Deriving Annual Double-Season Cropland Phenology Using Landsat Imagery

Tong Qiu,Conghe Song,Junxiang Li

doi:10.3390/rs12203275

Abstract

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.

Highlights

Cropland serves as the foundation of the stability of the society, producing foods and many other goods that are vital to human wellbeing [1]
We extended the algorithm proposed by Melaas, Friedl and Zhu [17] and Melaas et al [18] and developed a Landsat double-cropping phenology (LDCP) to extract annual phenological metrics of double-season cropland
The degree of fitnesses between the observed normalized multi-year enhanced vegetation index (EVI) and LDCP modeled results were centered at 0.83 ± 0.04, 0.85 ± 0.02, 0.82 ± 0.03, and 0.83 ± 0.03 for Jinshan, Fengxian, Qingpu, and Songjiang district, respectively (Figure S3), indicating that LDCP can effectively capture the temporal trajectories of double-season cropland phenology

Summary

Introduction

Cropland serves as the foundation of the stability of the society, producing foods and many other goods that are vital to human wellbeing [1]. Agricultural practices such as irrigation, fertilization, and crop rotation, affect energy, water, and carbon cycles between land and atmosphere [2]. Phenological stages of the cropland, i.e., the timing of start and peak of the growing season, can provide key information about agricultural practices [3]. Sakamoto et al [8] applied a wavelet transformation to a MODIS enhanced vegetation index (EVI) dataset and derived phenological stages of paddy rice in Japan. Studies have reported that multiple growth periods of cropland can be described by step-wise logistic functions [9,10]

Methods

Results

Conclusion