干旱区生态环境知识资源中心(Arid): A two-step mapping of irrigated corn with multi-temporal MODIS and Landsat analysis ready data

Arid

DOI	10.1016/j.isprsjprs.2021.04.007
	A two-step mapping of irrigated corn with multi-temporal MODIS and Landsat analysis ready data
	Ren, Jie; Shao, Yang; Wan, Heng; Xie, Yanhua; Campos, Adam
通讯作者	Shao, Y (corresponding author), Virginia Tech, Geog Dept, Coll Nat Resources & Environm, Blacksburg, VA 24061 USA.
来源期刊	ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING
ISSN	0924-2716
EISSN	1872-8235
出版年	2021
卷号	176 页码:69-82
英文摘要	Timely and reliable information about irrigated croplands is important for crop water stress analysis and studies of water, energy, and food security. This study mapped irrigated and non-irrigated corn at 30 m resolution for the state of Nebraska using a two-step multi-temporal image classification of MODIS and Landsat Analysis Ready Data (ARD). Starting from the drought year of 2012, when there was a high contrast between irrigated and non-irrigated fields, we first conducted image classification using the 250 m MODIS multi-temporal NDVI data. Training pixels were automatically derived, based on counties with predominant irrigated and non-irrigated cornfields. The MODIS-derived irrigated vs. non-irrigated map was further spatially filtered to generate training data covering the entire Nebraska to support automated Landsat ARD classification, footprint-by-footprint. Three classification algorithms of multi-layer perceptron (MLP) neural network, Random Forest (RF), and Support Vector Machine (SVM) were implemented to classify all available Landsat ARD images within the growing season (i.e. May to November). Given the issues of scanline corrector (SLC) error and cloud contamination, the provisional Landsat-based classifications were finally gap-filled to generate a seamless statewise irrigation map guided by decreasing cross-validation accuracy. Pixel-wise accuracy assessments showed similar overall accuracies of 89.6%, 89.3%, and 90.0% for MLP, RF, and SVM, respectively. They are 3-6% higher than a commonly used gap-filing procedure based on valid (cloud free) pixel count for growing season images. The estimated areas of irrigated corn from Landsat-based mapping were consistent with the 2012 USDA county level census data (R-2 = 0.97 and RMSE = 37.70 km(2)). Using the 2012 Landsat-derived irrigation map and the USDA's annual Cropland Data Layer as inputs, we further developed training data for annual irrigation mapping between 2013 and 2018. Pixel-wise assessment of the 2016 map showed reasonable overall accuracies of 78.4-79.6% for three classification algorithms. The annual maps yielded R-2 of 0.94-0.98 and RMSE values of 37.70-57.62 km(2) for various mapping years compared with USDA county statistics. These results suggest that our proposed two-step analytical method has a high potential for automated annual irrigation mapping at 30 m spatial resolution (especially for the arid and semi-arid western U.S.), providing clear field boundaries and irrigation frequency information that are vitally important for accurate agricultural water use analysis.
英文关键词	Multi-temporal classification Landsat ARD Gap-filling Annual irrigation mapping
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:000655474600007
WOS关键词	SUPPORT VECTOR MACHINE ; CONTERMINOUS UNITED-STATES ; NEURAL-NETWORK ; ANCILLARY DATA ; RANDOM FOREST ; CROPLAND ; CLASSIFICATION ; CLASSIFIERS ; FUSION ; TRENDS
WOS类目	Geography, Physical ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology
WOS研究方向	Physical Geography ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
来源机构	南京大学
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/367785
作者单位	[Ren, Jie] Nanjing Univ, Sch Geog & Ocean Sci, 163 Xianlin Ave, Nanjing 210023, Peoples R China; [Shao, Yang; Wan, Heng; Campos, Adam] Virginia Tech, Geog Dept, Coll Nat Resources & Environm, Blacksburg, VA 24061 USA; [Xie, Yanhua] Univ Wisconsin, Ctr Sustainabil & Global Environm, Nelson Inst Environm Studies, 1710 Univ Ave, Madison, WI 53726 USA
推荐引用方式 GB/T 7714	Ren, Jie,Shao, Yang,Wan, Heng,et al. A two-step mapping of irrigated corn with multi-temporal MODIS and Landsat analysis ready data[J]. 南京大学,2021,176:69-82.
APA	Ren, Jie,Shao, Yang,Wan, Heng,Xie, Yanhua,&Campos, Adam.(2021).A two-step mapping of irrigated corn with multi-temporal MODIS and Landsat analysis ready data.ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING,176,69-82.
MLA	Ren, Jie,et al."A two-step mapping of irrigated corn with multi-temporal MODIS and Landsat analysis ready data".ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING 176(2021):69-82.