Open Remote Sensing Data in Digital Soil Organic Carbon Mapping: A Review
Abstract
:1. Introduction
2. Digital SOC Mapping Studies Indexed in Web of Science Core Collection (WoSCC)
2.1. State of the Digital SOC Mapping Studies since 2000 on a Global Level
2.2. United Nations’ SDGs Addressed in Digital SOC Mapping Studies
2.3. Prediction Approaches and the Role of Remote Sensing Data in Digital SOC Mapping Studies
3. Environmental Covariates from Remote Sensing Data Sources in State-of-the-Art Digital SOC Mapping Studies Indexed in WoSCC
3.1. Topography Covariates
3.2. Climate Covariates
3.3. Spectral Covariates
3.4. Other Notable Environmental Covariate Groups
4. Conclusions and Future Directions
- Digital soil mapping is necessary to meet three SDGs, including goals 2 (Zero Hunger), 13 (Climate Action), and 15 (Life on Land), with SOC being a key soil property in managing these goals;
- The number of overall digital soil mapping studies has grown stably since 2000, with digital SOC mapping studies representing more than 20% of total studies in 2023;
- The United States, China, Germany, and Iran are leading countries in digital SOC mapping according to the total number of studies indexed in WoSCC;
- The lowest number of digital SOC mapping studies was observed in Africa, which should be encouraged in the future, as SDGs should be met globally;
- The application of machine and deep learning in digital SOC mapping studies has grown exponentially since 2010, replacing geostatistical digital soil mapping approaches;
- Machine and deep learning prediction consequentially require environmental covariates for digital SOC mapping, which consists of three primary groups, topography, climate, and spectral, along with auxiliary covariates;
- Available climate data primarily restrict the spatial resolution of digital soil mapping to 1 km, which typically requires downscaling to harmonize with topography (up to 30 m) and multispectral data (up to 10–30 m);
- Spectral indices derived from multispectral data were frequently used as covariates in digital SOC mapping, especially vegetation indices NDVI and EVI, as well as water (NDWI) and soil (BSI) indices;
- Auxiliary environmental covariates primarily include geology (parent material), land cover classes, and radar images.
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Country | Area (km2) | Environmental Covariates | Reference | |||
---|---|---|---|---|---|---|
Number | Topography | Climate | Spectral | |||
Switzerland | 13,000 | 178 | X | X | X | [52] |
Canada | 37 | 112 | X | X | [53] | |
Canada | 120 | 80 | X | X | [54] | |
China | 2667 | 57 | X | X | X | [55] |
China | 50,400 | 54 | X | X | X | [56] |
USA | 432,000 | 40 | X | X | X | [57] |
China | 739 | 39 | X | [58] | ||
Iran | 4829 | 37 | X | [59] | ||
USA | 9,834,000 | 31 | X | X | X | [60] |
South Africa | 1,272,150 | 31 | X | X | X | [61] |
Switzerland | 41,000 | 31 | X | X | X | [62] |
Iran | 100 | 30 | X | X | X | [63] |
China | 3930 | 29 | X | X | X | [64] |
Germany | 71,000 | 29 | X | X | X | [65] |
Morocco | 805 | 29 | X | X | X | [66] |
Australia | 233,877 | 28 | X | X | X | [67] |
Iran | 3000 | 28 | X | X | [68] | |
Colombia | 260,000 | 28 | X | X | [69] | |
India | 310 | 28 | X | X | [70] | |
China | 9,597,000 | 27 | X | X | [71] | |
Hungary | 93,000 | 26 | X | X | X | [72] |
Germany | 357,600 | 24 | X | X | X | [73] |
Russia | 51 | 23 | X | X | [74] | |
Iran | 1500 | 23 | X | X | [75] | |
China | 140,000 | 22 | X | X | X | [76] |
China | 5568 | 22 | X | X | X | [77] |
Dominican Republic | 48,198 | 20 | X | X | X | [78] |
China | 1833 | 17 | X | X | [79] | |
China | 650,000 | 17 | X | X | [80] | |
Australia | 810,000 | 15 | X | X | X | [81] |
Canada | 2824 | 15 | X | [82] | ||
Iran | 70 | 14 | X | X | [83] | |
Iran | 41 | 14 | X | X | [18] | |
Iran | 17 | 14 | X | X | [84] | |
Cameroon | 475,000 | 12 | X | X | X | [85] |
China | 2621 | 12 | X | X | X | [33] |
Italy | 314 | 9 | X | [86] | ||
Iran | 6.8 | 9 | X | X | [87] | |
Italy | 25,000 | / | X | X | X | [88] |
DEM | Responsible Organization | Maximum Spatial Resolution | Vertical Accuracy | Reference |
---|---|---|---|---|
ASTER GDEM | Japan Aerospace Exploration Agency (JAXA) | 30 m | 20 m | [94] |
SRTM30 | National Aeronautics and Space Administration (NASA) | 30 m | 16 m | [95] |
GTOPO30 | United States Geological Survey (USGS) | 1 km | 30 m | [96] |
Copernicus DEM | European Union (EU) Copernicus Programme | 30 m | 4 m | [97] |
Variable Name | CHELSA | WorldClim |
---|---|---|
Bioclimatic variables | X | X |
Monthly Mean Temperature | X | X |
Monthly Mean Minimum Temperature | X | X |
Monthly Mean Maximum Temperature | X | X |
Monthly Mean Diurnal Temperature Range | X | X |
Monthly Precipitation | X | X |
Monthly Wind Speed | X | X |
Monthly Relative Humidity | X | X |
Monthly Water Vapor Pressure | X | X |
Monthly Solar Radiation | X | X |
Monthly Cloud Area Fraction | X | |
Vapor Pressure Deficit | X | |
Potential Evapotranspiration | X | |
Climate Moisture Index | X | |
Site Water Balance | X |
Data Source | Spatial Resolution | Total Number of Bands | Temporal Resolution | Radiometric Resolution | Launch Year | Swath |
---|---|---|---|---|---|---|
MODIS | 250 m | Up to 36 | 1–2 days | 12-bit | 1999 | 2330 km |
Landsat 5 TM | 30 m | 7 | 16 days | 8-bit | 1984 | ~185 km |
Landsat 7 ETM+ | 30 m | 8 | 16 days | 8-bit | 1999 | ~185 km |
Landsat 8 OLI | 30 m | 8 | 16 days | 12-bit | 2013 | ~185 km |
ASTER | 15 to 90 m | 14 | Varies | 8-bit and 12-bit | 1999 | 60 km |
Sentinel-2 | 10 to 60 m | 13–14 | 5 days | 12-bit | 2015 | 290 km |
SPOT | 20 m | Varies | 1–3 days | 8-bit to 12-bit | 1986 | ~60 km |
RapidEye | 5 m | 5 | 1–5 days | 12-bit | 2008 | 77 km |
PlanetScope | 3 to 5 m | 4 | 1–3 days | 12-bit | 2016 | ~20–40 km |
SkySat | 0.8 to 1.1 m | 4 | Varies | 12-bit | 2013 | ~10–14.5 km |
Worldview-1 | 0.5 m | 1 | 1.1 days | 11-bit | 2007 | 15.2 km |
Worldview-2 | 0.46 m | 8 | 1.1 days | 11-bit | 2009 | 18.2 km |
Worldview-3 | 0.31 m | 8 | 1.1 days | 14-bit | 2014 | 13.1 km |
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Radočaj, D.; Gašparović, M.; Jurišić, M. Open Remote Sensing Data in Digital Soil Organic Carbon Mapping: A Review. Agriculture 2024, 14, 1005. https://doi.org/10.3390/agriculture14071005
Radočaj D, Gašparović M, Jurišić M. Open Remote Sensing Data in Digital Soil Organic Carbon Mapping: A Review. Agriculture. 2024; 14(7):1005. https://doi.org/10.3390/agriculture14071005
Chicago/Turabian StyleRadočaj, Dorijan, Mateo Gašparović, and Mladen Jurišić. 2024. "Open Remote Sensing Data in Digital Soil Organic Carbon Mapping: A Review" Agriculture 14, no. 7: 1005. https://doi.org/10.3390/agriculture14071005