Reducing the Influence of Soil Moisture on the Estimation of Clay from Hyperspectral Data: A Case Study Using Simulated PRISMA Data

Remote Sensing

Raffaele Casa¹  Fabio Castaldi¹  Stefano Pignatti²  Federico Santini²  Simone Pascucci²  Angelo Palombo² 

[1] Department of Agricultural and Forestry Sciences (DAFNE), Università degli Studi della Tuscia (DPV), Via San Camillo de Lellis, 01100 Viterbo, Italy;Institute of Methodologies for Environmental Analysis (IMAA), Consiglio Nazionale delle Ricerche (CNR), C.da S. Loja, 100, 85050 Tito, Italy;

关键词: clay indices;    remote sensing;    proximal sensing;    PLSR;    satellite;    spectral features;   

DOI  :  10.3390/rs71115561

来源: DOAJ

【 摘 要 】

Soil moisture hampers the estimation of soil variables such as clay content from remote and proximal sensing data, reducing the strength of the relevant spectral absorption features. In the present study, two different strategies have been evaluated for their ability to minimize the influence of soil moisture on clay estimation by using soil spectra acquired in a laboratory and by simulating satellite hyperspectral data. Simulated satellite data were obtained according to the spectral characteristics of the forthcoming hyperspectral imager on board of the Italian PRISMA satellite mission. The soil datasets were split into four groups according to the water content. For each soil moisture level a prediction model was applied, using either spectral indices or partial least squares regression (PLSR). Prediction models were either specifically developed for the soil moisture level or calibrated using synthetically dry soil spectra, generated from wet soil data. Synthetically dry spectra were obtained using a new technique based on the effects caused by soil moisture on the optical spectrum from 400 to 2400 nm. The estimation of soil clay content, when using different prediction models according to soil moisture, was slightly more accurate as compared to the use of synthetically dry soil spectra, both employing clay indices and PLSR models. The results obtained in this study demonstrate that the a priori knowledge of the soil moisture class can reduce the error of clay estimation when using hyperspectral remote sensing data, such as those that will be provided by the PRISMA satellite mission in the near future.

【 授权许可】

Unknown