Examinando por Materia "Google Earth Engine"

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Digital soil mapping of metals and metalloids in croplands using multiple geospatial data and machine learning, implemented in GEE, for the Peruvian Mantaro Valley
(Elsevier, 2024-03-29) Pizarro Carcausto, Samuel; Vera Vilchez, Jesús; Huamani, Joseph; Cruz, Juancarlos; Lastra, Sphyros; Solórzano Acosta, Richard; Verástegui Martínez, Patricia
Quality and safety of the soil are essential to ensure social and economic development and provides the supply of contaminant free food. With agriculture intensification, expansion of urban zones, construction of roads, and mining, some agricultural soils sites become polluted increasing environmental risks to ecosystems functions and human health. Hence the need know the spatial distribution of elements in soils, we mapped 25 elements, namely Ca, Mg, Sr, Ba, Be, K, Na, As, Sb, Se, Tl, Cd, Zn, Al, Pb, Hg, Cr, Ni, Cu, Mo, Ag, Fe, Co, Mn and V, using various geospatial datasets, such as remote sensing, climate, topography, soil data, and distance, to establish the spatial estimation models of spatial distribution trained trough machine learning model with a supervised dataset of 109 topsoil samples, into Google earth engine platform. Using R2, RMSE and MAE to assess the prediction accuracy. First Random Forest gave satisfactory results in predicting the distribution of analyzed elements in soil, being improved for some elements when adds more trees. Additionally, each element analyzed has a different combination of environmental covariates as predictor, mainly soil, climate, topographic and distance variables especially croplands close to rivers, with less importance for spectral variables. Our results suggest that is possible to identify polluted soils and improved regulations to minimize harm to environmental health and human health, for short-to-medium-term environmental risk control.
Implementing cloud computing for the digital mapping of agricultural soil properties from high resolution UAV multispectral imagery
(MDPI, 2023-06-20) Pizarro Carcausto, Samuel Edwin; Pricope, Narcisa G.; Figueroa Venegas, Deyanira Antonella; Carbajal Llosa, Carlos Miguel; Quispe Huincho, Miriam Rocío; Vera Vilchez, Jesús Emilio; Alejandro Méndez, Lidiana Rene; Achallma Mendoza, Lino; González Tovar, Izamar Estrella; Salazar Coronel, Wilian; Loayza, Hildo; Cruz Luis, Juancarlos Alejandro; Arbizu Berrocal, Carlos Irvin
The spatial heterogeneity of soil properties has a significant impact on crop growth, making it difficult to adopt site-specific crop management practices. Traditional laboratory-based analyses are costly, and data extrapolation for mapping soil properties using high-resolution imagery becomes a computationally expensive procedure, taking days or weeks to obtain accurate results using a desktop workstation. To overcome these challenges, cloud-based solutions such as Google Earth Engine (GEE) have been used to analyze complex data with machine learning algorithms. In this study, we explored the feasibility of designing and implementing a digital soil mapping approach in the GEE platform using high-resolution reflectance imagery derived from a thermal infrared and multispectral camera Altum (MicaSense, Seattle, WA, USA). We compared a suite of multispectral-derived soil and vegetation indices with in situ measurements of physical-chemical soil properties in agricultural lands in the Peruvian Mantaro Valley. The prediction ability of several machine learning algorithms (CART, XGBoost, and Random Forest) was evaluated using R2, to select the best predicted maps (R2 > 0.80), for ten soil properties, including Lime, Clay, Sand, N, P, K, OM, Al, EC, and pH, using multispectral imagery and derived products such as spectral indices and a digital surface model (DSM). Our results indicate that the predictions based on spectral indices, most notably, SRI, GNDWI, NDWI, and ExG, in combination with CART and RF algorithms are superior to those based on individual spectral bands. Additionally, the DSM improves the model prediction accuracy, especially for K and Al. We demonstrate that high-resolution multispectral imagery processed in the GEE platform has the potential to develop soil properties prediction models essential in establishing adaptive soil monitoring programs for agricultural regions.