东北三省2020-2022年间10 m空间分辨率耕地资源空间分布数据集

doi:10.19788/j.issn.2096-6369.230202

Abstract

Abstract:

Timely and accurate acquisition of cropland spatial distribution data is of great significance for agricultural production management, planting structure adjustment and food security. In this study, three northeastern provinces (Heilongjiang, Liaoning and Jilin) were selected as the research area. Based on massive Sentinel-2 data, a 12-month Normalized Difference Vegetation Index (NDVI) dataset of cropland and non-cropland samples in a specific year was established in each province. The Google Earth Engine remote sensing computing platform was used to conduct supervised classification according to the difference characteristics of NDVI between cropland and non-cropland samples, and the spatial distribution data set of cropland resources with 10 m spatial resolution in the three northeastern provinces during 2020-2022 was obtained. The data set is an update of the latest available cropland resource data set, which can provide data support and scientific services for the scientific protection of phaeozem in Northeast China, the implementation of the strategy of "storing grain in land, storing grain in technology" and the guarantee of food security.

Key words: cropland, remote sensing, Sentinel-2, Google Earth Engine

SHEN Ge, LIU Hang, LI DanDan, CHEN Shi, ZOU JinQiu. A 10 m Spatial Resolution Dataset for the Spatial Distribution of Cropland Resources in the Three Northeastern Provinces from 2020 to 2022[J].Journal of Agricultural Big Data, 2023, 5(2): 2-8.

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References 13

[1]	Wu M Q, Huang W J, Niu Z, et al. Validation of synthetic daily Landsat NDVI time series data generated by the improved spatial and temporal data fusion approach[J]. Information Fusion, 2018, 40: 34-44. doi: 10.1016/j.inffus.2017.06.005
[2]	Denize J, Hubert-Moy L, Betbeder J, et al. Evaluation of using sentinel-1 and -2 time-series to identify winter land use in agricultural landscapes[J], . Remote Sensing, 2019, 11(2): 37. doi: 10.3390/rs11010037
[3]	何蔓, 张军岩. 全球土地利用与覆盖变化(LUCC)研究及其进展[J], . 国土资源, 2005(9): 22-25.
	He M, Zhang J Y. Scientific research progress of global land-use and land-cover changes project[J]. Land and Resources, 2005(9): 22-25. (in Chinese)
[4]	White E V, Roy D P. A contemporary decennial examination of changing agricultural field sizes using Landsat time series data[J]. Geo: Geography and Environment, 2015, 2(1): 33-54. doi: 10.1002/geo2.4
[5]	蔡志文, 何真, 王文静, 等. 基于多源国产高分卫星时空信息的米级分辨率耕地提取[J]. 遥感学报, 2022, 26(7): 1368-1382. doi: 10.11834/jrs.20221069
	Cai Z W, He Z, Wang W J, et al. Mapping cropland at metric resolution using the spatiotemporal information from multi-source GF satellite data[J]. National Remote Sensing Bulletin, 2022, 26(7): 1368-1382. (in Chinese) doi: 10.11834/jrs.20221069
[6]	Fritz S, See L, Kummer S. Mapping global cropland and field size[J]. Global Change Biology, 2015, 21(5): 1980-1992. doi: 10.1111/gcb.12838 pmid: 25640302
[7]	Graesser J, Ramankutty N. Detection of cropland field parcels from Landsat imagery[J], . Remote Sensing of Environment 2017, 201: 165-180. doi: 10.1016/j.rse.2017.08.027
[8]	Xiong J, Thenkabail P S, Gumma K M, et al. Automated cropland mapping of continental Africa using Google Earth Engine cloud computing[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2017, 126: 225-244. doi: 10.1016/j.isprsjprs.2017.01.019
[9]	Xu L, Ming D P, Zhou W, et al. Farmland extraction from high spatial resolution remote sensing images based on stratified scale pre-estimation[J]. Remote Sensing, 2019, 11(2): 108. doi: 10.3390/rs11020108
[10]	Zhang D J, Pan Y Z, Zhang J S, et al. A generalized approach based on convolutional neural net-works for large area cropland mapping at very high resolution[J]. Remote Sensing of Environment, 2020, 247: 111912. doi: 10.1016/j.rse.2020.111912
[11]	Yang J W, Zhang J S, Zhu S, et al. Cropland mapping in fragmented agricultural landscape using modified pyramid scene parsing network[J]. IGARSS 2019-2019 IEEE International Geoscience and Remote Sensing Symposium. Yokohama, Japan: IEEE, 2019: 9831-9834.
[12]	宋德娟, 张承明, 杨晓霞, 等. 高分二号遥感影像提取冬小麦空间分布[J]. 遥感学报, 2020, 24(5): 596-608.
	Song D J, Zhang C M, Yang X X, et al. Extracting winter wheat spatial distribution information from GF-2 image[J]. Journal of Remote Sensing, 2020, 24(5): 596-608. (in Chinese)
[13]	徐俊峰, 张保明. 多特征融合的高分辨率遥感影像飞机目标变化检测[J]. 遥感学报, 2020, 24(1): 37-52.
	Xu J F, Zhang B M. Aircraft target change detection for high-resolution remote sensing images using multi-feature fusion[J]. Journal of Remote Sensing, 2020, 24(1): 37-52. (in Chinese)

波段	中央波长 (μm)	空间分辨率 (m)
波段1 -沿海气溶胶	0.443	60
波段2 - 蓝	0.49	10
波段3 - 绿	0.56	10
波段4 - 红	0.665	10
波段5 - 植被红边	0.705	20
波段6 -植被红边	0.74	20
波段7 -植被红边	0.783	20
波段8 - 近红外	0.842	10
波段8A -植被红边	0.865	20
波段9 - 水蒸气	0.945	60
波段10 - 短波红外线－卷云	1.375	60
波段11 -短波红外线	1.61	20
波段12 -短波红外线	2.19	20

时段	黑龙江省			辽宁省			吉林省
时段	2020	2021	2022	2020	2021	2022	2020	2021	2022
1月	1119	1109	1119	419	399	419	473	479	473
2月	1048	1030	1054	379	385	393	456	467	469
3月	1151	1152	1173	420	427	424	506	498	502
4月	1137	1129	1156	410	412	414	477	473	473
5月	1145	1156	1190	424	422	425	496	498	500
6月	1124	1093	1153	405	401	398	497	480	486
7月	1137	1157	1194	410	422	421	476	498	498
8月	1141	1160	1188	425	424	421	504	498	503
9月	1134	1105	1149	403	386	397	484	474	482
10月	1138	1151	1205	419	424	431	498	497	511
11月	1072	1099	1103	392	412	425	481	487	503
12月	1156	1122	0	441	424	0	503	488	0
小计	13502	13463	12684	4947	4938	4568	5851	5837	5400
总计	71190

地区	2020	2021	2022
黑龙江省	95.24	93.88	89.80
辽宁省	97.81	97.08	91.24
吉林省	96.27	95.65	93.16

A 10 m Spatial Resolution Dataset for the Spatial Distribution of Cropland Resources in the Three Northeastern Provinces from 2020 to 2022

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