Assessment of SMAP soil moisture for global simulation of gross primary production

Abstract

In this study, high‐quality soil moisture data derived from the Soil Moisture Active Passive (SMAP) satellite measurements are evaluated from a perspective of improving the estimation of the global gross primary production (GPP) using a process‐based ecosystem model, namely, the Boreal Ecosystem Productivity Simulator (BEPS). The SMAP soil moisture data are assimilated into BEPS using an ensemble Kalman filter. The correlation coefficient (r) between simulated GPP from the sunlit leaves and Sun‐induced chlorophyll fluorescence (SIF) measured by Global Ozone Monitoring Experiment‐2 is used as an indicator to evaluate the performance of the GPP simulation. Areas with SMAP data in low quality (i.e., forests), or with SIF in low magnitude (e.g., deserts), or both are excluded from the analysis. With the assimilated SMAP data, the r value is enhanced for Africa, Asia, and North America by 0.016, 0.013, and 0.013, respectively (p < 0.05). Significant improvement in r appears in single‐cropping agricultural land where the irrigation is not considered in the model but well captured by SMAP (e.g., 0.09 in North America, p < 0.05). With the assimilation of SMAP, areas with weak model performances are identified in double or triple cropping cropland (e.g., part of North China Plain) and/or mountainous area (e.g., Spain and Turkey). The correlation coefficient is enhanced by 0.01 in global average for shrub, grass, and cropland. This enhancement is small and insignificant because nonwater‐stressed areas are included.

Nitrogen Availability Dampens the Positive Impacts of CO2 Fertilization on Terrestrial Ecosystem Carbon and Water Cycles

“…The trend is then further extrapolated back to 2000, and forward to 2015. The trend of MERIS chlorophyll information was then used as a proxy of the trend of the maximum rate of carboxylation at 25°C ( V cmax, 25 ) in an ecosystem model named the Boreal Ecosystems Productivity Simulator (BEPS) (Chen et al, , , ; He et al, , ; Ju et al, ; Liu et al, ). The MERIS MTCI is derived based on Level 2 data which have been atmospherically corrected (Dash et al, ).…”

Data Assimilation to Extract Soil Moisture Information from SMAP Observations

“…For example, DA-NN exhibits drier conditions in the predominantly agricultural areas of the Midwest and parts of the Northwest (eastern Montana, eastern Oregon and the Dakotas). In these regions, SMAP observes the effects of agricultural practices (e.g., tile drainage or tillage) that are not represented in the model (see e.g., He et al [48]). For the agricultural areas subject to irrigation, these somewhat counter-intuitive results reflect the dry bias of the SMAP retrievals relative to the model (see e.g., Figure 4d in Kolassa et al [25]) .…”

Nonlinear Variations of Net Primary Productivity and Its Relationship with Climate and Vegetation Phenology, China

“…Although these factors have explained a total of 56% of the variation of NPP (Figure 8), other climate-related factors, such as sunshine duration, soil temperature, precipitation characteristics (e.g., effective precipitation, precipitation intensity), CO 2 concentrations, N enrichment and deposition (e.g., policy and planning changes), should be considered in future studies [67]. Moreover, other phonological metrics (e.g., Start of Season, End of Season), species competition, and disturbances such as anthropogenic activities (irrigation, fertilization [68,69], harvest, land use/land cover change), wildfires, plant diseases, pests, floods, and droughts as well as the time-lag effect of the above-mentioned variables can vary by region [50], and should also be considered in future studies.…”