收稿：2024-06-27，
纸质出版：2025-10-07
移动端阅览
高分辨率土壤湿度数据是农作物生长监测和干旱预测的重要参考，准确获取其时空分布和变化信息对保障农业生产管理和粮食安全具有重要意义。现有全球土壤湿度数据产品多以被动微波数据为主，分辨率较粗（≥3 km），无法满足田间尺度监测应用。合成孔径雷达（SAR）已被广泛应用于土壤湿度反演研究，但受植被覆盖、地表粗糙度及现有散射模型不精确等因素的影响，反演精度不足，尚未成熟应用于全球高分辨率土壤湿度产品制图。本文在alpha近似模型和时间序列变化检测土壤湿度反演算法框架下，提出通过引入粗分辨率被动微波土壤介电常数约束来提高SAR高分辨率土壤湿度反演精度。同时，采用入射角归一化处理来改善SAR土壤湿度反演结果的空间连续性。本文利用2022年1至12月日欧洲航天局Sentinel-1卫星SAR影像和美国国家航空航天局（NASA）SMAP 9 km日值（SPL3SMP_E）土壤湿度数据，将该算法应用于中国黄淮海平原农业区（Huanghuaihai Plain Agricultural Area）月尺度土壤湿度反演与制图。为验证本文算法的有效性，研究将2022年1至9月SMAP被动微波数据与MODIS地表温度降尺度1 km分辨率土壤湿度数据产品作为地面真值数据，对算法结果进行精度分析和评价。实验结果显示，本文算法在黄淮海平原农业区土壤湿度制图精度均方根误差0.04 ≤ RMSE ≤ 0.16
<math id="M1"><mi mathvariant="normal">c</mi><msup><mrow><mi mathvariant="normal">m</mi></mrow><mrow><mn mathvariant="normal">3</mn></mrow></msup><mo>/</mo><mi mathvariant="normal">c</mi><msup><mrow><mi mathvariant="normal">m</mi></mrow><mrow><mn mathvariant="normal">3</mn></mrow></msup></math>
https://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=92361341&type=
https://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=92361375&type=
10.49866676
2.53999996
、平均绝对误差0.04 ≤ MAE ≤ 0.12
<math id="M2"><mi mathvariant="normal">c</mi><msup><mrow><mi mathvariant="normal">m</mi></mrow><mrow><mn mathvariant="normal">3</mn></mrow></msup><mo>/</mo><mi mathvariant="normal">c</mi><msup><mrow><mi mathvariant="normal">m</mi></mrow><mrow><mn mathvariant="normal">3</mn></mrow></msup></math>
https://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=92361342&type=
https://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=92361353&type=
10.49866676
2.53999996
、决定系数0.41 ≤
R
2
≤ 0.84。此外，经过SAR入射角归一化处理后的反演结果，相比于未进行入射角归一化处理SAR土壤湿度制图的分带差异问题有所改善，空间连续性更好。以上研究结果表明，本文提出的土壤湿度反演算法能够融合主、被动观测数据优势，在实现高分辨土壤湿度反演的同时可以有效提高反演精度。
High-resolution Soil Moisture (SM) data are important for crop growth monitoring and drought prediction. Accurate acquisition of SM spatiotemporal distribution and variation is essential for ensuring agricultural production management and food security. However
the majority of existing global SM data products are based on passive microwave data having a coarse resolution that is larger than 3 km
which does not meet the needs of field-scale crop monitoring. Synthetic Aperture Radar (SAR) has been widely used in SM retrieval research. However
global high-resolution SM mapping with SAR has not been fully applied in practice because of the insufficient retrieval accuracy influenced by vegetation cover
surface roughness
and existing inaccurate scattering models. This study introduces an SM retrieval algorithm that uses passive and active microwave measurements and incorporates spatiotemporal physical constraints into soil dielectric properties. In the proposed algorithm
the alpha approximation model and Change Detection (CD) inversion theorem are used for SM retrieval. Given that the number of unknown time series of the SM value is larger than that of the unknown time series of SAR observations in the CD method
underdetermined soil dielectric constant inversion remains a problem and adversely affects SM estimates. Hence
the temporal and spatial constraints of coarse resolution soil permittivity derived from passive microwave SM products are introduced
for soil dielectric constant estimation. The proposed algorithm is applied to high-resolution SM retrieval and mapping over the Huanghuaihai Plain agricultural area in China to validate the effectiveness of the algorithm. Sentinel-1 backscatter measurements and SMAP 9 km daily SM data (SPL3SMP_E) from January 1 to December 31 in 2022 are collected for this study. SMAP-derived 1 km downscaled surface SM products from January to September in 2022 are employed as ground-truth measurements to evaluate the retrieval accuracy and verify the effectiveness of the proposed method. Experimental results show that 0.04 ≤ root mean square error ≤ 0.16 cm
3
/cm
3
0.04 ≤ mean absolute error ≤ 0.12 cm
3
/cm
3
and 0.41 ≤
R
2
≤ 0.84. The retrieval results with incidence angle normalization show better spatial continuity compared with those of without incidence angle normalization. The proposed SM retrieval algorithm that integrates the advantages of active and passive observation data not only enables high-resolution SM retrieval but also improves the accuracy of SM retrieval results.
Barrett B W , Dwyer E and Whelan P . 2009 . Soil moisture retrieval from active spaceborne microwave observations: an evaluation of current techniques . Remote Sensing , 1 ( 3 ): 210 - 242 [ DOI: 10.3390/rs1030210 http://dx.doi.org/10.3390/rs1030210 ]
Cheng Y , Li Y X , Li F and He L . 2021 . Soil moisture retrieval using extremely randomized trees over the Shandian river basin . National Remote Sensing Bulletin , 25 ( 4 ): 941 - 951
程渊 , 李玉霞 , 李凡 , 何磊 . 2021 . 基于极端随机树的闪电河流域土壤水分反演 . 遥感学报 , 25 ( 4 ): 941 - 951 [ DOI: 10.11834/jrs.20219396 http://dx.doi.org/10.11834/jrs.20219396 ]
Deng X D and Wang H Q . 2022 . Recent advances on algorithms and applications of soil moisture retrieval from microwave remote sensing . Journal of Zhejiang University (Agriculture and Life Sciences) , 48 ( 3 ): 289 - 302
邓小东 , 王宏全 . 2022 . 土壤水分微波遥感反演算法及应用研究进展 . 浙江大学学报(农业与生命科学版) , 48 ( 3 ): 289 - 302 [ DOI: 10.3785/j.issn.1008-9209.2021.04.281 http://dx.doi.org/10.3785/j.issn.1008-9209.2021.04.281 ]
Dubois P C , van Zyl J and Engman T . 1995 . Measuring soil moisture with imaging radars . IEEE Transactions on Geoscience and Remote Sensing , 33 ( 4 ): 915 - 926 [ DOI: 10.1109/36.406677 http://dx.doi.org/10.1109/36.406677 ]
Friedl M A , Sulla-Menashe D , Tan B , Schneider A , Ramankutty N , Sibley A and Huang X M . 2010 . MODIS collection 5 global land cover: algorithm refinements and characterization of new datasets . Remote Sensing of Environment , 114 ( 1 ): 168 - 182 [ DOI: 10.1016/j.rse.2009.08.016 http://dx.doi.org/10.1016/j.rse.2009.08.016 ]
He L , Qin Q M , Ren H Z , Du J , Meng J J and Du C . 2016 . Soil moisture retrieval using multi-temporal Sentinel-1 SAR data in agricultural areas . Transactions of the Chinese Society of Agricultural Engineering , 32 ( 3 ): 142 - 148
何连 , 秦其明 , 任华忠 , 都骏 , 孟晋杰 , 杜宸 . 2016 . 利用多时相Sentinel-1 SAR数据反演农田地表土壤水分 . 农业工程学报 , 32 ( 3 ): 142 - 148 [ DOI: 10.11975/j.issn.1002-6819.2016.03.020 http://dx.doi.org/10.11975/j.issn.1002-6819.2016.03.020 ]
He Y Z , Zhang K , Chao L J and Wang S . 2022 . Study on soil moisture merging and downscaling based on multi-source satellite remote sensing products . Journal of Hohai University (Natural Sciences) , 50 ( 6 ): 40 - 46
何涯舟 , 张珂 , 晁丽君 , 王晟 . 2022 . 基于多源卫星遥感产品的土壤湿度融合与降尺度研究 . 河海大学学报(自然科学版) , 50 ( 6 ): 40 - 46 [ DOI: 10.3876/j.issn.1000-1980.2022.06.006 http://dx.doi.org/10.3876/j.issn.1000-1980.2022.06.006 ]
Kim S and Garrison J L . 2021 . Retrieval of root-zone soil moisture profiles from multi-frequency signals of opportunity: a simulation study // IEEE International Geoscience and Remote Sensing Symposium IGARSS . Brussels : IEEE: 155 - 158 [ DOI: 10.1109/igarss47720.2021.9554561 http://dx.doi.org/10.1109/igarss47720.2021.9554561 ]
Kim S B and Liao T H . 2021 . Robust retrieval of soil moisture at field scale across wide-ranging SAR incidence angles for soybean, wheat, forage, oat and grass . Remote Sensing of Environment , 266 : 112712 [ DOI: 10.1016/j.rse.2021.112712 http://dx.doi.org/10.1016/j.rse.2021.112712 ]
Kornelsen K C and Coulibaly P . 2013 . Advances in soil moisture retrieval from synthetic aperture radar and hydrological applications . Journal of Hydrology , 476 : 460 - 489 [ DOI: 10.1016/j.jhydrol.2012.10.044 http://dx.doi.org/10.1016/j.jhydrol.2012.10.044 ]
Lal P , Singh G , Das N N , Entekhabi D , Lohman R , Colliander A , Pandey D K and Setia R K . 2023 . A multi-scale algorithm for the NISAR mission high-resolution soil moisture product . Remote Sensing of Environment , 295 : 113667 [ DOI: 10.1016/j.rse.2023.113667 http://dx.doi.org/10.1016/j.rse.2023.113667 ]
Li M Q , Fang S B , Zhu Y C , Wu Y J , Cao Y , Zhuo W and E Y H . 2022 . Spatial and temporal distributions of waterlogging disasters in the summer of 2021 in Mainland China and their possible impacts . National Remote Sensing Bulletin , 26 ( 9 ): 1886 - 1894
李梦倩 , 房世波 , 朱永超 , 武英洁 , 曹云 , 卓文 , 俄有浩 . 2022 . 2021年夏季中国大陆涝渍灾害时空分布分析 . 遥感学报 , 26 ( 9 ): 1886 - 1894 [ DOI: 10.11834/jrs.20221782 http://dx.doi.org/10.11834/jrs.20221782 ]
Liu C Y , Huang G X , Jing J H , Liu J T , Zhang Y and Guo W X . 2023 . Characteristics and driving mechanisms of evolution of groundwater chemistry in Huang-Huai-Hai Plain and its exploitation and utilization suggestions . Geology in China , 50 ( 6 ): 1705 - 1719
刘春燕 , 黄冠星 , 荆继红 , 刘景涛 , 张英 , 郭维轩 . 2023 . 黄淮海平原地下水化学演化特征、形成机制及其开发利用建议 . 中国地质 , 50 ( 6 ): 1705 - 1719 [ DOI: 10.12029/gc20211206002 http://dx.doi.org/10.12029/gc20211206002 ]
Liu R , Sun S , Ju L , Wang Q F , Zou Y F , Zhang Y Y and Miao L J . 2023 . Evolutionary characteristics and spatiotemporal trend of meteorological drought in the Huang-Huai-hai Plain . Transactions of the Chinese Society of Agricultural Engineering , 39 ( 19 ): 85 - 92
刘冉 , 孙劭 , 鞠蕾 , 王前锋 , 邹扬锋 , 张宇阳 , 缪丽娟 . 2023 . 黄淮海平原气象干旱的演化特征与时空规律 . 农业工程学报 , 39 ( 19 ): 85 - 92 [ DOI: 10.11975/j.issn.1002-6819.202304163 http://dx.doi.org/10.11975/j.issn.1002-6819.202304163 ]
Oh Y . 2004 . Quantitative retrieval of soil moisture content and surface roughness from multipolarized radar observations of bare soil surfaces . IEEE Transactions on Geoscience and Remote Sensing , 42 ( 3 ): 596 - 601 [ DOI: 10.1109/TGRS.2003.821065 http://dx.doi.org/10.1109/TGRS.2003.821065 ]
Ouellette J D , Johnson J T , Balenzano A , Mattia F , Satalino G , Kim S B , Dunbar R S , Colliander A , Cosh M H , Caldwell T G , Walker J P and Berg A A . 2017 . A time-series approach to estimating soil moisture from vegetated surfaces using L-band radar backscatter . IEEE Transactions on Geoscience and Remote Sensing , 55 ( 6 ): 3186 - 3193 [ DOI: 10.1109/TGRS.2017.2663768 http://dx.doi.org/10.1109/TGRS.2017.2663768 ]
Paloscia S , Macelloni G , Santi E and Tedesco M . 2002 . The capability of microwave radiometers in retrieving soil moisture profiles: an application of artificial neural networks // IEEE International Geoscience and Remote Sensing Symposium (IGARSS) . Toronto : IEEE , 3 : 1390 - 1392 [ DOI: 10.1109/igarss.2002.1026126 http://dx.doi.org/10.1109/igarss.2002.1026126 ]
Peng J and Loew A . 2017 . Recent advances in soil moisture estimation from remote sensing . Water , 9 ( 7 ): 530 [ DOI: 10.3390/w9070530 http://dx.doi.org/10.3390/w9070530 ]
Peng J , Loew A , Merlin O and Verhoest N E C . 2017 . A review of spatial downscaling of satellite remotely sensed soil moisture . Reviews of Geophysics , 55 ( 2 ): 341 - 366 [ DOI: 10.1002/2016RG000543 http://dx.doi.org/10.1002/2016RG000543 ]
Peng J , Shen H , He S W and Wu J S . 2013 . Soil moisture retrieving using hyperspectral data with the application of wavelet analysis . Environmental Earth Sciences , 69 ( 1 ): 279 - 288 [ DOI: 10.1007/s12665-012-1955-x http://dx.doi.org/10.1007/s12665-012-1955-x ]
Petropoulos G P , Ireland G and Barrett B . 2015 . Surface soil moisture retrievals from remote sensing: current status, products and future trends . Physics and Chemistry of the Earth, Parts A/B/C , 83 - 84 : 36 - 56 [ DOI: 10.1016/j.pce.2015.02.009 http://dx.doi.org/10.1016/j.pce.2015.02.009 ]
Rosen P A and Kumar R . 2021 . NASA-ISRO SAR (NISAR) mission status // 2021 IEEE Radar Conference (RadarConf21) . Atlanta : IEEE: 1 - 6 [ DOI: 10.1109/RadarConf2147009.2021.9455211 http://dx.doi.org/10.1109/RadarConf2147009.2021.9455211 ]
Shen X J , Walker J P , Ye N , Wu X L , Boopathi N , Yeo I Y , Zhang L L and Zhu L J . 2021 . Soil moisture retrieval depth of P- and L-band radiometry: predictions and observations . IEEE Transactions on Geoscience and Remote Sensing , 59 ( 8 ): 6814 - 6822 [ DOI: 10.1109/TGRS.2020.3026384 http://dx.doi.org/10.1109/TGRS.2020.3026384 ]
Shi H , Lopez-Sanchez J M , Yang J , Li P X , Zhao L L and Zhao J Q . 2021 . Contribution of polarimetry and multi-incidence to soil moisture estimation over agricultural fields based on time series of L -band SAR data . IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , 14 : 300 - 313 [ DOI: 10.1109/JSTARS.2020.3036732 http://dx.doi.org/10.1109/JSTARS.2020.3036732 ]
Shi H T . 2021 . Soil Moisture and Crop Biophysical Parameters Estimation from Time Series of PolSAR Imageries . Wuhan : Wuhan University
时洪涛 . 2021 . 时序极化SAR土壤湿度及农作物生物物理参数估计方法研究 . 武汉 : 武汉大学 [ DOI: 10.27379/d.cnki.gwhdu.2021.000142 http://dx.doi.org/10.27379/d.cnki.gwhdu.2021.000142 ]
Shi H T , Yang J , Zhao L L , Shi L , Li P X , Zhao J Q , Liu W S and Wang L . 2019 . Soil moisture retrieval using a modified decomposition method and multi-incidence polarimetric SAR data // 2019 IEEE International Geoscience and Remote Sensing Symposium . Yokohama : IEEE: 6957 - 6960 [ DOI: 10.1109/IGARSS.2019.8899225 http://dx.doi.org/10.1109/IGARSS.2019.8899225 ]
Shi J , Jiang L , Zhang L , Chen K S , Wigneron J P , Chanzy A and Jackson T J . 2006 . Physically based estimation of bare-surface soil moisture with the passive radiometers . IEEE Transactions on Geoscience and Remote Sensing , 44 ( 11 ): 3145 - 3153 [ DOI: 10.1109/TGRS.2006.876706 http://dx.doi.org/10.1109/TGRS.2006.876706 ]
Sun H , Zhou B C , Li H and Ruan L . 2021 . A primary study on downscaling microwave soil moisture with MOD16 and SMAP . National Remote Sensing Bulletin , 25 ( 3 ): 776 - 790
孙灏 , 周柏池 , 李欢 , 阮琳 . 2021 . 耦合MOD16和SMAP的微波土壤湿度降尺度研究 . 遥感学报 , 25 ( 3 ): 776 - 790 [ DOI: 10.11834/jrs.20209253 http://dx.doi.org/10.11834/jrs.20209253 ]
Wang L G , Zeng K , Li W J , Guo Y , Cheng Y Z , Liu T , He J , Zhang Y and Wei P P . 2023 . Exploration of the current situation and thinking of agricultural remote sensing development in the Huang-Huai-Hai region . China Agricultural Information , 35 ( 6 ): 60 - 70
王来刚 , 曾凯 , 李文娟 , 郭燕 , 程永政 , 刘婷 , 贺佳 , 张彦 , 位盼盼 . 2023 . 黄淮海地区农业遥感研究进展、发展现状与未来展望 . 中国农业信息 , 35 ( 6 ): 60 - 70 [ DOI: 10.12105/j.issn.1672-0423.20230606 http://dx.doi.org/10.12105/j.issn.1672-0423.20230606 ]
Wang M X and Wu Z P . 2013 . A review of researches on monitoring of soil moisture by remmote sensing . Arid Environmental Monitoring , 27 ( 4 ): 163 - 168
王明霞 , 毋兆鹏 . 2013 . 遥感监测土壤湿度综述 . 干旱环境监测 , 27 ( 4 ): 163 - 168 [ DOI: 10.3969/j.issn.1007-1504.2013.04.005 http://dx.doi.org/10.3969/j.issn.1007-1504.2013.04.005 ]
Zhang L , An R and Wang Z . 2015 . Improvement of CCI soil moisture product based on CDF technique . Science Technology and Engineering , 15 ( 30 ): 1 - 8
张玲 , 安如 , 王喆 . 2015 . 主/被动微波遥感数据融合的土壤湿度产品的CDF改进研究 . 科学技术与工程 , 15 ( 30 ): 1 - 8 [ DOI: 10.3969/j.issn.1671-1815.2015.30.001 http://dx.doi.org/10.3969/j.issn.1671-1815.2015.30.001 ]
Zhang W F , Chen E X , Li Z Y , Yang H and Zhao L . 2020 . Review of applications of radar remote sensing in agriculture . Journal of Radars , 9 ( 3 ): 444 - 461
张王菲 , 陈尔学 , 李增元 , 杨浩 , 赵磊 . 2020 . 雷达遥感农业应用综述 . 雷达学报 , 9 ( 3 ): 444 - 461 [ DOI: 10.12000/JR20051 http://dx.doi.org/10.12000/JR20051 ]
Zhang X . 2016 . Research on Soil Moisture Retrieval by Means of Multi-source and Time-series SAR Data . Xuzhou : China University of Mining and Technology
张祥 . 2016 . 多源时序SAR数据土壤水分反演研究 . 徐州 : 中国矿业大学
Zhao S H , Qin Q M , Shen X Y , Wang Z Q , Qiu G Y and Sui X X . 2010 . Review of microwave remote sensing on soil moisture monitoring . Journal of Microwaves , 26 ( 2 ): 90 - 96
赵少华 , 秦其明 , 沈心一 , 王志强 , 邱国玉 , 随欣欣 . 2010 . 微波遥感技术监测土壤湿度的研究 . 微波学报 , 26 ( 2 ): 90 - 96 [ DOI: 10.14183/j.cnki.1005-6122.2010.02.023 http://dx.doi.org/10.14183/j.cnki.1005-6122.2010.02.023 ]
Zhao T J . 2018 . Recent advances of L-band application in the passive microwave remote sensing of soil moisture and its prospects . Progress in Geography , 37 ( 2 ): 198 - 213
赵天杰 . 2018 . 被动微波反演土壤水分的L波段新发展及未来展望 . 地理科学进展 , 37 ( 2 ): 198 - 213 [ DOI: 10.18306/dlkxjz.2018.02.003 http://dx.doi.org/10.18306/dlkxjz.2018.02.003 ]
Zheng Y F , Hunag T N , Duan C C , Yin J F , Wu R J . 2017 . Research progress on microwave remote sensing soil moisture inversion algorithms and products , 45 ( 5 ): 1 - 7
郑有飞 , 黄图南 , 段长春 , 尹继福 , 吴荣军 . 2017 . 微波遥感土壤湿度反演算法及产品研究进展 . 江苏农业科学 , 45 ( 5 ): 1 - 7 [ DOI: 10.15889/j.issn.1002-1302.2017.05.001 http://dx.doi.org/10.15889/j.issn.1002-1302.2017.05.001 ]
Zhou C S , Zhang R R , Ning X J and Zheng Z C . 2020 . Spatial-temporal characteristics in grain production and its influencing factors in the Huang-Huai-Hai Plain from 1995 to 2018 . International Journal of Environmental Research and Public Health , 17 ( 24 ): 9193 [ DOI: 10.3390/ijerph17249193 http://dx.doi.org/10.3390/ijerph17249193 ]
相关作者
相关机构
京公网安备11010802024621
