公告:所有在
2025 年 4 月 15 日之前注册使用 Earth Engine 的非商业项目都必须
验证是否符合非商业性质的资格条件,才能继续使用 Earth Engine。
Concepts
使用集合让一切井井有条
根据您的偏好保存内容并对其进行分类。
此 API 可提供对各种地理空间图像数据的简单统一的访问。地理空间是指数据与地球表面的位置相关联。图像数据是指以像素值均匀网格形式结构化的数据,与传统图像非常相似。
此类数据最常见的示例是卫星图像,与用户在 Google 地图和 Google 地球中看到的图像非常相似。不过,许多其他数据集具有相同的结构,例如网格化天气和气候数据集、地形和土地覆盖数据集以及人口密度数据集。
身份验证
Earth Engine API 使用 OAuth 2.0 协议进行身份验证和授权。我们提供了便捷的库,可用于以各种语言通过 OAuth 2.0 发出 API 调用。如果您之前未使用过 OAuth 2.0 访问 Google API,不妨先熟悉一下使用 OAuth 2.0 访问 Google API 文档。
此 API 使用 Earth Engine OAuth 2.0 范围 https://www.googleapis.com/auth/earthengine.readonly
。如果您自行配置服务账号凭据,则需要明确请求该范围。如果您使用的是 Compute Engine 虚拟机实例中提供的默认应用凭据,则需要配置您的虚拟机实例以请求该范围。
素材资源
Earth Engine 数据模型的核心是类似文件系统的资源结构。资源主要有三种类型,具体说明如下:地理空间栅格数据本身存储在图片中,图片可以分组为大型集合,图片和集合还可以进一步整理到文件夹的层次结构中。
例如,以下路径描述了特定的 Landsat 影像:
LANDSAT/LC8_L1T/LC81180562013193LGN00
在此示例中,LANDSAT
是包含所有 Landsat 数据的顶级文件夹,LC8_L1T
用于标识一组特定的经过校准和地形校正的 Landsat 8 图像,而 LC81180562013193LGN00
用于标识该集合中的特定图像。资源路径可以包含字母、数字、下划线和连字符,并以正斜杠分隔。
每张图片都包含元数据,可用于轻松识别感兴趣的数据。这些图片级元数据属性通常包括:
- 图像的覆盖区几何图形,例如卫星图像或地形数据集的空间范围。
- 图片的相应时间戳,例如卫星图像采集时间,或气候模型数据集中的日期或月份。
- 任意键值属性,例如 Landsat 卫星图像的估计云覆盖百分比。
像素和地图投影
在 Earth Engine 中,每张图片都包含一个或多个数据波段。这些变量可能对应于普通 RGB 卫星影像的红色、绿色和蓝色波段,也可能对应于完全不同的内容,例如天气数据集中的温度和降水。每个波段中的像素都具有特定的数据类型,例如 uint8
或 float32
。
像素网格中的像素通过一种称为地图投影的数学函数对应于地球表面上的点。这种关系通常包含两个组成部分。首先,坐标参考系 (CRS) 定义了表示地球曲面上的点的二维坐标。不同的坐标参考系具有不同的属性,因此适合在不同的应用中使用不同类型的数据。坐标参考系通常使用标准标识符代码进行标识;常见的示例包括等距圆柱、Web 墨卡托和 UTM 坐标。
然后,像素坐标通常通过控制像素网格的物理比例和原点的仿射转换与空间坐标参考系统相关联。在 Earth Engine 中,我们会为每个图片存储一个数据金字塔:金字塔的基础层包含原始分辨率的原始数据,而金字塔的更高级别则存储分辨率降低的概览数据。金字塔的这些层级由仿射转换描述,其缩放比例相差 2 的连续倍数。
集合和文件夹
图片通常以大型数据集合的形式呈现,例如特定卫星采集的所有图片集合,或随时间变化的全球温度估计值集合。Earth Engine 会构建元数据索引,让您可以根据集合中图片的元数据过滤或查询图片,包括按位置和时间进行过滤。通常,单个集合中的所有图片都具有相同的结构,即相同的波段和相同的元数据属性。
图片和合集本身会整理成文件夹层次结构。文件夹非常类似于传统文件系统中的普通文件夹或目录:它是一个简单的容器,用于存放其他资源,即图片、合集和其他文件夹。并非所有图片都包含在集合中:某些数据集(例如特定的全球地形模型)可能只包含一张图片。无论如何,每项资源都由一个特定路径唯一标识,该路径用于标识资源在 Earth Engine 数据目录中的位置。
如未另行说明,那么本页面中的内容已根据知识共享署名 4.0 许可获得了许可,并且代码示例已根据 Apache 2.0 许可获得了许可。有关详情,请参阅 Google 开发者网站政策。Java 是 Oracle 和/或其关联公司的注册商标。
最后更新时间 (UTC):2025-07-26。
[null,null,["最后更新时间 (UTC):2025-07-26。"],[[["\u003cp\u003eThis API provides access to a wide variety of geospatial image data, including satellite imagery and gridded datasets like weather, climate, terrain, and population density.\u003c/p\u003e\n"],["\u003cp\u003eEarth Engine uses OAuth 2.0 for authentication, requiring the \u003ccode\u003ehttps://www.googleapis.com/auth/earthengine.readonly\u003c/code\u003e scope.\u003c/p\u003e\n"],["\u003cp\u003eEarth Engine data is organized as assets, including images, collections, and folders, with images containing bands of pixels and metadata.\u003c/p\u003e\n"],["\u003cp\u003eImages in Earth Engine are georeferenced using map projections and coordinate reference systems for accurate location representation.\u003c/p\u003e\n"],["\u003cp\u003eCollections allow filtering and querying of images based on metadata, facilitating efficient data discovery and analysis.\u003c/p\u003e\n"]]],["This API offers access to geospatial image data, structured as pixel grids, through a filesystem-like asset system. Access requires OAuth 2.0 authentication with a specific scope. Data is organized into **images**, **collections**, and **folders**. Each image has metadata, including footprint geometry and timestamps. Images have bands of data with defined pixel data types, linked to Earth's surface via a map projection. Images can be filtered in collections using metadata. A pyramid of data is also available.\n"],null,["# Concepts\n\nThis API provides simple, uniform access to a wide variety geospatial image data. By *geospatial* we mean that the data is associated with locations on the Earth's surface. By *image data* we mean data that is structured as a uniform grid of pixel values, much like a traditional image.\n\nThe most familiar example of this type of data is satellite imagery, much like users are accustomed to seeing in Google Maps and Earth. However, many other datasets have the same structure, including for example gridded weather and climate datasets, terrain and land cover datasets, and population density datasets.\n\nAuthentication\n--------------\n\nThe Earth Engine API uses the **OAuth 2.0** protocol for authentication and authorization. There are convenient libraries for using OAuth 2.0 to issue API calls in a wide range of languages. If you have not used OAuth 2.0 to access Google APIs before, you may wish to familiarize yourself with the documentation on [Using OAuth 2.0 to Access Google APIs](https://developers.google.com/identity/protocols/OAuth2).\n\nThis API uses the Earth Engine OAuth 2.0 **scope** `https://www.googleapis.com/auth/earthengine.readonly`. If you are configuring service account credentials yourself then you will need to request that scope explicitly. If you are using the default application credentials available in Compute Engine virtual machine instances, you will need to configure your VM instance to request that scope.\n\nAssets\n------\n\nAt the heart of the Earth Engine data model is a filesystem-like structure of **assets** . There are three primary types of assets, described in more detail below: geospatial raster data itself is stored in **images** , images can be grouped into large **collections** , and images and collections are further organized in a hierarchy of **folders**.\n\nFor example, the following path describes a particular Landsat image: \n\n LANDSAT/LC8_L1T/LC81180562013193LGN00\n\nIn this example, `LANDSAT` is a top-level folder that contains all the Landsat data, `LC8_L1T` identifies a particular collection of calibrated and terrain-corrected Landsat 8 images, and `LC81180562013193LGN00` identifies a particular image within that collection. Asset paths may contain letters, numbers, underscores, and hyphens, separated by forward slashes.\n\nEach image has metadata that makes it easy to identify data of interest. These per-image metadata properties typically include:\n\n- The footprint geometry of the image, such as the spatial extent of a satellite image or terrain dataset.\n- The timestamp of the image, such as the satellite image acquisition time, or the day or month in a climate model dataset.\n- Arbitrary key/value properties, such as the estimated cloud cover percentage of a Landsat satellite image.\n\nPixels and Map Projections\n--------------------------\n\nIn Earth Engine each image consists of one or more **bands** of data. These might correspond to the red, green, and blue bands of an ordinary RGB satellite image, or they might correspond to something completely different, such as temperature and precipitation in a weather dataset. The **pixels** in each band have a particular data type, e.g. `uint8` or `float32`.\n\nPixels in a pixel grid correspond to points on the Earth's surface via a mathematical function known as a **map projection** . This relationship typically has two components. First a **coordinate reference system** (CRS) defines 2D coordinates that represent points on the curved surface of the Earth. Different coordinate reference systems have different properties that make them appropriate to use with different types of data in different applications. Coordinate reference systems are typically identified using [standard identifier codes](http://epsg.io/); common examples are [equirectangular](https://en.wikipedia.org/wiki/Equirectangular_projection), [web Mercator](https://en.wikipedia.org/wiki/Web_Mercator), and [UTM](https://en.wikipedia.org/wiki/Universal_Transverse_Mercator_coordinate_system) coordinates.\n\nPixel coordinates are then typically related to the spatial coordinate reference system via an **affine transformation** that controls the physical scale and origin of the pixel grid. In Earth Engine we store a **pyramid** of data for each image: the base level of the pyramid contains the original data in its native resolution, and higher levels of the pyramid store reduced-resolution overview data. These levels of the pyramid are described by affine transformations whose scales differ by successive factors of two.\n\nCollections and Folders\n-----------------------\n\nImages often come in large **collections** of data, such as a collection of all images acquired by a particular satellite, or a collection of global temperature estimates over time. Earth Engine builds metadata indexes that allow you to **filter** or **query** the images in collections based on their metadata, including filtering by location and time. Typically, all images within a single collection have the same structure, i.e. the same bands and the same metadata properties.\n\nImages and collections are themselves organized into a hierarchy of **folders**. A folder is much like an ordinary folder or directory in a traditional filesystem: it is a simple container for other assets, i.e. for images, collections, and other folders. Not all images are contained in collections: some datasets, such as a particular global terrain model, may consist of only a single image. Regardless, every asset is uniquely identified by a particular path that identifies its location in the Earth Engine data catalog."]]