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

Abstract

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. Soils are directly related to global issues, such as food supply, water security and climate regulation 1-4. Considering that soil health is easily disturbed by modifications of physical, chemical and biological conditions, soil management must be carefully delineated and constantly monitored 5. The high occurrence of bare surfaces in agricultural areas may increase soil degradation, influencing soil health and affecting important ecosystem functions 6. Surface exposure triggers a sequence of events, which may result in soil erosion 7 , contamination 8 , desertification 9 , salinization 10 , acidification 11 , compaction 12 , biodiversity loss 13 , nutrient depletion 14 , and loss of soil organic carbon (SOC) 15. Despite the need to preserve soil ecosystems for future generations, we will still need to feed 9.7 billion people by the middle of this century 16. To date, the total area of worldwide crop and pasture lands is estimated at 49 million km 2 , which represents approximately 30% of the total global land area 17. The world's future demand for water, energy and food is closely related to the intensification of agriculture and the preservation of ecosystems. Sustainable land use requires data-driven policymaking and management strategies, which are currently hampered by the high cost and time-consuming characteristics of soil data acquisition 2. The remote sensing (RS) technique is an indispensable tool for multitemporal and spatial data acquisition, as it enables the collection of soil information at gl...

Digital Soil Mapping Using Multispectral Modeling with Landsat Time Series Cloud Computing Based

“…Study Area Time Series Percentage (%) [5] Swiss Plateau 1984-2016 43 [7] Brazil, Southeast 1984-2011 68 [8] Brazil, Southeast 1984-2017 53 [9] Germany 1984-2014 26 [10] Brazil, Midwest 1984-2018 74 [16] Brazil, Southeast 1984-2018 68 [17] Brazil, Midwest 1984-2019 100 * [46] Worldwide 1985-2015 34 * Using Krigagem on the achieved SySI area.…”

African Soil Properties and Nutrients Mapped at 30--m Spatial Resolution using Two-scale Ensemble Machine Learning

“…The combined variable importance plots derived using Random Forest with all 250 m and 30 m covariates used together ( Fig. 2) reveal that, on average, climatic images such as SM2RAIN monthly rainfall estimates and CHELSA bioclimatic images (3,7,4), are the most important covariates to inform mapping of soil properties and nutrients in Africa. This result is consistent with our previous global results 22 , where soil chemical properties were primarily correlated with climate images, and soil physical properties with a combination of landform parameters, parent material and climatic images.…”

“…Training points used to build predictive models are usually provided by data from soil samples (fixed depth intervals) or soil profiles (pedogenetic soil horizons) that were geolocated in the field and then entered into a soil profile database. Covariate layers commonly used to train models include terrain attributes 2 -especially hydrological terrain parameters -parent material maps, climatic and vegetation maps and surface reflectances, including bare soil surface reflectances 4 . Predictions of soil properties and classes are generated by (1) training the learners i.e.…”

Sentinel-2 and Landsat-8 Multi-Temporal Series to Estimate Topsoil Properties on Croplands

“…However, in case of non-availability of a robust IT infrastructure with large storage capability and high computing power, a cloud platform that offers catalogs of satellite imagery and geospatial analysis capabilities by an Application Programming Interface (API) is necessary to analyze this plethora of available data within a large time series in a most effective way. In this regard, Google Earth Engine [28] was successfully employed for obtaining a bare Earth's surface spectra using L8 multi-temporal data [29].…”