PLS) regression analysis. Different spiking strategies and spiking ratios were investigated and results revealed that the best prediction accuracy was obtained after 20% spiking ratio with samples whose spectra were measured in the laboratory. Evaluated by the values of residual prediction deviation (RPD), which is the ratio of standard deviation to root mean square error of prediction (RMSEP), the accuracy of the on-line measurement was classified as excellent for MC (RPD=2.76–3.96), good to very good for OC (RPD=1.88–2.38) and good to excellent for TN (RPD=1.96–2.52). Reducing the number of samples used for spiking resulted in deteriorating the prediction accuracy, although 1–2 samples per ha were found to provide good predictions. There was a distinguishable spatial similarity between the on-line and laboratory measured maps for all studied properties, although the full-data point maps provided more detailed information about the spatial variation. This confirms that the on-line vis–NIR soil sensor provides correct and detailed information about soil OC, TN and MC at high sampling resolutions.
This study evaluated the agronomic and economic prospects of Site-Specific Seeding (SSS) for consumption and seed potato production based on Management Zone (MZ) maps delineated with the fusion of ...multiple soil and crop attributes at four experimental sites in Belgium. Soil pH, organic carbon, P, K, Mg, Ca, Na, moisture content, cation exchange capacity, apparent electrical conductivity and crop normalized difference vegetation index were measured with an on-line visible and near-infrared reflectance spectroscopy sensor, electromagnetic induction sensor, and Sentinel-2 constellation, respectively. Spatial alignment of the different data layers generated a co-georeferenced data matrix for data fusion by k-means clustering. Per field MZ classes were ranked according to their fertility status and the prescription rule of sowing more seeds to the more fertile zones and vice versa was adopted and compared against a Uniform Rate Seeding (URS) treatment in a strip plot experiment. Cost–benefit analysis revealed that the SSS improved tuber yields, hence, increased gross margin (137.81 to 457.83 €/ha) of production compared to the URS, although SSS consumed relatively higher amount of seeds. The percentage of gross margin increase varied between 2.34 and 27.21%, with the highest profitability in fields with low productivity. Larger seed-to-seed spacing than the control increased the proportion of the most demanded and profitable tuber category, suggesting the seeding interval is a key determinant of tuber size distribution. It is suggested to adopt SSS for potato production using the proposed multi-sensor data-fusion approach to manage in-field soil and crop variabilities, and improve productivity and profitability.
Digital mapping of soil organic carbon (SOC) is essential for visualizing the spatial distribution at different regions and scales. However, existing studies using remote sensing are limited by the ...low spectral resolution of multispectral data for accurate estimation of SOC, and the failure of laboratory visible and near-infrared and shortwave infrared (Vis-NIR) spectroscopy to perform pixel-level mapping. To address these limitations, this study proposes a SOC mapping framework that integrates satellite earth observations and proximal sensing spectral data to reconstruct images having a spectral resolution of 400–2500 nm and a spatial resolution of 10 m, then the clustering probability analysis was applied on the reconstructed images, to develop a high accuracy SOC prediction model and demonstrate its effectiveness across geographical and spatial scales. Specifically, a total of 324 topsoil samples were collected from Baoqing and Suiling counties in northeast China, whose laboratory Vis-NIR reflectance spectra and SOC were measured. Sentinel-2A images of bare soils averaged over three years before and after the samples collection (i.e., 2018 to 2021) were collected. A total of 264 samples from eight fields in Belgium were used to evaluate the performance of the model at the field scale. Then, two models were developed (i) a global regression model with direct prediction using random forest and (ii) a clustering probability model with Gaussian Mixture Model-random forest. Results demonstrated that (1) reconstructed images produced more accurate prediction results than the individual laboratory Vis-NIR spectroscopy and Sentinel-2A models. (2) At the regional scale, clustering probability model achieved better performance in quantification of SOC (coefficient of determination R2 = 0.78, and root mean square error RMSE = 0.78 %) than global regression model (R2 = 0.65, and RMSE = 1.06 %). (3) For field scale, the reconstructed image-based clustering probability SOC prediction model was shown to be effective when using the Belgium data, providing a R2 of 0.67, and RMSE of 0.15 %. (4) Integrating remote sensing multispectral and proximal Vis-NIR information can lead to a smoother and more continuous SOC distribution within the region, and can better show its spatial heterogeneity within the field. This study highlights the advantages of integrating remote sensing with proximal sensing for improving the accuracy of SOC prediction and mapping, enabling applicability of the model across geographical and spatial scales with appreciable accuracy.
•Absorption peak around 2310 nm was identified as the most important for SIC.•An urban SSL with 3492 samples was developed for SIC prediction.•CWT scale 1 showed the optimal performance in predicting ...SIC.•Performance of CNN was superior to other common machine learning models.•Stratifying SSL by LULC in more homogeneous classes achieved better SIC prediction.
Urban soils and cultural layers may accumulate carbon over a long period, so certain carbon stocks may be stored beneath cities. Studies have shown that the turnover rate of soil inorganic carbon (SIC) is also fast, and its role in global carbon pool and regulation of atmospheric CO2 should be noticed. Visible and near infrared (vis–NIR) spectroscopy, as a rapid and cost-effective approach, has demonstrated its huge potential for soil characterization. However, little has been reported on the potential of vis–NIR spectra for estimating SIC in urban and suburban areas that are greatly affected by human activities. Here, an urban soil spectral library (SSL) with 3492 samples in Wuhan City, China was established. Our main objective was to evaluate whether stratification of an urban SSL by land-use/land-cover (LULC) strategy can generate better estimation, compared with non-stratified (global) model for predicting SIC. Five modeling approaches were developed for SIC prediction: linear regression using an optimal spectral ratio index, and partial least squares regression, Cubist, convolutional neural network (CNN), and autoencoder-based residual deep network using full-spectrum. Raw absorbance was processed by continuous wavelet transform (CWT), thus decomposing spectral data at six scales. Results demonstrated that in almost all cases, CWT scale 1 obtained better performances in predicting SIC than raw absorbance and other five CWT scales. Stratification by LULC resulted in improved accuracy, compared with non-stratified model, with CWT scale 1 obtaining the highest validation R2 of 0.73. Among the non-stratified modeling, CNN based on CWT scale 1 generated the optimal validation result with an R2 value of 0.69. The most important spectral absorption for SIC was peaked around 2310 nm. This study confirms that vis–NIR technique combined with LULC stratification and machine/deep learning has a huge potential for SIC prediction in urban soils.
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