The Joint Assimilation of Remotely Sensed Leaf Area Index and Surface Soil Moisture into a Land Surface Model

Abstract

This work tests the hypothesis that jointly assimilating satellite observations of leaf area index and surface soil moisture into a land surface model improves the estimation of land vegetation and water variables. An Ensemble Kalman Filter is used to test this hypothesis across the Contiguous United States from April 2015 to December 2018. The performance of the proposed methodology is assessed for several modeled vegetation and water variables (evapotranspiration, net ecosystem exchange, and soil moisture) in terms of random errors and anomaly correlation coefficients against a set of independent validation datasets (i.e., Global Land Evaporation Amsterdam Model, FLUXCOM, and International Soil Moisture Network). The results show that the assimilation of the leaf area index mostly improves the estimation of evapotranspiration and net ecosystem exchange, whereas the assimilation of surface soil moisture alone improves surface soil moisture content, especially in the western US, in terms of both root mean squared error and anomaly correlation coefficient. The joint assimilation of vegetation and soil moisture information combines the results of individual vegetation and soil moisture assimilations and reduces errors (and increases correlations with the reference datasets) in evapotranspiration, net ecosystem exchange, and surface soil moisture simulated by the land surface model. However, because soil moisture satellite observations only provide information on the water content in the top 5 cm of the soil column, the impact of the proposed data assimilation technique on root zone soil moisture is limited. This work moves one step forward in the direction of improving our estimation and understanding of land surface interactions using a multivariate data assimilation approach, which can be particularly useful in regions of the world where ground observations are sparse or missing altogether.

Improving Soil Moisture Estimation via Assimilation of Remote Sensing Product into the DSSAT Crop Model and Its Effect on Agricultural Drought Monitoring

“…Multi-source remote sensing can provide land surface monitoring from different perspectives. Some scholars carried out data assimilation based on various remote sensing data sources [24,61,62,64,65]; however, so far collaborative applications based on multi-source remote sensing data are still rare in agricultural drought monitoring. With the launch of subsequent remote sensing satellites, data quality and spatial-temporal resolution will be further improved.…”

Is It Possible to Quantify Irrigation Water‐Use by Assimilating a High‐Resolution Satellite Soil Moisture Product?

“…Moreover, by keeping the default soil layer setup, the optimal soil parameter remains relevant to the community that uses the model (Pinnington et al, 2021). This is a common practice in many other studies that assimilated satellite soil moisture data with VIC (Lievens et al, 2016;Xia et al, 2012aXia et al, , 2012bZhou et al, 2020) or other LSMs (Pinnington et al, 2021;Rahman et al, 2022;Rodríguez-Fernández et al, 2019;Santanello et al, 2016). On the other hand, The SMAP sensing depth can vary based on the soil moisture content from ∼5 cm (when the soil is saturated) to more than 15 cm (in the drier soil) according to a recent study by Feldman et al (2022).…”

Assimilation of Remotely Sensed Leaf Area Index for Improving Land Surface Simulation Performance at a Global Scale

“…Previous studies have demonstrated that the integration of satellite-based products can enhance the simulation accuracy of related land surface variables at both global and regional scales 2 JSTARS-2023-01363 [14][15][16][17][18][19]. For instance, replacing default LAI values with field observations or remote sensing products has been shown to improve simulations of momentum and trace gas exchanges [20][21][22][23].…”