China's annual dryness index data set

Introduction:

China's annual dryness index, with a spatial resolution of 1km and a time period of 1901-2022, is a ratio without units. This data set is calculated based on the monthly potential evapotranspiration (PET) and precipitation (PRE) of 1km in China using the ratio method (annual AI = annual PET/annual PRE). AI is an indicator of the degree of dryness and humidity in an area. Generally speaking, according to the AI classification, the area can be generally divided into humid (AI<1, equivalent to forest) and semi-humid (AI between 1-1.5, equivalent to forest grassland). , semi-arid (AI between 1.5-4, equivalent to steppe) and arid areas (AI ≥ 4, equivalent to desert). Preface – Artificial Intelligence Tutorial

The dryness index refers to the ratio of the water vapor content in the air to the maximum water vapor content at that temperature, expressed as a percentage. Its function is to measure the degree of dryness in the air and is an important indicator for evaluating the degree of dryness and humidity of the air.

Dryness index is widely used in meteorology, agriculture, industry, medical and other fields. In meteorology, it is used to evaluate air humidity, grasp precipitation potential, predict meteorological disasters, etc.; in agriculture, it is used to judge the degree of soil dryness and moisture, and provide reasonable irrigation; in industry, it is used to control humidity in the production process to ensure product quality. Quality; in medical treatment, used to assess the degree of dehydration in a patient's body and provide appropriate treatment.

The larger the dryness index, the higher the relative dryness in the air and the lower the water vapor content in the air; conversely, the smaller the dryness index, the higher the relative humidity in the air and the higher the water vapor content in the air. Normally, the dryness index is between 40% and 60%, which is suitable for human health and production activities.

Dataset ID:

TPDC/CHINA_ARID_INDEX

Time range: 1901-2022

Scope: Nationwide

Source: National Tibetan Plateau Scientific Data Center

Copy code snippet:

var images = pie.ImageCollection("TPDC/CHINA_SOIL_EROSION_K")

There is a bug in PIE, that is, the band name is not as shown below but uses "B1"

name	type	Spatial resolution (meters)	Invalid value
dryness index	Float32	1000	-32768

date

string

Image date

Code:

var img =  pie.ImageCollection("TPDC/CHINA_SOIL_EROSION_K")
            //.filterDate("2000-01-01", "2006-01-01")
            .select("B1").first()
            //.mean();
print(img);
//设定颜色预览组合
visParams = {
            palette: ['#0D0887','#5B02A3','#9A179B','#CB4678','#EB7852','#FBB32F','#F0F921']};
//定位地图中心
Map.centerObject(img, 3);
//加载影像
Map.addLayer(img, visParams, "img");

China's annual dryness index data set

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