Using Sentinel-2 Images for Soil Organic Carbon Content Mapping in Croplands of Southwestern France. The Usefulness of Sentinel-1/2 Derived Moisture Maps and Mismatches between Sentinel Images and Sampling Dates

Performance of soil quality indicators in estimation and distribution of rapeseed yield

This research aimed to evaluate the quality of soils for rapeseed crop production by Boolean and fuzzy-analytical hierarchy process (FAHP) approach in northwest of Iran. To this purpose, the physical, chemical, and topography quality indicators of land were selected based on agricultural considerations that were obtained from 83 fields. The spatial distribution of soil quality indicators was prepared using inverse distance weighting (IDW) technique. Also, validation of the developed model was performed using composite operator. The results showed that physical and chemical properties were key deciding parameters for the evaluation of soil quality. In the developed models, clay, sand, silt, soil organic matter, pH, calcium carbonate equivalent, electrical conductivity, and elevation were selected as modeling parameters. AHP technique showed that soil texture and elevation had the strongest and weakest influences on rapeseed yield, respectively. By dividing lands into four suitability categories, FAHP could more easily classify lands into soil quality classes where 36.3% of the study area was permanently unsuitable, 39.7% was marginally suitable, 22.6% was moderately suitable, and 1.4% was suitable. The comparison results of soil quality and rapeseed yield map by composite operator showed that FAHP with 77% agreement provided better results than Boolean approach with 39% agreement. Finally, this research will provide a reasonable record in ensuring crop yield security, agronomic use and management of rapeseed as well as increasing crop income. Hence, FAHP was introduced as an efficient approach.

Spatial distribution of arsenic and metals suggest a high ecotoxicological potential in Puno Bay, Lake Titicaca, Peru

Spatial distribution and interpolation methods provide a summarized overview about the pollution dispersion, concerning the environment's quality. A high-altitude lake was taken as a model to assess the metalloid As and metals Cr, Cu, Ni, Pb, Zn distribution in superficial sediment and classify them according to their ecotoxicological potential in the aquatic environment. Surface sediments were collected from 11 sites along Puno Bay located at the western area of Lake Titicaca, Peru, and analyzed for pseudo total-metals. Sediment concentration data and quality were plotted using the Inverse Distance Weighting (IDW) as an interpolation method. High concentrations of As were found especially in the outer bay (81.73 mg.kg^-1). Spatial heterogeneity was evidenced for metal by the coefficient of variation, although no significative differences were observed between the two bays applying a Kruskall Wallis test (p < 0.05, df = 1). Sediment quality classification showed that most metal values were below TEL and toxicity was unlikely to occur, only As exceeded threefold PEL values, which categorized sediment as "Very Bad", indicating a rather high ecotoxicological potential to the aquatic environment. In conclusion, spatial analysis connected to interpolation methods demonstrated the superficial sediment heterogeneity in Puno Bay.

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

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.