Agricultural greenhouses datasets of 2010, 2016, and 2022 in China

Agricultural greenhouses, serving as facilities to protect crops and enhance farmland production efficiency, play a crucial role in the modernization process of agricultural production. With continuous innovation and promotion of greenhouse technology, the area covered by greenhouses in China has been steadily increasing over the past 20 years, far surpassing that of other countries.
In this study, we utilized Landsat-7 imagery on the Google Earth Engine platform and employed a Random Forest classifier to extract the distribution of agricultural greenhouses across China mainland for the years 2010, 2016, and 2022. The classification results underwent further visual inspection and refinement through morphological checks, hole filling, and manual contour adjustments. The resulting dataset achieved an overall accuracy exceeding 97% for each of the years 2010, 2016, and 2022.
The dataset, China GH, encompasses both the distribution of greenhouse sample points across mainland China and the precise geographic coordinates of agricultural greenhouses. It is stored in Shapefile format, TIFF format, and uses the WGS-84 coordinate system. The dataset includes:
• Vector data of agricultural greenhouse sample points in mainland China.( Shapefile )
• Raster data of agricultural greenhouse distribution in mainland China. ( TIFF )
• Relevant thematic maps. ( TIFF )
All raster files are in unsigned 8-bit integer format and use 255 as no-data value (pixels ignored by prediction), following an specific naming convention:
1. Project name: China Greenhouse (China GH)
2. Procedure combination: Random Forest ( RF )
3. Spatial resolution: 30m
4. Begin of time reference, date of first Landsat composite used by the modeling (20100101)
5. End of time reference: date of last Landsat composite used by the modeling (20221231)
6. Spatial extent: china mainland
7. Coordinate system: World Geodetic System 1984
8. Version: v1
Users can employ our China GH dataset in the following aspects :(1) Assist in agricultural layout and land use planning. (2) Assist agricultural producers to master the layout and scale of greenhouses to achieve the optimization of crop planting and management. (3) Research on the layout of agricultural greenhouses and crop growth in combination with other agriculture-related data. (3) Analyze the supply and demand of agricultural products in facilities, and guide the market pricing and sales strategy of agricultural products.

农业温室作为保护作物、提升农田生产效率的核心设施，在农业生产现代化进程中发挥着至关重要的作用。随着温室技术的持续创新与推广，近20年来中国温室种植面积稳步增长，远超其他国家。

本研究依托谷歌地球引擎（Google Earth Engine）平台的陆地卫星7号（Landsat-7）影像，采用随机森林（Random Forest）分类器，提取了2010年、2016年及2022年中国大陆地区的农业温室空间分布。分类结果还通过形态学检查、孔洞填充及手动轮廓调整进行了进一步的目视校验与优化。最终数据集在2010、2016、2022三个年份的总体精度均超过97%。

本数据集命名为China GH，涵盖中国大陆地区温室样本点分布数据与农业温室精确地理坐标信息。数据集采用Shapefile与TIFF格式存储，并使用WGS-84坐标系。数据集包含以下内容：
• 中国大陆地区农业温室样本点矢量数据（Shapefile格式）
• 中国大陆地区农业温室分布栅格数据（TIFF格式）
• 相关专题地图（TIFF格式）

所有栅格文件均采用无符号8位整数格式，以255作为无数据值（即预测过程中忽略的像素），并遵循特定命名规范，具体如下：
1. 项目名称：中国温室（China GH）
2. 处理流程组合：随机森林（Random Forest, RF）
3. 空间分辨率：30米
4. 时间基准起始：建模所用首幅陆地卫星合成影像日期（20100101）
5. 时间基准结束：建模所用末幅陆地卫星合成影像日期（20221231）
6. 空间范围：中国大陆地区
7. 坐标系：1984世界大地测量系统（World Geodetic System 1984, WGS-84）
8. 版本：v1

用户可将本China GH数据集应用于以下场景：
(1) 支撑农业布局与土地利用规划工作；
(2) 帮助农业生产者掌握温室布局与规模情况，实现作物种植与管理优化；
(3) 结合其他农业相关数据，开展农业温室布局与作物生长相关研究；
(4) 分析设施农产品供需状况，为农产品市场定价与销售策略制定提供参考。

搜集汇总

背景与挑战

背景概述

该数据集涵盖了中国大陆2010年、2016年和2022年农业温室的分布情况，数据精度高，格式多样，适用于农业布局规划、作物种植优化及农业产品市场分析等研究。