Southern China, one of the traditional rice production bases, has experienced significant declines in the area of rice paddy since the beginning of this century. Monitoring the rice cropping area is becoming an urgent need for food security policy decisions. One of the main challenges for mapping rice in this area is the quantity of cloud-free observations that are vulnerable to frequent cloud cover. Another relevant issue that needs to be addressed is determining how to select the appropriate classifier for mapping paddy rice based on the cloud-masked observations. Therefore, this study was organized to quickly find a strategy for rice mapping by evaluating cloud-mask algorithms and machine-learning methods for Sentinel-2 imagery. Specifically, we compared four GEE-embedded cloud-mask algorithms (QA60, S2cloudless, CloudScore, and CDI (Cloud Displacement Index)) and analyzed the appropriateness of widely accepted machine-learning classifiers (random forest, support vector machine, classification and regression tree, gradient tree boost) for cloud-masked imagery. The S2cloudless algorithm had a clear edge over the other three algorithms based on its overall accuracy in evaluation and visual inspection. The findings showed that the algorithm with a combination of S2cloudless and random forest showed the best performance when comparing mapping results with field survey data, referenced rice maps, and statistical yearbooks. In general, the research highlighted the potential of using Sentinel-2 imagery to map paddy rice with multiple combinations of cloud-mask algorithms and machine-learning methods in a cloud-prone area, which has the potential to broaden our rice mapping strategies.

The maximum light use efficiency (LUE) (ε₀) is a key essential parameter of the LUE model, and its accurate estimation is crucial for quantifying gross primary production (GPP) and better understanding the global carbon budget. Currently, a comprehensive understanding of the potential of seasonal variations of ε₀ in GPP estimation across different plant functional types (PFTs) is still lacking. In this study, we used a phenology-based strategy for the estimation of ε₀ to find the optimal photosynthetic responses of the parameter in different phenological stages. The start and end of growing season (SOS and EOS) from time series vegetation indices and the camera-derived greenness index were extracted across seven PFT flux sites using the methods of the hybrid generalized additive model (HGAM) and double logistic function (DLF). Optimal extractions of SOS and EOS were evaluated, and the ε₀ was estimated from flux site observations during the optimal phenological stages with the light response equation. Coupled with other obligatory parameters of the LUE model, phenology-based GPP (GPP_phe-based) was estimated over 21 site-years and compared with vegetation photosynthesis model (VPM)-based GPP (GPP_VPM) and eddy covariance-measured GPP (GPP_EC). Generally, GPP_phe-based basically tracked both the seasonal dynamics and inter-annual variation of GPP_EC well, especially at forest, cropland, and wetland flux sites. The R² between GPP_phe-based and GPP_EC was stable between 0.85 and 0.95 in forest ecosystems, between 0.75 and 0.85 in cropland ecosystems, and around 0.9 in wetland ecosystems. Furthermore, we found that GPP_phe-based was significantly improved compared to GPP_VPM in cropland, grassland, and wetland ecosystems, implying that phenology-based ε₀ is more appropriate in the GPP estimation of herbaceous plants. In addition, we found that GPP_phe-based was significantly improved over GPP_VPM in cropland, grassland, and wetland ecosystems, and the R² between GPP_phe-based and GPP_EC was improved by up to 0.11 in cropland ecosystems and 0.05 in wetland ecosystems compared to GPP_VPM, and RMSE was reduced by up to 5.90 and 2.11 g C m⁻² 8 day⁻¹, respectively, implying that phenology-based ε₀ in herbaceous plants is more appropriate for GPP estimation. This work highlights the potential of phenology-based ε₀ in understanding the seasonal variation of vegetation photosynthesis and production.