Earth Observation Data-Driven Cropland Soil Monitoring: A Review

Tziolas, Nikolaos; Tsakiridis, Nikolaos L.; Chabrillat, Sabine; Demattê, José Alexandre Melo; Ben‐Dor, Eyal; Gholizadeh, Asa; Zalidis, George; Wesemael, Bas van

doi:10.3390/rs13214439

Cited by 34 publications

(15 citation statements)

References 112 publications

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“…reviewed recent studies on crop yield prediction by analyzing remote sensing images with machine learning techniques to find chemical, physical, and biological soil quality indicators. Tziolas et al 11 . reviewed the research covering a 3-year period (2019 to 2021) on soil mapping and monitoring with spaceborne and aerial EO techniques.…”

Section: Literature Surveymentioning

confidence: 99%

Soil classification with multi-temporal hyperspectral imagery using spectral unmixing and fusion

Kaba,

Leloglu

2023

J. Appl. Rem. Sens.

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.Soil maps are essential sources for a diverse range of agricultural and environmental studies; hence, the detection of soil properties using remote sensing technology is a hot topic. Satellites carrying hyperspectral sensors provide possibilities for the estimation of soil properties. But, the main obstacle in soil classification with remote sensing methods is the vegetation that has a spectral signature that mixes with that of the soil. The objective of this study is to detect soil texture properties after eliminating the effects of vegetation using hyperspectral imaging data and reducing the noise by fusion. First, the endmembers common to all images and their abundances are determined. Then the endmembers are classified as stable ones (soil, rock, etc.) and unstable ones (green vegetation, dry vegetation, etc.). This method eliminates vegetation from the images with orthogonal subspace projection (OSP) and fuses multiple images with the weighted mean for a better signal-to-noise-ratio. Finally, the fused image is classified to obtain the soil maps. The method is tested on synthetic images and hyperion hyperspectral images of an area in Texas, United States. With three synthetic images, the individual classification results are 89.14%, 89.81%, and 93.79%. After OSP, the rates increase to 92.23%, 93.13%, and 95.38%, respectively, whereas it increases to 96.97% with fusion. With real images from the dates 22/06/2013, 25/09/2013, and 24/10/2013, the classification accuracies increase from 70.51%, 68.87%, and 63.18% to 71.96%, 71.78%, and 64.17%, respectively. Fusion provides a better improvement in classification with a 75.27% accuracy. The results for the analysis of the real images from 2016 yield similar improvements. The classification accuracies increase from 57.07%, 62.81%, and 63.80% to 58.99%, 63.93%, and 66.33%, respectively. Fusion also provides a better classification accuracy of 69.02% for this experiment. The results show that the method can improve the classification accuracy with the elimination of vegetation and with the fusion of multiple images. The approach is promising and can be applied to various other classification tasks.

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Section: Literature Surveymentioning

confidence: 99%

Soil classification with multi-temporal hyperspectral imagery using spectral unmixing and fusion

Kaba,

Leloglu

2023

J. Appl. Rem. Sens.

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“…Standardization refers to reproducible data processing and modeling, as well as their evaluation based on accuracy metrics [6,[69][70][71]. In contrast to static conventional soil maps, the scale-specific suitability can be determined, which helps to communicate map quality to end-users [71][72][73], to provide additional information about data fitness-for-use [74,75], to improve the model's interpretability [4] as well as to get a additional geospatial provenance description [76]. Although the process chain presented is reproducible, individual steps are based on expert knowledge.…”

Section: Data Quality and Fitness-for-usementioning

confidence: 99%

“…Therefore, the soil of agricultural ecosystems can contribute to the mitigation of greenhouse gas (GHG) emissions and thus to climate change mitigation through increased carbon sequestration [2]. In order to assess this potential and promote it through adaptation of land use systems, as well as to localize adaptation needs on an area-by-area basis in the context of the European Common Agricultural Policy (CAP) and the Sustainable Development Goals (SDGs), up-to-date, area-wide, and high-resolution information on carbon contents of agricultural soils is needed [3,4]. Germany-wide maps of the carbon content of agricultural soils are currently only available as static maps with a spatial resolution of 200 m 2 to 1 km 2 [5].…”

Section: Introductionmentioning

confidence: 99%

“…Since the nationwide availability of digital elevation models at various spatial resolutions, digital soil mapping has transitioned from the research phase to operational use [11]. The increasing availability of multi-temporal satellite imagery allows an expansion of the data space to distinguish both spatial and temporal patterns of SOC content [4,12]. Multi-temporal soil reflectance composites (SRC), based on Landsat or Sentinel-2 time series, have proven as explanatory variables for the prediction of (top)soil organic carbon (SOC) content [5,[13][14][15][16][17][18][19][20].…”

Section: Introductionmentioning

confidence: 99%

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Scale-specific Prediction of Topsoil Organic Carbon Contents using Terrain Attributes and SCMaP Soil Reflectance Composites

Möller¹,

Zepp²,

Wiesmeier³

et al. 2022

Preprint

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There is a growing need for an area-wide knowledge of SOC contents in agricultural soils at field scale for food security, monitoring long-term changes related to soil health and climate change. In Germany, large-scale SOC maps are mostly available with a spatial resolution of 250 m to 1 km2. The nationwide availability of both digital elevation models at various spatial resolutions and multi-temporal satellite imagery enables the derivation of multi-scale terrain attributes and Landsat-based multi-temporal soil reflectance composites (SRC) as explanatory variables. On the example of an Bavarian test of about 8000 km2, the scale-specific dependencies between the representativeness of 220 soil samples and different aggregation levels of the explanatory variables were analyzed for their scale-specific predictive power. The aggregation levels were generated by applying a region-growing segmentation procedure, the SOC content prediction was realized by the Random Forest algorithm. In doing so, established approaches of (geographic) object-based image analysis (GEOBIA) and machine learning were combined. The modeling results revealed scale-specific differences. Compared to terrain attributes, the use of SRC parameters lead to a significant model improvement at large field-related scale levels. The joint use of both terrain attributes and SRC parameters resulted in further model improvements. The best modeling variant is characterized by an accuracy of R2=0.84 and RMSE=1.99.

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“…Conventional high-resolution soil maps are static and often based on obsolete data in relation to their current application [1][2][3]. Hence, there is an urgent need to update soil map information and monitor soil properties regularly for enabling decision support and land management, while complying with global policies such as the sustainable development goals of United Nations (UN), land neutrality target of the UN Framework Convention on Climate Change-Intergovernmental Panel on Climate Change (UNFCC-IPCC), or the "Caring for Soil" mission of the European Commission [1]. Such monitoring can be carried out either temporally or spatially.…”

Section: Introductionmentioning

confidence: 99%

Satellite Imagery to Map Topsoil Organic Carbon Content over Cultivated Areas: An Overview

et al. 2022

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There is a need to update soil maps and monitor soil organic carbon (SOC) in the upper horizons or plough layer for enabling decision support and land management, while complying with several policies, especially those favoring soil carbon storage. This review paper is dedicated to the satellite-based spectral approaches for SOC assessment that have been achieved from several satellite sensors, study scales and geographical contexts in the past decade. Most approaches relying on pure spectral models have been carried out since 2019 and have dealt with temperate croplands in Europe, China and North America at the scale of small regions, of some hundreds of km2: dry combustion and wet oxidation were the analytical determination methods used for 50% and 35% of the satellite-derived SOC studies, for which measured topsoil SOC contents mainly referred to mineral soils, typically cambisols and luvisols and to a lesser extent, regosols, leptosols, stagnosols and chernozems, with annual cropping systems with a SOC value of ~15 g·kg−1 and a range of 30 g·kg−1 in median. Most satellite-derived SOC spectral prediction models used limited preprocessing and were based on bare soil pixel retrieval after Normalized Difference Vegetation Index (NDVI) thresholding. About one third of these models used partial least squares regression (PLSR), while another third used random forest (RF), and the remaining included machine learning methods such as support vector machine (SVM). We did not find any studies either on deep learning methods or on all-performance evaluations and uncertainty analysis of spatial model predictions. Nevertheless, the literature examined here identifies satellite-based spectral information, especially derived under bare soil conditions, as an interesting approach that deserves further investigations. Future research includes considering the simultaneous analysis of imagery acquired at several dates i.e., temporal mosaicking, testing the influence of possible disturbing factors and mitigating their effects fusing mixed models incorporating non-spectral ancillary information.

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Earth Observation Data-Driven Cropland Soil Monitoring: A Review

Cited by 34 publications

References 112 publications

Soil classification with multi-temporal hyperspectral imagery using spectral unmixing and fusion

Soil classification with multi-temporal hyperspectral imagery using spectral unmixing and fusion

Scale-specific Prediction of Topsoil Organic Carbon Contents using Terrain Attributes and SCMaP Soil Reflectance Composites

Satellite Imagery to Map Topsoil Organic Carbon Content over Cultivated Areas: An Overview

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