Mapping soil organic carbon in Tuscany through the statistical combination of ground observations with ancillary and remote sensing data

Gardin, Lorenzo; Chiesi, Marta; Fibbi, Luca; Maselli, Fabio

doi:10.1016/j.geoderma.2021.115386

Cited by 21 publications

(10 citation statements)

References 48 publications

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“…In both locations and sowing dates, soils were characterized by a medium level of total nitrogen and average organic matter content (Table 1). Soil texture differences observed in ALB were due to the different fields used for experimentation, pointing out the high soil variability that characterizes the Tuscan territory [26]. Daily meteorological data (rainfall and temperature), of both growing seasons and long-term period (30 years) trends were obtained via automatic meteorological stations located near each experimental site.…”

Section: Experimental Setup and Plant Materialsmentioning

confidence: 99%

Variety and Sowing Date Affect Seed Yield and Chemical Composition of Linseed Grown under Organic Production System in a Semiarid Mediterranean Environment

2022

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The use of suitable species and varieties in organic cropping systems is essential for improving resource use efficiency, biodiversity, and agroecosystem resilience. Within the SIC-OLEAT project, a 2-year field trial was carried out in two contrasting environments of Central Italy, with the aim to hypothesize a production path for linseed inclusion within organic farming. The effects of location, genotype and sowing date on crop phenology, agronomic performances, and qualitative traits were evaluated. Generally, linseed showed good agronomic traits that make it suitable to be introduced in organic systems. Autumn sowing coupled with milder and wetter conditions seemed to be more favorable for linseed cultivation, allowing a higher seed yield (2.1 vs. 1.3 Mg ha−1) and oil content (47.2 vs. 45.2%). From multivariate analysis, the superior genotypes were Kaolin > Szafir > Galaad, and among these Kaolin had the highest production stability. On the contrary, Libra was the lowest performing one and the most unstable. These findings underline the importance of a site-specific approach for choosing the most suitable variety, since both sowing date and location are meteoclimatic-related factors. Definitively, our results demonstrated that linseed might be a valuable autumn alternative for organic cropping system diversification, contributing to the local production of vegetable oils and proteins.

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Section: Experimental Setup and Plant Materialsmentioning

confidence: 99%

Variety and Sowing Date Affect Seed Yield and Chemical Composition of Linseed Grown under Organic Production System in a Semiarid Mediterranean Environment

2022

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“…Work on the humus state of soils has been carried out for a long time, in the territory of the Russian Federation this indicator is determined by agrochemical services during regular rounds of soil surveys. At the same time, remote sensing data can be more widely used in the assessment of this subindicator, using a semi-quantitative method for remotely determining the humus content in the soil [9][10].…”

Section: Discussionmentioning

confidence: 99%

The use of remote sensing data in the assessment of the Land Degradation Neutrality

Ogorodnikov

2022

IOP Conf. Ser.: Earth Environ. Sci.

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An adapted methodology for assessing Land Degradation Neutrality is proposed. It is shown that the factor of overgrowth of agricultural lands and settlements should be assessed as a sign of degradation. The ways of adjusting the calculation of indicators of land quality are considered. According to the updated data, the area of degraded lands of the Tver region for the period 2001-2017 increased by 4% and is 5.1%.

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“…(3) Combination of spectral information from optical images with non-spectral covariates, such as digital elevation models, soil and land use maps, or meteorological and environmental data [43][44][45][46][47]. Other studies have used both synthetic aperture radar (SAR) data and derive predictors (e.g., radar indices, soil moisture) as covariates [48][49][50][51] or as a way to select relevant Sentinel-2 dates by estimating the state of the soil surface [38,50].…”

Section: Introductionmentioning

confidence: 99%

Using Machine-Learning Algorithms to Predict Soil Organic Carbon Content from Combined Remote Sensing Imagery and Laboratory Vis-NIR Spectral Datasets

Zayani,

Fouad,

Michot

et al. 2023

Remote Sensing

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Understanding spatial and temporal variability in soil organic carbon (SOC) content helps simultaneously assess soil fertility and several parameters that are strongly associated with it, such as structural stability, nutrient cycling, biological activity, and soil aeration. Therefore, it appears necessary to monitor SOC regularly and investigate rapid, non-destructive, and cost-effective approaches for doing so, such as proximal and remote sensing. To increase the accuracy of predictions of SOC content, this study evaluated combining remote sensing time series with laboratory spectral measurements using machine and deep-learning algorithms. Partial least squares (PLS) regression, random forest (RF), and deep neural network (DNN) models were developed using Sentinel-2 (S2) time series of 58 sampling points of bare soil and according to three approaches. In the first approach, only S2 bands were used to calibrate and compare the performance of the models. In the second, S2 indices, Sentinel-1 (S1) indices, and S1 soil moisture were added separately during model calibration to evaluate their effects individually and then together. In the third, we added the laboratory indices incrementally and tested their influence on model accuracy. Using only S2 bands, the DNN model outperformed the PLS and RF models (ratio of performance to the interquartile distance RPIQ = 0.79, 1.36 and 1.67, respectively). Additional information improved performances only for model calibration, with S1 soil moisture yielding the most stable improvement among three iterations. Including equivalent indices of the S2 indices calculated using soil spectra obtained under laboratory conditions improved prediction of SOC, and the use of only two indices achieved good validation performances for the RF and DNN models (mean RPIQ = 2.01 and 1.77, respectively).

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Mapping soil organic carbon in Tuscany through the statistical combination of ground observations with ancillary and remote sensing data

Cited by 21 publications

References 48 publications

Variety and Sowing Date Affect Seed Yield and Chemical Composition of Linseed Grown under Organic Production System in a Semiarid Mediterranean Environment

Variety and Sowing Date Affect Seed Yield and Chemical Composition of Linseed Grown under Organic Production System in a Semiarid Mediterranean Environment

The use of remote sensing data in the assessment of the Land Degradation Neutrality

Using Machine-Learning Algorithms to Predict Soil Organic Carbon Content from Combined Remote Sensing Imagery and Laboratory Vis-NIR Spectral Datasets

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