Spatial evaluation of L-band satellite-based soil moisture products in the upper Huai River basin of China
(2019) In European Journal of Remote Sensing 52(1). p.194-205- Abstract
Using dense soil moisture (SM) measurements in the upper Huai River basin of China, this study evaluated the spatial patterns of L-band satellite-based SM products, including Soil Moisture Active Passive (SMAP) L3, Soil Moisture and Ocean Salinity (SMOS) L3 and the European Space Agency’s Climate Change Initiative (ESA CCI) SM products. The mean difference (MD), root mean squared error (RMSE), unbiased root mean square error (ubRMSE) and Pearson correlation coefficient (R), were used in the evaluation. The evaluation results presented that SMAP and ESA CCI products can well capture the temporal variation of SM at single points quite well, with average R values of 0.51 and 0.46, respectively. And SMAP had the highest overall accuracy... (More)
Using dense soil moisture (SM) measurements in the upper Huai River basin of China, this study evaluated the spatial patterns of L-band satellite-based SM products, including Soil Moisture Active Passive (SMAP) L3, Soil Moisture and Ocean Salinity (SMOS) L3 and the European Space Agency’s Climate Change Initiative (ESA CCI) SM products. The mean difference (MD), root mean squared error (RMSE), unbiased root mean square error (ubRMSE) and Pearson correlation coefficient (R), were used in the evaluation. The evaluation results presented that SMAP and ESA CCI products can well capture the temporal variation of SM at single points quite well, with average R values of 0.51 and 0.46, respectively. And SMAP had the highest overall accuracy among the three satellite-based products in study area. We also analyzed the correlations between the four accuracy indexes and six environmental factors including the proportions of five land use/land cover types (i.e. water bodies, paddy fields, construction land, dryland and forest) and the average NDVI (Normalized Difference Vegetation Index) in 2016 in each grid. Analysis showed that the proportions of paddy fields and water bodies in each grid had significant positive correlations with MD, RMSE and ubRMSE, while NDVI, and the proportions of dryland and construction land had significant negative correlations with these three indexes. The significant correlations between the accuracy of SMAP, SMOS and ESA CCI SM products and environmental factors indicate that there exist systematic biases in these products, which can provide valuable insights into algorithm improvements.
(Less)
- author
- Zhu, Liming ; Liu, Junzhi ; Zhu, A. Xing and Duan, Zheng LU
- publishing date
- 2019-01-01
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- ESA CCI, L-band, satellite-based soil moisture products, SMAP, SMOS
- in
- European Journal of Remote Sensing
- volume
- 52
- issue
- 1
- pages
- 12 pages
- publisher
- Taylor & Francis
- external identifiers
-
- scopus:85062074665
- ISSN
- 2279-7254
- DOI
- 10.1080/22797254.2019.1579618
- language
- English
- LU publication?
- no
- id
- fa321927-6bf2-4f63-9cd6-1cf3216347a3
- date added to LUP
- 2019-12-22 20:08:28
- date last changed
- 2022-04-18 19:49:15
@article{fa321927-6bf2-4f63-9cd6-1cf3216347a3,
abstract = {{<p>Using dense soil moisture (SM) measurements in the upper Huai River basin of China, this study evaluated the spatial patterns of L-band satellite-based SM products, including Soil Moisture Active Passive (SMAP) L3, Soil Moisture and Ocean Salinity (SMOS) L3 and the European Space Agency’s Climate Change Initiative (ESA CCI) SM products. The mean difference (MD), root mean squared error (RMSE), unbiased root mean square error (ubRMSE) and Pearson correlation coefficient (R), were used in the evaluation. The evaluation results presented that SMAP and ESA CCI products can well capture the temporal variation of SM at single points quite well, with average R values of 0.51 and 0.46, respectively. And SMAP had the highest overall accuracy among the three satellite-based products in study area. We also analyzed the correlations between the four accuracy indexes and six environmental factors including the proportions of five land use/land cover types (i.e. water bodies, paddy fields, construction land, dryland and forest) and the average NDVI (Normalized Difference Vegetation Index) in 2016 in each grid. Analysis showed that the proportions of paddy fields and water bodies in each grid had significant positive correlations with MD, RMSE and ubRMSE, while NDVI, and the proportions of dryland and construction land had significant negative correlations with these three indexes. The significant correlations between the accuracy of SMAP, SMOS and ESA CCI SM products and environmental factors indicate that there exist systematic biases in these products, which can provide valuable insights into algorithm improvements.</p>}},
author = {{Zhu, Liming and Liu, Junzhi and Zhu, A. Xing and Duan, Zheng}},
issn = {{2279-7254}},
keywords = {{ESA CCI; L-band; satellite-based soil moisture products; SMAP; SMOS}},
language = {{eng}},
month = {{01}},
number = {{1}},
pages = {{194--205}},
publisher = {{Taylor & Francis}},
series = {{European Journal of Remote Sensing}},
title = {{Spatial evaluation of L-band satellite-based soil moisture products in the upper Huai River basin of China}},
url = {{http://dx.doi.org/10.1080/22797254.2019.1579618}},
doi = {{10.1080/22797254.2019.1579618}},
volume = {{52}},
year = {{2019}},
}