紫色土光谱特征及其碱解氮的预测研究

文章分析探索了应用可见近红外光谱技术快速、高效、便捷测定土壤营养参数的可能性。采集蓬莱镇组紫色土样本，比对分析了不同肥力水平、土壤厚度和土壤粒径条件下采集土壤光谱对可见近红外光谱特征的影响，筛选出不同厚度、粒径土壤条件下的碱解氮含量预测模型。研究结果表明，土壤样本厚度为30mm时具有最大的光谱反射率，建立的氮含量预测模型效果最佳，校正集和验证集的相关系数分别为0.84和0.83，均方根误差分别为1.79和1.87。土样粒径在0.25-0.85mm时氮含量的预测效果最佳，校正集和验证集的相关系数均超过0.8，且均方根误差较小；但当土样粒径<0.25mm时，氮含量预测模型效果明显下降。采用20目（<0.85mm）过筛、30mm厚度土壤样本采集可见近红外光谱和偏最小二乘法（PLS）模型预测，可以实现对蓬莱镇组紫色土碱解氮含量的较好光谱预测。     

Design of a hyperspectral nitrogen sensing system for orange leaves

The orange (Citrus sinensis) is one of the most important agricultural crops in Florida. Heavy reliance on agricultural chemicals and low fertilizer use efficiencies in citrus production have raised environmental and economic concerns. In this study, a nitrogen sensor was developed to predict nitrogen concentrations in orange leaves. Four design criteria were chosen to maximize the sensing efficiency and reliability. They were: (1) coverage of the spectral N sensing range, (2) no moving parts, (3) single leaf detection, and (4) diffuse reflectance measurement. Based on chlorophyll and protein spectral absorption bands, the sensor's wavelength ranges were chosen to be 620–950 nm and 1400–2500 nm. A reflectance housing was designed to block environmental noise and to ensure single leaf measurement. A halogen light source, two detector arrays, two linear variable filters, and data acquisition cards with 16-bit analog-to-digital converters were used to collect data. The designed N sensor had a spectral resolution less than 30 nm. Test results showed that the nitrogen sensor had good linearity (r > 0.99) and stability. With averaged signal-to-noise ratio (SNR) of 299, the system was able to predict N content with a root mean square difference (RMSD) of l.69 g kg⁻¹ for the validation data set. Using the N sensor, unknown leaf samples could be classified into low, medium and high N levels with 70% accuracy.     

Using a global VNIR soil-spectral library for local soil characterization and landscape modeling in a 2nd-order Uganda watershed

Combining global soil-spectral libraries with local calibration samples has the potential to provide improved visible and near-infrared (VNIR, 400–2500 nm) diffuse reflectance spectroscopy (DRS) soil characterization predictions than with either global or local calibrations alone. In this study, a geographically diverse “global” soil-spectral library with 4184 samples was augmented with up to 418 “local” calibration soil samples distributed across a 2nd-order Ugandan watershed to predict the amount of clay-size material (CLAY), soil organic carbon (SOC) and proportion of expansible 2:1 clays (termed “montmorillonite” or MT in the global library). Stochastic gradient boosted regression trees (BRT) were employed for model construction, with a variety of calibration and validation schemes tested. Using the global library combined with 13- and 14-fold cross-validation by local profile for CLAY and SOC, respectively, yielded dambo/upland RMSD values of 89/68 g kg^− 1 for CLAY (N = 429/410) and 4.2/2.6 g kg^− 1 for SOC (N = 272/105). These results were obtained despite the challenge of combining spectral libraries constructed using different spectroradiometers and laboratory reference measurements (total combustion vs. Walkley–Black, hydrometer vs. pipette). Using only the global library, a VNIR-derived index of MT content was significantly correlated with the square root of X-ray diffraction (XRD) MT peak intensity for local dambo soils (r² = 0.52, N = 59, p < 0.0001), an acceptable result given the semi-quantitative nature of the reference XRD method. Though VNIR predictions did not approach laboratory precision, for soil-landscape modeling VNIR characterization worked remarkably well for clay mineralogy, was adequate for mapping dambo “depth to 35% clay”, and was insufficiently accurate for SOC mapping.     

Continuum removal versus PLSR method for clay and calcium carbonate content estimation from laboratory and airborne hyperspectral measurements

Reflectance spectroscopy provides an alternate method to classical physical and chemical laboratory soil analysis for estimation of a large range of key soil properties. Techniques including classical chemometrics approaches and specific absorption features studies have been developed for deriving estimates of soil characteristics from visible and near-infrared (VNIR, 400-1200 nm) and shortwave infrared (SWIR, 1200-2500 nm) reflectance measurements. This paper examines the performances of two distinct methods for clay and calcium carbonate (CaCO₃) content estimation (two key soil properties for erosion prediction) by VNIR/SWIR spectroscopy: i) the Continuum Removal (CR) has been used to correlate spectral absorption bands centred at 2206 and 2341 nm with clay and CaCO₃ concentrations and ii) the partial least-squares regression (PLSR) method with leave-one-out cross-validation, which is a classical chemometrics technique, has been used to predict clay and CaCO₃ concentrations from VNIR/SWIR full spectra. We tried to respond to the question “should we use all bands in the 400-2500 nm range or should we focus our analysis on selected spectral absorption bands to determine soil properties from reflectance data?” In this paper, the CR and PLSR methods were applied to VNIR/SWIR laboratory and airborne HYMAP reflectance measurements collected over the La Peyne Valley area in southern France.This study shows that the performance of both techniques is dependent on the spectral feature for the soil property of interest and on the level data acquisition (lab or airborne) face to the instrument specifications. When airborne HYMAP reflectance measurements are used, the PLSR technique performs better than the CR approach. As well, when the soil property of interest has no well-identified spectral feature, which is the case of clay, the PLSR technique performs better than the CR approach. In this last situation, PLSR is able to find surrogate spectral features that retain satisfactory estimations of the studied soil properties. However, parts of these spectral features remain difficult to explain or relate to area-specific correlations between soil properties, which means that extrapolation to larger pedological contexts must be envisaged with care. In the near future, VNIR/SWIR airborne hyperspectral data processed by the PLSR technique will allow for accurate mapping of clay and CaCO₃ contents, which will contribute significantly to the digital mapping of soil properties.     

Quantification and depth distribution analysis of carbon to nitrogen ratio in forest soils using reflectance spectroscopy

Forest soils have large contents of carbon (C) and total nitrogen (TN), which have significant spatial variability laterally across landscapes and vertically with depth due to decomposition, erosion and leaching. Therefore, the ratio of C to TN contents (C:N), a crucial indicator of soil quality and health, is also different depending on soil horizon. These attributes can cost-effectively and rapidly be estimated using visible–near infrared–shortwave infrared (VNIR–SWIR) spectroscopy. Nevertheless, the effect of different soil layers, particularly over large scales of highly heterogeneous forest soils, on the performance of the technique has rarely been attempted. This study evaluated the potential of VNIR–SWIR spectroscopy in quantification and variability analysis of C:N in soils from different organic and mineral layers of forested sites of the Czech Republic. At each site, we collected samples from the litter (L), fragmented (F) and humus (H) organic layers, and from the A₁ (depth of 2–10 cm) and A₂ (depth of 10–40 cm) mineral layers providing a total of 2505 samples. Support vector machine regression (SVMR) was used to train the prediction models of the selected attributes at each individual soil layer and the merged layer (profile). We further produced the spatial distribution maps of C:N as the target attribute at each soil layer. Results showed that the prediction accuracy based on the profile spectral data was adequate for all attributes. Moreover, F was the most accurately predicted layer, regardless of the soil attribute. C:N models and maps in the organic layers performed well although in mineral layers, models were poor and maps were reliable only in areas with low and moderate C:N. On the other hand, the study indicated that reflectance spectra could efficiently predict and map organic layers of the forested sites. Although, in mineral layers, high values of C:N (≥ 50) were not detectable in the map created based on the reflectance spectra. In general, the study suggests that VNIR–SWIR spectroscopy has the feasibility of modelling and mapping C:N in soil organic horizons based on national spectral data in the forests of the Czech Republic.