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Deriving Annual Double-Season Cropland Phenology Using Landsat Imagery

by 1,2,*, 2 and 3
Nicholas School of Environment, Duke University, Durham, NC 27710, USA
Department of Geography, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
School of Design, Shanghai Jiao Tong University, Shanghai 200240, China
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(20), 3275;
Received: 30 August 2020 / Revised: 28 September 2020 / Accepted: 2 October 2020 / Published: 9 October 2020
(This article belongs to the Collection Feature Papers for Section Environmental Remote Sensing)
Cropland phenology provides key information in managing agricultural practices and modelling crop yield. However, most of the existing phenological products have coarse spatial resolution ranging from 250 to 8000 m, which is not sufficient to capture the critical spatial details of cropland phenology at the landscape scale. Landsat imagery provides an unprecedented data source to generate 30-m spatial resolution phenological products. This paper explored the potential of utilizing multi-year Landsat enhanced vegetation index to derive annual phenological metrics of a double-season agricultural land from 1993 to 2009 in a sub-urban area of Shanghai, China. We used all available Landsat TM and ETM+ observations (538 scenes) and developed a Landsat double-cropping phenology (LDCP) algorithm. LDCP captures the temporal trajectory of multi-year enhanced vegetation index time series very well, with the degree of fitness ranging from 0.78 to 0.88 over the study regions. We found good agreements between derived annual phenological metrics and in situ observation, with root mean square error ranging from 8.74 to 18.04 days, indicating that the proposed LDCP is capable of detecting double-season cropland phenology. LDCP could reveal the spatial heterogeneity of cropland phenology at parcel scales. Phenology metrics were retrieved for approximately one-third and two-thirds of the 17 years for the first and second cropping cycles, respectively, depending on the number of good quality Landsat data. In addition, we found an advanced peak of season for both cropping cycles in 50–60% of the study area, and a delayed start of season for the second cropping cycle in 50–70% of the same area. The potential drivers of those trends might be climate warming and changes in agricultural practices. The derived cropland phenology can be used to help estimate historical crop yields at Landsat spatial resolution, providing insights on evaluating the effects of climate change on temporal variations of crop growth, and contributing to food security policy making. View Full-Text
Keywords: double-cropping; phenology; Landsat; enhanced vegetation index; cubic spline models double-cropping; phenology; Landsat; enhanced vegetation index; cubic spline models
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MDPI and ACS Style

Qiu, T.; Song, C.; Li, J. Deriving Annual Double-Season Cropland Phenology Using Landsat Imagery. Remote Sens. 2020, 12, 3275.

AMA Style

Qiu T, Song C, Li J. Deriving Annual Double-Season Cropland Phenology Using Landsat Imagery. Remote Sensing. 2020; 12(20):3275.

Chicago/Turabian Style

Qiu, Tong, Conghe Song, and Junxiang Li. 2020. "Deriving Annual Double-Season Cropland Phenology Using Landsat Imagery" Remote Sensing 12, no. 20: 3275.

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