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Received：27 June 2024，
Published：07 October 2025
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
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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
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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.
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