Google Earth Engine——干旱指数（KBDI）是一个连续的参考量表，用于估计土壤和煤层的干燥程度。该指数在没有下雨的每一天都会增加（增加的数量取决于每日的最高温度）-优快云博客

本文链接：https://blog.youkuaiyun.com/qq_31988139/article/details/120658217

Keetch-Byram干旱指数（KBDI）是一种评估土壤湿度的指标，广泛应用于干旱监测、野火预防。该指数基于降雨模式，随无雨天数增加而上升，降雨时下降。其范围从0到800，反映了从无水分缺失到极端干旱的状况。KBDI在雨水灌溉农业区特别有用，可用作实时干旱管理和预警系统。

摘要生成于 C知道，由 DeepSeek-R1 满血版支持，前往体验 >

Keetch-Byram Drought Index (KBDI) is a continuous reference scale for estimating the dryness of the soil and duff layers. The index increases for each day without rain (the amount of increase depends on the daily high temperature) and decreases when it rains. This system is based primarily on recent rainfall patterns. It is a measure of meteorological drought; it reflects water gain or loss within the soil.

The scale ranges from 0 (no moisture deficit) to 800 (extreme drought). The range of the index is determined by assuming that there is 20 cm of moisture in a saturated soil that is readily available to the vegetation (Keetch and Byram, 1968). KBDI is world widely used for drought monitoring for national weather forecast, wildfire prevention and usefully especially in regions with rain-fed crops.

Keetch-Byram干旱指数（KBDI）是一个连续的参考量表，用于估计土壤和煤层的干燥程度。该指数在没有下雨的每一天都会增加（增加的数量取决于每日的最高温度），而在下雨的时候会减少。这个系统主要是基于最近的降雨模式。它是对气象干旱的测量；它反映了土壤内的水分增减。

等级范围从0（无水分缺失）到800（极端干旱）。该指数的范围是通过假设饱和土壤中有20厘米的水分可供植被使用而确定的（Keetch和Byram，1968）。KBDI被广泛用于国家天气预报的干旱监测、野火预防，特别是在有雨水灌溉作物的地区非常有用。

Dataset Availability

2007-01-01T00:00:00 - 2021-10-05T00:00:00

Dataset Provider

Institute of Industrial Science, The University of Tokyo, Japan

Collection Snippet

Copied

ee.ImageCollection("UTOKYO/WTLAB/KBDI/v1")

Resolution

4000 meters

Bands Table

Name	Description	Min	Max
KBDI	Keetch-Byram Drought Index	0	800

引用：

Wataru Takeuchi, Soni Darmawan, Rizatus Shofiyati, Mai Van Khiem, Kyaw San Oo, Uday Pimple and Suthy Heng, 2015. Near-real time meteorological drought monitoring and early warning system for croplands in Asia. 36th Asian conference on remote sensing (ACRS): Manila, Philippines, Oct. 20, 2015.

代码：

var collection = ee.ImageCollection('UTOKYO/WTLAB/KBDI/v1')
  .select('KBDI')
  .filterDate('2019-01-01', '2019-01-10');
var bandViz = {
  min: 0,
  max: 800,
  palette: [
    '001a4d', '003cb3', '80aaff', '336600', 'cccc00', 'cc9900', 'cc6600',
    '660033'
  ]
};
Map.addLayer(collection.mean(), bandViz, 'Keetch-Byram Drought Index');
Map.setCenter(120, 3, 3);