Modeling and Mapping of Salt-Affected Soils through Spectral Indices in Inland Plains of Semi-arid Agro-Ecological Region

<p>Arid and semiarid regions represent about 47% of the total land area of the world and around 40% of the world&#8217;s food is produced there. In these areas, soil salinization is an emerging problem due to unsustainable land management practices and climate change. However, the use of sustainable land management practices in salt-affected soils can offset the negative effects of salinization and increase soil carbon stocks. In a Citrus tree orchard under semi-arid climate conditions, we evaluated the effect of (i) intensive tillage along with flood irrigation (IT); (ii) combination of no-tillage with pruning residues (branches and leaves) as mulch, and drip-irrigation (NT+PM); and (iii) combination of reduced tillage with the incorporation of pruning residues and drip-irrigation (RT+PI), on aggregate stability, amount and quality of organic matter fractions and soil organic carbon (OC) sequestration. Our results showed that the incorporation of pruning residues through reduced tillage decreased bulk density and salinity while soil porosity, soil OC and N stocks, and percentage of OC-rich macroaggregates increased compared to the IT system.&#160; However, the positive effects of the NT+PM system on soil properties were limited to the topsoil. The IT management system showed the highest values of bulk density and salinity and lower amounts of macroaggregates and soil OC stocks. In conclusion, the combination of pruning residues through the reduced tillage and drip-irrigation was the most effective systems to improve soil structure and OC sequestration and reduced the salt content under Citrus tree orchard in semi-arid soils</p>

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SPECTRAL INDICES OF VEGETATION TO CAATINGA OF THE AREA OF SEMI-ARID OF RIO GRANDE OF NORTE, BRAZIL

Journal of Hyperspectral Remote Sensing ◽

10.5935/2237-2202.20120002 ◽

2012 ◽

Vol 2 (2) ◽

pp. 10-24

Author(s):

Joel Medeiros Bezerra ◽

Rochele Sheila Vasconcelos ◽

Geber Barbosa de Albuquerque Moura ◽

José Espínola Sobrinho

Keyword(s):

Rio Grande ◽

Spectral Indices ◽

Semi Arid

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An Assessment of Surface Water Detection Methods for Water Resource Management in the Nigerien Sahel

Sensors ◽

10.3390/s20020431 ◽

2020 ◽

Vol 20 (2) ◽

pp. 431 ◽

Cited By ~ 4

Author(s):

Kelsey Herndon ◽

Rebekke Muench ◽

Emil Cherrington ◽

Robert Griffin

Keyword(s):

Remote Sensing ◽

Surface Water ◽

Decision Tree ◽

Water Bodies ◽

Spectral Indices ◽

Dynamic Surface ◽

Tree Methods ◽

Global Surface ◽

Decision Tree Methods ◽

Semi Arid

Water is a scarce, but essential resource in the Sahel. Rainfed ephemeral ponds and lakes that dot the landscape are necessary to the livelihoods of smallholder farmers and pastoralists who rely on these resources to irrigate crops and hydrate cattle. The remote location and dispersed nature of these water bodies limits typical methods of monitoring, such as with gauges; fortunately, remote sensing offers a quick and cost-effective means of regularly measuring surface water extent in these isolated regions. Dozens of operational methods exist to use remote sensing to identify waterbodies, however, their performance when identifying surface water in the semi-arid Sahel has not been well-documented and the limitations of these methods for the region are not well understood. Here, we evaluate two global dynamic surface water datasets, fifteen spectral indices developed to classify surface water extent, and three simple decision tree methods created specifically to identify surface water in semi-arid environments. We find that the existing global surface water datasets effectively minimize false positives, but greatly underestimate the presence and extent of smaller, more turbid water bodies that are essential to local livelihoods, an important limitation in their use for monitoring water availability. Three of fifteen spectral indices exhibited both high accuracy and threshold stability when evaluated over different areas and seasons. The three simple decision tree methods had mixed performance, with only one having an overall accuracy that compared to the best performing spectral indices. We find that while global surface water datasets may be appropriate for analysis at the global scale, other methods calibrated to the local environment may provide improved performance for more localized water monitoring needs.

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Organic matter dynamics along a salinity gradient in Siberian steppe soils

10.5194/bg-2017-53 ◽

2017 ◽

Author(s):

Norbert Bischoff ◽

Robert Mikutta ◽

Olga Shibistova ◽

Reiner Dohrmann ◽

Daniel Herdtle ◽

...

Keyword(s):

Organic Matter ◽

Microbial Community ◽

Community Composition ◽

Salinity Gradient ◽

Microbial Community Composition ◽

Arid Ecosystems ◽

Sodic Soils ◽

Kulunda Steppe ◽

Salt Affected Soils ◽

Semi Arid

Abstract. Salt-affected soils will become increasingly important in the next decades as arid and semi-arid ecosystems are predicted to expand as a result of climate change. Nevertheless, little is known about organic matter (OM) dynamics in these soils, though OM is largely controlling soil fertility and represents an important C sink. We aimed at investigating OM dynamics along a salinity and sodicity gradient in soils of the south-western Siberian Kulunda steppe (Kastanozem, Non-sodic Solonchak, Sodic Solonchak) by assessing the organic carbon (OC) stocks, the quantity and quality of particulate and mineral-associated OM in terms of non-cellulosic neutral sugar contents and carbon isotopes (δ13C, 14C activity), and the microbial community composition based on phospholipid fatty acid (PLFA) patterns. Our hypotheses were that (i) soil OC stocks decrease along the salinity gradient, (ii) the proportion and stability of particulate OM is larger in salt-affected Solonchaks as compared to non-salt-affected Kastanozems, and (iii) sodicity reduces the proportion and stability of mineral-associated OM. Against our first hypothesis, OC stocks increased along the salinity gradient with most pronounced differences between topsoils. In contrast to our second hypothesis, the proportion of particulate OM was unaffected by salinity, thereby accounting for only 90 %. Isotopic data (δ13C, 14C activity) and neutral sugars in the OM fractions indicated a comparable degree of OM transformation along the salinity gradient, thus particulate OM was not more persistent under saline conditions. This we attribute to a resilient microbial community composition and function, which was nearly unaffected by salt occurrence, and capable of decomposing OM at a similar rate in salt-affected and non-salt-affected soils. Also our third hypothesis was rejected, as saline-sodic soils contained more than twice as much mineral-bound OC than non-salt-affected soils, what we ascribe to the flocculation of OM and mineral components under higher ionic strength conditions. We conclude that salt-affected soils contribute significantly to the OC storage in the semi-arid soils of the Kulunda steppe while most of the OC is associated to minerals and therefore effectively sequestered in the long-term.

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Applying Multi-Index approach from Sentinel-2 Imagery to Extract Urban Area in dry season (Semi-Arid Land in North East Algeria)

Revista de Teledetección ◽

10.4995/raet.2020.13787 ◽

2020 ◽

pp. 89

Author(s):

K. Rouibah ◽

M. Belabbas

Keyword(s):

Urban Area ◽

Urban Areas ◽

Bare Soil ◽

Dry Season ◽

Spectral Indices ◽

Multi Index ◽

North East ◽

Sensing Applications ◽

Semi Arid ◽

Sentinel 2

<p>The mapping of urban areas mostly presents a big difficulty, particularly, in arid and semi-arid environments. For that reason, in this research, we expect to increase built up accuracy mapping for Bordj Bou Arreridj city in semi-arid regions (North-East Algeria) by focusing on the identification of appropriate combination of the remotely sensed spectral indices. The study applies the ‘k–means’ classifier. In this regard, four spectral indexes were selected, namely normalized difference tillage index (NDTI) for built-up, and both bare soil index (BSI) and dry bare-soil index (DBSI), which are related to bare soil, as well as the normalized difference vegetation index (NDVI). All previous spectral indices mentioned were derived from Sentinel-2 data acquired during the dry season. Two combinations of them were generated using layer stack process, keeping both of NDTI and NDVI index constant in both combinations so that the multi-index NDTI/BSI/NDVI was the first single dataset combination, and the multi-index NDTI/DBSI/NDVI as the second component. The results show that BSI index works better with NDTI index compared to the use of DBSI index. Therefore, BSI index provides improvements: bare soil classes and built-up were better discriminated, where the overall accuracy increased by 5.67% and the kappa coefficient increased by 12.05%. The use of k-means as unsupervised classifier provides an automatic and a rapid urban area detection. Therefore, the multi-index dataset NDTI/ BSI / NDVI was suitable for mapping the cities in dry climate, and could provide a better urban management and future remote sensing applications in semi-arid areas particularly.</p>

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