Generating soil electrical conductivity maps at regional level by integrating measurements on the ground and remote sensing data

In arid and semi‐arid areas, salinization of soil and water resources is one of the major threats to irrigated agriculture. For management purposes, quantifying both the extent and distribution of salinization is important, but accurate data with sufficient spatial resolution are often not available. Commonly used techniques such as soil sampling and geophysical methods are time‐consuming and yield only point data. A method is described in which multispectral remote sensing images can be used to regionalize point data measured on the field. Field data consist of measurements of electrical conductivity and are obtained by the combination of geophysical methods and the analysis of field soil samples. Uncalibrated salinity maps were calculated with spectral correlation mapping using image‐based reference spectra of saline areas. As an alternative indicator for soil salinity, the NDVI was used. The method was verified in the Yanqi Basin, northwestern China. Correlations between field data and the uncalibrated salinity maps were found over non‐irrigated sites for all images. Good correlations (R ² up to 0.85) resulted for images collected during the winter months. The high correlation coefficients allow the uncalibrated salinity maps to be scaled to electrical conductivity maps.     

The effects of vegetation on the ability to map soils using imaging spectrometer data

Remote sensing applied to tasks of mapping soil and rock surfaces must address the problem of vegetation cover in all but the most arid terrain. Masking out pixels with a high proportion of vegetation using a threshold on the near-infrared/red ratio is a popular strategy for live vegetation. The important effects of dead vegetation on the SWIR reflectance is usually ignored. Data gathered by the GER-II imaging spectrometer over a semi-arid area near Almaden, south central Spain were used to test the sensitivity of thematic soil mapping to variable cover of live and dead vegetation. After calibration to reflectance a least-squares unmixing analysis was performed using image end-members and proportions maps of vegetation and soil/rock components generated. Despite a low signal-to-noise ratio, three soil/rock and four vegetation endmembers were successfully mapped and validated from field estimates. A quantitative assessment was made of the effects of live and dead vegetation on the ability of the unmixing analysis to distinguish between granite and shale soils using synthetically mixed spectra gathered using field spectroradiometry and statistical analysis of the imaging spectrometer data. Dead vegetation was shown to have a greater impact on soil spectra than live vegetation. The ability to distinguish between the soils was lost at 50-60 per cent vegetation cover.     

Digitally mapping the information content of visible-near infrared spectra of surficial Australian soils

We can use soil mapping to gain a better understanding of the soil and how it varies in the landscape. Good quality data sets that represent the survey area are important to develop quantitative spatial models for soil mapping and to evaluate their outputs. Over the past three decades, scientists have become interested in rapid, non-destructive measurements of the soil using visible-near infrared (vis-NIR) (400-2500 nm) and mid infrared (mid-IR) (2500-25,000 nm) diffuse reflectance spectra. These spectra provide an integrative technique that measures the fundamental characteristics and composition of the soil, including colour, iron oxide, clay and carbonate mineralogy, organic matter content and composition, the amount of water present and particle size. If adequately summarised and exhaustively available over large areas, this information might be useful in situations where reliable, quantitative soil information is needed, such as agricultural, environmental and ecological modelling, or for digital soil mapping. The aims of this paper are to summarise the information content of vis-NIR spectra of Australian soils and to use a predictive spatial modelling approach to digitally map this information across Australia on a 3-arc second grid (around 90 m). We measured the spectra of 4606 surface soil samples from across Australia using a vis-NIR spectrometer. The soil information content of the spectra was summarised using a principal component analysis (PCA). We used model trees to derive statistical relationships between the scores of the PCA and 31 predictors that were readily available and we thought might best represent the factors of soil formation (climate, organisms, relief, parent material, time and the soil itself). The models were validated and subsequently used to produce digital maps of the information content of the spectra, as summarised by the PCA, with estimates of prediction error at 3-arc seconds pixel resolution. The most frequently used predictors at the continental scale were factors related to climate, parent material (and time), while at landscape and more local scales, they were factors related to relief, organisms and the soil. Finally, we use our maps for pedologic interpretations of the distribution of soils in Australia. Our results might be useful in situations requiring high-resolution, quantitative soil information e.g. in agricultural, environmental and ecologic modelling and for soil mapping and classification.     

Analysis of a small agricultural watershed using remote sensing techniques

Salinization of land and sweet water is an increasing problem worldwide. In the Carpathian Basin, particularly in arid and semi‐arid regions, irrigation is a contributing factor to the secondary salinization problems, one of the major problems affecting soils in Hungary. Conventional broadband sensors such as SPOT, Landsat MSS, and Landsat ETM+ are not suitable for mapping soil properties, because their bandwidth of 100–200 mm cannot resolve diagnostic spectral features of terrestrial materials. Analytical techniques, developed for analysis of broadband spectral data, are incapable of taking advantage of the full range of information present in hyperspectral remote sensing imagery. In our pilot project in Tedej farm in the Great Plain Region, Hungary, the DAIS sensor was used to assess salinity risk, covering the spectral range from the visible to the thermal infrared wavelengths at 5 m spatial resolution, and other major indicators of soil salinization (NDVI, SAVI, canopy cover) were quantified with advanced remote sensing techniques using the TETRACAM ADC agricultural multispectral camera which offers red/green and NIR imaging at megapixel resolution. As a result, prominent absorption bands around 1450 nm and 1950 nm wavelength in most soil spectra are attributed to water and hydroxyl ions. Occasional weaker absorption bands caused by water also occur at 970, 1200, and 1700 nm. Absorption features near the 400 nm wavelength for all samples are also noticeable. Absorption bands at 1800 and 2300 nm are attributed to gypsum, while strong absorption features near 2350 nm are assigned to calcite (CaCo₃). Saline soils exhibited significantly higher reflectance values all throughout the 325–2500 nm wavelengths of the spectrum. Soils with a high amount of soluble salts gave a higher average reflectance than soils with a low salt content. In the project, an ADC camera‐based real‐time integrated system was developed to take advantage of more specialized spectral information and to provide even more accurate and useful data directly from the field. The results revealed that the NDVI and SAVI index and the canopy cover mapping taken with multispectral cameras can be useful as an indirect marker and help for detecting salinization. However, we did not find a strong correlation between NDVI and soil salinity. This is probably because the detection and assessment of lower levels of salinity are difficult, mainly owing to the nature of the remotely sensed images; with such images, it is not possible to obtain information on the third dimension of the 3‐D soil body. Also, the impact of salinity on electromagnetic properties needs to be explored further to understand how it can be derived indirectly from remotely sensed information. With the rapid validation of remotely sensed hyperspectral data, the decision in the future, with the best trade‐off between irrigation and sustainable land use made by agricultural specialists in this region, can be more environmentally sound and more accurate using the results from the pilot.     

Unmixing of soil types and estimation of soil exposure with simulated hyperspectral imagery

Hyperspectral determination of soil types has the potential to become an important addition to the methods used for classification and mapping of soils. In this study laboratory measured spectra of different soils, vegetation and crop residue were combined to simulate hyperspectral remote sensing imagery. The overall aim was to examine the spectral unmixing of these materials under laboratory conditions to better understand the limits to prediction of soil types and determination of cover fractions. Two different methods were utilized to mix spectra of the soil and vegetation and substantial differences were observed in the unmixing results from the different image types, particularly in mixed pixels. Results found pure soils were easily distinguished from each other when not mixed with vegetation, while some mixes of soil and vegetation were confused as pure soil spectra. The accuracy of abundance fractions retrieved in the unmixing process also varied substantially with soil type and vegetation cover.     

Monitoring desertification and land degradation over sub-Saharan Africa

A desertification monitoring system is developed that uses four indicators derived using continental-scale remotely sensed data: vegetation cover, rain use efficiency (RUE), surface run-off and soil erosion. These indicators were calculated on a dekadal time step for 1996. Vegetation cover was estimated using the Normalized Difference Vegetation Index (NDVI). The estimation of RUE also employed NDVI and, in addition, rainfall derived from Meteosat cold cloud duration data. Surface run-off was modelled using the Soil Conservation Service (SCS) model parametrized using the rainfall estimates, vegetation cover, land cover, and digital soil maps. Soil erosion, one of the most indicative parameters of the desertification process, was estimated using a model parametrized by overland flow, vegetation cover, the digital soil maps and a digital elevation model (DEM). The four indicators were then combined to highlight the areas with the greatest degradation susceptibility. The system has potential for near-real time monitoring and application of the methodology to the remote sensing data archives would allow both spatial and temporal trends in degradation to be determined.     

A spectral matching quality indicator for material mapping using spectral library search methods

Spectral library search methods are being used increasingly as an efficient approach for exploiting hyperspectral remotely sensed data in material identification and mapping applications. The aim of this study was to develop a quantitative method, using an indicator called the Quality factor (Q-factor), for providing quantitative information on the reliability of spectral identifications in the interpretation (classification) of unknown spectra by library search methods. This was achieved by summing the two main requirements of a typical reflectance spectral library search for material mapping: (1) a reliable correlation between spectral matching scores and material similarity, and (2) a reliable separation ability between the relevant and non-relevant parts of the candidate reference spectra. These form a metric whose values reflect the closeness of the output reference spectra to the input unknown spectra for a chosen library search method. The Q-factor was tested as an indicator of the reliability of the material identifications by the library search for a range of unknown reflectance spectra of various types of vegetation, soils and minerals collected from the US Geological Survey (USGS) Spectral Library and from our in-house spectral database. The results indicate that this approach has the potential to separate correct and incorrect spectral identifications resulting from a particular spectral library search method using a reference similarity logic. The method may be applied to any combination of deterministic spectral matching alternatives using reflectance spectra. Spectrum-level quality information provided by the Q-factor is useful for optimizing a particular search method or for choosing the most appropriate method for distinct identification and classification problems.     

Mapping of flood dynamics and spatial distribution of vegetation in the Amazon floodplain using multitemporal SAR data

This paper presents the use of time series of SAR images to map the flood temporal dynamics and the spatial distribution of vegetation over a large Amazonian floodplain. The region under study (3500 km²) presents a diversity of landscape units with open lakes, bogs, large meadows, savannahs, alluvial forests and terra firma forest, covered by 21 images acquired by J-ERS between 1993 and 1997. Ground data include in situ observations of vegetation structure and flood extent as well as water level records. Image analysis demonstrates that temporal variations of the radar backscatter can be used to monitor efficiently the flood extent regardless of the landscape units. Also, analysis of the backscatter temporal variation greatly reduces the confusion between smooth surfaces (e.g. open water bodies, bare soils) inherent to L-band backscatter. The mapping method is based on decision rules over two decision variables: 1) the mean backscatter coefficient computed over the whole time series; 2) the total change computed using an “Absolute Change” estimator. The first variable provides classification into rough vegetation types while the second variable yields a direct estimate of the intensity of change that is related to flood dynamics. The classifier is first applied to the whole time series to map the maximum and minimum flood extent by defining 3 flood conditions: never flooded (NF); occasionally flooded (OF); permanently flooded (PF). It also furnishes the broad land cover type: open water/bare soils/low vegetation/forest. The accuracy of the flood extent mapping shows a kappa value of 0.82. Then, the classifier is run iteratively on the OF pixels to monitor flood stages during which the occasionally flooded areas get submerged. The mapping accuracy is assessed on one intermediate flood stage, showing a precision in excess of 90%. The importance of the time sampling for flood mapping is discussed along with the influence of SAR backscatter accuracy and the number of images. Then general guidelines for floodplain mapping are presented. By combining water level reports with maps of different flood stages the flooding pattern can be retrieved along with the vegetation succession processes. It is shown that the spatial distribution of vegetation communities is governed by flood stress and can be modelled as a function of the mean annual exposure to floods.     

Remote sensing of soil salinity: potentials and constraints

Soil salinity caused by natural or human-induced processes is a major environmental hazard. The global extent of primary salt-affected soils is about 955 M ha, while secondary salinization affects some 77 M ha, with 58% of these in irrigated areas. Nearly 20% of all irrigated land is salt-affected, and this proportion tends to increase in spite of considerable efforts dedicated to land reclamation. This requires careful monitoring of the soil salinity status and variation to curb degradation trends, and secure sustainable land use and management. Multitemporal optical and microwave remote sensing can significantly contribute to detecting temporal changes of salt-related surface features. Airborne geophysics and ground-based electromagnetic induction meters, combined with ground data, have shown potential for mapping depth of salinity occurrence. This paper reviews various sensors (e.g. aerial photographs, satellite- and airborne multispectral sensors, microwave sensors, video imagery, airborne geophysics, hyperspectral sensors, and electromagnetic induction meters) and approaches used for remote identification and mapping of salt-affected areas. Constraints on the use of remote sensing data for mapping salt-affected areas are shown related to the spectral behaviour of salt types, spatial distribution of salts on the terrain surface, temporal changes on salinity, interference of vegetation, and spectral confusions with other terrain surfaces.As raw remote sensing data need substantial transformation for proper feature recognition and mapping, techniques such as spectral unmixing, maximum likelihood classification, fuzzy classification, band ratioing, principal components analysis, and correlation equations are discussed. Lastly, the paper presents modelling of temporal and spatial changes of salinity using combined approaches that incorporate different data fusion and data integration techniques.     

Use of conditional probability networks for environmental monitoring

Causal or conditional probability networks (CPNs) are shown to provide a natural framework for combining a time sequence of classified satellite images with other maps for environmental monitoring. The key features of CPNs are described by way of application to an example involving the monitoring of salinization of farmland over time using satellite images and an ancillary dataset derived from a digital terrain model. It is shown that CPNs can be used to improve mapping accuracies by incorporating knowledge about the spatial and temporal variation of the map classes of interest. The methods provide a practical solution to the challenging problem of mapping and monitoring salt in farmland. The representation and propagation of uncertainty within this framework is discussed, as well as the spatial and temporal prediction of images and maps.