Abstract
The onus for monitoring crop growth from space is its ability to be applied anytime and anywhere, to produce crop yield estimates that are consistent at both the subfield scale for farming management strategies and the country level for national crop yield assessment. Historically, the requirements for satellites to successfully monitor crop growth and yield differed depending on the extent of the area being monitored. Diverging imaging capabilities can be reconciled by blending images from high-temporal-frequency (HTF) and high-spatial-resolution (HSR) sensors to produce images that possess both HTF and HSR characteristics across large areas. We evaluated the relative performance of Moderate Resolution Imaging Spectroradiometer (MODIS), Landsat, and blended imagery for crop yield estimates (2009–2015) using a carbon-turnover yield model deployed across the Australian cropping area. Based on the fraction of missing Landsat observations, we further developed a parsimonious framework to inform when and where blending is beneficial for nationwide crop yield prediction at a finer scale (i.e., the 25-m pixel resolution). Landsat provided the best yield predictions when no observations were missing, which occurred in 17% of the cropping area of Australia. Blending was preferred when <42% of Landsat observations were missing, which occurred in 33% of the cropping area of Australia. MODIS produced a lower prediction error when ≥42% of the Landsat images were missing (~50% of the cropping area). By identifying when and where blending outperforms predictions from either Landsat or MODIS, the proposed framework enables more accurate monitoring of biophysical processes and yields, while keeping computational costs low.
Highlights
IntroductionTo contribute to improved global food security, the generation of crop models and agricultural decision support tools needs to efficiently and consistently operate across various scales [2]
The world’s human population is projected to increase by more than 35% by 2050 [1]
C-Crop performs the best with Landsat images at field (R2 = 0.68; Figure 4a), 250-m (R2 = 0.85; Figure 4d), and 25-m (R2 = 0.48; Figure 4g) pixel resolutions for yield prediction pooled for wheat, barley, and canola
Summary
To contribute to improved global food security, the generation of crop models and agricultural decision support tools needs to efficiently and consistently operate across various scales [2]. Accurate nationwide crop yield forecasts may ensure food security to the citizens. More accurate crop yield prediction at the subfield scale can provide farmers with more detailed information for guiding, within the growing season, in-field variable rate applications of fertilizer, herbicides, and pesticides. An efficient approach to monitor crop growth uses satellite observations providing repeated synoptic regional assessments [3,4,5,6]. High-temporal-frequency (HTF) observations are required to accurately track crop development [7] and predict yield [8], and high-spatial-resolution (HSR) observations are necessary to capture within-field heterogeneity [9]
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