Estimating Gross Primary Productivity (GPP) over Rice–Wheat-Rotation Croplands by Using the Random Forest Model and Eddy Covariance Measurements: Upscaling and Comparison with the MODIS Product

Zexia Duan,Zhiqiu Gao,Yuanjian Yang,Sihui Fan,Lian Zong,Shaohui Zhou,Jian Yin

doi:10.3390/rs13214229

Abstract

Despite advances in remote sensing–based gross primary productivity (GPP) modeling, the calibration of the Moderate Resolution Imaging Spectroradiometer (MODIS) GPP product (GPPMOD) is less well understood over rice–wheat-rotation cropland. To improve the performance of GPPMOD, a random forest (RF) machine learning model was constructed and employed over the rice–wheat double-cropping fields of eastern China. The RF-derived GPP (GPPRF) agreed well with the eddy covariance (EC)-derived GPP (GPPEC), with a coefficient of determination of 0.99 and a root-mean-square error of 0.42 g C m−2 d−1. Therefore, it was deemed reliable to upscale GPPEC to regional scales through the RF model. The upscaled cumulative seasonal GPPRF was higher for rice (924 g C m−2) than that for wheat (532 g C m−2). By comparing GPPMOD and GPPEC, we found that GPPMOD performed well during the crop rotation periods but underestimated GPP during the rice/wheat active growth seasons. Furthermore, GPPMOD was calibrated by GPPRF, and the error range of GPPMOD (GPPRF minus GPPMOD) was found to be 2.5–3.25 g C m−2 d−1 for rice and 0.75–1.25 g C m−2 d−1 for wheat. Our findings suggest that RF-based GPP products have the potential to be applied in accurately evaluating MODIS-based agroecosystem carbon cycles at regional or even global scales.

Highlights

Gross primary productivity (GPP), defined as the total photosynthetic carbon uptake by terrestrial plants, is the first phase of atmospheric CO2 reaching the biosphere [1,2].Currently, farmland accounts for ~12% of the Earth’s land surface [3], and around 15%of global CO2 fixation is contributed by crop GPP [4]
The GPP estimated from eddy covariance (EC) flux measurements over rice–wheat-rotation cropland can represent the amount of carbon uptake by the main land cover type in the northern Yangtze River Delta (NYRD) area
To obtain multiple samples for calibration of the MOD17A2H GPP product, a random forest (RF) model for estimating GPP was designed by integrating multi-source satellite retrievals and in situ ground observations during the period of 2014–2018 over the rice–wheat doublecropping fields of eastern China

Summary

Introduction

Gross primary productivity (GPP), defined as the total photosynthetic carbon uptake by terrestrial plants, is the first phase of atmospheric CO2 reaching the biosphere [1,2]. Of global CO2 fixation is contributed by crop GPP [4]. Accurately quantifying crop GPP can provide valuable information on the ecosystem’s carbon cycle, agricultural applications and climate change [5]. For assessing GPP in crops, eddy covariance (EC) systems, satellite-driven methods, and process-based models are frequently employed. The gathered NEE data are routinely partitioned into GPP and ecosystem respiration [7]. These EC measurements only represent the fluxes at the scale of the tower footprint, with an along-wind extent ranging between hundreds of meters and Remote Sens.

Methods

Results

Discussion

Conclusion