Cartographie Du Risque De Salinité Des Sols À L’aide De L’approche Des Indices Et Des Données Multi-Sources: Cas De La Plaine De Tadla Au Maroc

La salinisation est l’une des formes de dégradation des sols qui connait une extension spectaculaire et qui révèle des aspects de plus en plus inquiétants. Elle contribue à la diminution de la production agricole dans la plupart des zones irriguées du monde, notamment celles soumises à un climat aride ou semi-aride. Ce phénomène résulte des effets synergiques du climat, de la roche mère, de l’agressivité des conditions naturelles et des activités anthropiques. Dans ce contexte, la présente étude se fixe comme objectif de cartographier le risque de salinité des sols de la plaine de Tadla. Pour atteindre cet objectif, nous avons adopté l’approche de l’Indice de Risque de Salinisation des Sols (IRSS) calculé à l’aide des données multi-sources (pédologiques, climatiques, hydrologiques et de salinité). La superposition des variables mises en jeu (la conductivité électrique de l’eau de nappe, la conductivité électrique de l’eau d’irrigation, la conductivité électrique du sol, la profondeur de la nappe, l’indice d’aridité, le type de climat, la pente, la texture et l’efficacité géologique) et leur pondération ont été réalisées à l’aide des SIG. Cette opération a permis de calculer l’indice IRSS et d’élaborer la carte de risque de salinité des sols de la zone d’étude. Cette approche basée sur l’IRSS a montré la présence de trois classes de risque de salinisation : léger, modéré et sévère. La classe de risque modéré domine avec une couverture de 76% de la superficie totale. Les résultats obtenus montrent l’intérêt de cette approche pour déterminer les zones à risque de salinisation afin de mieux gérer le risque de la salinisation des sols et réduire les effets de celle-ci sur la production agricole. Salinization is one of the forms of soil degradation that is expanding dramatically and is revealing increasingly worrying aspects. It contributes to the decline in agricultural production in most of the world's irrigated areas, especially those subject to arid or semi-arid climate. This phenomenon is the result of the synergistic effects of climate, bedrock, the aggressiveness of natural conditions and anthropogenic activities. In this context, the present study focuses on the mapping of salinity risk in the soils of Tadla Plain. To achieve this objective, we adopted the approach of the Soil Salinity Risk Index (SSRI) calculated using data from multiple sources (pedological, climatic, hydrological and salinity). The necessary variables (electrical conductivity of groundwater, electrical conductivity of irrigation water, electrical conductivity of soil, depth to groundwater, aridity index, climate type, slope, texture and geological efficiency) were overlaid and weighted using GIS. This operation allowed to calculate the SSRI and develop the soil salinity risk map of the study area. The use of the SSRI-based approach indicates the presence of three risk classes: light, moderate and severe. The moderate risk class dominates with a coverage representing 76% of the total area. The results obtained show the prospect of this approach to delineate areas of salinization risk to manage soil salinization and reduce its effects on agricultural production

Procjena utjecaja atmosferske korekcije na podatke Landsat 8 i Sentinel-2 za određivanje saliniteta tla

Remote sensing technology effectively determines and evaluates salinity-affected areas\u27 spatial and temporal distribution. Soil salinity maps for large areas can be obtained with low cost and low effort using remote sensing methods and techniques. Remote sensing data are delivered raw as Level-1 data, and they can be further atmospherically corrected to surface reflectance values, Level-2 data. This study evaluates the atmospheric correction impact on Landsat 8 and Sentinel-2 data for soil salinity determination. The study has been supported with in-situ measurements in Alpu, Eskisehir, Turkey, where samples were collected from various agricultural fields simultaneously with the overpass of the satellites. Two different analysis cases have been used to determine the effect of atmospheric correction. The first is to examine the relationship between the measurements taken from the areas with mixed product groups and the salinity indices for both data types. The other is to investigate the relationship between the measurement values taken only from the wheat and beet groups and the salinity index values. The results show that atmospheric correction has a high effect on the relationship between spectral indices and in situ salinity measurement values. Especially in all cases examined in Landsat, it was observed that atmospheric correction led to an improvement of over 140%, while nearly 50% was observed in Sentinel on a product basis.Uz pomoć tehnologije daljinskih istraživanja učinkovito se određuje i procjenjuje prostorna i vremenska rasprostranjenost područja zahvaćenih salinitetom. Karte saliniteta tla za velika područja mogu se izraditi uz niske troškove i malo truda koristeći metode i tehnike daljinskih istraživanja. Podaci dobiveni daljinskim istraživanjima isporučuju se neobrađeni kao podaci Level-1 te se zatim mogu atmosferski korigirati na vrijednosti površinske refleksije, podaci Level-2. Ova studija procjenjuje utjecaje atmosferske korekcije na podatke Landsat 8 i Sentinel-2 za određivanje saliniteta tla. Studija je potkrijepljena mjerenjima in situ u Alpu, Eskisehir, Turska, gdje su uzorci bili prikupljeni na različitim poljoprivrednim poljima istovremeno s preletima satelita. Upotrijebljene su dvije različite analize kako bi se odredio učinak atmosferske korekcije. Prva je analiza primijenjena kako bi se ispitao odnos između mjerenja provedenih na područjima s miješanim skupinama proizvoda i indeksima saliniteta za obje vrste podataka. Druga je analiza primijenjena kako bi se istražio odnos između vrijednosti mjerenja dobivenih samo iz skupina pšenice i repe te vrijednosti indeksa saliniteta. Rezultati pokazuju da atmosferska korekcija ima visok učinak na odnos između spektralnih indeksa i vrijednosti mjerenja saliniteta in situ. Posebno se u svim slučajevima ispitivanja putem Landsata moglo primijetiti da je atmosferska korekcija dovela do poboljšanja za više od 140%, dok je gotovo 50% primijećeno za Sentinel na temelju proizvoda

Remote sensing approaches and mapping methods for monitoring soil salinity under different climate regimes

Soil salinization is one of the severe land-degradation problems due to its adverse effects on land productivity. Each year several hectares of lands are degraded due to primary or secondary soil salinization, and as a result, it is becoming a major economic and environmental concern in different countries. Spatio-temporal mapping of soil salinity is therefore important to support decisionmaking procedures for lessening adverse effects of land degradation due to the salinization. In that sense, satellite-based technologies provide cost effective, fast, qualitative and quantitative spatial information on saline soils. The main objective of this work is to highlight the recent remote sensing (RS) data and methods to assess soil salinity that is a worldwide problem. In addition, this study indicates potential linkages between salt-affected land and the prevailing climatic conditions of the case study areas being examined. Web of science engine is used for selecting relevant articles. "Soil salinity" is used as the main keyword for finding "articles" that are published from January 1, 2007 up to April 30, 2018. Then, 3 keywords; "remote sensing", "satellite" and "aerial" were used to filter the articles. After that, 100 case studies from 27 different countries were selected. Remote sensing based researches were further overviewed regarding to their location, spatial extent, climate regime, remotely sensed data type, mapping methods, sensing approaches together with the reason of salinity for each case study. In addition, soil salinity mapping methods were examined to present the development of different RS based methods with time. Studies are shown on the Köppen-Geiger climate classification map. Analysis of the map illustrates that 63% of the selected case study areas belong to arid and semi-arid regions. This finding corresponds to soil characteristics of arid regions that are more susceptible to salinization due to extreme temperature, high evaporation rates and low precipitation

Procjena utjecaja atmosferske korekcije na podatke Landsat 8 i Sentinel-2 za određivanje saliniteta tla

Remote sensing technology effectively determines and evaluates salinity-affected areas\u27 spatial and temporal distribution. Soil salinity maps for large areas can be obtained with low cost and low effort using remote sensing methods and techniques. Remote sensing data are delivered raw as Level-1 data, and they can be further atmospherically corrected to surface reflectance values, Level-2 data. This study evaluates the atmospheric correction impact on Landsat 8 and Sentinel-2 data for soil salinity determination. The study has been supported with in-situ measurements in Alpu, Eskisehir, Turkey, where samples were collected from various agricultural fields simultaneously with the overpass of the satellites. Two different analysis cases have been used to determine the effect of atmospheric correction. The first is to examine the relationship between the measurements taken from the areas with mixed product groups and the salinity indices for both data types. The other is to investigate the relationship between the measurement values taken only from the wheat and beet groups and the salinity index values. The results show that atmospheric correction has a high effect on the relationship between spectral indices and in situ salinity measurement values. Especially in all cases examined in Landsat, it was observed that atmospheric correction led to an improvement of over 140%, while nearly 50% was observed in Sentinel on a product basis.Uz pomoć tehnologije daljinskih istraživanja učinkovito se određuje i procjenjuje prostorna i vremenska rasprostranjenost područja zahvaćenih salinitetom. Karte saliniteta tla za velika područja mogu se izraditi uz niske troškove i malo truda koristeći metode i tehnike daljinskih istraživanja. Podaci dobiveni daljinskim istraživanjima isporučuju se neobrađeni kao podaci Level-1 te se zatim mogu atmosferski korigirati na vrijednosti površinske refleksije, podaci Level-2. Ova studija procjenjuje utjecaje atmosferske korekcije na podatke Landsat 8 i Sentinel-2 za određivanje saliniteta tla. Studija je potkrijepljena mjerenjima in situ u Alpu, Eskisehir, Turska, gdje su uzorci bili prikupljeni na različitim poljoprivrednim poljima istovremeno s preletima satelita. Upotrijebljene su dvije različite analize kako bi se odredio učinak atmosferske korekcije. Prva je analiza primijenjena kako bi se ispitao odnos između mjerenja provedenih na područjima s miješanim skupinama proizvoda i indeksima saliniteta za obje vrste podataka. Druga je analiza primijenjena kako bi se istražio odnos između vrijednosti mjerenja dobivenih samo iz skupina pšenice i repe te vrijednosti indeksa saliniteta. Rezultati pokazuju da atmosferska korekcija ima visok učinak na odnos između spektralnih indeksa i vrijednosti mjerenja saliniteta in situ. Posebno se u svim slučajevima ispitivanja putem Landsata moglo primijetiti da je atmosferska korekcija dovela do poboljšanja za više od 140%, dok je gotovo 50% primijećeno za Sentinel na temelju proizvoda

Метод автоматизации оценки индексов подстилающей поверхности и их изменения во времени по космическим изображениям и его применение при оценке состояния окружающей среды в окрестности полигонов твердых бытовых отходов

Актуальность работы обусловлена упрощением процедур получения и обработки космических изображений посредством автоматизации обработки и расчета тех или иных индексов подстилающей поверхности и их временных изменений. Предлагаемый метод позволяет с высокой скоростью и в широких масштабах обнаруживать признаки деградации почвы, в частности, объекты захоронения отходов размера, кратного пространственному разрешению снимков, расчетом соответствующих индексов подстилающей поверхности. Основной недостаток действующих методов детектирования объектов захоронения отходов состоит в том, что их поиск и обнаружение, оценка состояния окружающей среды осуществляются наземными «ручными» методами. Все это и, прежде всего, само выявление объектов дает низкую производительность работы в области мониторинга свалок. В результате внедрения метода автоматизации детектирования объектов захоронения отходов может быть существенно улучшена защита территории от воздействия негативных факторов. Данные факторы, в частности фактор замусоривания окружающей среды, с точки зрения космических изображений могут быть выражены в виде различных индексов подстилающей поверхности. Цель: описание метода автоматического получения индексов подстилающей поверхности и их временных рядов с использованием космических изображений, привязанных к географической проекции UTM, и базы их метаданных. Методы исследования: методы получения масок облачности (метод пороговой фильтрации) и регрессионного анализа (метод среднеквадратического отклонения) для проведения темпоральной обработки. Результаты. Приведен алгоритм автоматической обработки, и описаны основные шаги его работы. Обозначена модель пересчета параметров изменения индексов подстилающей поверхности при добавлении новых значений коэффициентов спектральной яркости или удалении прежних. Работа алгоритма показана на примере расчета временных рядов NDVI заданной области наблюдения и применения метода для анализа данных обработки, в частности, автоматически просчитаны изображения NDVI окрестности полигона ТБО Торбеево Люберецкого района за период 2003-2011 гг. Проиллюстрированы структурные изменения биопродуктивности почвы в окрестности 4 муниципальных свалок городского округа Железнодорожный Московского региона: Кучино, Саввино, Лисьи Горы и Некрасовка.The relevance of the work due to the simplification of the procedures for receiving and processing satellite images through processing automation and calculation of various indices of the underlying surface, and their temporal changes. The proposed method allows a high speed, and on a large scale to detect signs of soil degradation, in particular, the size of the waste disposal facilities, the multiple spatial resolution images, calculating respective indices underlying surface. The main drawback of the existing detection methods of waste disposal sites is that their search and discovery, environmental assessment carried out by ground, «manual» methods. All this is primarily self identifying objects gives low productivity in landfills monitoring. As a result of the automation of security detection method of waste disposal facilities can be significantly improved protection of the territory from the impact of negative factors. These factors, inter alia, debris environmental factor, in terms of space images may be expressed in a variety of indices of the underlying surface. The main aim of the study is to develop a method for obtaining automatically the underlying surface indices and time series using satellite images linked to UTM geographical projection and their metadata. The methods used in the study: methods of getting cloud masks (threshold filtering method) and regression analysis (method of standard deviation) for temporal processing. The results. The paper introduces the algorithm of automatic data processing and describes the main steps of its operation. The authors define the model of conversion parameters of underlying surface indices change when adding new values ??of the spectral brightness coefficients or deleting the previous ones. The algorithm operation is shown by the example of calculation of NDVI time-series of the given observation area and their application to the analyze the processing data, in particular, the NDVI images are automatically calculated in neighborhood landfill of Torbeevo Lyubertsky district for 2003-2011. The paper illustrates structural changes in soil bio-productivity within four municipal landfills of Zheleznodorozhny urban district in Moscow region: Kuchino, Savvino, Licyi Gory, Nekrasovka

Review of soil salinity assessment for agriculture across multiple scales using proximal and/or remote sensors

Mapping and monitoring soil spatial variability is particularly problematic for temporally and spatially dynamic properties such as soil salinity. The tools necessary to address this classic problem only reached maturity within the past 2 decades to enable field- to regional-scale salinity assessment of the root zone, including GPS, GIS, geophysical techniques involving proximal and remote sensors, and a greater understanding of apparent soil electrical conductivity (ECa) and multi- and hyperspectral imagery. The concurrent development and application of these tools have made it possible to map soil salinity across multiple scales, which back in the 1980s was prohibitively expensive and impractical even at field scale. The combination of ECa-directed soil sampling and remote imagery has played a key role in mapping and monitoring soil salinity at large spatial extents with accuracy sufficient for applications ranging from field-scale site-specific management to statewide water allocation management to control salinity within irrigation districts. The objective of this paper is: (i) to present a review of the geophysical and remote imagery techniques used to assess soil salinity variability within the root zone from field to regional scales; (ii) to elucidate gaps in our knowledge and understanding of mapping soil salinity; and (iii) to synthesize existing knowledge to give new insight into the direction soil salinity mapping is heading to benefit policy makers, land resource managers, producers, agriculture consultants, extension specialists, and resource conservation field staff. The review covers the need and justification for mapping and monitoring salinity, basic concepts of soil salinity and its measurement, past geophysical and remote imagery research critical to salinity assessment, current approaches for mapping salinity at different scales, milestones in multi-scale salinity assessment, and future direction of field- to regional-scale salinity assessment

Are Global Environmental Uncertainties Inevitable? Measuring Desertification for the SDGs

Continuing uncertainty about the present magnitudes of global environmental change phenomena limits scientific understanding of human impacts on Planet Earth, and the quality of scientific advice to policy makers on how to tackle these phenomena. Yet why global environmental uncertainties are so great, why they persist, how their magnitudes differ from one phenomenon to another, and whether they can be reduced is poorly understood. To address these questions, a new tool, the Uncertainty Assessment Framework (UAF), is proposed that builds on previous research by dividing sources of environmental uncertainty into categories linked to features inherent in phenomena, and insufficient capacity to conceptualize and measure phenomena. Applying the UAF shows that, based on its scale, complexity, areal variability and turnover time, desertification is one of the most inherently uncertain global environmental change phenomena. Present uncertainty about desertification is also very high and persistent: the Uncertainty Score of a time series of five estimates of the global extent of desertification shows limited change and has a mean of 6.8, on a scale from 0 to 8, based on the presence of four conceptualization uncertainties (terminological difficulties, underspecification, understructuralization and using proxies) and four measurement uncertainties (random errors, systemic errors, scalar deficiencies and using subjective judgment). This suggests that realization of the Land Degradation Neutrality (LDN) Target 15.3 of the UN Sustainable Development Goal (SDG) 15 (“Life on Land”) will be difficult to monitor in dry areas. None of the estimates in the time series has an Uncertainty Score of 2 when, according to the UAF, evaluation by statistical methods alone would be appropriate. This supports claims that statistical methods have limitations for evaluating very uncertain phenomena. Global environmental uncertainties could be reduced by devising better rules for constructing global environmental information which integrate conceptualization and measurement. A set of seven rules derived from the UAF is applied here to show how to measure desertification, demonstrating that uncertainty about it is not inevitable. Recent review articles have advocated using ‘big data’ to fill national data gaps in monitoring LDN and other SDG 15 targets, but an evaluation of a sample of three exemplar studies using the UAF still gives a mean Uncertainty Score of 4.7, so this approach will not be straightforward

Mapping Soil Salinity and Its Impact on Agricultural Production in Al Hassa Oasis in Saudi Arabia

Soil salinity is considered as one of the major environmental issues globally that restricts agricultural growth and productivity, especially in arid and semi-arid regions. One such region is Al Hassa Oasis in the eastern province of Saudi Arabia, which is one of the most productive date palm (Phoenix dactylifera L.) farming regions in Saudi Arabia and is seriously threatened by soil salinity. Development of remote sensing techniques and modelling approaches that can assess and map soil salinity and the associated agricultural impacts accurately and its likely future distribution should be useful in formulating more effective, long-term management plans. The main objective of this study was to detect, assess and map soil salinity and and its impact on agricultural production in the Al Hassa Oasis. The presented research first started by reviewing the related literature that have utilized the use of remote sensing data and techniques to map and monitor soil salinity. This review started by discussing soil salinity indicators that are commonly used to detect soil salinity. Soil salinity can be detected either directly from the spectral reflectance patterns of salt features visible at the soil surface, or indirectly using the vegetation reflectance since it impacts vegetation. Also, it investigated the most commonly used remote sensors and techniques for monitoring and mapping soil salinity in previous studies. Both spectral vegetation and salinity indices that have been developed and proposed for soil salinity detection and mapping have been reviewed. Finally, issues limiting the use of remote sensing for soil salinity mapping, particularly in arid and semi-arid regions have been highlighted. In the second study, broadband vegetation and soil salinity indices derived from IKONOS images along with ground data in the form of soil samples from three sites across the Al Hassa Oasis were used to assess soil salinity in the Al-Hassa Oasis. The effectiveness of these indices to assess soil salinity over a dominant date palm region was examined statistically. The results showed that very strongly saline soils with different salinity level ranges are spread across the three sites in the study area. Among the investigated indices, the Soil Adjusted Vegetation Index (SAVI), Normalized Differential Salinity Index (NDSI) and Salinity Index (SI-T) yielded the best results for assessing the soil salinity in densely vegetated area, while NDSI and SI-T revealed the highest significant correlation with salinity for less densely vegetated lands and bare soils. In the third study, combined spectral-based statistical regression models were developed using IKONOS images to model and map the spatial variation of the soil salinity in the Al Hassa Oasis. Statistical correlation between Electrical Conductivity (EC), spectral indices and IKONOS original bands showed that the Salinity Index (SI) and red band (band 3) had the highest correlation with EC. Integrating SI and band 3 into one model produced the best fit with R2 = 0.65. The high performance of this combined model is attributed to: (i) the spatial resolution of the images; (ii) the great potential of SI in enhancing and delineating the spatial variation of soil salinity; and (iii) the superiority of band 3 in retrieving soil salinity features and patterns. Soil salinity maps generated using the selected model showed that strongly saline soils (>16 dS/m) with variable spatial distribution were the dominant class over the study area. The spatial variability of this class over the investigated areas was attributed to a variety factors, including soil factors, management related factors and climate factors.16 dS/m) with variable spatial distribution were the dominant class over the study area. The spatial variability of this class over the investigated areas was attributed to a variety factors, including soil factors, management related factors and climate factors. In the fourth study, Landsat time series data of years 1985, 2000 and 2013 were used to detect the temporal change in soil salinity and vegetation cover in the Al Hassa Oasis and investigate whether there is any linkage of vegetation cover change to the change in soil salinity over a 28-year period. Normalized Difference Vegetation Index (NDVI) and Soil Salinity Index (SI) differencing images were used to identify vegetation and salinity change/no-change for the two periods. The results revealed that soil salinity during 2000-2013 exhibited much higher increase compared to 1985-2000, while the vegetation cover declined for the same period. Highly significant (p In the fifth study, the effects of physical and proximity factors, including elevation, slope, soil salinity, distance to water, distance to built-up areas, distance to roads, distance to drainage and distance to irrigation factors on agricultural expansion in the Al Hassa Oasis were investigated. A logistic regression model was used for two time periods of agricultural change in 1985 and 2015. The probable agricultural expansion maps based on agricultural changes in 1985 was used to test the performance of the model to predict the probable agricultural expansion after 2015. This was achieved by comparing the probable maps of 1985 and the actual agricultural land of 2015 model. The Relative Operating Characteristic (ROC) method was also used and together these two methods were used to validate the developed model. The results showed that the prediction model of 2015 provides a reliable and consistent prediction based on the performance of 1985. The logistic regression results revealed that among the investigated factors, distance to water, distance to built-up areas and soil salinity were the major factors having a significant influence on agricultural expansion. In the last study, the potential distribution of date palm was assessed under current and future climate scenarios of 2050 and 2100. Here, CLIMEX (an ecological niche model) and two different Global Climate Models (GCMs), CSIRO-Mk3.0 (CS) and MIROC-H (MR), were employed with the A2 emission scenario to model the potential date palm distribution under current and future climates in Saudi Arabia. A sensitivity analysis was conducted to identify the CLIMEX model parameters that had the most influence on date palm distribution. The model was also run with the incorporation of six non-climatic parameters, which are soil taxonomy, soil texture, soil salinity, land use, landform and slopes, to further refine the distributions. The results from both GCMs showed a significant reduction in climatic suitability for date palm cultivation in Saudi Arabia by 2100 due to increment of heat stress. The lower optimal soil moisture, cold stress temperature threshold and wet stress threshold parameters had the greatest impact on sensitivity, while other parameters were moderately sensitive or insensitive to change. A more restricted distribution was projected with the inclusion of non-climatic parameters. Overall, the research demonstrated the potential of remote sensing and modeling techniques for assessing and mapping soil salinity and providing the essential information of its impacts on date palm plantation. The findings provide useful information for land managers, environmental decision makers and governments, which may help them in implementing more suitable adaptation measures, such as the use of new technologies, management practices and new varieties, to overcome the issue of soil salinity and its impact on this important economic crop so that long-term sustainable production of date palm in this region can be achieved. Additionally, the information derived from this research could be considered as a useful starting point for public policy to promote the resilience of agricultural systems, especially for smallholder farmers who might face more challenges, if not total loss, not only due to soil salinity but also due to climate change

Sustainable Agriculture and Advances of Remote Sensing (Volume 1)

Agriculture, as the main source of alimentation and the most important economic activity globally, is being affected by the impacts of climate change. To maintain and increase our global food system production, to reduce biodiversity loss and preserve our natural ecosystem, new practices and technologies are required. This book focuses on the latest advances in remote sensing technology and agricultural engineering leading to the sustainable agriculture practices. Earth observation data, in situ and proxy-remote sensing data are the main source of information for monitoring and analyzing agriculture activities. Particular attention is given to earth observation satellites and the Internet of Things for data collection, to multispectral and hyperspectral data analysis using machine learning and deep learning, to WebGIS and the Internet of Things for sharing and publishing the results, among others