Soil Moisture Content Retrieval from Remote Sensing Data by Artificial Neural Network Based on Sample Optimization

Liu, Qixin1,2; Gu, Xingfa1,2,3; Chen, Xinran1,2; Mumtaz, Faisal1,2; Liu, Yan1; Wang, Chunmei1; Yu, Tao1; Zhang, Yin4; Wang, Dakang5; Zhan, Yulin1

2022-02-01

10.3390/s22041611

SENSORS   影响因子和分区

1424-8220

Soil moisture content (SMC) plays an essential role in geoscience research. The SMC can be retrieved using an artificial neural network (ANN) based on remote sensing data. The quantity and quality of samples for ANN training and testing are two critical factors that affect the SMC retrieving results. This study focused on sample optimization in both quantity and quality. On the one hand, a sparse sample exploitation (SSE) method was developed to solve the problem of sample scarcity, resultant from cloud obstruction in optical images and the malfunction of in situ SMC-measuring instruments. With this method, data typically excluded in conventional approaches can be adequately employed. On the other hand, apart from the basic input parameters commonly discussed in previous studies, a couple of new parameters were optimized to improve the feature description. The Sentinel-1 SAR and Landsat-8 images were adopted to retrieve SMC in the study area in eastern Austria. By the SSE method, the number of available samples increased from 264 to 635 for ANN training and testing, and the retrieval accuracy could be markedly improved. Furthermore, the optimized parameters also improve the inversion effect, and the elevation was the most influential input parameter.

soil moisture content artificial neural network sample optimization synthetic aperture radar optical remote sensing image

SCI ; EI

英语

其他

Chemistry ; Engineering ; Instruments & Instrumentation

Chemistry, Analytical ; Engineering, Electrical & Electronic ; Instruments & Instrumentation

WOS:000769051200001

MDPI

20220911716178

Geometrical optics ; Moisture determination ; Radar imaging ; Remote sensing ; Soil moisture ; Synthetic aperture radar

Soils and Soil Mechanics:483.1 ; Radar Systems and Equipment:716.2 ; Light/Optics:741.1 ; Moisture Measurements:944.2

CHEMISTRY

Web of Science

1.Chinese Acad Sci, Aerosp Informat Res Inst, Beijing 100094, Peoples R China
2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
3.North China Inst Aerosp Engn, Sch Remote Sensing & Informat Engn, Langfang 065000, Peoples R China
4.Beijing Inst Space Long March Vehicle, Beijing 100076, Peoples R China
5.Southern Univ Sci & Technol, Sch Environm Sci & Engn, Shenzhen 518055, Peoples R China

Liu, Qixin,Gu, Xingfa,Chen, Xinran,et al. Soil Moisture Content Retrieval from Remote Sensing Data by Artificial Neural Network Based on Sample Optimization[J]. SENSORS,2022,22(4).

Liu, Qixin.,Gu, Xingfa.,Chen, Xinran.,Mumtaz, Faisal.,Liu, Yan.,...&Zhan, Yulin.(2022).Soil Moisture Content Retrieval from Remote Sensing Data by Artificial Neural Network Based on Sample Optimization.SENSORS,22(4).

Liu, Qixin,et al."Soil Moisture Content Retrieval from Remote Sensing Data by Artificial Neural Network Based on Sample Optimization".SENSORS 22.4(2022).