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.     

基于特征空间的黄河三角洲垦利县土壤盐分遥感提取

土壤盐渍化是实现土地资源可持续利用所面临的重要挑战,在我国滨海的黄河三角洲区域遥感定量反演适宜方法可为区域盐渍化监测与防治提供技术方法参考。研究以Landsat 8 OLI数据和野外实测数据为基础,提取关键地表特征参量,定量化探讨土壤盐分与地表生物物理参数之间的规律及关系,建立黄河三角洲土壤盐分最优反演模型。结果表明:Albedo-MSAVI、SI-Albedo、SI-NDVI反演精度分别为83.4%、88.8%和80.6%。分析认为SI-Albedo模型最适用于滨海地区盐渍化程度反演,对滨海地区土壤盐分的预测能力较强;Albedo-MSAVI、SI-NDVI模型对内陆干旱、半干旱地区的盐渍化信息提取具有一定的参考意义。基于精度最高的SI-Albedo所反演的结果来看,垦利县盐渍化程度自东向西总体呈高低高走向,与该区域盐分积聚的成因机理相符。     

Remote Sensing Extraction of Soil Salinity in Yellow River Delta Kenli County based on Feature Space

Soil salinization is an important challenge to achieve sustainable use of land resources. The appropriate method for remote sensing quantitative inversion in the coastal Yellow River Delta region of China can provide technical reference for regional salinization monitoring and prevention. Utilizing Landsat 8 OLI image and field measured data, we extracted key surface characteristic parameters, quantitatively discussed the law and relationship between soil salinity and surface biophysical parameters and established a soil salinity inversion model. The results show that the inversion precisions of Albedo-MSAVI, SI-Albedo and SI-NDVI feature space are 83.4%, 88.8% and 80.6% respectively. The analysis shows the SI-Albedo model is suitable for the inversion of salinization level in Binhai areas. For Albedo-MSAVI and SI-NDVI models, they have certain reference significance for salinization information extraction in inland arid and semi-arid areas. Based on the inversion of the SI-Albedo feature space with the highest accuracy, the level of salinization in Kenli County is generally high-low-high trends from the east to the west, which is consistent with the formation mechanism of salt accumulation in this area.     

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.     

Comparing Sentinel-2 MSI and Landsat 8 OLI in soil salinity detection: a case study of agricultural lands in coastal North Carolina

This study presents the first comparison of Landsat 8 Operational Land Imager (OLI) and Sentinel-2 Multispectral Instrument (MSI) in identifying soil salinity using soil physiochemical, spectral, statistical, and image analysis techniques. By the end of the century, intermediate sea level rise scenarios project approximately 1.3 meters of sea level rise along the coast of the southeastern United States. One of the most vulnerable areas is Hyde County, North Carolina, where 1140 km² of agricultural lands are being salinized, endangering 4,200 people and $40 million USD of property. To determine the best multispectral sensor to map the extent of salinization, this study compared the feasibility of OLI and MSI to estimate electrical conductivity (EC). The EC of field samples were correlated with handheld spectrometer spectra resampled into multispectral sensor bands. Using an iterative ordinary least squares regression, it was found that EC was sensitive to OLI bands 2 (452 nm – 512 nm) and 4 (636 nm – 673 nm) and MSI bands 2 (457.5 nm – 522.5 nm) and 4 (650 nm – 680 nm). Respectively, the R²_Adj and Root Mean Square Error (RMSE) of 0.04–0.54 and 1.15 for OLI, and 0.05–0.67 and 1.17 for MSI, suggests that the two sensors have similar salinity modelling skill. The extracted saline soils make up approximately 1,703 hectares for OLI and 118 hectares for MSI, indicating overestimation from the OLI image due to its coarser spatial resolution. Additionally, field samples indicate that nearby vegetated land is saline, indicating an underestimation of total impacted land. As sea levels rise, accurately monitoring soil salinization will be critical to protecting coastal agricultural lands. MSI’s spatial and temporal resolution makes it superior to OLI for salinity tracking though they have roughly equivalent spectral resolutions. This study demonstrates that visible spectral bands are sensitive to soil salinity with the Blue and Red spectral ranges producing the highest model accuracy; however, the low accuracies for both sensors indicate the need of narrowband sensors. The HyspIRI to be launched in the early 2020s by NASA may provide ideal data source in soil salinity studies.     

Mapping and monitoring of degraded lands in part of Jaunpur district of Uttar Pradesh using temporal spaceborne multispectral data

Apart from soil erosion by wind and water, the major land degradation processes operating in irrigated commands in arid and semi-arid regions are waterlogging and subsequent salinization/alkalinization. Remote sensing data have been used successfully in studies of the spatial extent, magnitude and temporal behaviour of lands affected by such processes. In this work we interpreted Landsat Multispectral Scanner images acquired during 1975 and Landsat Thematic Mapper data acquired during 1993, in conjunction with ancillary information and adequate ground data, to derive information on the extent and spatial distribution of various degraded lands, namely salt-affected soils, waterlogged areas and eroded lands in part of the Jaunpur district of Uttar Pradesh. The results indicate a significant shrinkage in the spatial extent of salt-affected soils (of the order of 49.76%) over the period 1975 to 1993. A similar trend was observed in the temporal behaviour of waterlogged areas, but an increase (6.45%) was found in the spatial extent of eroded lands. The methodology employed and the observations made are described here in detail.     

Characterizing the spatiotemporal evolution of soil salinization in Hetao Irrigation District (China) using a remote sensing approach

ABSTRACT

Soil salinization is a major problem of land degradation in arid and semiarid irrigation districts. This study aims to characterize the spatiotemporal evolution of soil salinization in Hetao Irrigation District (HID) in Inner Mongolia, China, using Landsat Thematic Mapper/Enhanced Thematic Mapper Plus/Operational Land Imager datasets. Salty barren land and farmland are extracted using supervised classification. Then, we develop four integrated soil salinity models (ISSMs) to quantify the intensity of saline farmland. ISSMs are generated through deriving the parameters (E_VI-SIs), which integrate enhanced vegetation index (EVI) and Salinity Index-1 (SI1), EVI and Salinity Index-3 (SI3), Modified Soil Adjusted Vegetation Index (MSAVI) and SI1, and MSAVI and SI3, respectively, from the scatter plots of farmland soils with different salinity in four spectral feature spaces (SFSs). Exponential regression analyses reveal that the E_VI-SI from MSAVI-SI3 SFS has the best fit with in situ soil electrical conductivity measurements (R² = 0.74, root mean square error = 0.31 dS m^–1). Salty barren land clustered in the central and northeast of HID, while the area of salty barren land decreased during 1986–2016. After employing water-saving irrigation since 2000, saline farmland decreased and then remained relatively stable. This study indicates that the SFS integrating MSAVI and SI3 contains effective information for quantifying the saline farmland. Employing water-saving irrigation had a positive effect on controlling salinization.     

Change detection technique based on the radiometric rotation controlled by no‐change axis,applied on a semi‐arid landscape

We demonstrate a new digital technique for change detection that integrates field survey data in a methodology. The field survey was specifically designed to control the operation of this new technique for change detection. The Radiometric Rotation Controlled by No‐change Axis (RCNA) technique was applied with spectral bands in the visible spectral range without atmospheric correction. The 190.37 km² study area is located in the centre of the semi‐arid region of northeast Brazil. This region is occupied by a natural shrub vegetation and low arboreal type steppe. This is known locally as ‘caatinga’, and in this area the predominant use is grazing by cattle and goat. Two TM/Landsat images in digital format of the dry seasons of 1984 and 1996 were used together with two ETM+ /Landsat images for the dry seasons of 1999 and 2001. Three change maps were generated, all with accuracy index values of around 0.70, which is acceptable for areas with complex patterns like semi‐arid lands. In conclusion, there is no need to apply atmospheric correction methods to a pair of multi‐temporal images to obtain an acceptable accuracy of change detection in semi‐arid regions. Although this technique can combine images from different sensors, this requires that both have similar spectral ranges.     

吉林西部盐碱地特性微波双频差分无损探测

针对吉林西部盐碱地特性(内地苏打盐类型),应用车载双频段被动微波遥感系统,对不同盐碱状态的4个裸盐碱区进行双极化多角度微波辐射无损探测。基于多角度双频率双极化的观测数据优势,选择逼近式迭代算法来反演其介电常数虚部,Dobson模型反演介电常数实部。在此基础上,应用双频差分法研究了该区域盐碱地介电特性与含水量、含盐量的关系。双频差分结果表明:实部双频差分与盐碱土含水量呈线性关系,相关系数为0.9996;虚部双频差分与含盐量呈线性关系,相关系数为0.9977。这为应用被动微波遥感定量反演盐碱地特性(含水量、含盐量)奠定了理论基础。     

Field-derived spectra of salinized soils and vegetation as indicators of irrigation-induced soil salinization

Salinization is a major cause of soil degradation in the Murray–Darling Basin of Australia. The objective of this research is to evaluate the utility of field-derived spectra of saline soils and related vegetation for characterizing and mapping the spatial distribution of irrigation-induced soil salinization. A FieldSpec FR hand-held spectrometer was used to measure the spectra of a range of salinized soils and associated vegetation. Strategies for mapping field-derived spectra using hyperspectral (HyMap) imagery were assessed, and a continuum-removed Spectral-Feature-Fitting (SFF) approach adopted. Field-derived spectra of the vegetation comprising of samphire, sea blite, and native grass species are also useful indicators of salinization; however, their absence is not necessarily an indicator of healthy soils. Distribution maps created using the SFF method and a restricted wavelength range of field-derived spectra provide an accurate record of the distribution of both vegetation and soil indicators of salinization at the time of image acquisition. Salinized soil and vegetation indicator class maps show a similar spatial distribution to soil salinization as mapped by ground-based geophysical surveys.     

Monitoring surface water content using visible and short-wave infrared SPOT-5 data of wheat plots in irrigated semi-arid regions

Irrigated agriculture is an important strategic sector in arid and semi-arid regions. Given the large spatial coverage of irrigated areas, operational tools based on satellite remote sensing can contribute to their optimal management. The aim of this study was to evaluate the potential of two spectral indices, calculated from SPOT-5 high-resolution visible (HRV) data, to retrieve the surface water content values (from bare soil to completely covered soil) over wheat fields and detect irrigation supplies in an irrigated area. These indices are the normalized difference water index (NDWI) and the moisture stress index (MSI), covering the main growth stages of wheat. These indices were compared to corresponding in situ measurements of soil moisture and vegetation water content in 30 wheat fields in an irrigated area of Morocco, during the 2012–2013 and 2013–2014 cropping seasons. NDWI and MSI were highly correlated with in situ measurements at both the beginning of the growing season (sowing) and at full vegetation cover (grain filling). From sowing to grain filling, the best correlation (R² = 0.86; p < 0.01) was found for the relationship between NDWI values and observed soil moisture values. These results were validated using a k-fold cross-validation methodology; they indicated that NDWI can be used to estimate and map surface water content changes at the main crop growth stages (from sowing to grain filling). NDWI is an operative index for monitoring irrigation, such as detecting irrigation supplies and mitigating wheat water stress at field and regional levels in semi-arid areas.     

Detecting date palm trees health and vegetation greenness change on the eastern coast of the United Arab Emirates using SAVI

Due to shortage of fresh water resources, the vegetation of the eastern region of the United Arab Emirates (UAE) has experienced a series of declines resulting from salinization of groundwater, which is the major source of irrigation. To assess these changes, field measurements combined with Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) based Soil Adjusted Vegetation Index (SAVI) were analysed. TM and ETM+ images from two dates, 1987 and 2000 were acquired to enable the computation of the greenness anomalies for three sites in the eastern region, Fujairah, Kalba and Hatta. The results show an overall increase in agricultural area, associated with a severe decrease in vegetation greenness and health conditions, particularly in the Kalba study area. The SAVI values decreased with increased soil salinity, permitting the identification of salt‐affected areas. This remotely sensed data offered valuable information regarding vegetation health conditions, especially when using greenness indices. However, in open canopies, like date palm trees, soil line indices, such as, SAVI are more robust, since they account for the contribution of the soil background. This research suggests, that in order for the date palm trees of this region to stay productive, considerable attention needs to be placed in managing and monitoring soil salinity conditions and progress. Potential areas of further research range from studying the effects of tree spacing and understory crops as immediate and potential solutions to maintain productivity and mitigate the salinity problem.     

Soil wetness as an indicator of stream salinity: a landscape position index approach

Dryland salinity is a major environmental issue in much of Australia’s agricultural lands and is expressed as salt affected land or degraded stream water quality. Maps showing areas at risk of land and stream degradation are needed by regional, state and national management and planning authorities, as well as farmers. Part of the management involves establishing end-of-valley targets for water quality. Developing maps of salinity risk is limited by the availability of appropriate spatial data. Elevation data at appropriate scales are available for all Australia.This paper explores the potential to develop catchment and regional scale soil wetness maps, based only on elevation data, as a surrogate for stream salinity risk. Soil wetness indices were derived through the Fuzzy Landscape Analysis GIS (FLAG) model. While FLAG avoids the direct use of process models it uses process understanding. It does this through an index-based approach that requires a training set of areas of wetness, salinity or other attribute associated with position in the landscape. We test whether only one of the FLAG landscape position indices (UPNESS), that has been shown to characterise depositional zones, is correlated to baseflow stream salinity.UPNESS is the uphill area monotonically above each point in the landscape, and is a specialised form of contributing area, a measure of surface and sub-surface water accumulation. This measure characterises catchments as the ‘connectedness’ of drainage or prevalence of runoff sinks. It distinguishes, for example, between freely drained catchments and those with more extensive depositional zones that allow the accumulation and storage of salt and formation of preferential pathways in the system.FLAG analysis was applied over an area of ~12 000 km² in southeastern Australia where salinity research was being conducted by state agencies. Stream electrical conductivity measurements were obtained to compare with the UPNESS index. The results suggest that the model is useful for targeting further investigations in regional scale salinity management planning and research. FLAG is suggested as a first step for obtaining a highly visual rapid assessment of potential wetness, discharge and salinisation at catchment scales.     

Semi‐automated methods for mapping wetlands using Landsat ETM+ and SRTM data

The overarching goal of this study was to develop a comprehensive methodology for mapping natural and human‐made wetlands using fine resolution Landsat enhanced thematic mapper plus (ETM+), space shuttle radar topographic mission digital elevation model (SRTM DEM) data and secondary data. First, automated methods were investigated in order to rapidly delineate wetlands; this involved using: (a) algorithms on SRTM DEM data, (b) thresholds of SRTM‐derived slopes, (c) thresholds of ETM+ spectral indices and wavebands and (d) automated classification techniques using ETM+ data. These algorithms and thresholds using SRTM DEM data either over‐estimated or under‐estimated stream densities (S _d) and stream frequencies (S _f), often generating spurious (non‐existent) streams and/or, at many times, providing glaring inconsistencies in the precise physical location of the streams. The best of the ETM+‐derived indices and wavebands either had low overall mapping accuracies and/or high levels of errors of omissions and/or errors of commissions.

Second, given the failure of automated approaches, semi‐automated approaches were investigated; this involved the: (a) enhancement of images through ratios to highlight wetlands from non‐wetlands, (b) display of enhanced images in red, green, blue (RGB) false colour composites (FCCs) to highlight wetland boundaries, (c) digitizing the enhanced and displayed images to delineate wetlands from non‐wetlands and (d) classification of the delineated wetland areas into various wetland classes. The best FCC RGB displays of ETM+ bands for separating wetlands from other land units were: (a) ETM+4/ETM+7, ETM+4/ETM+3, ETM+4/ETM+2, (b) ETM+4, ETM+3, ETM+5 and (c) ETM+3, ETM+2, ETM+1. In addition, the SRTM slope threshold of less than 1% was very useful in delineating higher‐order wetland boundaries. The wetlands were delineated using the semi‐automated methods with an accuracy of 96% as determined using field‐plot data.

The methodology was evaluated for the Ruhuna river basin in Sri Lanka, which has a diverse landscape ranging from sea shore to hilly areas, low to very steep slopes (0° to 50°), arid to semi‐arid zones and rain fed to irrigated lands. Twenty‐four per cent (145 733 ha) of the total basin area was wetlands as a result of a high proportion of human‐made irrigated areas, mainly under rice cropping. The wetland classes consisted of irrigated areas, lagoons, mangroves, natural vegetation, permanent marshes, salt pans, lagoons, seasonal wetlands and water bodies. The overall accuracies of wetland classes varied between 87% and 94% (K _hat = 0.83 to 0.92) with errors of omission less than 13% and errors of commission less than 1%.     

Localization of local soil classes in an arid region of Mexico,using satellite imagery

A map of local soil classes (LSCs) was used to make digital maps of soil classes using computer-assisted cartography. The objectives were to establish classes of the local soils of an arid locality; to determine their physical and chemical characteristics; to relate the content of clay (Cl), organic matter (OM) and electrical conductivity (EC) to the levels of reflectance from the classes of soil; and to obtain a digital map of classes of local soils using a supervised classification of the image and verify its precision. The map of LSCs was used as basis for generating information about the physical and chemical characteristics of soils. The information was related to digital levels of a Landsat 7 Enhanced Thematic Mapper Plus (ETM+) image, and a supervised classification was performed. The precision of the digital maps was verified. The results show that a map of LSCs can be used as basis for generating digital maps.     

Land degradation monitoring using multi‐temporal Landsat TM/ETM data in a transition zone between grassland and cropland of northeast China

Land degradation is one of the most pressing problems of environments. This research presents a methodology to monitor land degradation in a transition zone between grassland and cropland of northeast China, where soil salinization and grassland degradation, even desertification, have been observed in the past few decades. Landsat TM/ETM data in 1988, 1996 and 2001 were selected to determine the rate and status of grassland degradation and soil salinization together based on both decision tree (DT) classifier and the field investigation. The thermal radiance values of TM/ETM 6 data, the Normalized Difference Vegetation Index (NDVI), and new variables (brightness, greenness, and wetness) generated by the Kauth–homas Transforms (KT) algorithms from Landsat TM/ETM data served as the feature nodes of a DT classifer and contributed to improving the classification results. It showed an overall accuracy of more than 85% and a Kappa statistic of agreement of about 0.79 in 1996 and 2001 with the exception of about 0.69 in 1988. The statistical areas of land degradation in the observation periods revealed that land degradation, especially the salt‐affected soil, is accelerating. The distribution maps of land degradation in the years of 1988, 1996 and 2001 were generated respectively based on the classification results. Their change maps were created by the difference between the distribution maps from 1988 to 1996 and from 1996 to 2001 respectively. The changes of salt‐affected soil occurred near the water bodies due to variations of water sizes, and most of the degraded grassland appeared around the salt‐affected soil. Although climate variations play an important role in this region, human activities are also crucial to land degradation.     

Assessing the capability of broadband indices derived from Landsat 8 Operational Land Imager to monitor above ground biomass and salinity in semiarid saline environments of the Bahía Blanca Estuary,Argentina

In arid and semi-arid ecosystems, salinisation and desertification are the most common processes of land degradation, and satellite data may provide a valuable tool to assess land surface condition and vegetation status. The aim of this study was to evaluate the capability of Landsat 8 OLI (Operational Land Imager) remote sensing information and broadband indices derived from it, to monitor above ground biomass (AGB) and salinity in two different semiarid saline environments (unit a and unit b) in the Bahía Blanca Estuary. Unit a (Ua) is composed of bushes of Cyclolepis genistoides in association with Atriplex undulata and 41% of bare soil. Unit b (Ub) is composed of dense thickets of Allenrolfea patagonica in association with C. genistoides and 34% of bare soil. Pearson’s correlation analyses were performed between field estimates of AGB and salinity (soil salinity and interstitial water salinity) and remote sensing estimates. Satellite data include surface reflectance of individual bands, vegetation indices (NDVI [normalised difference vegetation index], SAVI [soil-adjusted vegetation index], MSAVI2 [modified soil-adjusted vegetation index], NDII [normalised difference infrared index], GNDVI [green normalised difference vegetation index], GRNDI [green-red normalised difference index], OSAVI [optimised soil-adjusted vegetation index], SR [simple ratio]), and salinity indices (SI1, SI2, SI3 [salinity index 1, 2 and 3, respectively] and BI [brightness index]). Correlation analyses involving AGB were performed twice; first considering all months and then again excluding the months with higher soil salinities. In Ua, soil adjusted vegetation indices SAVI and MSAVI2 showed to be suitable to detect changes in the total green AGB and C. genistoides green AGB (the major contributor to total green AGB). After excluding data from December and January (the months with the highest soil salinity), green AGB of A. undulata also showed a significant positive correlation with soil adjusted indices SAVI, MSAVI2 and OSAVI. Although proportionally this species was not a large contributor to the total biomass, it is characterised by a high leaf reflectance, which makes it suitable for biomass retrieval. In Ub, significant positive correlations were obtained between NDVI, SAVI, NDII, OSAVI and SR indices and the AGB green ratio, but significant negative correlations were obtained between A. patagonica red AGB and these vegetation indices. When December and January were excluded from the analysis the negative correlations between vegetation indices NDVI, OSAVI and SR and red AGB remained significant (r = ?0.68, ?0.76 and ?0.7, respectively). The positive correlations between these indices and AGB green ratio (r = 0.73, 0.78 and 0.75, respectively) remained significant as well. Significant negative correlations were also found between NDVI, NDII, GNDVI, OSAVI and SR indices and field salinity estimates. As soil salinisation induces A. patagonica reddening, red AGB and soil salinity covariate in the field, and the negative correlation with vegetation indices may be useful to retrieve information on both variables combined, which are indicative of water stress. Correlation analysis between field estimates of salinity and spectral salinity indices showed significant positive correlation for all the tested indices. The obtained results highlight the importance of a thoughtful selection of remote sensing indices to account for changes in vegetation biomass, especially in arid and semiarid environments particularly sensitive to desertification and salinisation. Also, ground truth cannot be overlooked, and field work is necessary to test index performance in every case.     

基于多源数据的土地盐碱化遥感快速监测

通过分析干旱区土地盐碱化环境的地表景观特征和遥感信息特征，基于SPOT、ASTER多平台多波段遥感数据和DEM、土壤样品分析数据等多源数据，采用光谱角度制图（SAM）的遥感图像分类方法对实验区土地盐碱化程度进行了分级制图。该方法对常规数据的依赖性较小，适于西部干旱地区的土地盐碱化快速监测和评估。     

Irrigated area mapping in heterogeneous landscapes with MODIS time series,ground truth and census data,Krishna Basin,India

Diverse irrigated areas were mapped in the Krishna River Basin (258,912 km²), southern India, using an irrigated fraction approach and multiple ancillary data sources. Unsupervised classification of a monthly time series of net difference vegetation index (NDVI) images from the Moderate Resolution Imaging Spectrometer (MODIS) over January–December 2002 generated 40 classes. Nine generalized classes included five irrigated classes with distinct NDVI time signatures: continuous irrigation, double‐cropped, irrigated with low biomass, minor irrigation, and groundwater irrigation. Areas irrigated by surface water began greening 45 days after groundwater‐irrigated areas, which allowed separation of surface and groundwater irrigation in the classification. The fraction of each class area irrigated was determined using three different methods: ground truth data, a linear regression model calibrated to agricultural census data, and visual interpretation of Landsat TM imagery. Irrigated fractions determined by the three methods varied least for the double‐cropped irrigated class (0.62–0.79) and rangeland (0.00–0.02), and most for the minor irrigated class (0.06–0.43). Small irrigated patches (<0.1 km²) accounted for more irrigated area than all major surface water irrigated areas combined. The irrigated fractions of the minor and groundwater‐irrigated classes differed widely by method, suggesting that mapping patchy and small irrigated areas remains challenging, but comparison of multiple data sources improves confidence in the classification and highlights areas requiring more intensive fieldwork.     

An inversion approach to generate electromagnetic conductivity images from signal data

The alluvial clay plains of the Murray–Darling Basin (MDB) have been extensively developed for irrigated agricultural production. Whilst irrigation has brought economic prosperity, there have been isolated environmental impacts. This is because the plains were formed by a system of ancient streams (i.e. prior stream and palaeochannels) that are characterised by coarse textured sediments and which are susceptible to deep drainage. To improve irrigation efficiency and natural resource management outcomes, information is required to characterise the connectivity of prior stream channels with underlying migrational channel deposits (i.e. palaeochannels). One option is the use of electromagnetic (EM) induction instruments which measure the apparent soil electrical conductivity (σ_a – mS/m). In this paper, we describe how σ_a collected using a next-generation DUALEM-421 and an EM34 can be used in conjunction with a joint-inversion algorithm (EM4Soil) to generate a 2d model of electrical conductivity (σ – mS/m) across an irrigated cotton growing field located on Quaternary alluvial clay plain in the lower Gwydir valley of NSW (Australia). The results compare favourably with existing pedological and stratigraphic knowledge. On the clay alluvial plain the accumulation of Aeolian and cyclical salt in the root zone and depth of clay alluvium are discerned by the DUALEM-421 and EM34, respectively. In addition, the approach is able to resolve the location of buried migrational channel deposits (i.e. palaeochannel) underlying the clay plain and the connectivity of these coarser sediments with a prior stream channel. Quantitatively the best correlation between estimated σ and measured soil properties, was found to be greatest when the DUALEM-421 and EM34 data were jointly inverted and when predicting EC_1:5 (r² = 0.61).