scholarly journals USING LEGACY SOIL DATA FOR STANDARDIZING PREDICTIONS OF TOPSOIL CLAY CONTENT OBTAINED FROM VNIR/SWIR HYPERSPECTRAL AIRBORNE IMAGES

2015 ◽  
Vol XL-3/W3 ◽  
pp. 439-444
Author(s):  
C. Gomez ◽  
A. Gholizadeh ◽  
L. Borůvka ◽  
P. Lagacherie
Keyword(s):  
Soil Properties ◽  
Spectral Index ◽  
Near Infrared ◽  
Soil Property ◽  
Clay Content ◽  
Short Wave ◽  
Soil Samples ◽  
Spectral Bands ◽  
Large Sets ◽  
The One

Mapping of topsoil properties using Visible, Near-Infrared and Short Wave Infrared (VNIR/SWIR) hyperspectral imagery requires large sets of ground measurements for calibrating the models that estimate soil properties. To avoid collecting such expensive data, we proposed a procedure including two steps that involves only legacy soil data that were collected over and?or around the study site: <i>1)</i> estimation of a soil property using a spectral index of the literature and <i>2)</i> standardisation of the estimated soil property using legacy soil data. This approach was tested for mapping clay contents in a Mediterranean region in which VNIR/SWIR AISA-DUAL hyperspectral airborne data were acquired. The spectral index was the one proposed by Levin et al (2007) using the spectral bands at 2209, 2133 and 2225 nm. Two legacy soil databases were tested as inputs of the procedure: the <i>Focused-Legacy</i> database composed of 67 soil samples collected in 2000 over the study area, and the No-Focused-Legacy database composed of 64 soil samples collected between 1973 and 1979 around but outside of the study area. The results were compared with those obtained from 120 soil samples collected over the study area during the hyperspectral airborne data acquisition, which were considered as a reference. <br><br> Our results showed that: <i>1)</i> the spectral index with no further standardisation offered predictions with high accuracy in term of coefficient of correlation <i>r</i> (0.71), but also high <i>bias</i> (&minus;414 g/kg) and <i>SEP</i> (439 g/kg), <i>2)</i> the standardisation using both legacy soil databases allowed an increase of accuracy (<i>r</i> = 0.76) and a reduction of <i>bias</i> and <i>SEP</i> and <i>3)</i> a better standardisation was obtained by using the <i>Focused-Legacy</i> database rather than the <i>No-Focused-Legacy</i> database. Finally, the clay predicted map obtained with standardisation using the <i>Focused-Legacy</i> database showed pedologically-significant soil spatial structures with clear short-scale variations of topsoil clay contents in specific areas. <br><br> This study, associated with the coming availability of a next generation of hyperspectral VNIR/SWIR satellite data for the entire globe, paves the way for inexpensive methods for delivering high resolution soil properties maps.

scholarly journals Understanding Vine Hyperspectral Signature through Different Irrigation Plans: A First Step to Monitor Vineyard Water Status

2021 ◽  
Vol 13 (3) ◽  
pp. 536
Author(s):  
Eve Laroche-Pinel ◽  
Mohanad Albughdadi ◽  
Sylvie Duthoit ◽  
Véronique Chéret ◽  
Jacques Rousseau ◽  
...  
Keyword(s):  
Near Infrared ◽  
Water Status ◽  
Vegetation Indices ◽  
Remote Sensing Data ◽  
Short Wave ◽  
Spatial And Temporal Scales ◽  
Spectral Bands ◽  
Main Challenge ◽  
Spectral Scale ◽  
Grape Varieties

The main challenge encountered by Mediterranean winegrowers is water management. Indeed, with climate change, drought events are becoming more intense each year, dragging the yield down. Moreover, the quality of the vineyards is affected and the level of alcohol increases. Remote sensing data are a potential solution to measure water status in vineyards. However, important questions are still open such as which spectral, spatial, and temporal scales are adapted to achieve the latter. This study aims at using hyperspectral measurements to investigate the spectral scale adapted to measure their water status. The final objective is to find out whether it would be possible to monitor the vine water status with the spectral bands available in multispectral satellites such as Sentinel-2. Four Mediterranean vine plots with three grape varieties and different water status management systems are considered for the analysis. Results show the main significant domains related to vine water status (Short Wave Infrared, Near Infrared, and Red-Edge) and the best vegetation indices that combine these domains. These results give some promising perspectives to monitor vine water status.

scholarly journals A Comparison, Validation, and Evaluation of the S-world Global Soil Property Database

Land ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 544
Author(s):  
Jetse J. Stoorvogel ◽  
Vera L. Mulder
Keyword(s):  
Comparative Analysis ◽  
Soil Properties ◽  
Soil Property ◽  
Evaluation Criteria ◽  
Clay Content ◽  
Soil Database ◽  
Global Environmental ◽  
Global Soil ◽  
Mean Square Difference ◽  
Methodological Evaluation

Despite the increased usage of global soil property maps, a proper review of the maps rarely takes place. This study aims to explore the options for such a review with an application for the S-World global soil property database. Global soil organic carbon (SOC) and clay content maps from S-World were studied at two spatial resolutions in three steps. First, a comparative analysis with an ensemble of seven datasets derived from five other global soil databases was done. Second, a validation of S-World was done with independent soil observations from the WoSIS soil profile database. Third, a methodological evaluation of S-world took place by looking at the variation of soil properties per soil type and short distance variability. In the comparative analysis, S-World and the ensemble of other maps show similar spatial patterns. However, the ensemble locally shows large discrepancies (e.g., in boreal regions where typically SOC contents are high and the sampling density is low). Overall, the results show that S-World is not deviating strongly from the model ensemble (91% of the area falls within a 1.5% SOC range in the topsoil). The validation with the WoSIS database showed that S-World was able to capture a large part of the variation (with, e.g., a root mean square difference of 1.7% for SOC in the topsoil and a mean difference of 1.2%). Finally, the methodological evaluation revealed that estimates of the ranges of soil properties for the different soil types can be improved by using the larger WoSIS database. It is concluded that the review through the comparison, validation, and evaluation provides a good overview of the strengths and the weaknesses of S-World. The three approaches to review the database each provide specific insights regarding the quality of the database. Specific evaluation criteria for an application will determine whether S-World is a suitable soil database for use in global environmental studies.

scholarly journals Hyperspectral Inversion Model of Chlorophyll Content in Peanut Leaves

2020 ◽  
Vol 10 (7) ◽  
pp. 2259 ◽  
Author(s):  
Haixia Qi ◽  
Bingyu Zhu ◽  
Lingxi Kong ◽  
Weiguang Yang ◽  
Jun Zou ◽  
...  
Keyword(s):  
Chlorophyll Content ◽  
Spectral Index ◽  
Spectral Reflectance ◽  
Near Infrared ◽  
Plant Density ◽  
Cell Structure ◽  
Spectral Bands ◽  
Study Results ◽  
Leaf Spectral Reflectance ◽  
Estimation Models

The purpose of this study is to determine a method for quickly and accurately estimating the chlorophyll content of peanut plants at different plant densities. This was explored using leaf spectral reflectance to monitor peanut chlorophyll content to detect sensitive spectral bands and the optimum spectral indicators to establish a quantitative model. Peanut plants under different plant density conditions were monitored during three consecutive growth periods; single-photon avalanche diode (SPAD) and hyperspectral data derived from the leaves under the different plant density conditions were recorded. By combining arbitrary bands, indices were constructed across the full spectral range (350–2500 nm) based on blade spectra: the normalized difference spectral index (NDSI), ratio spectral index (RSI), difference spectral index (DSI) and soil-adjusted spectral index (SASI). This enabled the best vegetation index reflecting peanut-leaf SPAD values to be screened out by quantifying correlations with chlorophyll content, and the peanut leaf SPAD estimation models established by regression analysis to be compared and analyzed. The results showed that the chlorophyll content of peanut leaves decreased when plant density was either too high or too low, and that it reached its maximum at the appropriate plant density. In addition, differences in the spectral reflectance of peanut leaves under different chlorophyll content levels were highly obvious. Without considering the influence of cell structure as chlorophyll content increased, leaf spectral reflectance in the visible (350–700 nm): near-infrared (700–1300 nm) ranges also increased. The spectral bands sensitive to chlorophyll content were mainly observed in the visible and near-infrared ranges. The study results showed that the best spectral indicators for determining peanut chlorophyll content were NDSI (R520, R528), RSI (R748, R561), DSI (R758, R602) and SASI (R753, R624). Testing of these regression models showed that coefficient of determination values based on the NDSI, RSI, DSI and SASI estimation models were all greater than 0.65, while root mean square error values were all lower than 2.04. Therefore, the regression model established according to the above spectral indicators was a valid predictor of the chlorophyll content of peanut leaves.

scholarly journals Field Proximal Soil Sensor Fusion for Improving High-Resolution Soil Property Maps

Soil Systems ◽  
2020 ◽  
Vol 4 (3) ◽  
pp. 52
Author(s):  
Gustavo M. Vasques ◽  
Hugo M. Rodrigues ◽  
Maurício R. Coelho ◽  
Jesus F. M. Baca ◽  
Ricardo O. Dart ◽  
...  
Keyword(s):  
Soil Properties ◽  
Ordinary Kriging ◽  
Soil Property ◽  
Clay Content ◽  
Exchange Capacity ◽  
Organic C ◽  
Covariate Data ◽  
Independent Validation ◽  
Soil Sensors ◽  
Dense Grid

Mapping soil properties, using geostatistical methods in support of precision agriculture and related activities, requires a large number of samples. To reduce soil sampling and measurement time and cost, a combination of field proximal soil sensors was used to predict and map laboratory-measured soil properties in a 3.4-ha pasture field in southeastern Brazil. Sensor soil properties were measured in situ on a 10 × 10-m dense grid (377 samples) using apparent electrical conductivity meters, apparent magnetic susceptibility meter, gamma-ray spectrometer, water content reflectometer, cone penetrometer, and portable X-ray fluorescence spectrometer (pXRF). Soil samples were collected on a 20 × 20-m thin grid (105 samples) and analyzed in the laboratory for organic C, sum of bases, cation exchange capacity, clay content, soil volumetric moisture, and bulk density. Another 25 samples collected throughout the area were also analyzed for the same soil properties and used for independent validation of models and maps. To test whether the combination of sensors enhances soil property predictions, stepwise multiple linear regression (MLR) models of the laboratory soil properties were derived using individual sensor covariate data versus combined sensor data—except for the pXRF data, which were evaluated separately. Then, to test whether a denser grid sample boosted by sensor-based soil property predictions enhances soil property maps, ordinary kriging of the laboratory-measured soil properties from the thin grid was compared to ordinary kriging of the sensor-based predictions from the dense grid, and ordinary cokriging of the laboratory properties aided by sensor covariate data. The combination of multiple soil sensors improved the MLR predictions for all soil properties relative to single sensors. The pXRF data produced the best MLR predictions for organic C content, clay content, and bulk density, standing out as the best single sensor for soil property prediction, whereas the other sensors combined outperformed the pXRF sensor for the sum of bases, cation exchange capacity, and soil volumetric moisture, based on independent validation. Ordinary kriging of sensor-based predictions outperformed the other interpolation approaches for all soil properties, except organic C content, based on validation results. Thus, combining soil sensors, and using sensor-based soil property predictions to increase the sample size and spatial coverage, leads to more detailed and accurate soil property maps.

scholarly journals Mobile Proximal Sensing with Visible and Near Infrared Spectroscopy for Digital Soil Mapping

Soil Systems ◽  
2020 ◽  
Vol 4 (3) ◽  
pp. 40
Author(s):  
Masakazu Kodaira ◽  
Sakae Shibusawa
Keyword(s):  
Soil Properties ◽  
Regression Models ◽  
Near Infrared ◽  
Soil Property ◽  
Coefficient Of Determination ◽  
Clay Loam ◽  
Soil Maps ◽  
Local Regression ◽  
Predicted Values ◽  
Digital Soil Maps

The objective of this study was to estimate multiple soil property local regression models, confirm the accuracy of the predicted values using visible near-infrared subsurface diffuse reflectance spectra collected by a mobile proximal soil sensor, and show that digital soil maps predicted by multiple soil property local regression models are able to visualize empirical knowledge of the grower. The parent materials in the experimental fields were light clay, clay loam, and sandy clay loam. The study was conducted in Saitama Prefecture, Japan. To develop local regression models for the 30 chemical and 4 physical properties, a total of 231 samples were collected; to evaluate accuracy of prediction, 65 samples were collected. The local regression models were developed using 2nd derivative pretreatment by the Savitzky–Golay algorithm and partial least squares regression. The local regression models were evaluated using the coefficient of determination (R2), residual prediction deviation (RPD), range error ratio (RER), and the ratio of prediction error to interquartile range (RPIQ). The R2 accuracy of the 34 local regression models was 0.81 or higher. In the predicted values for 65 unknown samples, the local regression models could ‘distinguish between high and low’ for 3 of the 34 soil properties, but were ‘not useful’ as absolute quantitative values for the other 31 soil properties. However, it was confirmed that the predicted values followed the transition in measured values, and thus that the developed 34 regression models could be used for generating digital soil maps based on relative quantitative values. The grower changed the ridge direction in the field from east–west to north–south just looking at the digital soil maps.

Emergence response of subterranean clover to dissolved gypsum in relation to soil properties and evaporative conditions

Soil Research ◽  
1966 ◽  
Vol 4 (1) ◽  
pp. 55 ◽  
Author(s):  
J Loveday ◽  
DR Scotter
Keyword(s):  
Soil Properties ◽  
Surface Soil ◽  
Subterranean Clover ◽  
Clay Content ◽  
Exchangeable Sodium ◽  
Loam Soil ◽  
Highly Correlated ◽  
The One ◽  
Relationship Of ◽  
Emergence Response

Using small plots set in the earthen floor of an open glasshouse, the emergence response of subterranean clover to dissolved gypsum has been determined on 10 soils covering a range of clay and exchangeable sodium levels. The response on a loam soil of low exchangeable sodium percentage (E.S.P.) has been examined at three times of differing evaporative potential. For loams and clay loams, the appearance of a response depends on the severity of evaporative conditions as well as on the E.S.P. On clay soils not naturally self mulching, some response is probably always obtained but, in general, the higher the clay content and the higher the E.S.P. and evaporative potential, the more likely is an emergence response to dissolved gypsum. Emergence was found to be highly correlated with the matric potential of the surface 1/2 in. at the time emergence began. From a consideration of the relationships between emergence and moisture status, the most significant effect of the gypsum treatment seems to be the delay of several days it causes in the air drying of the surface soil, probably because of improved transmission of moisture from beneath. Parallel effects on surface soil temperature to those on moisture can be explained in terms of differences in amount of evaporative cooling. Relationships found between emergence and emergence response to gypsum on the one hand and clay content and E.S.P. on the other are presumably a reflection of the relationship of these soil properties to porosity and moisture transmission.

scholarly journals Quantitative Analysis of Spectral Response to Soda Saline-AlkaliSoil after Cracking Process: A Laboratory Procedure to Improve Soil Property Estimation

2019 ◽  
Vol 11 (12) ◽  
pp. 1406
Author(s):  
Jianhua Ren ◽  
Xiaojie Li ◽  
Sijia Li ◽  
Honglei Zhu ◽  
Kai Zhao
Keyword(s):  
Soil Properties ◽  
Soil Property ◽  
Spectral Response ◽  
Texture Feature ◽  
Soil Surface ◽  
Soil Samples ◽  
Spectral Measurements ◽  
Spectral Difference ◽  
Property Estimation ◽  
Alkali Soil

Cracking on the surface of soda saline-alkali soil is very common. In most previous studies, spectral prediction models of soil salinity were less accurate since spectral measurements were usually performed on 2 mm soil samples which cannot represent true soil surface condition very well. The objective of our research is to provide a procedure to improve soil property estimation of soda saline-alkali soil based on spectral measurement considering the texture feature of the soil surface with cracks. To achieve this objective, a cracking test was performed with 57 soil samples from Songnen Plain of China, the contrast (CON) texture feature of crack images of soil samples was then extracted from grey level co-occurrence matrix (GLCM). The original reflectance was then measured and the mixed reflectance considering the CON texture feature was also calculated from both the block soil samples (soil blocks separated by crack regions) and the comparison soil samples (soil powders with 2 mm particle size). The results of analysis between spectra and the main soil properties indicate that surface cracks can reduce the overall reflectivity of the soda saline-alkali soil and thus increasing the spectral difference among the block soil samples with different salinity levels. The results also show that both univariate and multivariate linear regression models considering the CON texture feature can greatly improve the prediction accuracy of main soil properties of soda saline-alkali soils, such as Na+, EC and salinity, which also can reduce the intensity of field spectral measurements under natural condition.

Classification of Soil Texture Classes by Using Soil Visual near Infrared Spectroscopy and Factorial Discriminant Analysis Techniques

2005 ◽  
Vol 13 (4) ◽  
pp. 231-240 ◽  
Author(s):  
A.M. Mouazen ◽  
R. Karoui ◽  
J. De Baerdemaeker ◽  
H. Ramon
Keyword(s):  
Soil Properties ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Soil Samples ◽  
Data Sets ◽  
Validation Data ◽  
Light Reflectance ◽  
Validation Set ◽  
Soil Groups

Texture is one of the main properties affecting the accuracy of visible (vis) and near infrared (NIR) spectroscopy during on-the-go measurement of soil properties. Classification of soil spectra into predefined texture classes is expected to increase the accuracy of measurement of other soil properties using separate groups of calibration models, each developed for one texture class. A mobile, fibre-type, vis-NIR spectrophotometer (Zeiss Corona 1.7 vis-NIR fibre), with a light reflectance measurement range of 306.5–1710.9 nm was used to measure the light reflectance from fresh soil samples collected from many fields in Belgium and northern France. A total of 365 soil samples were classified into four different texture classes, namely, coarse sandy, fine sandy, loamy and clayey soils. The factorial discriminant analysis (FDA) was applied on the first five principal components obtained from the principal component analysis performed on the vis-NIR spectra in order to classify soils into the four assigned groups. Correct classification (CC) of 85.7% and 81.8% was observed for the calibration and validation data sets, respectively. However, validation of the vis-NIR-FDA technique on the validation set showed poor discrimination between the coarse sandy and fine sandy soil groups, with a great deal of overlapping. Therefore, the soil groups were reduced to three groups by combining the coarse sandy and fine sandy soil groups into one group and FDA was applied again. A better classification was obtained with CC of 89.9 and 85.1% for the calibration and validation data sets, respectively. However, the CC for the sand group in the validation set was rather small (46.7%), which was attributed to the small sample number and poor correlation between sand fraction and vis-NIR spectroscopy. It was concluded that vis-NIR-FDA is an efficient technique to classify soil into three main groups of sandy (light soils), loamy (medium soils) and clayey (heavy soils). Additional samples from the sandy and clayey groups should be included to improve the accuracy of the vis-NIR-FDA classification models to be used for an on-the-go vis-NIR measurement system of soil properties.

scholarly journals Thermal remote sensing from Airborne Hyperspectral Scanner data in the framework of the SPARC and SEN2FLEX projects: an overview

2009 ◽  
Vol 6 (3) ◽  
pp. 4107-4124
Author(s):  
J. A. Sobrino ◽  
J. C. Jiménez-Muñoz ◽  
P. J. Zarco-Tejada ◽  
G. Sepulcre-Cantó ◽  
E. de Miguel ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Near Infrared ◽  
Short Wave ◽  
Scanner Data ◽  
Data Errors ◽  
Spectral Bands ◽  
Field Campaigns ◽  
First Time

Abstract. The AHS (Airborne Hyperspectral Scanner) instrument has 80 spectral bands covering the visible and near infrared (VNIR), short wave infrared (SWIR), mid infrared (MIR) and thermal infrared (TIR) spectral range. The instrument is operated by Instituto Nacional de Técnica Aerospacial (INTA), and it has been involved in several field campaigns since 2004. This paper presents an overview of the work performed with the AHS thermal imagery provided in the framework of the SPARC and SEN2FLEX campaigns, carried out respectively in 2004 and 2005 over an agricultural area in Spain. The data collected in both campaigns allowed for the first time the development and testing of algorithms for land surface temperature and emissivity retrieval as well as the estimation of evapotranspiration from AHS data. Errors were found to be around 1.5 K for land surface temperature and 1 mm/day for evapotranspiration.

scholarly journals Prediction of soil organic matter and clay contents by near-infrared spectroscopy - NIRS

2020 ◽  
Vol 50 (1) ◽  
Author(s):  
Bruno Pedro Lazzaretti ◽  
Leandro Souza da Silva ◽  
Gerson Laerson Drescher ◽  
André Carnieletto Dotto ◽  
Darines Britzke ◽  
...  
Keyword(s):  
Organic Matter ◽  
Infrared Spectroscopy ◽  
Soil Organic Matter ◽  
Near Infrared Spectroscopy ◽  
Calibration Curve ◽  
Near Infrared ◽  
Soil Analysis ◽  
Clay Content ◽  
Soil Samples ◽  
Support Vector

ABSTRACT: Among the soil constituents, special attention is given to soil organic matter (SOM) and clay contents, since, among other aspects, they are key factors to nutrient retention and soil aggregates formation, which directly affect the crop production potential. The methods commonly used for the quantification of these constituents have some disadvantages, such as the use of chemical reactants and waste generation. An alternative to these methods is the near-infrared spectroscopy (NIRS) technique. The aim of this research is to evaluate models for SOM and clay quantification in soil samples using spectral data by NIRS. A set (n = 400) of soil samples previously analyzed by traditional methods were used to generate a NIRS calibration curve. The clay content was determined by the hydrometer method while SOM content was determined by sulfochromic solution. For calibration, we used the original spectra (absorbance) and spectral pretreatment (Savitzky-Golay smoothing derivative) in the following models: multiple linear regression (MLR), partial last squares regression (PLSR), support vector machine (SVM) and Gaussian process regression (GPR). The curve validation was performed with the SVM model (best performance in the calibration based on R² and RMSE) in two ways: with 40 random samples from the calibration set and another set with 200 new unknown samples. The soil clay content affects the predictive ability of the calibration curve to estimate SOM content by NIRS. Validation curves showed poorer performance (lower R² and higher RMSE) when generated from unknown samples, where the model tends to overestimate the lower levels and to underestimate the higher levels of clay and SOM. Despite the potential of NIRS technique to predict these attributes, further calibration studies are still needed to use this technique in soil analysis laboratories.

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