Soil moisture as a potential variable for tracking and quantifying irrigation: A case study with proximal gamma-ray spectroscopy data. (February 2020)

• Record Type: Journal Article
• Authors: Filippucci, Paolo; Tarpanelli, Angelica; Massari, Christian; Serafini, Andrea; Strati, Virginia; Alberi, Matteo; Raptis, Kassandra Giulia Cristina; Mantovani, Fabio; Brocca, Luca
• Is Part Of: Advances in water resources. Volume 136(2020)
• Publication Date: 2020-02
• Subjects: Sm2rain -- Irrigation -- Soil moisture -- Proximal gamma-ray method; Hydrology -- Periodicals; Hydrodynamics -- Periodicals; Hydraulic engineering -- Periodicals; 551.48
• DOI: 10.1016/j.advwatres.2019.103502
• Languages: English
• ISSNs: 0309-1708

Abstract

Highlights: Soil Moisture from proximal gamma–ray spectroscopy keep trace of irrigation. It is possible to estimate irrigation from soil moisture and precipitation data High resolution remote sensing soil moisture data could be use as potential input. Abstract: The global warming effects put in danger global water availability and make necessary to decrease water wastage, e.g., by monitoring global irrigation. Despite this, global irrigation information is scarce due to the absence of a solid estimation technique. In this study, we applied an innovative approach to retrieve irrigation water from high spatial and temporal resolution Soil Moisture (SM) data obtained from an advanced sensor based on Proximal Gamma-Ray (PGR) spectroscopy, in a field located in Emilia Romagna (Italy). The results show that SM is a key variable to obtain information about the amount of water applied to plants, with Pearson correlation between observed and estimated daily irrigation data ranges from 0.88 to 0.91 by using different calibration methodology. With the aim of reproducing the working conditions of satellites measuring soil moisture, we sub-sampled SM hourly time series at larger time steps. The results demonstrated that the methodology is still capable to perform the daily (weekly) irrigation estimation with Pearson Correlation around 0.6 (0.7) if the time step is not greater than 36 (48) hours.