Using Landsat and soil clay content to map soil organic carbon of oxisols and Ultisols near São Paulo, Brazil

Padilha, Manuela Corrêa de Castro; Vicente, L. E.; Demattê, José A.M.; Loebmann, Daniel; Koga-Vicente, Andrea; Salazar, Diego Fernando Urbina; Guimarães, Carlos Henrique

doi:10.1016/j.geodrs.2020.e00253

Cited by 17 publications

(6 citation statements)

References 41 publications

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“…Although no big dataset was evaluated, both indices were helpful to track SOC stock distribution with high accuracy (R 2 = 0.82). Previous studies also used NDVI and BSI to predict SOC concentrations and showed good performance [10,11,49].…”

Section: Prediction Of Soc Stock By Spectral Indicesmentioning

confidence: 99%

Spectral and Soil Quality Index for Monitoring Environmental Rehabilitation and Soil Carbon Stock in an Amazonian Sandstone Mine

et al. 2022

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Rehabilitation is the key factor for improving soil quality and soil carbon stock after mining operations. Monitoring is necessary to evaluate the progress of rehabilitation and its success, but the use of repeated field surveys is costly and time-consuming at a large scale. This study aimed to monitor the environmental/soil rehabilitation process of an Amazonian sandstone mine by applying spectral indices for predicting soil organic carbon (SOC) stock and comparing them to soil quality index. The studied area has different chronological rehabilitation stages: initial, intermediate, and advanced with 2, 10, and 12 years of onset rehabilitation activities, respectively. Non-rehabilitated (NR) and two native forest areas (RA) were used as controls. Soil samples were analyzed for physical, chemical, and biological attributes. After determination of Normalized Difference Vegetation Index and Bare Soil Index, simple regression analysis comparing these indices with SOC stock showed a good fit (R2 = 0.82). Rehabilitated areas presented higher soil quality index (~1.50-fold) and SOC stock (~10.6-fold) than NR; however, they did not differ of RA. The use of spectral indices was effective for monitoring the soil quality in this study, with a positive correlation between the predicted SOC stock and the calculated soil quality index.

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Section: Prediction Of Soc Stock By Spectral Indicesmentioning

confidence: 99%

Spectral and Soil Quality Index for Monitoring Environmental Rehabilitation and Soil Carbon Stock in an Amazonian Sandstone Mine

et al. 2022

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“…The second example is the Soil Composite Mapping Processor (ScMAP) that del ered exposed soil masks that were run on high-performance local computing clusters [4 Leveraging the application of current multispectral EO data for mapping croplan soils several applications have been recorded on the regional [43][44][45], national [46], an continental scale [30]. A significant number of these studies were implemented in Bra [47], India [48], Indonesia [49], and China [50] since detailed information about soils abundant in those countries. This data were acquired to address the challenges generat by the cropland expansion there during recent decades.…”

Section: The Temporal Dimensionmentioning

confidence: 99%

Earth Observation Data-Driven Cropland Soil Monitoring: A Review

et al. 2021

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We conducted a systematic review and inventory of recent research achievements related to spaceborne and aerial Earth Observation (EO) data-driven monitoring in support of soil-related strategic goals for a three-year period (2019–2021). Scaling, resolution, data characteristics, and modelling approaches were summarized, after reviewing 46 peer-reviewed articles in international journals. Inherent limitations associated with an EO-based soil mapping approach that hinder its wider adoption were recognized and divided into four categories: (i) area covered and data to be shared; (ii) thresholds for bare soil detection; (iii) soil surface conditions; and (iv) infrastructure capabilities. Accordingly, we tried to redefine the meaning of what is expected in the next years for EO data-driven topsoil monitoring by performing a thorough analysis driven by the upcoming technological waves. The review concludes that the best practices for the advancement of an EO data-driven soil mapping include: (i) a further leverage of recent artificial intelligence techniques to achieve the desired representativeness and reliability; (ii) a continued effort to share harmonized labelled datasets; (iii) data fusion with in situ sensing systems; (iv) a continued effort to overcome the current limitations in terms of sensor resolution and processing limitations of this wealth of EO data; and (v) political and administrative issues (e.g., funding, sustainability). This paper may help to pave the way for further interdisciplinary research and multi-actor coordination activities and to generate EO-based benefits for policy and economy.

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“…In addition, the United Nations Convention to Combat Desertification (UNCCD), which is an indicator of the goal of the Sustainable Development Agency 15.3.1, recognized the SOC reserve map as an indicator for assessing the area of degraded land in the context of achieving neutral land degradation (IUCN, 2015). Soil mapping has long been widely used all over the world, and with the development of GIS technologies, the development of digital mapping has leaped forward (Castaldi et al, 2019;Lupachev et al, 2020;Padilha et al, 2020). However, such maps describe and predict SOM stocks rather well on large areas of land, while local heterogeneities are left without due attention (Scull et al, 2003;Lamichhane et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Soil-geomorphological mapping of Samoylov Island based on UAV imaging

Polyakov

Kartoziia

Nizamutdinov

et al. 2022

Front. Environ. Sci.

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Soil-geomorphological mapping is a reliable tool for analyzing the patterns of soil distribution in various parts of Earth’s surface. Cryogenic and watershed areas are the most dynamic landscapes with relatively rapid transformation under the influence of climate change and river activity. The soil-geomorphological map obtained by unmanned aerial vehicle (UAV) imaging, classical soil sections, geomorphological observation, and determination of the main chemical parameters of soils are presented. Mapping of the spatial distribution was performed using QGIS 3.22, SAGA GIS 7.9.1, and ArcGIS 10.6 software. The investigation of soil cover was performed according to WRB soil classification. From the obtained data, four types of soils were identified due to their position in the relief and chemical parameters. The dominant soil type is Folic Cryosol (Siltic) (41.1%) which is formed on the periglacial landscape of wet polygons on Samoylov Island. The application of high-resolution UAV imaging to construct soil-geomorphological maps is the most relevant method for analyzing soils formed in cryogenic, watershed, and mountainous landscapes. Based on SOC distribution, it was found that the highest SOC content corresponds to Holocene terrace (Cryosol and Histosol soil types), in areas that are not subject to the flooding process. According to the analysis of the chemical composition of soils, it was found that the main elements accumulating in the soil are SiO2, Al2O3, CaO, and K2O, which have a river origin. The soil-geomorphological maps can be used to analyze the reserves and contents of organic and inorganic components with high accuracy.

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Using Landsat and soil clay content to map soil organic carbon of oxisols and Ultisols near São Paulo, Brazil

Cited by 17 publications

References 41 publications

Spectral and Soil Quality Index for Monitoring Environmental Rehabilitation and Soil Carbon Stock in an Amazonian Sandstone Mine

Spectral and Soil Quality Index for Monitoring Environmental Rehabilitation and Soil Carbon Stock in an Amazonian Sandstone Mine

Earth Observation Data-Driven Cropland Soil Monitoring: A Review

Soil-geomorphological mapping of Samoylov Island based on UAV imaging

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