Spectral behavior of some modal soil profiles from São Paulo State, Brazil

Remote sensing has a high potential for environmental evaluation. However, a necessity exists for a better understanding of the relations between the soil attributes and spectral data. The objective of this work was to analyze the spectral behavior of some soil profiles from the region of Piracicaba, São Paulo State, using a laboratory spectroradiometer (400 to 2500 nm). The relations between the reflected electromagnetic energy and the soil physical, chemical and mineralogical attributes were analyzed, verifying the spectral variations of soil samples in depth along the profiles with their classification and discrimination. Sandy soil reflected more, presenting a spectral curve with an ascendant form, opposite to clayey soils. The 1900 nm band discriminated soil with 2:1 mineralogy from the 1:1 and oxidic soils. It was possible to detect the presence of kaolinite, gibbsite, hematite and goethite in the soils through the descriptive aspects of curves, absorption features and reflectance intensity. A relation exists between the weathering stage and spectral data. The evaluation of the superficial and subsuperficial horizon samples allowed characterizing and discriminating the analytical variability of the profile, helping to soil distinguishing and classification.

The relationship between diffuse spectral reflectance of the soil and its cation exchange capacity is scale-dependent

Diffuse reflectance spectroscopy (DRS) is increasingly being used to predict numerous soil physical, chemical and biochemical properties. However, soil properties and processes vary at different scales and, as a result, relationships between soil properties often depend on scale. In this paper we report on how the relationship between one such property, cation exchange capacity (CEC), and the DRS of the soil depends on spatial scale. We show this by means of a nested analysis of covariance of soils sampled on a balanced nested design in a 16 km × 16 km area in eastern England. We used principal components analysis on the DRS to obtain a reduced number of variables while retaining key variation. The first principal component accounted for 99.8% of the total variance, the second for 0.14%. Nested analysis of the variation in the CEC and the two principal components showed that the substantial variance components are at the > 2000-m scale. This is probably the result of differences in soil composition due to parent material. We then developed a model to predict CEC from the DRS and used partial least squares (PLS) regression do to so. Leave-one-out cross-validation results suggested a reasonable predictive capability (R2 = 0.71 and RMSE = 0.048 molc kg− 1). However, the results from the independent validation were not as good, with R2 = 0.27, RMSE = 0.056 molc kg− 1 and an overall correlation of 0.52. This would indicate that DRS may not be useful for predictions of CEC. When we applied the analysis of covariance between predicted and observed we found significant scale-dependent correlations at scales of 50 and 500 m (0.82 and 0.73 respectively). DRS measurements can therefore be useful to predict CEC if predictions are required, for example, at the field scale (50 m). This study illustrates that the relationship between DRS and soil properties is scale-dependent and that this scale dependency has important consequences for prediction of soil properties from DRS data

Alteration of soil properties through a weathering sequence as evaluated by spectral reflectance

In the study of physical, chemical, and mineralogical data related to the weathering of soils and the quantification of their properties, remote sensing constitutes an important technique that, in addition to conventional analyses, can contribute to soil survey. The objectives of this research were to characterize and differentiate soils developed from basaltic rocks that occur in the Parana state, Brazil and to quantify soil properties based on their spectral reflectance. These observations were used to verify the relationship between the soils and reflectance with regard to weathering, organic matter (OM), and forms of Fe. From the least to the most weathered soil, we used a Typic Argiudoll (Reddish Brunizem), Rhodudalf (Terra Roxa Estruturada), and Rhodic Hapludox (Very Dark Red Latosol). The spectral reflectances between 400 and 2500 nm were obtained in the laboratory from soil samples collected at two depth increments, 0- to 20- and 40- to 60-cm, using an Infra Red Intelligent Spectroradiometer (IRIS). Correlation, regression, and discriminant estimates were used in analyzing the soil and spectral data. Results of this study indicated that soils could be separated at the soil-type level based on reflectance intensity in various absorption bands. Soil collected in the 40- to 60-cm depth appeared to have higher reflectance intensities than those from the 0- to 20-cm depth. Removal of OM from soil samples promoted higher reflectance intensity in the entire spectrum. Amorphous and crystalline Fe influenced reflectance differently. Weathering of basaltic soils was correlated with alterations in the reflectance intensities and absorption features of the spectral curves. Multivariate analysis demonstrated that this technique was efficient in the estimation of clay, silt, kaolinite, crystalline Fe, amorphous Fe, and Mg through the use of reflected energy of the soils.

Magnetic susceptibility and diffuse reflectance spectroscopy to characterize the spatial variability of soil properties in a Brazilian Haplustalf

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Relationships between soil nitrate desorbed from anion-exchange membranes, canopy reflectance and nitrate leaching losses from cool-season lawn turf.

Nutrient leaching studies are expensive and require expertise in water collection and analyses. Less expensive or easier methods that estimate leaching losses would be desirable. The objective of this study was to determine if anion-exchange membranes (AEMs) and reflectance meters could predict nitrate (NO3-N) leaching losses from a cool-season lawn turf. A two-year field study used an established 90% Kentucky bluegrass (Poa pratensis L.)-10% creeping red fescue (Festuca rubra L.) turf that received 0 to 98 kg N ha-1 month-1, from May through November. Soil monolith lysimeters collected leachate that was analyzed for NO3-N concentration. Soil NO3-N was estimated with AEMs. Spectral reflectance measurements of the turf were obtained with chlorophyll and chroma meters. No significant (p > 0.05) increase in percolate flow-weighted NO3-N concentration (FWC) or mass loss occurred when AEM desorbed soil NO3-N was below 0.84 µg cm-2 d-1. A linear increase in FWC and mass loss (p < 0.0001) occurred, however, when AEM soil NO3-N was above this value. The maximum contaminant level (MCL) for drinking water (10 mg L-1 NO3-N) was reached with an AEM soil NO3-N value of 1.6 µg cm-2 d-1. Maximum meter readings were obtained when AEM soil NO3 N reached or exceeded 2.3 µg cm-2 d-1. As chlorophyll index and hue angle (greenness) increased, there was an increased probability of exceeding the NO3-N MCL. These data suggest that AEMs and reflectance meters can serve as tools to predict NO3-N leaching losses from cool-season lawn turf, and to provide objective guides for N fertilization.

Detection of nitrogen deficiency in wheat from spectral reflectance indices and basic crop eco-physiological concepts

We tested the capacity of several published multispectral indices to estimate the nitrogen nutrition of wheat canopies grown under different levels of water supply and plant density and derived a simple canopy reflectance index that is greatly independent of those factors. Planar domain geometry was used to account for mixed signals from the canopy and soil when the ground cover was low. A nitrogen stress index was developed, which adjusts shoot %N for plant biomass and area, thereby accounting for environmental conditions that affect growth, such as crop water status. The canopy chlorophyll content index (CCCi) and the modified spectral ratio planar index (mSRPi) could explain 68 and 69% of the observed variability in the nitrogen nutrition of the crop as early as Zadoks 33, irrespective of water status or ground cover. The CCCi was derived from the combination of 3 wavebands 670, 720 and 790 nm, and the mSRPi from 445, 705 and 750 nm, together with broader bands in the NIR and RED. The potential for their spatial application over large fields/paddocks is discussed.

Modelling spectral reflectance of open cork oak woodland: a simulation analysis of the effects of vegetation structure and background

This study analyses the influence of vegetation structure (i.e. leaf area index and canopy cover) and seasonal background changes on moderate-resolution imaging spectrometer (MODIS)-simulated reflectance data in open woodland. Approximately monthly spectral reflectance and transmittance field measurements (May 2011 to October 2013) of cork oak tree leaves (Quercus suber) and of the herbaceous understorey were recorded in the region of Ribatejo, Portugal. The geometric-optical and radiative transfer (GORT) model was used to simulate MODIS response (red, near-infrared) and to calculate vegetation indices, investigating their response to changes in the structure of the overstorey vegetation and to seasonal changes in the understorey using scenarios corresponding to contrasting phenological status (dry season vs. wet season). The performance of normalized difference vegetation index (NDVI), soil-adjusted vegetation index (SAVI), and enhanced vegetation index (EVI) is discussed. Results showed that SAVI and EVI were very sensitive to the emergence of background vegetation in the wet season compared to NDVI and that shading effects lead to an opposing trend in the vegetation indices. The information provided by this research can be useful to improve our understanding of the temporal dynamic of vegetation, monitored by vegetation indices.

Nitrogen content identification in crop plants using spectral reflectance and artificial neural networks

The accurate identification of the nitrogen content in crop plants is extremely important since it involves economic aspects and environmental impacts. Several experimental tests have been carried out to obtain characteristics and parameters associated with the health of plants and its growing. The nitrogen content identification involves a lot of nonlinear parametes and complexes mathematical models. This paper describes a novel approach for identification of nitrogen content thought spectral reflectance of plant leaves using artificial neural networks. The network acts as identifier of relationships among pH of soil, fertilizer treatment, spectral reflectance and nitrogen content in the plants. So, nitrogen content can be estimated and generalized from an input parameter set. This approach can be form the basis for development of an accurate real time nitrogen applicator.

Iron oxides as proxies for characterizing anisotropy in soil CO2 emission in sugarcane areas under green harvest

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Mapping of clay, iron oxide and adsorbed phosphate in Oxisols using diffuse reflectance spectroscopy

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Spectroradiometry of soil and comparison with orbital sensors

The aim of this study was to characterize and compare the spectral behavior of different soil classes obtained by orbital and terrestrial sensors. For this, an area of 184 ha in Rafard (SP) Brazil was staked on a regular grid of 100x100 m and soil samples were collected and georeferenced. After that, soil spectral curves were obtained with IRIS sensor and the sample points were overlaid at Landsat and ASTER images for spectral data collection. The soil samples were classified and mean soil curves for all sensors were generated by soil classes. The soil classes were differentiated by texture, organic matter and total iron for all sensors studied, the orbital sensors despite the lower spectral resolution, maintained the characteristics of the soil and the curves of reflectance intensity.

Spatial Assessment of Erosion and its Impact on Soil Fertility in the Tajik Foothills

Efficient planning of soil conservation measures requires, first, to understand the impact of soil erosion on soil fertility with regard to local land cover classes; and second, to identify hot spots of soil erosion and bright spots of soil conservation in a spatially explicit manner. Soil organic carbon (SOC) is an important indicator of soil fertility. The aim of this study was to conduct a spatial assessment of erosion and its impact on SOC for specific land cover classes. Input data consisted of extensive ground truth, a digital elevation model and Landsat 7 imagery from two different seasons. Soil spectral reflectance readings were taken from soil samples in the laboratory and calibrated with results of SOC chemical analysis using regression tree modelling. The resulting model statistics for soil degradation assessments are promising (R2=0.71, RMSEV=0.32). Since the area includes rugged terrain and small agricultural plots, the decision tree models allowed mapping of land cover classes, soil erosion incidence and SOC content classes at an acceptable level of accuracy for preliminary studies. The various datasets were linked in the hot-bright spot matrix, which was developed to combine soil erosion incidence information and SOC content levels (for uniform land cover classes) in a scatter plot. The quarters of the plot show different stages of degradation, from well conserved land to hot spots of soil degradation. The approach helps to gain a better understanding of the impact of soil erosion on soil fertility and to identify hot and bright spots in a spatially explicit manner. The results show distinctly lower SOC content levels on large parts of the test areas, where annual crop cultivation was dominant in the 1990s and where cultivation has now been abandoned. On the other hand, there are strong indications that afforestations and fruit orchards established in the 1980s have been successful in conserving soil resources.

Quantitative assessment of soil parameters in Western Tajikistan using a soil spectral library approach

Soil degradation is a major problem in the agriculturally dominated country of Tajikistan, which makes it necessary to determine and monitor the state of soils. For this purpose a soil spectral library was established as it enables the determination of soil properties with relatively low costs and effort. A total of 1465 soil samples were collected from three 10x10 km test sites in western Tajikistan. The diffuse reflectance of the samples was measured with a FieldSpec PRO FR from ASD in the spectral range from 380 to 2500 nm in laboratory. 166 samples were finally selected based on their spectral information and analysed on total C and N, organic C, pH, CaCO₃, extractable P, exchangeable Ca, Mg and K, and the fractions clay, silt and sand. Multiple linear regression was used to set up the models. Two third of the chemically analysed samples were used to calibrate the models, one third was used for hold-out validation. Very good prediction accuracy was obtained for total C (R² = 0.76, RMSEP = 4.36 g kg⁻¹), total N (R² = 0.83, RMSEP = 0.30 g kg⁻¹) and organic C (R² = 0.81, RMSEP = 3.30 g kg⁻¹), good accuracy for pH (R² = 0.61, RMSEP = 0.157) and CaCO3(R² = 0.72, RMSEP = 4.63 %). No models could be developed for extractable P, exchangeable Ca, Mg and K, and the fractions clay, silt and sand. It can be concluded that the spectral library approach has a high potential to substitute standard laboratory methods where rapid and inexpensive analysis is required.