Use of the time series and multi-temporal features of Sentinel-1/2 satellite imagery to predict soil inorganic and organic carbon in a low-relief area with a semi-arid environment

Garosi, Younes; Ayoubi, Shamsollah; Nussbaum, Madlene; Sheklabadi, Mohsen; Nael, Mohsen; Kimiaee, Iman (2022). Use of the time series and multi-temporal features of Sentinel-1/2 satellite imagery to predict soil inorganic and organic carbon in a low-relief area with a semi-arid environment International Journal of Remote Sensing, 43(18), pp. 6856-6880. Taylor & Francis 10.1080/01431161.2022.2147037

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Accurate mapping of soil organic carbon (SOC) and inorganic carbon (SIC) contents at regional scales can be very important for sustainable agriculture and soil management. Low variation in terrain attributes (classically used for digital soil mapping) at low relief areas calls for additional spatial data to explain soil variability. The main objective of this study was to evaluate the predictive capability of Sentinel-1 (radar) and Sentinel-2 (optical) time series and statistics, summarized as multi-temporal features (MTF) to improve the spatial predictions of SOC and SIC in Ghorveh plain, located in Kurdistan Province, Western Iran. A systematic grid sampling was then employed to collect 150 soil surface samples (0–30 cm) for SOC and SIC measurements. We applied boosted regression trees (BRT) and random forest (RF) to predict SOC and SIC contents by using covariate sets compiled from radar and optical time series and topographic attributes. Model performance, evaluated by 10-fold cross-validation, showed that RF using the covariate set containing time series of Sentinel-1, Sentinel-2 and topographic attributes performed the best in predicting SOC (RMSE = 0.23, ME = 0.005, R2 = 0.29). On the other hand, for SIC, the covariate set containing MTF of Sentinel-1, Sentinel-2 and topographic attributes ranked the best with BRT (RMSE = 0.77, ME= −0.001, R2 = 0.48). The study indicates that using the time series and MTF from multiple dates of remote sensing data with topographic attributes results in improved predictions. However, model performance for SIC and SOC was moderate to poor, respectively. Therefore more substantial studies would be required to verify if the computational effort is likely justified by an increase in accuracy in general.

Item Type:

Journal Article (Original Article)

Division/Institute:

School of Agricultural, Forest and Food Sciences HAFL
School of Agricultural, Forest and Food Sciences HAFL > Agriculture
School of Agricultural, Forest and Food Sciences HAFL > Agriculture > Soils and Geoinformation

Name:

Garosi, Younes;
Ayoubi, Shamsollah;
Nussbaum, Madlene0000-0002-6808-8956;
Sheklabadi, Mohsen;
Nael, Mohsen and
Kimiaee, Iman

Subjects:

G Geography. Anthropology. Recreation > G Geography (General)
G Geography. Anthropology. Recreation > GA Mathematical geography. Cartography
G Geography. Anthropology. Recreation > GB Physical geography
S Agriculture > S Agriculture (General)

ISSN:

0143-1161

Publisher:

Taylor & Francis

Language:

English

Submitter:

Madlene Nussbaum

Date Deposited:

20 Dec 2022 10:50

Last Modified:

20 Dec 2022 10:50

Publisher DOI:

10.1080/01431161.2022.2147037

Uncontrolled Keywords:

Time series; Multi-temporal features; Soil organic carbon; Soil inorganic carbon; Uncertainty analysis

ARBOR DOI:

10.24451/arbor.18454

URI:

https://arbor.bfh.ch/id/eprint/18454

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