Airborne electromagnetics supporting salinity and natural resource management decisions at the field scale in Australia

Airborne geophysics has been used at the catchment scale to map salt stores, conduits and soil variability, but few studies have evaluated its usefulness as a land management tool at the field scale. We respond to questions posed by land managers with: (1) comparison of airborne and ground-based electromagnetic surveys in the Lower Balonne catchment, Queensland, and (2) comparison with historical and anecdotal knowledge of landscape response in the country around Jamestown in mid-South Australia.In the Lower Balonne, direct comparison between ground electromagnetic survey (EM) and airborne electromagnetics (AEM) showed a strong relationship for both the absolute values and spatial patterns of conductivity. The penetration of AEM to greater than 100 m is valuable in defining hydrological barriers. In the Jamestown area, AEM conductivity corresponded well with specific outbreaks of salinity and observed variability in crop response; local inconsistencies at the ground surface could be resolved when sub-surface data were considered. AEM can provide valuable information at the field scale that is relevant to salinity management. Farmers can have confidence in any of these techniques (historical information, EM and AEM) and they may directly compare or integrate the results.     

基于Argo浮标和EOF建立区域海水三维声速场的方法

本文提出了一种利用Argo浮标数据和经验正交函数(EOF)建立区域海水三维声速场的方法。利用局部区域内的Argo观测数据对该方法进行了验证,利用少量Argo浮标数据即可建立精度较高的三维海水声速场模型,预测区域内任意剖面的中误差在1 m/s左右。     

Estimating salinity stress in sugarcane fields with spaceborne hyperspectral vegetation indices

The presence of salt in the soil profile negatively affects the growth and development of vegetation. As a result, the spectral reflectance of vegetation canopies varies for different salinity levels. This research was conducted to (1) investigate the capability of satellite-based hyperspectral vegetation indices (VIs) for estimating soil salinity in agricultural fields, (2) evaluate the performance of 21 existing VIs and (3) develop new VIs based on a combination of wavelengths sensitive for multiple stresses and find the best one for estimating soil salinity. For this purpose a Hyperion image of September 2, 2010, and data on soil salinity at 108 locations in sugarcane (Saccharum officina L.) fields were used. Results show that soil salinity could well be estimated by some of these VIs. Indices related to chlorophyll absorption bands or based on a combination of chlorophyll and water absorption bands had the highest correlation with soil salinity. In contrast, indices that are only based on water absorption bands had low to medium correlations, while indices that use only visible bands did not perform well. From the investigated indices the optimized soil-adjusted vegetation index (OSAVI) had the strongest relationship (R² = 0.69) with soil salinity for the training data, but it did not perform well in the validation phase. The validation procedure showed that the new salinity and water stress indices (SWSI) implemented in this study (SWSI-1, SWSI-2, SWSI-3) and the Vogelmann red edge index yielded the best results for estimating soil salinity for independent fields with root mean square errors of 1.14, 1.15, 1.17 and 1.15 dS/m, respectively. Our results show that soil salinity could be estimated by satellite-based hyperspectral VIs, but validation of obtained models for independent data is essential for selecting the best model.     

土壤含盐量反演的研究

运用Hyperion数据,以黑龙江省大庆市某一实验区为例,开展对土壤含盐量定量提取的研究,通过对图像预处理、特征提取、建立BP神经网络模型（Back Propagation Network）等研究工作,探讨反演土壤含盐量的方法。研究结果表明：神经网络模型具有极强的线性和非线性拟合能力,模拟遥感影像特征与土壤盐分之间比较复杂的关系上有很大优势。研究结果不但为利用Hyperion数据反演土壤含盐量提供理论依据,而且还为其它地表参数的反演提供参考。     

Spectral responses of jojoba under salinity stress

Spectral reflectance characteristics of jojoba (Simmondsia chinensis (Link) Schneid.), a dioecious member of Buxaceae have been studied, especially under salinity stress. Reflectance is minimum at bands 1 and 2 (450–520 nm and 520–590 run) of visible range and maximum at bands 3 and 4 (620–680 nm and 770–860 nm) of near infrared range. At all wavelength intervals, male plants have greater reflectance than females. Reflectance in near infrared range (band 4) decreases with increasing age and leaf area index (LAI). A reverse trend occurs at band 3. Absorptance increases in visible as well as Infrared ranges with increasing salinity from control to 10 PSU of sea water concentration.     

Towards decadal soil salinity mapping using Landsat time series data

Salinization is one of the major soil problems around the world. However, decadal variation in soil salinization has not yet been extensively reported. This study exploited thirty years (1985–2015) of Landsat sensor data, including Landsat-4/5 TM (Thematic Mapper), Landsat-7 ETM+ (Enhanced Thematic Mapper Plus) and Landsat-8 OLI (Operational Land Imager), for monitoring soil salinity of the Yellow River Delta, China. The data were initially corrected for atmospheric effects, and then matched the spectral bands of EO-1 (Earth Observing One) ALI (Advanced Land Imager). Subsequently, soil salinity maps were derived with a previously developed PLSR (Partial Least Square Regression) model. On intra-annual scale, the retrievals showed that soil salinity increased in February, stabilized in March, and decreased in April. On inter-annual scale, soil salinity decreased within 1985–2000 (−0.74 g kg⁻¹/10a, p < 0.001), and increased within 2000–2015 (0.79 g kg⁻¹/10a, p < 0.001). Our study presents a new perspective for use of multiple Landsat data in soil salinity retrieval, and further the understanding of soil salinization development over the Yellow River Delta.     

Assessing the accuracy of hyperspectral and multispectral satellite imagery for categorical and Quantitative mapping of salinity stress in sugarcane fields

This study evaluates the feasibility of hyperspectral and multispectral satellite imagery for categorical and quantitative mapping of salinity stress in sugarcane fields located in the southwest of Iran. For this purpose a Hyperion image acquired on September 2, 2010 and a Landsat7 ETM+ image acquired on September 7, 2010 were used as hyperspectral and multispectral satellite imagery. Field data including soil salinity in the sugarcane root zone was collected at 191 locations in 25 fields during September 2010. In the first section of the paper, based on the yield potential of sugarcane as influenced by different soil salinity levels provided by FAO, soil salinity was classified into three classes, low salinity (1.7–3.4 dS/m), moderate salinity (3.5–5.9 dS/m) and high salinity (6–9.5) by applying different classification methods including Support Vector Machine (SVM), Spectral Angle Mapper (SAM), Minimum Distance (MD) and Maximum Likelihood (ML) on Hyperion and Landsat images. In the second part of the paper the performance of nine vegetation indices (eight indices from literature and a new developed index in this study) extracted from Hyperion and Landsat data was evaluated for quantitative mapping of salinity stress. The experimental results indicated that for categorical classification of salinity stress, Landsat data resulted in a higher overall accuracy (OA) and Kappa coefficient (KC) than Hyperion, of which the MD classifier using all bands or PCA (1–5) as an input performed best with an overall accuracy and kappa coefficient of 84.84% and 0.77 respectively. Vice versa for the quantitative estimation of salinity stress, Hyperion outperformed Landsat. In this case, the salinity and water stress index (SWSI) has the best prediction of salinity stress with an R² of 0.68 and RMSE of 1.15 dS/m for Hyperion followed by Landsat data with an R² and RMSE of 0.56 and 1.75 dS/m respectively. It was concluded that categorical mapping of salinity stress is the best option for monitoring agricultural fields and for this purpose Landsat data are most suitable.     

Estimating soil salinity in Pingluo County of China using QuickBird data and soil reflectance spectra

Soil salinization is a worldwide environmental problem with severe economic and social consequences. In this paper, estimating the soil salinity of Pingluo County, China by a partial least squares regression (PLSR) predictive model was carried out using QuickBird data and soil reflectance spectra. At first, a relationship between the sensitive bands of soil salinity acquired from measured reflectance spectra and the spectral coverage of seven commonly used optical sensors was analyzed. Secondly, the potentiality of QuickBird data in estimating soil salinity by analyzing the correlations between the measured reflectance spectra and reflectance spectra derived from QuickBird data and analyzing the contributions of each band of QuickBird data to soil salinity estimation Finally, a PLSR predictive model of soil salinity was developed using reflectance spectra from QuickBird data and eight spectral indices derived from QuickBird data. The results indicated that the sensitive bands covered several bands of each optical sensor and these sensors can be used for soil salinity estimation. The result of estimation model showed that an accurate prediction of soil salinity can be made based on the PLSR method (R² = 0.992, RMSE = 0.195). The PLSR model's performance was better than that of the stepwise multiple regression (SMR) method. The results also indicated that using spectral indices such as intensity within spectral bands (Int1, Int2), soil salinity indices (SI1, SI2, SI3), the brightness index (BI), the normalized difference vegetation index (NDVI) and the ratio vegetation index (RVI) as independent model variables can help to increase the accuracy of soil salinity mapping. The NDVI and RVI can help to reduce the influences of vegetation cover and soil moisture on prediction accuracy. The method developed in this paper can be applied in other arid and semi-arid areas, such as western China.     

Predicting salinity impacts of land-use change: Groundwater modelling with airborne electromagnetics and field data,SE Queensland,Australia

Throughout Australia, there is concern that land use change is mobilizing salt stored in the landscape, causing salinity in soil and water resources. Salt in the landscape becomes a salinity risk only if it is mobilized by groundwater movement. A combination of modelled groundwater behaviour under various land uses with three-dimensional salt-load maps developed from airborne electromagnetic survey (AEM) provides a practical tool to assess potential salt movement.AEM survey of the country around St. George, SE Queensland, revealed a potential salinity threat: significant salt stores in the uplands adjacent to flood plains which support important irrigation developments and which drain to the Darling River system. A conceptual model of the regional hydrogeology was built upon three-dimensional AEM data, an investigation-drilling program, and direct field measurement of hydraulic conductivities. This information was incorporated in a Flowtube groundwater model and groundwater responses to five different land management options were tested over a 100-year period. Surface water storage on relatively permeable soils and continuous irrigated cotton both resulted in water tables reaching the soil surface; rain-fed wheat and pasture both resulted in a raised watertable, but both established a new equilibrium without the water table reaching the ground surface.     

基于局部体样条函数的海洋盐度场建模

海洋盐度是海洋水文要素的重要组成部分,是海洋战场环境研究的重点。在介绍海水盐度数据特点的基础上,采用局部体样条函数法对散乱的大规模盐度场标量数据进行建模,并利用遗传算法和非线性规划相结合的方法解决体样条函数法的区域划分问题。通过一组实测数据进行建模实验并将得到的数据场进行可视化,表明该方法适用于大规模散乱数据场的建模,可为海洋战场环境的数据分析和场景构建提供建模手段。     

Assessing soil salinity using WorldView-2 multispectral images in Timpaki,Crete, Greece

Salinization is one of the major soil degradation threats occurring worldwide. This study evaluates the feasibility of operational surface soil salinity mapping based on state-of-the-art Earth Observation (EO) products captured by sensors on-board WorldView-2 (WV2) and Landsat 8 satellites. The proposed methods are tested in Timpaki, south-central Crete,Greece, where brackish water irrigation puts soil health at risk of soil salinization. In all cases, EO products are calibrated against soil samples collected from bare soil locations. Results indicate a moderate correlation of observed EC_e values with the investigated remote sensing parameters. Regarding sensitivity to saline soil, the yellow band displays higher values. Comparison between methods used in the literature shows that those developed specifically for soil salinity, and especially index S5, perform better. The proposed ‘detection index’ and 3D PCA transformation methodology perform reasonably well in detecting areas with high EC_e values and provide a simple and effective operational alternative for saline topsoil detection and mapping.     

Soil salinity and vegetation cover change detection from multi-temporal remotely sensed imagery in Al Hassa Oasis in Saudi Arabia

Detecting soil salinity changes and its impact on vegetation cover are necessary to understand the relationships between these changes in vegetation cover. This study aims to determine the changes in soil salinity and vegetation cover in Al Hassa Oasis over the past 28 years and investigates whether the salinity change causing the change in vegetation cover. Landsat time series data of years 1985, 2000 and 2013 were used to generate Normalized Difference Vegetation Index (NDVI) and Soil Salinity Index (SI) images, which were then used in image differencing to identify vegetation and salinity change/no-change for two periods. Soil salinity during 2000–2013 exhibits much higher increase compared to 1985–2000, while the vegetation cover declined to 6.31% for the same period. Additionally, highly significant (p < 0.0001) negative relationships found between the NDVI and SI differencing images, confirmed the potential long-term linkage between the changes in soil salinity and vegetation cover.     

Environmental geophysics mapping salinity and water resources

Salinity and fresh water are two sides of the same coin, most conveniently measured by electrical conductivity; they can now be mapped rapidly in three dimensions using airborne electromagnetics (AEM). Recent developments in the calibration of airborne data against in-field measurements and additional information from radiometrics, magnetics and digital elevation models lend new insights into salinity, groundwater flow systems and water resources. Freshwater resources can be mapped, and salinity risk and the outcome of management interventions may be forecast, on the basis of the specific architecture of complete groundwater flow systems-enabling practical, cost-effective protection and development of water resources.     

Monitoring of salt affected soils — A case study using aerial photographs,salyut‐7 space photographs,and landsat TM data

Soils of part of Ukai‐kakarapar Command area, Gujarat (India) have been mapped at 1:25, 000 scale using aerial photographs of December 1977. It was observed that about 36.3% of the area was affected by soil salinity/alkalinity. The test area has been remapped using Salyut‐7 space photographs taken during Indo‐Soviet joint flight in April, 1984. The area affected by soil salinity/alkalinity was found to be substantially higher (80.3%). The earlier mapping using aerial photographs was done when the soil surface was compartively moist (December 1977) as compared to date of Salyut‐7 photography (April 1984), when the soil surface was likely to be devoid of moisture and the salts moved to the surface. To have easy comparision with the map prepared by using aerial photographs, Landsat TM data of December, 1985 was used in which 45.7% of the total area was mapped as salt affected. The extent of area delineated using Landsat TM was higher than that of 1977 but much lesser than the area delineated using Salyut‐7 (MKF‐6M) photographs. This indicated that the increase in the extent of salt affected area in the map prepared using the MKF‐6M photographs might be partly due to actual increase in the salinity/alkalinity and partly due to the seasonal affects. Among the various bands of MKF‐6M, band ‐4 was found to be the best for delineating the salt affected soils. The boundaries were sharper in the FCC and band No.4 of MKF‐6M than in the aerial photographs.     

Visual discrimination of surface features of salt affected soils using satellite images in arid region of Rajasthan (India)

The study has been carried for visual discrimination of natural salt affected soils on FCC images of IRS 1 B in Pali district of Rajasthan. The salt affected soils show wide variations in salinity (EC_2.53.7 to 28 dSm^-1), alkalinity (pH 8.5-9.8), cover ofP. juliflora (10-90%), salt tolerant grasses (10–55%) and gravelly surface (20–35%). ThoughP. juliflora and grasses were present at most of the observation points their cover decreased with soil EC_2.5 values more than 10 and 13 dSm^-1, respectively. Five darkness categories derived as the result of visual interpretation of FCCs; and ground and laboratory studies revealed that the darkness category 1 represented fewer plant community with high salinity (EC 28.7 dSm^-1) and gravelly surface, categories 2 and 3 were characterised by grass cover and moderate salt affected soils (EC 3-10 dSm^-1) whereas category 4 was dominated by thicket ofP. juliflora. The derived numerical darkness categories of the FCC images were slightly low for February images. The darkness values of observation pixel on February images correlated positively withP. juliflora cover and negatively with grass cover and soil pH indicating that surface features on FCC were related with the immediate observation pixels.     

High-resolution airborne infrared measurements of ocean skin temperature

Airborne measurements of ocean skin temperature T/sub s/ are presented from the Coupled Boundary Layers, Air-Sea Transfer in Low Winds (CBLAST-Low) Pilot Experiment in August 2001 off Martha's Vineyard, MA. We used an infrared (IR) camera with a spatial resolution of 1 m or less and temperature resolution of roughly 0.02/spl deg/C. Using subframe sampling of the IR imagery, we achieve lower noise and higher spatial resolution than reported by previous investigators using IR radiometers. Fine-scale maps of T/sub s/ exhibit horizontal variability over spatial scales ranging from O(10 km) down to O(1 m) that are related to atmospheric and subsurface phenomena under low to moderate wind conditions. Based on supporting measurements of wind and waves, we identify coherent ramp-like structures in T/sub s/ with stratification breakdown and meandering streaky features with internal waves. Regional maps of T/sub s/ show the standard deviation for the region is /spl plusmn/1.04/spl deg/C, while the meridional and zonal variability is 0.23/spl deg/C /spl middot/ km/sup -1/ and 0.27/spl deg/C /spl middot/ km/sup -1/, respectively. This temperature variability results in meridional and zonal scalar heat flux variability of 7.0 W /spl middot/ m/sup -2/ /spl middot/ km/sup -1/ and 7.6 W /spl middot/ m/sup -2/ /spl middot/ km/sup -1/, respectively. Our results demonstrate the potential for airborne IR imagery accompanied by high-quality ocean data to identify T/sub s/ features produced by subsurface circulation.     

Sea Surface Height Variability in the Tropical Indian Ocean: Steric Contribution

Variability of Sea level and its steric contribution in the Tropical Indian Ocean (TIO) was studied based on 15?years (1993?C2007) satellite altimeter observations of sea surface height (SSH) anomaly and steric height (STH) anomaly computed using temperature and salinity fields obtained from Simple Ocean Data Assimilation (SODA) product. Complex Empirical Orthogonal Function (CEOF) analysis was carried out to decompose variability of SSH and STH into various modes to examine the coherency between them. It is revealed that both the parameters exhibit variability in all the time scales. First three major modes of CEOF corresponds to 90% and 84% of the total variability of SSH and STH respectively. There exists strong coherence between the respective CEOF modes of SSH and STH. The first mode of CEOF contributes around ~50% of the total signal corresponds to the annual cycle exhibit large variability in the western Arabian Sea along the Somali and Arabia Coast, latitudinal strip between 2 and 10°N extending from Somali-coast to the west coast of India, coastal oceans around India, and the south eastern TIO. The second CEOF with 25% of total signal contains mixed signature of intra-seasonal and inter-annual periodicities. This exhibit large amplitude in the central south TIO, western and eastern parts of Equatorial Indian Ocean (EIO). Computed long term linear growth rate of sea level anomaly suggests that increase of sea level varies from small (1?C3?mm?yr^?1) in the north TIO to large (8?mm?yr^?1) in the south TIO. Further analysis suggests that SSH trend in the south TIO was mostly governed by steric contribution while the variability of SSH trend in the north TIO could be explained partially by the variability in STH.     

Use oe aerial photograph for study of soil salinity and landscape relationship in parts of Haryana

In order to know the development of soil salinity and alkalinity in relation to landform, a systematic approach has been adopted by using aerial photo-interpretation techniques followed by field checks. The major land form units identified in the area are (i) alluvial upland (ii) low land (iii) back-swamp areas (iv) remanent horstland (v) interfiuve (vi) abandoned channel (vii) plain undifferentiated and (viii) plain with aeolian activity. salt accumulation is pronounced in landform units (i), (ii), (iii) and (iv) due to configurating slopes and basement ridge control, whereas in landform units (v) and (vi), a patchy distribution of salinity has been found. Landform units (vii) and (viii) are free from this hazard The elements of landscape have controlled the distriburion of salts within each landscape.     

藏东南冰川地区250 m空间分辨率全天候地表温度生成

遥感全天候地表温度产品在多云雾地区意义重大,对冰川泥石流多发的藏东南地区极具应用价值,但遥感全天候地表温度空间分辨率不足限制了其在精细化灾害监测中的应用。以藏东南冰川地区为研究区,采用高程、坡度、坡向、地表覆盖类型、植被指数、地表反射率、积雪指数作为全天候地表温度的影响因子,结合移动窗口,进行多种地表温度降尺度方法的对比,进而使用最优的降尺度方法将现有的遥感全天候地表温度产品（TRIMS LST）的空间分辨率从1 km提升至250 m。利用地面站点实测数据的评价结果表明,基于梯度提升决策树（LightGBM）的降尺度方法得到的250 m空间分辨率全天候地表温度的均方根误差在白天/夜间为2.25 K/2.15 K,优于基于多元线性回归和随机森林的降尺度方法,且比原始1 km分辨率全天候地表温度的精度高0.25 K左右。基于Q指数与SIFI指数的图像质量评价结果表明,降尺度得到的250 m地表温度不仅在空间格局和幅值上与原始1 km遥感全天候地表温度一致,而且补充了大量的地表温度空间细节信息。生成得到的250 m分辨率的地表温度对于藏东南冰川地区的灾害分析具有积极的意义。