Abstract
Climate change and anthropogenic activities, including agricultural irrigation have significantly altered the global and regional hydrological cycle. However, human-induced modification to the natural environment is not well represented in land surface models (LSMs). In this study, we utilize microwave-based soil moisture products to aid the detection of under-represented irrigation processes throughout China. The satellite retrievals used in this study include passive microwave observations from the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) and its successor AMSR2, active microwave observations from the Advanced Scatterometer (ASCAT), and the blended multi-sensor soil moisture product from the European Space Agency (i.e., ESA CCI product). We first conducted validations of the three soil moisture retrievals against in-situ observations (collected from the nationwide agro-meteorological network) in irrigated areas in China. It is found that compared to the conventional Spearman’s rank correlation and Pearson correlation coefficients, entropy-based mutual information is more suitable for evaluating soil moisture anomalies induced by irrigation. In general, around 60% of uncertainties in the anomaly of “ground truth” time series can be resolved by soil moisture retrievals, with ASCAT outperforming the others. Following this, the potential utility of soil moisture retrievals in mapping irrigation patterns in China is investigated by examining the difference in probability distribution functions (detected by two-sample Kolmogorov-Smirnov test) between soil moisture retrievals and benchmarks of the numerical model ERA-Interim without considering the irrigation process. Results show that microwave remote sensing provides a promising alternative to detect the under-represented irrigation process against the reference LSM ERA-Interim. Specifically, the highest performance in detecting irrigation intensity is found when using ASCAT in Huang-Huai-Hai Plain, followed by advanced microwave scanning radiometer (AMSR) and ESA CCI. Compared to ASCAT, the irrigation detection capabilities of AMSR exhibit higher discrepancies between descending and ascending orbits, since the soil moisture retrieval algorithm of AMSR is based on surface temperature and, thus, more affected by irrigation practices. This study provides insights into detecting the irrigation extent using microwave-based soil moisture with aid of LSM simulations, which has great implications for numerical model development and agricultural managements across the country.
Highlights
Soil moisture plays a crucial role in regulating water and energy exchange between land surface and atmosphere, and is recognized as an essential climate variable in earth science [1]
We focus on the potential utility of the microwave-based soil moisture products in identifying irrigation patterns that are typically not well represented in land surface models
The advanced microwave scanning radiometer (AMSR)-E is onboard the National Aeronautics and Space Administration (NASA) Aqua platform and has provided soil moisture estimates since 2002
Summary
Soil moisture plays a crucial role in regulating water and energy exchange between land surface and atmosphere, and is recognized as an essential climate variable in earth science [1]. It is of importance for understanding the hydrological cycle in the context of climate change and increasingly intensified human interference. Information on long-term soil moisture variations largely relies on simulations from land surface models [6,7,8,9]. Quantification of the uncertainties in simulated and observed (both ground-based and satellite-based) soil moisture has drawn increased attention [10,11,12]. Tian et al [13]
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