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

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 GPP MOD (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.

A comparative study on the spatio-temporal patterns and drivers of net primary productivity in three basins across different climate gradients in China

“…This is possibly due to fact that light deficiency in the rainy season limits the plant photosynthesis and results in the decrease of NPP (more obvious in natural ecosystems like forest and shrubland) (Figure 10d) (Liang et al, 2015). Another reason is that the cropland is also under rotation period from wheat harvesting to rice planting in June and July in the LYRB (Figure 10d) (Duan et al, 2021).…”

Machine Learning in Weather Prediction and Climate Analyses—Applications and Perspectives

“…Based on the findings in this section, we summarize that machine learning helps to improve analysis and find links between different predictors and climate conditions in different issues. Simultaneously, it can also be used to generate high-resolution data and to explore the drivers of climate change [82][83][84][85][86][87].…”

Correction to a Simple Biosphere Model 2 (SiB2) Simulation of Energy and Carbon Dioxide Fluxes over a Wheat Cropland in East China Using the Random Forest Model

“…In this study, we applied the RF framework to correct heat and carbon fluxes simulated by the SiB2 model (Figure 3). First, a set of explanatory variables (Table A2) were selected based on previous research [43,[59][60][61][62] and currently available in situ measurements. Second, 90% of the outputs of the SiB2 model and explanatory variables (Table A2) from January to May 2016-2017 were used to train the RF model, with the remaining 10% of them and 100% of the data in 2015 used to validate estimation performance of the model.…”

“…Recently, the random forest (RF) model has become a popular machine learning technique owing to its success in selecting and ranking numerous predictor variables [40]. Several works have investigated land surface processes with the RF model, such as the exchange of energy [41] and CO 2 [42,43], and obtained satisfactory results. Accordingly, there is reason to believe that the RF model could also be a promising method to correct SiB2 outputs.…”