GEE数据集:欧美1950-2022年扩展春季指数(SI-x)

2024-09-05 08:19:46 浏览数 (2)

高分辨率扩展春季指数数据库

简介

扩展春季指数(SI-x)为研究春季开始的时间及其与气候变化的关系提供了一个全面的数据集。 这些由每日最低和最高气温得出的模型可追踪主要植物物种的初叶和初花事件。 通过将温度数据转化为一致的指数,SI-x 可以计算冻害指数。 该数据集对北美(1980-2022 年)和欧洲(1950-2020 年)的春季物候进行了多年代、高分辨率(1 公里)分析。

数据集说明

空间信息

目前,1 公里 SI-x 产品可用于北美和中美两个研究区域(位于北纬 14°02'31.3 "N 至 55°37'04.1 "N 和西经 56°05'50.7 "W 至 126°22'06.1 "W 之间)。 该数据集采用了 1980 年至 2022 年的 Daymet 第 4 版。 每日最高和最低气温以及昼长可在 GEE 中获得。 这些 SI-x 产品是 Izquierdo-Verdiguier 等人的研究成果的更新版。

欧洲(位于北纬 35°55'48.7 "N 和 73°32'47.1 "N 之间,西经 10°36'29.5 "W 和 44°50'29.5 "E 之间)。 日最高气温和日最低气温来自 1950 年至 2020 年欧洲观测资料缩减版(E-OBS)第 3 版,可在此处下载。 昼长是在将数据导入 GEE 后计算(模拟)得出的(导入是因为 GEE 无法直接获得该数据)。

代码

代码语言:javascript复制
var geometry = /* color: #d63000 */ee.Geometry.MultiPoint();
function linspace(a,b,n) {
    if(typeof n === "undefined") n = Math.max(Math.round(b-a) 1,1);
    if(n<2) { return n===1?[a]:[]; }
    var i,ret = Array(n);
    n--;
    for(i=n;i>=0;i--) { ret[i] = (i*b (n-i)*a)/n; }
    return ret;
}

var lf_eu = ee.ImageCollection('projects/sat-io/open-datasets/SIx_products/LeafEuropev3')
.map(function(img){return img.mask(img.neq(0)).select([3],['leaf']).copyProperties(img)});
var bl_eu = ee.ImageCollection('projects/sat-io/open-datasets/SIx_products/BloomEuropev3')
.map(function(img){return img.mask(img.neq(0)).select([3],['bloom']).copyProperties(img)});
var di_am = ee.ImageCollection('projects/sat-io/open-datasets/SIx_products/DI_Daymetv4')
.map(function(img){return img.select([3],['damage']).copyProperties(img)});
var di_er = ee.ImageCollection('projects/sat-io/open-datasets/SIx_products/DI_Europev3')
.map(function(img){return img.select([3],['damage']).copyProperties(img)});

var yrs = linspace(1950,2022,73);

function compositeAreas(yr){
var image = ee.Image(ee.ImageCollection('projects/sat-io/open-datasets/SIx_products/LeafDaymetv4')
  .filter(ee.Filter.eq('system:index',yr.toString())).select('leaf').first());

var image2 = ee.Image(lf_eu.filter(ee.Filter.eq('system:index',yr.toString())).first());

var image3 = ee.Image(ee.ImageCollection('projects/sat-io/open-datasets/SIx_products/BloomDaymetv4')
  .filter(ee.Filter.eq('system:index',yr.toString())).select('bloom').first());

var image4 = ee.Image(bl_eu.filter(ee.Filter.eq('system:index',yr.toString())).first());
  
var image5 = ee.Image(di_am.filter(ee.Filter.eq('system:index',yr.toString())).first());

var image6 = ee.Image(di_er.filter(ee.Filter.eq('system:index',yr.toString())).first());

var final = ee.ImageCollection([image,image2]).mosaic()
.addBands(ee.ImageCollection([image3,image4]).mosaic())
.addBands(ee.ImageCollection([image5,image6]).mosaic());
return final.set('year',yr);
}

var pheno = ee.ImageCollection(yrs.map(compositeAreas));
print(pheno);

var vis_vi1 = {bands:['leaf'],min:0 , max: 250, palette:["af0000","eb1e00","ff6400","ffb300","ffeb00","9beb4a","33db80","00b4ff","0064ff","000096"]};
Map.addLayer(pheno,vis_vi1,'Leaf');

var vis_vi2 = {bands:['bloom'],min:0, max: 250, palette:["af0000","eb1e00","ff6400","ffb300","ffeb00","9beb4a","33db80","00b4ff","0064ff","000096"]};
Map.addLayer(pheno,vis_vi2,'Bloom');

var vis_vi3 = {bands:['damage'],min:-150 , max: 150, palette:["ff3636","ff7b71","ffafaf","ffe8de","ffffff","dae4ff","b1c5ff","6d93ff","3e70ff"]};
Map.addLayer(pheno,vis_vi3,'DI');

Map.setCenter(-32.639,40.407,3)

代码链接

https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:analysis-ready-data/EXTENDED-SPRING-INDICES

APP链接

https://emma.users.earthengine.app/view/spring-onset

结果

引用

Izquierdo-Verdiguier, Emma, Raúl Zurita-Milla, Toby R. Ault, and Mark D. Schwartz. "Development and analysis of spring plant phenology products: 36 years of 1-km grids over the conterminous US." Agricultural and forest meteorology 262 (2018): 34-41.

许可

This work is licensed under a CC BY-NC 4.0 license.

Created by: Izquierdo-Verdiguier. 2024

Curated in GEE by : Samapriya Roy

Keyworks: spring onset, phenology, climate change

Last updated in GEE: 2024-08-29

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