Potential and limits of vegetation indices compared to evaporite mineral indices for soil salinity discrimination and mapping

Mapping Intimacies ◽

10.5194/soil-2021-55 ◽

2021 ◽

Author(s):

Abderrazak Bannari ◽

Abdelgader Abuelgasim

Keyword(s):

Optical Properties ◽

Soil Salinity ◽

Vegetation Indices ◽

Salt Content ◽

Ground Truth ◽

Field Work ◽

Spectral Signature ◽

Spectral Space ◽

Spectral Signatures ◽

Salt Pan

Abstract. The study aims to analyze the ability of the most popular and widely used vegetation indices (VI’s), including NDVI, SAVI, EVI and TDVI, to discriminate and map soil salt contents compared to the potential of evaporite mineral indices such as SSSI and NDGI. The proposed methodology leverages on two complementary parts exploiting simulated and imagery data acquired over two study areas, i.e. Kuwait-State and Omongwa salt-pan in Namibia. In the first part, a field survey was conducted on the Kuwait site and 100 soil samples with various salinity levels and contents were collected; as well as, herbaceous vegetation cover canopy (alfalfa and forage plants) with various LAI coverage rates. In a Goniometric-Laboratory, the spectral signatures of all samples were measured and transformed using the continuum removed reflectance spectrum (CRRS) approach. Subsequently, they were resampled and convolved in the solar-reflective spectral bands of Landsat-OLI, and converted to the considered indices. Meanwhile, soil laboratory analyses were accomplished to measure pHs, electrical conductivity (EC-Lab), the major soluble cations and anions; thereby the sodium adsorption ratio was calculated. These elements support the investigation of the relationship between the spectral signature of each soil sample and its salt content. Furthermore, on the Omongwa salt-pan site, a Landsat-OLI image was acquired, pre-processed and converted to the investigated indices. Mineralogical ground-truth information collected during previous field work and an accurate Lidar DEM were used for the characterization and validation procedures on this second site. The obtained results demonstrated that regardless of the data source (simulation or image), the study site and the applied analysis methods, it is impossible for VI's to discriminate or to predict soil salinity. In fact, the spectral analysis revealed strong confusion between signals resulting from salt-crust and soil optical properties in the VNIR wavebands. The CRRS transformation highlighted the complete absence of salt absorption features in the blue, red and NIR wavelengths. As well as the analysis in 2D spectral-space pointed-out how VI’s compress and completely remove the signal fraction emitted by the soil background. Moreover, statistical regressions (p ˂ 0.05) between VI's and EC-Lab showed insignificant fits for SAVI, EVI and TDVI (R2 ≤ 0.06), and for NDVI (R2 of 0.35). Although the Omongwa is a natural flat salt playa, the four derived VI’s from OLI image are completely unable to detect the slightest grain of salt in the soil. Contrariwise, analyses of spectral signatures and CRRS highlighted the potential of the SWIR spectral domain to distinguish salt content in soil regardless of its optical properties. Likewise, according to Kuwait spectral data and EC-Lab analysis, NDGI and SSSI incorporating SWIR wavebands have performed very well and similarly (R2 of 0.72) for the differentiation of salt-affected soil classes. These statistical results were also corroborated visually by the maps derived from these evaporite indices over the salt-pan site, as well as by their consistency with the validation points representing the ground truth. However, although both the indices have mapped the salinity patterns almost similarly, NDGI further highlights the gypsum content. While the SSSI show greater sensitivity to salt crusts present in the pan area that are formed from different mineral sources (i.e., halite, gypsum, etc.).

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Application of multispectral sensors and unmanned aerial system in Stara Planina region

Sustainable Forestry: Collection ◽

10.5937/sustfor2081175q ◽

2020 ◽

pp. 175-186

Author(s):

Nenad Šurjanac ◽

Marija Milosavljević ◽

Mara Tabaković-Tošić ◽

Miroslava Marković

Keyword(s):

Vegetation Index ◽

Physiological Activity ◽

Vegetation Indices ◽

Ground Truth ◽

Sensor Data ◽

Spectral Signature ◽

Unmanned Aerial System ◽

Positive Ratio ◽

Pest Infestation ◽

Leaf Mining

In the area of Stara Planina mountain, a multispectral survey of forest vegetation was performed. Data acquisition was done with unmanned aerial system DJI Phantom 4 Pro, equipped with integrated RGB 20Mpix sensor, and MicaSense RedEdge M, 5-channel narrowband multispectral sensor. Data was collected in the form of images, and 4 composite orthomosaics were produced-broadband visible RGB, narrowband visible RGB, and with vegetation indices applied NDVI and NDRE. RGB orthomosaic showed no significant changes in canopies apart from the variability of levels of green. Orthomosaics with vegetation indices applied showed changes in the level of physiological activities of leaves. NDVI map showed the negative changes of the top of the canopies, while NDRE map showed more dramatic changes within the canopy as well. Besides the map, 5 polygons with different NDRE values were selected and their respective spectral signature graphs were produced. The areas with the lowest NDRE values had the highest reflectance values in all wavelengths, while the absorption of light is much higher in physiologically active vegetation. Values of NDRE lower than 0.479 were inspected from the ground. This kind of ground-truth provided evidence that the areas coded in red were with lower physiological activity due to the infestation by beech leaf-mining weevil Orchestes fagi L. Another interesting finding was that both NDVI and NDRE values were higher in the areas not directly exposed to the sunlight. The areas shaded by surrounding canopies received only diffuse light but it showed a more positive ratio between absorbed and reflected wavelength which could be characteristic of the Fagus Sylvatica species. The findings in this study showed a strong correlation between low values NDRE vegetation index and negative changes deep within the canopy of high vegetation, which can serve as an indicator of pest infestation in forestry.

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Comparison of Different Interpolation Methods for Prediction of Soil Salinity in Arid Irrigation Region in Northern China

Agronomy ◽

10.3390/agronomy11081535 ◽

2021 ◽

Vol 11 (8) ◽

pp. 1535

Author(s):

Tonggang Fu ◽

Hui Gao ◽

Jintong Liu

Keyword(s):

Soil Salinity ◽

Subsurface Layer ◽

Salt Content ◽

Spatial Prediction ◽

Northern China ◽

Sodium Adsorption Ratio ◽

Interpolation Methods ◽

Distance Weighting ◽

Total Salt Content ◽

Inverse Distance

Numerous methods have been used in the spatial prediction of soil salinity. However, the most suitable method is still unknown in arid irrigation regions. In this paper, 78 locations were sampled in salt-affected land caused by irrigation in an arid area in northern China. The geostatistical characteristics of the soil pH, Sodium Adsorption Ratio (SAR), Total Salt Content (TSC), and Soil Organic Matter (SOM) of the surface (0–20 cm) and subsurface (20–40 cm) layers were analyzed. The abilities of the Inverse Distance Weighting (IDW), Ordinary Kriging (OK), and CoKriging (CK) interpolation methods were compared, and the Root Mean Square Error (RMSE) was used to justify the results of the methods. The results showed that the spatial distributions of the soil properties obtained using the different interpolation methods were similar. However, the surface layer exhibits more spatial heterogeneity than the subsurface layer. Based on the RSME, the nugget/sill value and range significantly affected which method was the most suitable. Lower nugget/sill values and lower ranges can be fitted using the IDW method, but higher nugget/sill values and higher ranges can be fitted using the OK method. These results provide a valuable reference for the prediction of soil salinity.

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The soil-adjusted vegetation index for soil salinity assessment in Uzbekistan

Proceedings of the International conference “InterCarto/InterGIS” ◽

10.35595/2414-9179-2020-3-26-324-333 ◽

2020 ◽

Vol 26 (3) ◽

pp. 324-333

Author(s):

Nozimjon Teshaev ◽

Bunyod Mamadaliyev ◽

Azamjon Ibragimov ◽

Sayidjakhon Khasanov

Keyword(s):

Soil Salinity ◽

Information Technologies ◽

Vegetation Index ◽

Arable Land ◽

Salt Content ◽

Secondary Data ◽

Soil Surface ◽

Soil Salinization ◽

Arid Zones ◽

Agronomic Practices

Soil salinization, as one of the threats of land degradation, is the main environmental issue in Uzbekistan due to its aridic climate. One of the most vulnerable areas to soil salinization is Sirdarya province in Uzbekistan. The main human-induced causes of soil salinization are the insufficient operation of drainage and irrigation systems, irregular observations of the agronomic practices, and non-efficient on-farm water use. All of these causes considerably interact with the level of the groundwater, leading to an increase in the level of soil salinity. The availability of historical data on actual soil salinity in agricultural lands helps in formulating validated generic state-of-the-art approaches to control and monitor soil salinization by remote sensing and geo-information technologies. In this paper, we hypothesized that the Soil-Adjusted Vegetation Index-based results in soil salinity assessment give statistically valid illustrations and salinity patterns. As a study area, the Mirzaabad district was taken to monitor soil salinization processes since it is the most susceptible territory of Sirdarya province to soil salinization and provides considerably less agricultural products. We mainly formulated this paper based on the secondary data, as we downloaded satellite images and conducted an experiment against the in-situ method of soil salinity assessment using the Soil-Adjusted Vegetation Index. As a result, highly saline areas decreased by a factor of two during the studied period (2005–2014), while non-saline areas increased remarkably from a negligible value to over 10 000 ha. Our study showed that arable land suitability for agricultural purposes has been improving year by year, and our research held on this district also proved that there was a gradual decrease in high salt contents on the soil surface and land quality has been improved. The methodology has proven to be statistically valid and significant to be applied to other arid zones for the assessment of soil salinity. We assume that our methodology is surely considered as a possible vegetation index to evaluate salt content in arable land of either Uzbekistan or other aridic zones and our hypothesis is not rejected by this research.

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Mobile Application Development for Spectral Signature of Weed Species in Rice Farming

Pertanika Journal of Science and Technology ◽

10.47836/pjst.29.4.01 ◽

2021 ◽

Vol 29 (4) ◽

Author(s):

Nor Athirah Roslin ◽

Nik Norasma Che’Ya ◽

Nursyazyla Sulaiman ◽

Lutfi Amir Nor Alahyadi ◽

Mohd Razi Ismail

Keyword(s):

Mobile Application ◽

Near Infrared ◽

Spectral Signature ◽

Weed Species ◽

Application Development ◽

Spectral Signatures ◽

Weed Infestation ◽

Access Information ◽

Master Builder ◽

And Control

Weed infestation happens when there is intense competition between rice and weeds for light, nutrients and water. These conditions need to be monitored and controlled to lower the growth of weeds as they affected crops production. The characteristics of weeds and rice are challenging to differentiate macroscopically. However, information can be acquired using a spectral signature graph. Hence, this study emphasises using the spectral signature of weed species and rice in a rice field. The study aims to generate a spectral signature graph of weeds in rice fields and develop a mobile application for the spectral signature of weeds. Six weeds were identified in Ladang Merdeka using Fieldspec HandHeld 2 Spectroradiometer. All the spectral signatures were stored in a spectral database using Apps Master Builder, viewed using smartphones. The results from the spectral signature graph show that the jungle rice (Echinochloa spp.) has the highest near-infrared (NIR) reflectance. In contrast, the saromacca grass (Ischaemum rugosum) shows the lowest NIR reflectance. Then, the first derivative (FD) analysis was run to visualise the separation of each species, and the 710 nm to 750 nm region shows the highest separation. It shows that the weed species can be identified using spectral signature by FD analysis with accurate separation. The mobile application was developed to provide information about the weeds and control methods to the users. Users can access information regarding weeds and take action based on the recommendations of the mobile application.

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A simple approach to estimate coastal soil salinity using digital camera images

Soil Research ◽

10.1071/sr20009 ◽

2020 ◽

Vol 58 (8) ◽

pp. 737

Author(s):

Lu Xu ◽

Raphael A. Viscarra Rossel ◽

Juhwan Lee ◽

Zhichun Wang ◽

Hongyuan Ma

Keyword(s):

Soil Salinity ◽

Precision Agriculture ◽

Mean Squared Error ◽

Rapid Development ◽

Salt Content ◽

Digital Camera ◽

Soil Surface ◽

Coefficient Of Determination ◽

Least Squares Regression ◽

High Concentration

Soil salinisation is a global problem that hinders the sustainable development of ecosystems and agricultural production. Remote and proximal sensing technologies have been used to effectively evaluate soil salinity over large scales, but research on digital camera images is still lacking. In this study, we propose to relate the pixel brightness of soil surface digital images to the soil salinity information. We photographed the surface of 93 soils in the field at different times and weather conditions, and sampled the corresponding soils for laboratory analyses of soil salinity information. Results showed that the pixel digital numbers were related to soil salinity, especially at the intermediate and higher brightness levels. Based on this relationship, we employed random forest (RF) and partial least-squares regression (PLSR) to model soil salt content and ion concentrations, and applied root mean squared error, coefficient of determination and Lin’s concordance correlation coefficient to evaluate the accuracy of models. We found that ions with high concentration were estimated more accurately than ions with low concentrations, and RF models performed overall better than PLSR models. However, the method is only suitable for bare land of coastal soil, and verification is needed for other conditions. In conclusion, a new approach of using digital camera images has good potential to predict and manage soil salinity in the context of precision agriculture with the rapid development of unmanned aerial vehicles.

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Efficient Unsupervised Classification of Hyperspectral Images Using Voronoi Diagrams and Strong Patterns

Sensors ◽

10.3390/s20195684 ◽

2020 ◽

Vol 20 (19) ◽

pp. 5684

Author(s):

Laura Bianca Bilius ◽

Ştefan Gheorghe Pentiuc

Keyword(s):

Heuristic Method ◽

Voronoi Diagrams ◽

Ground Truth ◽

Unsupervised Classification ◽

Hyperspectral Data ◽

Hyperspectral Images ◽

Spectral Signature ◽

Area Of Interest ◽

Parafac Decomposition

Hyperspectral images (HSIs) are a powerful tool to classify the elements from an area of interest by their spectral signature. In this paper, we propose an efficient method to classify hyperspectral data using Voronoi diagrams and strong patterns in the absence of ground truth. HSI processing consumes a great deal of computing resources because HSIs are represented by large amounts of data. We propose a heuristic method that starts by applying Parafac decomposition for reduction and to construct the abundances matrix. Furthermore, the representative nodes from the abundances map are searched for. A multi-partition of these nodes is found, and based on this, strong patterns are obtained. Then, based on the hierarchical clustering of strong patterns, an optimum partition is found. After strong patterns are labeled, we construct the Voronoi diagram to extend the classification to the entire HSI.

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Agronomic Traits Analysis of Ten Winter Wheat Cultivars Clustered by UAV-Derived Vegetation Indices

Remote Sensing ◽

10.3390/rs12020249 ◽

2020 ◽

Vol 12 (2) ◽

pp. 249 ◽

Cited By ~ 4

Author(s):

Stefano Marino ◽

Arturo Alvino

Keyword(s):

Winter Wheat ◽

Agronomic Traits ◽

Vegetation Indices ◽

Ground Truth ◽

Accurate Estimation ◽

Wheat Cultivars ◽

Yield Data ◽

Related Data ◽

High Data ◽

Winter Wheat Cultivars

Timely and accurate estimation of crop yield variability before harvest is crucial in precision farming. This study is aimed to evaluate the ability of cluster analysis based on Vegetation Indices (VIs) that were obtained from UAVs to predict the spatial variability on agronomic traits of ten winter wheat cultivars. Five VIs groups were identified and the ground truth yield-related data were analyzed for clusters validation. The yield data revealed a value of 6.91 t ha−1 for the first cluster with the highest VIs value and a decrease of −12%, −21%, and −27% for the 2nd, 3rd, and 4th clusters; respectively; the 5th cluster; with the lowest VIs value showed the lower yield values (4 t ha−1). Agronomic traits, such as dry biomass, spike numbers, and weight were grouped according to VIs clusters and analyzed and showed the same trends. The analysis of spatial distribution and agronomic data of the ten cultivars within the single clusters highlighted that the most productive varieties showing a greater value of spike weight and numbers and a greater presence of areas with high values of VIs and vice versa the less productive once, though two cultivars showed productions not linked to cluster classification and high data range variability were recorded. Cluster identified by high-resolution UAV vegetation indices can be a valid strategy although its effectiveness is closely linked to the cultivar component and, therefore, requires extensive verification.

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SPECTRAL SIGNATURES OF PARTICULATE MINERALS IN THE VISIBLE AND NEAR INFRARED

Geophysics ◽

10.1190/1.1440721 ◽

1977 ◽

Vol 42 (3) ◽

pp. 501-513 ◽

Cited By ~ 657

Author(s):

Graham R. Hunt

Keyword(s):

Near Infrared ◽

Infrared Region ◽

Spectral Signature ◽

Remotely Sensed Data ◽

Oh Groups ◽

Spectral Signatures ◽

Multispectral Remote Sensing ◽

Remote Sensing Techniques ◽

Bidirectional Reflection ◽

Selection Of

The utility of multispectral remote sensing techniques for discriminating among materials is based on the differences that exist among their spectral properties. As distinct from spectral variations that occur as a consequence of target condition and environmental factors, intrinsic spectral features that appear in the form of bands and slopes in the visible and near infrared (.325 to 2.5 μm) bidirectional reflection spectra of minerals (and, consequently, rocks) are caused by a variety of electronic and vibrational processes. These processes, such as crystal field effects, charge‐transfer, color centers, transitions to the conduction band, and overtone and combination tone vibrational transitions are discussed and illustrated with reference to specific minerals. Spectral data collected from a large selection of minerals are used to generate a “spectral signature” diagram that summarizes the optimum intrinsic information available from the spectra of particulate minerals. The diagram provides a ready reference for the interpretation of visible and near infrared features that typically appear in remotely sensed data. In the visible‐near infrared region, the most commonly observed features in naturally occurring materials are due to the presence of iron in some form or other, or to the presence of water or OH groups.

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Quantifying bioalbedo: a new physically based model and discussion of empirical methods for characterising biological influence on ice and snow albedo

The Cryosphere ◽

10.5194/tc-11-2611-2017 ◽

2017 ◽

Vol 11 (6) ◽

pp. 2611-2632 ◽

Cited By ~ 22

Author(s):

Joseph M. Cook ◽

Andrew J. Hodson ◽

Alex S. Gardner ◽

Mark Flanner ◽

Andrew J. Tedstone ◽

...

Keyword(s):

Optical Properties ◽

Radiative Transfer ◽

Surface Energy Balance ◽

Empirical Methods ◽

Physically Based Model ◽

Snow Albedo ◽

Spectral Signatures ◽

Physically Based ◽

The Impact ◽

Reflectance Data

Abstract. The darkening effects of biological impurities on ice and snow have been recognised as a control on the surface energy balance of terrestrial snow, sea ice, glaciers and ice sheets. With a heightened interest in understanding the impacts of a changing climate on snow and ice processes, quantifying the impact of biological impurities on ice and snow albedo (bioalbedo) and its evolution through time is a rapidly growing field of research. However, rigorous quantification of bioalbedo has remained elusive because of difficulties in isolating the biological contribution to ice albedo from that of inorganic impurities and the variable optical properties of the ice itself. For this reason, isolation of the biological signature in reflectance data obtained from aerial/orbital platforms has not been achieved, even when ground-based biological measurements have been available. This paper provides the cell-specific optical properties that are required to model the spectral signatures and broadband darkening of ice. Applying radiative transfer theory, these properties provide the physical basis needed to link biological and glaciological ground measurements with remotely sensed reflectance data. Using these new capabilities we confirm that biological impurities can influence ice albedo, then we identify 10 challenges to the measurement of bioalbedo in the field with the aim of improving future experimental designs to better quantify bioalbedo feedbacks. These challenges are (1) ambiguity in terminology, (2) characterising snow or ice optical properties, (3) characterising solar irradiance, (4) determining optical properties of cells, (5) measuring biomass, (6) characterising vertical distribution of cells, (7) characterising abiotic impurities, (8) surface anisotropy, (9) measuring indirect albedo feedbacks, and (10) measurement and instrument configurations. This paper aims to provide a broad audience of glaciologists and biologists with an overview of radiative transfer and albedo that could support future experimental design.

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