An Integration Approach for Mapping Field Capacity of China Based on Multi-Source Soil Datasets

Abstract

Field capacity is one of the most important soil hydraulic properties in water cycle, agricultural irrigation, and drought monitoring. It is difficult to obtain the distribution of field capacity on a large scale using manual measurements that are both time-consuming and labor-intensive. In this study, the field capacity ensemble members were established using existing pedotransfer functions (PTFs) and multiple linear regression (MLR) based on three soil datasets and 2388 in situ field capacity measurements in China. After evaluating the accuracy of each ensemble member, an integration approach was proposed for estimating the field capacity distribution and development of a 250 m gridded field capacity dataset in China. The spatial correlation coefficient (R) and root mean square error (RMSE) between the in situ field capacity and ensemble field capacity were 0.73 and 0.048 m 3 ·m −3 in region scale, respectively. The ensemble field capacity shows great consistency with practical distribution of field capacity, and the deviation is revised when compared with field capacity datasets provided by previous researchers. It is a potential product for estimating field capacity in hydrological and agricultural practices on both large and fine scales, especially in ungauged regions.

Becsült talajhidrológiai paraméterek szimulációs vizsgálata a NAIK Erdészeti Tudományos Intézet két mintaterületén

“…Legtöbbször PTF-ekkel számítják a talaj víztartó-és vízvezető képességét jellemző paramétereket (VAN LOOY et al, 2017) is. A közelmúltban más módszertannal (matematikai háttér, alapadatok, térbeliség, leírt talajtulajdonságok) több hazai és nemzetközi talajhidrológiai adatbázist is kidolgoztak (BAKACSI et al, 2012;DAI et al, 2013;MARTHEWS et al, 2014;CHANEY et al, 2016;MONTZKA et al, 2017;TÓTH et al, 2017;WU et al, 2018).…”

author comments

“…decision tree, or support vector machine, or neural network algorithms, but can consist of different models and are derived from the average of all. It has been shown that often, but not always, the more models are combined for the prediction the more accurate the results are (Baker and Ellison, 2008;Cichota et al, 2013;Nussbaum et al, 2018;Wu et al, 2018). However, the significance of improvement is often not tested.…”

“…Point data can be measured or predicted by PTFs. Several studies analysed the efficiency of geostatistical methods to map water retention at specific matric potential (Farkas et al, 2008) and saturated hydraulic conductivity (Motaghian and Mohammadi, 2011;Xu et al, 2017). Ferrer Julià et al (2004) mapped soil hydraulic conductivity for the Spanish area of the Iberian Peninsula at 1 km resolution with both methods (i) and (ii).…”

“…In most cases there are no available point data for applying geostatistical methods; therefore in several studies soil hydraulic maps were generated with a PTF applied on easily available spatial soil data (Chaney et al, 2016;Dai et al, 2013;Marthews et al, 2014;Montzka et al, 2017;Wu et al, 2018).…”

“…in Nguyen et al, 2017;Zhang and Schaap, 2017;or Román Dobarco et al, 2019; to mention some of the latest results). When PTFs are used for mapping, the uncertainty of the input soil layers will further increase the uncertainty of the calculated soil hydraulic properties; for example in point-based validation RMSE was 0.073 cm 3 cm −3 for water content at field capacity mapped for China in Wu et al (2018); Leenaars et al (2018) found that mean RMSE for water content at saturation, field capacity and wilting point together was 0.102 cm 3 cm −3 for African soils; in EU-SoilHydroGrids RMSE was 0.095, 0.096 and 0.084 cm 3 cm −3 for water content at saturation, field capacity and wilting point respectively for European soils.…”