- Ketersediaan Dataset
- 2001-01-01T00:00:00Z–2017-01-01T00:00:00Z
- Produsen Dataset
- iSDA
- Tag
Deskripsi
Kerapatan bulk, fraksi <2 mm pada kedalaman tanah 0-20 cm dan 20-50 cm, rata-rata dan standar deviasi yang diprediksi.
Nilai piksel harus ditransformasi kembali dengan x/100.
Di area hutan lebat (umumnya di Afrika Tengah), akurasi model rendah sehingga artefak seperti banding (garis-garis) mungkin terlihat.
Prediksi properti tanah dibuat oleh Innovative Solutions for Decision Agriculture Ltd. (iSDA) dengan ukuran piksel 30 m menggunakan machine learning yang dipadukan dengan data penginderaan jauh dan set pelatihan lebih dari 100.000 sampel tanah yang dianalisis.
Informasi selengkapnya dapat ditemukan dalam dokumentasi informasi teknis dan FAQ . Untuk mengirimkan masalah atau meminta dukungan, buka situs iSDAsoil.
Band
Band
Ukuran piksel: 30 meter (semua band)
| Nama | Unit | Min | Maks | Ukuran Piksel | Deskripsi |
|---|---|---|---|---|---|
mean_0_20 |
g/cm^3 | 44 | 197 | 30 meter | Kerapatan bulk, fraksi <2 mm, rata-rata yang diprediksi pada kedalaman 0-20 cm |
mean_20_50 |
g/cm^3 | 44 | 196 | 30 meter | Kerapatan bulk, fraksi <2 mm, rata-rata yang diprediksi pada kedalaman 20-50 cm |
stdev_0_20 |
g/cm^3 | 0 | 92 | 30 meter | Kerapatan bulk, fraksi <2 mm, standar deviasi pada kedalaman 0-20 cm |
stdev_20_50 |
g/cm^3 | 0 | 92 | 30 meter | Kerapatan bulk, fraksi <2 mm, standar deviasi pada kedalaman 20-50 cm |
Persyaratan Penggunaan
Persyaratan Penggunaan
Kutipan
Hengl, T., Miller, M.A.E., Križan, J., et al. African soil properties and nutrients mapped at 30 m spatial resolution using two-scale ensemble machine learning. Sci Rep 11, 6130 (2021). doi:10.1038/s41598-021-85639-y
Mengeksplorasi dengan Earth Engine
Editor Kode (JavaScript)
var mean_0_20 = '<RasterSymbolizer>' + '<ColorMap type="ramp">' + '<ColorMapEntry color="#00204D" label="0.8-1.05" opacity="1" quantity="105"/>' + '<ColorMapEntry color="#002D6C" label="1.05-1.19" opacity="1" quantity="119"/>' + '<ColorMapEntry color="#16396D" label="1.19-1.23" opacity="1" quantity="123"/>' + '<ColorMapEntry color="#36476B" label="1.23-1.25" opacity="1" quantity="125"/>' + '<ColorMapEntry color="#4B546C" label="1.25-1.28" opacity="1" quantity="128"/>' + '<ColorMapEntry color="#5C616E" label="1.28-1.31" opacity="1" quantity="131"/>' + '<ColorMapEntry color="#6C6E72" label="1.31-1.34" opacity="1" quantity="134"/>' + '<ColorMapEntry color="#7C7B78" label="1.34-1.36" opacity="1" quantity="136"/>' + '<ColorMapEntry color="#8E8A79" label="1.36-1.38" opacity="1" quantity="138"/>' + '<ColorMapEntry color="#A09877" label="1.38-1.41" opacity="1" quantity="141"/>' + '<ColorMapEntry color="#B3A772" label="1.41-1.43" opacity="1" quantity="143"/>' + '<ColorMapEntry color="#C6B66B" label="1.43-1.45" opacity="1" quantity="145"/>' + '<ColorMapEntry color="#DBC761" label="1.45-1.48" opacity="1" quantity="148"/>' + '<ColorMapEntry color="#F0D852" label="1.48-1.51" opacity="1" quantity="151"/>' + '<ColorMapEntry color="#FFEA46" label="1.51-1.85" opacity="1" quantity="154"/>' + '</ColorMap>' + '<ContrastEnhancement/>' + '</RasterSymbolizer>'; var mean_20_50 = '<RasterSymbolizer>' + '<ColorMap type="ramp">' + '<ColorMapEntry color="#00204D" label="0.8-1.05" opacity="1" quantity="105"/>' + '<ColorMapEntry color="#002D6C" label="1.05-1.19" opacity="1" quantity="119"/>' + '<ColorMapEntry color="#16396D" label="1.19-1.23" opacity="1" quantity="123"/>' + '<ColorMapEntry color="#36476B" label="1.23-1.25" opacity="1" quantity="125"/>' + '<ColorMapEntry color="#4B546C" label="1.25-1.28" opacity="1" quantity="128"/>' + '<ColorMapEntry color="#5C616E" label="1.28-1.31" opacity="1" quantity="131"/>' + '<ColorMapEntry color="#6C6E72" label="1.31-1.34" opacity="1" quantity="134"/>' + '<ColorMapEntry color="#7C7B78" label="1.34-1.36" opacity="1" quantity="136"/>' + '<ColorMapEntry color="#8E8A79" label="1.36-1.38" opacity="1" quantity="138"/>' + '<ColorMapEntry color="#A09877" label="1.38-1.41" opacity="1" quantity="141"/>' + '<ColorMapEntry color="#B3A772" label="1.41-1.43" opacity="1" quantity="143"/>' + '<ColorMapEntry color="#C6B66B" label="1.43-1.45" opacity="1" quantity="145"/>' + '<ColorMapEntry color="#DBC761" label="1.45-1.48" opacity="1" quantity="148"/>' + '<ColorMapEntry color="#F0D852" label="1.48-1.51" opacity="1" quantity="151"/>' + '<ColorMapEntry color="#FFEA46" label="1.51-1.85" opacity="1" quantity="154"/>' + '</ColorMap>' + '<ContrastEnhancement/>' + '</RasterSymbolizer>'; var stdev_0_20 = '<RasterSymbolizer>' + '<ColorMap type="ramp">' + '<ColorMapEntry color="#fde725" label="low" opacity="1" quantity="2"/>' + '<ColorMapEntry color="#5dc962" label=" " opacity="1" quantity="4"/>' + '<ColorMapEntry color="#20908d" label=" " opacity="1" quantity="5"/>' + '<ColorMapEntry color="#3a528b" label=" " opacity="1" quantity="7"/>' + '<ColorMapEntry color="#440154" label="high" opacity="1" quantity="9"/>' + '</ColorMap>' + '<ContrastEnhancement/>' + '</RasterSymbolizer>'; var stdev_20_50 = '<RasterSymbolizer>' + '<ColorMap type="ramp">' + '<ColorMapEntry color="#fde725" label="low" opacity="1" quantity="2"/>' + '<ColorMapEntry color="#5dc962" label=" " opacity="1" quantity="4"/>' + '<ColorMapEntry color="#20908d" label=" " opacity="1" quantity="5"/>' + '<ColorMapEntry color="#3a528b" label=" " opacity="1" quantity="7"/>' + '<ColorMapEntry color="#440154" label="high" opacity="1" quantity="9"/>' + '</ColorMap>' + '<ContrastEnhancement/>' + '</RasterSymbolizer>'; var raw = ee.Image("ISDASOIL/Africa/v1/bulk_density"); Map.addLayer( raw.select(0).sldStyle(mean_0_20), {}, "Bulk density, mean visualization, 0-20 cm"); Map.addLayer( raw.select(1).sldStyle(mean_20_50), {}, "Bulk density, mean visualization, 20-50 cm"); Map.addLayer( raw.select(2).sldStyle(stdev_0_20), {}, "Bulk density, stdev visualization, 0-20 cm"); Map.addLayer( raw.select(3).sldStyle(stdev_20_50), {}, "Bulk density, stdev visualization, 20-50 cm"); var converted = raw.divide(100); var visualization = {min: 1, max: 1.5}; Map.setCenter(25, -3, 2); Map.addLayer(converted.select(0), visualization, "Bulk density, mean, 0-20 cm");