Real-Time Monitoring of Crop Phenology in the Midwestern United States Using VIIRS Observations

Lingling Liu,Xiaoyang Zhang,Zhengwei Yang,Yunyue Yu,Feng Gao

doi:10.3390/rs10101540

Abstract

Real-time monitoring of crop phenology is critical for assisting farmers managing crop growth and yield estimation. In this study, we presented an approach to monitor in real time crop phenology using timely available daily Visible Infrared Imaging Radiometer Suite (VIIRS) observations and historical Moderate Resolution Imaging Spectroradiometer (MODIS) datasets in the Midwestern United States. MODIS data at a spatial resolution of 500 m from 2003 to 2012 were used to generate the climatology of vegetation phenology. By integrating climatological phenology and timely available VIIRS observations in 2014 and 2015, a set of temporal trajectories of crop growth development at a given time for each pixel were then simulated using a logistic model. The simulated temporal trajectories were used to identify spring green leaf development and predict the occurrences of greenup onset, mid-greenup phase, and maximum greenness onset using curvature change rate. Finally, the accuracy of real-time monitoring from VIIRS observations was evaluated by comparing with summary crop progress (CP) reports of ground observations from the National Agricultural Statistics Service (NASS) of the United States Department of Agriculture (USDA). The results suggest that real-time monitoring of crop phenology from VIIRS observations is a robust tool in tracing the crop progress across regional areas. In particular, the date of mid-greenup phase from VIIRS was significantly correlated to the planting dates reported in NASS CP for both corn and soybean with a consistent lag of 37 days and 27 days on average (p < 0.01), as well as the emergence dates in CP with a lag of 24 days and 16 days on average (p < 0.01), respectively. The real-time monitoring of maximum greenness onset from VIIRS was able to predict the corn silking dates with an advance of 9 days (p < 0.01) and the soybean blooming dates with a lag of 7 days on average (p < 0.01). These findings demonstrate the capability of VIIRS observations to effectively monitor temporal dynamics of crop progress in real time at a regional scale.

Highlights

Crop phenology refers to the developments, differentiation and initiation of organs of a crop [1]
To evaluate real-time monitoring of crop phenology from Visible Infrared Imaging Radiometer Suite (VIIRS) data, field observations of crop progress collected from United States Department of Agriculture (USDA) National Agricultural Statistics Service (NASS) in 2014 and 2015 were used, which could allow us to establish the relationships between the two datasets and to use VIIRS-derived phenology to estimate field-based crop progress
It is due to the following three reasons: (1) the accuracy of real-time monitoring of greenup onset is relatively low due to a limited number of available VIIRS observations [35] greenup onset is correspondent to the timing of crop emergences; (2) planting and emergence dates are most close to greenup onset or mid-greenup phase while silking or blooming date closely corresponds to maximum greenness onset according to crop physiology; (3) planting date is investigated because it is critical for crop yield modelling [5,6]

Summary

Introduction

Crop phenology refers to the developments, differentiation and initiation of organs of a crop [1]. Crop progress (CP) at key phenological dates is a very dynamic crop attribute and an important indicator of crop production for decision-making [20]. It is estimated by the National Agricultural Statistics Service (NASS) of the United States Department of Agriculture (USDA) using ground observations provided by about 4000 reporters based on NASS standard definitions Thermal unit methods require both real-time temperature data and information on the planting dates and the thermal properties of cultivars, which could limit their practical applications to a great extent [23,24]. Flying on board UAV is quite expensive and labor costly, making it impractical for regional or continental-scale crop monitoring [25,26]

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