Generating high accuracy multi-layer soil moisture at daily scale in the black soil region of China

The black soil region of China is one of the four major black soil regions in the world and is an important grain production base. High-accuracy soil moisture data are therefore crucial for understanding the mechanisms of drought and waterlogging on crop yields. Here, we present a 16 years (2008-2023) soil moisture dataset (DMSN) with both high spatiotemporal resolution and multi-layer spatiotemporal consistency (2 km, daily, 0-10, 10-20, 20-50, 50-100 cm),using Community Land Model 3.5 (CLM3.5). The DMSM showed excellent performance, with validation at seven field monitoring sites in the black soil region, the correlation coefficient (R) was generally greater than 0.7, the RMSE varied between 0.035 and 0.07, the MAE ranged from 0.03 to 0.06, and the bias remained between -0.02 and 0.02. The DMSM effectively captured daily, inter-annual, and seasonal soil moisture variations, providing important insights into soil moisture dynamics in this critical black soil region. These results help in understanding the potential drivers of drought and waterlogging and their impact on crop yields.

Authors

Institutions

Publication Details

Journal
Scientific Data
Published
2025-10-28
DOI
https://doi.org/10.1038/s41597-025-05986-7
Primary Topic
Soil Moisture and Remote Sensing
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Generating high accuracy multi-layer soil moisture at daily scale in the black soil region of China

Haiqing Song, Jingxuan Sun, Yangguang Wang, Jia Mu et al.
Scientific Data
Soil Moisture and Remote Sensing
article

Generating high accuracy multi-layer soil moisture at daily scale in the black soil region of China

Haiqing Song, Jingxuan Sun, Yangguang Wang, Jia Mu, Jingquan Ren, Guangxin Zhang, Lijun Wang, Liwen Chen, Cong Liu, Mei-Yu Wang
article en

Abstract

The black soil region of China is one of the four major black soil regions in the world and is an important grain production base. High-accuracy soil moisture data are therefore crucial for understanding the mechanisms of drought and waterlogging on crop yields. Here, we present a 16 years (2008-2023) soil moisture dataset (DMSN) with both high spatiotemporal resolution and multi-layer spatiotemporal consistency (2 km, daily, 0-10, 10-20, 20-50, 50-100 cm),using Community Land Model 3.5 (CLM3.5). The DMSM showed excellent performance, with validation at seven field monitoring sites in the black soil region, the correlation coefficient (R) was generally greater than 0.7, the RMSE varied between 0.035 and 0.07, the MAE ranged from 0.03 to 0.06, and the bias remained between -0.02 and 0.02. The DMSM effectively captured daily, inter-annual, and seasonal soil moisture variations, providing important insights into soil moisture dynamics in this critical black soil region. These results help in understanding the potential drivers of drought and waterlogging and their impact on crop yields.

Scientific DataVol. 12(1)
Chinese Academy of Sciences (CN), Banner Health (US), Jilin Jianzhu University (CN), Northeast Institute of Geography and Agroecology (CN), Inner Mongolia Autonomous Region Meteorological Bureau (CN), Jilin Meteorological Bureau (CN)
Chinese Academy of Sciences, China Meteorological Administration, People's Government of Jilin Province, Education Department of Jilin Province
Openalex Percentile: Top 71%
Soil Moisture and Remote Sensing
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.