Creating Multi-Temporal Composites of Airborne Imaging Spectroscopy Data in Support of Digital Soil Mapping

Diek, Sanne; Schaepman, Michael E.; Jong, Rogier de

doi:10.3390/rs8110906

Cited by 44 publications

(22 citation statements)

References 62 publications

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“…and the climate can be described as warm temperate humid, with a yearly mean temperature around 9.3 • C and annual precipitation around 1134 mm [52]. Soils comprise mainly clay loam or loam, and Cambisol [53].…”

Section: Study Areamentioning

confidence: 99%

Crop Classification in a Heterogeneous Arable Landscape Using Uncalibrated UAV Data

2018

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Land cover maps are indispensable for decision making, monitoring, and management in agricultural areas, but they are often only available after harvesting. To obtain a timely crop map of a small-scale arable landscape in the Swiss Plateau, we acquired uncalibrated, very high-resolution data, with a spatial resolution of 0.05 m and four spectral bands, using a consumer-grade camera on an unmanned aerial vehicle (UAV) in June 2015. We resampled the data to different spatial and spectral resolutions, and evaluated the method using textural features (first order statistics and mathematical morphology), a random forest classifier for best performance, as well as number and size of the structuring elements. Our main findings suggest the overall best performing data consisting of a spatial resolution of 0.5 m, three spectral bands (RGB—red, green, and blue), and five different sizes of the structuring elements. The overall accuracy (OA) for the full set of crop classes based on a pixel-based classification is 66.7%. In case of a merged set of crops, the OA increases by ~7% (74.0%). For an object-based classification based on individual field parcels, the OA increases by ~20% (OA of 86.3% for the full set of crop classes, and 94.6% for the merged set, respectively). We conclude the use of UAV to be most relevant at 0.5 m spatial resolution in heterogeneous arable landscapes when used for crop classification.

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Section: Study Areamentioning

confidence: 99%

Crop Classification in a Heterogeneous Arable Landscape Using Uncalibrated UAV Data

2018

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“…However, testing all unlabeled samples exhaustively from a large dataset is inefficient. It is especially true for hyperspectral imaging applications, in which a large population of soil spectra can be collected [48][49][50]. In such a scenario, testing all available unlabeled samples is impractical.…”

Section: Discussionmentioning

confidence: 99%

Improving Spectral Estimation of Soil Organic Carbon Content through Semi-Supervised Regression

et al. 2017

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Visible and near infrared (VIS-NIR) spectroscopy has been applied to estimate soil organic carbon (SOC) content with many modeling strategies and techniques, in which a crucial and challenging problem is to obtain accurate estimations using a limited number of samples with reference values (labeled samples). To solve such a challenging problem, this study, with Honghu City (Hubei Province, China) as a study area, aimed to apply semi-supervised regression (SSR) to estimate SOC contents from VIS-NIR spectroscopy. A total of 252 soil samples were collected in four field campaigns for laboratory-based SOC content determinations and spectral measurements. Semi-supervised regression with co-training based on least squares support vector machine regression (Co-LSSVMR) was applied for spectral estimations of SOC contents, and it was further compared with LSSVMR. Results showed that Co-LSSVMR could improve the estimations of SOC contents by exploiting samples without reference values (unlabeled samples) when the number of labeled samples was not excessively small and produce better estimations than LSSVMR. Therefore, SSR could reduce the number of labeled samples required in calibration given an accuracy threshold, and it holds advantages in SOC estimations from VIS-NIR spectroscopy with a limited number of labeled samples. Considering the increasing popularity of airborne platforms and sensors, SSR might be a promising modeling technique for SOC estimations from remotely sensed hyperspectral images.

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“…However, we can also see that there are fixed moments in time where the increase is stronger than the average. These moments are mainly in spring and at the end of summer, which are known to be seeding and harvest periods [16]. The exact moment is weather dependent and therefore hard to predict.…”

Section: Resultsmentioning

confidence: 99%

“…This variability can be caused by differences between the Landsat sensors, including differences in atmospheric correction, differences between the bands, or differences in view angle. Additionally, the variability can be caused by differences in weather conditions and land management in the different time periods cause differences in soil moisture and soil surface roughness, which both influence the surface reflectance of the soil (also discussed in Diek et al [16]). Using the mean over the time periods reduces the variability; however, as Figure 11 shows, the bareness frequency changes for each time period.…”

Section: Variability Of the Resultsmentioning

confidence: 99%

Barest Pixel Composite for Agricultural Areas Using Landsat Time Series

et al. 2017

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Many soil remote sensing applications rely on narrow-band observations to exploit molecular absorption features. However, broadband sensors are invaluable for soil surveying, agriculture, land management and mineral exploration, amongst others. These sensors provide denser time series compared to high-resolution airborne imaging spectrometers and hold the potential of increasing the observable bare-soil area at the cost of spectral detail. The wealth of data coming along with these applications can be handled using cloud-based processing platforms such as Earth Engine. We present a method for identifying the least-vegetated observation, or so called barest pixel, in a dense time series between January 1985 and March 2017, based on Landsat 5, 7 and 8 observations. We derived a Barest Pixel Composite and Bare Soil Composite for the agricultural area of the Swiss Plateau. We analysed the available data over time and concluded that about five years of Landsat data were needed for a full-coverage composite (90% of the maximum bare soil area). Using the Swiss harmonised soil data, we derived soil properties (sand, silt, clay, and soil organic matter percentages) and discuss the contribution of these soil property maps to existing conventional and digital soil maps. Both products demonstrate the substantial potential of Landsat time series for digital soil mapping, as well as for land management applications and policy making.

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Creating Multi-Temporal Composites of Airborne Imaging Spectroscopy Data in Support of Digital Soil Mapping

Cited by 44 publications

References 62 publications

Crop Classification in a Heterogeneous Arable Landscape Using Uncalibrated UAV Data

Crop Classification in a Heterogeneous Arable Landscape Using Uncalibrated UAV Data

Improving Spectral Estimation of Soil Organic Carbon Content through Semi-Supervised Regression

Barest Pixel Composite for Agricultural Areas Using Landsat Time Series

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