Determination of rice paddy parameters in the global gross primary production capacity estimation algorithm using 6 years of JP-MSE flux observation data

Abstract

Gross primary production GPP capacity is defined as GPP under low stress, and the algorithm for its estimation was developed by Thanyapraneedkul et al. 2012 using a light-response curve. The idea behind this algorithm is that the light response curve under low stress is related to chlorophyll content. The parameter is estimated from a vegetation index derived from satellite observations of the green chlorophyll index CI green for seven vegetation types, including rice paddy. These previous studies included 1 year of data for the flux site and MODIS reflectance data. Recently, long-term data have become publicly available for flux data covering a period of 6 years, and MODIS reflectance data covering a period of more than 16 years.This study determined the parameters in the GPP capacity estimation algorithm for rice paddies using 6 years of Mase paddy flux site data and clear daytime reflectance data observed using MODIS. The fitted parameter-related initial slopes of the light -photosynthesis curves for each year were identical within the fitting error. Using the averaged parameter-related initial slope over 6 years, we were able to determine a linear relationship between CI green and the maximum photosynthesis rate at 2000 PAR μmol m 2 s 1 , the slope of which was slightly higher than has been reported previously. Using the parameters for the period 2001 -2006, we investigated how GPP capacity varied for irrigated rice paddy. The ratio of the average GPP capacity to the GPP after transplanting until harvesting was 0.91 for the period 2001 to 2006. This result shows that GPP capacity provides a useful first approximation of GPP for irrigated rice paddies as a framework of the global GPP estimation algorithm.

Comparative assessment of net CO2 exchange across an urbanization gradient in Korea based on eddy covariance measurements

“…NEE shows rapid changes in sign during the harvest of crops in June (barley) and November (rice) at the CP site. The maximum CO 2 absorption rate is approximately − 40 μmol m −2 s −1 for rice in July-August, which is comparable to previous results from rice paddies in East Asian countries (e.g., [36][37][38][39][40]). The maximum CO 2 absorption rate of barley is smaller (approximately − 20 μmol m −2 s −1 ) during the mid-growing period of barley (from April to May).…”

Gap-filling eddy covariance methane fluxes: Comparison of machine learning model predictions and uncertainties at FLUXNET-CH4 wetlands

“…Water table depth, a proxy for the balance of anaerobic CH 4 -producing and aerobic CH 4 -consuming soil volumes (Bridgham et al 2013), was an important predictor at rice and swamp sites that undergo larger changes in seasonal inundation (Dalmagro et al 2018;Muramatsu et al 2017), but not at other wetland types. Although WTD has been found to be important in bogs and fens (Moore et al 2011;Goodrich et al 2015;Koebsch et al 2020), it was only an important gap-filling predictor at one of the five bogs in this study.…”

Intercomparison on Four Irrigated Cropland Maps in Mainland China

“…Mapping communities treat them as references against which to contrast the accuracy of novel methods, such as in the work by Zhu et al [ 10 ], Dong et al [ 11 ], and Salmon et al [ 6 ]. Further, they are primary inputs in calculations of other metrics [ 12 , 13 , 14 , 15 , 16 ], e.g., estimating gross primary or crop yield, establishing land surface models, and conducting land use analysis, which has highlighted their limitations in bringing uncertainties in modeling and the final outputs of the metrics calculated using the datasets. Data quality reports such as GMIA and GRIPC reveal a possibility of regional or national superiority on accuracy of irrigation maps, which may lead to in homogenous outcomes based on them as well.…”