SSURGO(POLARIS)土壤属性的概率重绘--美国大陆(CONUS)30米土壤属性概率图数据库。绘制的CONUS变量包括土壤质地、有机物、pH值、饱和导水率、Brooks-Corey和Van Genuchten保水曲线参数、体积密度和饱和含水量。
| Variable |
Description |
Units |
| silt |
silt percentage |
% |
| sand |
sand percentage |
% |
| clay |
clay percentage |
% |
| bd |
bulk density |
g/cm3 |
| theta_s |
saturated soil water content |
m3/m3 |
| theta_r |
residual soil water content |
m3/m3 |
| ksat |
saturated hydraulic conductivity |
log10(cm/hr) |
| ph |
soil pH in H20 |
N/A |
| om |
organic matter |
log10(%) |
| lambda |
pore size distribution index (brooks corey) |
N/A |
| hb |
bubbling pressure (brooks corey) |
log10(kPa) |
| n |
measure of the pore size distribution (van genuchten) |
N/A |
| alpha |
scale parameter inversely proportional to mean pore diameter (van genuchten) |
log10(kPa-1) |
文献引用：
Chaney, Nathaniel W., Budiman Minasny, Jonathan D. Herman, Travis W. Nauman, Colby W. Brungard, Cristine LS Morgan Alexander B. McBratney, Eric F. Wood, and Yohannes Yimam. "POLARIS soil properties: 30‐m probabilistic maps of soil properties over the contiguous United States." Water Resources Research 55, no. 4 (2019): 2916-2938.
数据特点¶
POLARIS提供了一个空间上连续的、内部一致的、定量预测的土壤系列。它为SSURGO的主要弱点提供了潜在的解决方案：1）利用周边地区的调查数据填补未绘制区域的空白；2）消除政治边界上的人为不连续性；3）使用高分辨率的环境协变量数据导致粗大多边形的空间分解。
该数据集可从地表获得不同深度的数据，而提供的统计数据包括平均数、模式、中位数和百分位数，只有中位数被包括在所创建的集合中。
| Depth from Surface |
| 0-5 cm |
| 5-15 cm |
| 15-30 cm |
| 30-60 cm |
| 60-100 cm |
| 100-200 cm |
总体数据集包括处理大约80,000个文件，这些文件在不同深度的每个属性的集合中被转换为单个图像。例如，bd_mean集合包括bd_0_5，代表连续的美国bd值在距离地表0-5厘米深度的单一图像。
数据提供者的说明¶
05/01/2019 - 变量hb、alpha、ksat、om是在log10空间。
05/01/2019 - 由于文件大小的限制，1 arcsec数据库被分割成1x1度的tiffs。每个变量/层/统计数字都有自己的虚拟栅格，作为所有较小的1x1度块的 "粘合剂"。关于虚拟栅格的更多信息，见
https://www.gdal.org/gdal_vrttut.html.
06/02/2019 - 变量hb和alpha最初报告的单位分别为log10（cm）和log10（cm-1）。这是一个打字错误。正确的单位分别是log10（kPa）和log10（kPa-1）。
代码：
var bd_mean = ee.ImageCollection('projects/sat-io/open-datasets/polaris/bd_mean');
var clay_mean = ee.ImageCollection('projects/sat-io/open-datasets/polaris/clay_mean');
var ksat_mean = ee.ImageCollection('projects/sat-io/open-datasets/polaris/ksat_mean');
var n_mean = ee.ImageCollection('projects/sat-io/open-datasets/polaris/n_mean');
var om_mean = ee.ImageCollection('projects/sat-io/open-datasets/polaris/om_mean');
var ph_mean = ee.ImageCollection('projects/sat-io/open-datasets/polaris/ph_mean');
var sand_mean = ee.ImageCollection('projects/sat-io/open-datasets/polaris/sand_mean');
var silt_mean = ee.ImageCollection('projects/sat-io/open-datasets/polaris/silt_mean');
var theta_r_mean = ee.ImageCollection('projects/sat-io/open-datasets/polaris/theta_r_mean');
var theta_s_mean = ee.ImageCollection('projects/sat-io/open-datasets/polaris/theta_s_mean');
var lambda_mean = ee.ImageCollection('projects/sat-io/open-datasets/polaris/lambda_mean');
var hb_mean = ee.ImageCollection('projects/sat-io/open-datasets/polaris/hb_mean');
var alpha_mean = ee.ImageCollection('projects/sat-io/open-datasets/polaris/alpha_mean');
//Import palette
var palettes = require('users/gena/packages:palettes');
Map.addLayer(bd_mean.first(),{min:0.67,max:1.6,palette:palettes.cmocean.Delta[7]},'bd_mean_0_5',false)
Map.addLayer(clay_mean.first(),{min:3,max:55,palette:palettes.cmocean.Speed[7]},'clay_mean_0_5')
Map.addLayer(ksat_mean.first(),{min:-1,max:1.8,palette:palettes.cmocean.Haline[7]},'ksat_mean_0_5',false)
Map.addLayer(n_mean.first(),{min:1.22,max:1.6,palette:palettes.cmocean.Solar[7]},'n_mean_0_5')
Map.addLayer(om_mean.first(),{min:-0.8,max:1.8,palette:palettes.cmocean.Gray[7]},'om_mean_0_5',false)
Map.addLayer(ph_mean.first(),{min:4,max:9,palette:palettes.cmocean.Oxy[7]},'ph_mean_0_5')
Map.addLayer(sand_mean.first(),{min:5,max:90,palette:palettes.cmocean.Dense[7]},'sand_mean_0_5',false)
Map.addLayer(silt_mean.first(),{min:2,max:80,palette:palettes.cmocean.Curl[7]},'silt_mean_0_5')
Map.addLayer(theta_r_mean.first(),{min:0.022,max:0.15,palette:palettes.cmocean.Algae[7]},'theta_r_mean_0_5',false)
Map.addLayer(theta_s_mean.first(),{min:0.4,max:0.8,palette:palettes.cmocean.Turbid[7]},'theta_s_mean_0_5',false)
Map.addLayer(alpha_mean.first(),{min:-0.15,max:0.2,palette:palettes.cmocean.Speed[7]},'alpha_mean_0_5',false)
Map.addLayer(hb_mean.first(),{min:-0.15,max:0.75,palette:palettes.cmocean.Matter[7]},'hb_mean_0_5',false)
Map.addLayer(lambda_mean.first(),{min:0.2,max:0.5,palette:palettes.cmocean.Balance[7]},'lambda_mean_0_5')
代码连接：
https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:soil-properties/POLARIS-PROBABILISTIC-SOIL-PROPERTIES-30
不适用GEE可以在这里下载: Index of /POLARIS
License¶
POLARIS is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Curated by: Samapriya Roy
Keywords: Digital soil mapping, Soil, Environmental modeling, High performance computing
Last updated dataset: 2019-05-04
Last curated: 2022-03-05
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