Remote sensing of the Earth's soil color in space and time

Soil color is a key indicator of soil properties and conditions, exerting influence on both agronomic and environmental variables. Conventional methods for soil color determination have come under scrutiny due to their limited accuracy and reliability. In response to these concerns, we developed an innovative system that leverages 35 years of satellite imagery in conjunction with in-situ soil spectral measurements. This approach enables the creation of a global soil color map with a fine spatial resolution of 30 m x 30 m. The system initially identifies bare earth areas worldwide using reflectance bands acquired from Landsat 4 through Landsat 8 between 1985 and 2020. Soil color was quantified using the CIE-XYZ coordinates, utilizing 8005 soil spectral measurements within the visible range (380–780 nm) as ground truth data. We established transfer functions to convert Landsat reflectance bands to standardized XYZ color coordinates. These transfer functions were subsequently applied to images of bare surfaces, covering approximately 38.5% of the Earth's surface. We validated the resulting global soil color map using statistical indices derived from an independent set of ground-truth spectral data, demonstrating a high degree of agreement. By creating the world's first global soil color map, we have set a baseline for future spatial and temporal monitoring of soil conditions, thus enhancing our understanding and management of our planet's vital soil resources.

Authors
Rizzo Rodnei , Wadoux A.M. , Demattê J.A. , Minasny Budiman , Barrón Vidal , Ben-Dor Eyal , Francos Nicolas , Savin Igor 1, 2 , Poppiel Raul , Silvero N.E. , Da Silva Terra Fabrício , Rosin N.A. , Rosas J.T. , Greschuk L.T. , Ballester M.V. , Gómez A.M. , Belllinaso Henrique , Safanelli J.L. , Chabrillat Sabine , Fiorio P.R. , Das B.S. , Malone B.P. , Zalidis George , Tziolas Nikolaos , Tsakiridis Nikolaos , Karyotis Konstantinos , Samarinas Nikiforos , Kalopesa Eleni , Gholizadeh Asa , Shepherd K.D. , Milewski Robert , Vaudour Emmanuelle , Wang Changkun , Salama E.S. 2
Publisher
Elsevier Inc.
Language
English
Pages
113845
Status
Published
Volume
299
Year
2023
Organizations
  • 1 V.V. Dokuchaev Soil Science Institute
  • 2 Peoples' Friendship University of Russia
Keywords
soil spectroscopy; spectral library; Color space models; remote sensing; landsat; Soil spatio-temporal monitoring
Date of creation
28.12.2023
Date of change
28.12.2023
Short link
https://repository.rudn.ru/en/records/article/record/105265/
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