Peatlands spectral data influence in global spectral modelling of soil organic carbon and total nitrogen using visible-near-infrared spectroscopy.

Peatlands ecosystem is one of the largest global terrestrial carbon pools. However, there is a shortness of its characterisation and information through new proximal sensing approaches. The visible and near-infrared spectroscopy is an inexpensive, quick, non-evasive, proximal sensing and low-cost analysis employed in field and/or laboratory. Despite that, there is another current issue in using this tool for creating global models, which is how it can retrieve local characteristics such as soil organic carbon (SOC) and total nitrogen (TN) in peatlands ecosystems. The aims in this study were to: (i) create a local model for quantifying SOC and TN finding the best pre-processing and machine learning methods in peatlands ecosystem, and (ii) evaluate the contribution of SOC and TN data collected in that ecosystem to global models in European Union. The hypothesis was that the SOC and TN data sampled in peatlands ecosystem can improve analytical quantification of those soil properties. The soil and spectral datasets were retrieved from the Land Use/Cover Area frame Statistical Survey with 21,771 observations at 0-20 cm depth and 63 soil cores in a degraded peatland in Germany with 262 observations up to 2 m depth. We evaluated three spectral pre-processing techniques with the Partial Least Square Regression (PLSR), Random Forest (RF), and Cubist machine learning algorithms. The best pre-processing technique was achieved applying Savitzky-Golay smoothing with a window size of 71 points, 2nd order polynomial, and zero derivative with Cubist algorithm for both SOC and TN predictions. Furthermore, merging the local with global data for global modelling demonstrated to improve SOC and TN predictions because of the local data representativeness and quality. Therefore, the SOC and TN data sampled in peatlands ecosystem can improve quantification of those soil properties in field and laboratory, which are crucial proxies for GHG emissions and climate change.

A remote sensing framework to map potential toxic elements in agricultural soils in the humid tropics.

Soil contamination by potentially toxic elements (PTEs) is one of the greatest threats to environmental degradation. Knowing where PTEs accumulated in soil can mitigate their adverse effects on plants, animals, and human health. We evaluated the potential of using long-term remote sensing images that reveal the bare soils, to detect and map PTEs in agricultural fields. In this study, 360 soil samples were collected at the superficial layer (0-20 cm) in a 2574 km2 agricultural area located in São Paulo State, Brazil. We tested the Soil Synthetic Image (SYSI) using Landsat TM/ETM/ETM+, Landsat OLI, and Sentinel 2 images. The three products have different spectral, temporal, and spatial resolutions. The time series multispectral images were used to reveal areas with bare soil and their spectra were used as predictors of soil chromium, iron, nickel, and zinc contents. We observed a strong linear relationship (-0.26 > r > -0.62) between the selected PTEs and the near infrared (NIR) and shortwave infrared (SWIR) bands of Sentinel (ensemble of 4 years of data), Landsat TM (35 years data), and Landsat OLI (4 years data). The clearest discrimination of soil PTEs was obtained from SYSI using a long term Landsat 5 collection over 35 years. Satellite data could efficiently detect the contents of PTEs in soils due to their relation with soil attributes and parent materials. Therefore, distinct satellite sensors could map the PTEs on tropics and assist in understanding their spatial dynamics and environmental effects.

Bare Earth's Surface Spectra as a Proxy for Soil Resource Monitoring.

The Earth's surface dynamics provide essential information for guiding environmental and agricultural policies. Uncovered and unprotected surfaces experience several undesirable effects, which can affect soil ecosystem functions. We developed a technique to identify global bare surface areas and their dynamics based on multitemporal remote sensing images to aid the spatiotemporal evaluation of anthropic and natural phenomena. The bare Earth's surface and its changes were recognized by Landsat image processing over a time range of 30 years using the Google Earth Engine platform. Two additional products were obtained with a similar technique: a) Earth's bare surface frequency, which represents where and how many times a single pixel was detected as bare surface, based on Landsat series, and b) Earth's bare soil tendency, which represents the tendency of bare surface to increase or decrease. This technique enabled the retrieval of bare surfaces on 32% of Earth's total land area and on 95% of land when considering only agricultural areas. From a multitemporal perspective, the technique found a 2.8% increase in bare surfaces during the period on a global scale. However, the rate of soil exposure decreased by ~4.8% in the same period. The increase in bare surfaces shows that agricultural areas are increasing worldwide. The decreasing rate of soil exposure indicates that, unlike popular opinion, more soils have been covered due to the adoption of conservation agriculture practices, which may reduce soil degradation.