Soil Moisture and Irrigation Mapping in A Semi-Arid Region, Based on the Synergetic Use of Sentinel-1 and Sentinel-2 Data

This paper presents a technique for the mapping of soil moisture and irrigation, at the scale of agricultural fields, based on the synergistic interpretation of multi-temporal optical and Synthetic Aperture Radar (SAR) data (Sentinel-2 and Sentinel-1). The Kairouan plain, a semi-arid region in central Tunisia (North Africa), was selected as a test area for this study. Firstly, an algorithm for the direct inversion of the Water Cloud Model (WCM) was developed for the spatialization of the soil water content between 2015 and 2017. The soil moisture retrieved from these observations was first validated using ground measurements, recorded over 20 reference fields of cereal crops. A second method, based on the use of neural networks, was also used to confirm the initial validation. The results reported here show that the soil moisture products retrieved from remotely sensed data are accurate, with a Root Mean Square Error (RMSE) of less than 5% between the two moisture products. In addition, the analysis of soil moisture and Normalized Difference Vegetation Index (NDVI) products over cultivated fields, as a function of time, led to the classification of irrigated and rainfed areas on the Kairouan plain, and to the production of irrigation maps at the scale of individual fields. This classification is based on a decision tree approach, using a combination of various statistical indices of soil moisture and NDVI time series. The resulting irrigation maps were validated using reference fields within the study site. The best results were obtained with classifications based on soil moisture indices only, with an accuracy of 77%.

[1]  Amin Elshorbagy,et al.  On the relevance of using artificial neural networks for estimating soil moisture content , 2008 .

[2]  G. Guyot,et al.  Estimating surface soil moisture and leaf area index of a wheat canopy using a dual-frequency (C and X bands) scatterometer , 1993 .

[3]  J. Bruinsma,et al.  World agriculture towards 2030/2050: the 2012 revision , 2012 .

[4]  Prasad S. Thenkabail,et al.  Mapping Irrigated Areas of Ghana Using Fusion of 30 m and 250 m Resolution Remote-Sensing Data , 2011, Remote. Sens..

[5]  Mutlu Ozdogan,et al.  A new methodology to map irrigated areas using multi-temporal MODIS and ancillary data: An application example in the continental US , 2008 .

[6]  Emanuele Santi,et al.  Soil moisture mapping using Sentinel-1 images: Algorithm and preliminary validation , 2013 .

[7]  Mohammad Reza Mobasheri,et al.  Soil moisture estimation using land surface temperature and soil temperature at 5 cm depth , 2018, International Journal of Remote Sensing.

[8]  Mehrez Zribi,et al.  Irrigation Mapping Using Sentinel-1 Time Series at Field Scale , 2018, Remote. Sens..

[9]  Thomas J. Jackson,et al.  Validation of Advanced Microwave Scanning Radiometer Soil Moisture Products , 2010, IEEE Transactions on Geoscience and Remote Sensing.

[10]  Mehrez Zribi,et al.  Potential of Sentinel-1 Radar Data for the Assessment of Soil and Cereal Cover Parameters , 2017, Sensors.

[11]  Zohra Lili-Chabaane,et al.  Monitoring Irrigation Consumption Using High Resolution NDVI Image Time Series: Calibration and Validation in the Kairouan Plain (Tunisia) , 2015, Remote. Sens..

[12]  Abdelghani Chehbouni,et al.  Estimation spatialisée de l’évapotranspiration des cultures irriguées par télédétection : application à la gestion de l’irrigation dans la plaine du Haouz (Marrakech, Maroc) , 2009 .

[13]  Richard K. Moore,et al.  Microwave Remote Sensing, Active and Passive , 1982 .

[14]  Frédéric Baup,et al.  A New Empirical Model for Radar Scattering from Bare Soil Surfaces , 2016, Remote. Sens..

[15]  Francesco Mattia,et al.  LAI estimation of agricultural crops from optical data at different spatial resolution , 2009, 2009 IEEE International Geoscience and Remote Sensing Symposium.

[16]  W. Mauser,et al.  A global approach to estimate irrigated areas – a comparison between different data and statistics , 2018 .

[17]  Mehrez Zribi,et al.  A new soil roughness parameter for the modelling of radar backscattering over bare soil , 2014, IGARSS 2014.

[18]  J. Wigneron,et al.  An empirical calibration of the integral equation model based on SAR data, soil moisture and surface roughness measurement over bare soils , 2002 .

[19]  Qi Gao,et al.  Synergetic Use of Sentinel-1 and Sentinel-2 Data for Soil Moisture Mapping at 100 m Resolution , 2017, Sensors.

[20]  W. Wagner,et al.  Soil moisture mapping in a semiarid region, based on ASAR/Wide Swath satellite data , 2014 .

[21]  Austin M. Jensen,et al.  Assessment of Surface Soil Moisture Using High-Resolution Multi-Spectral Imagery and Artificial Neural Networks , 2015, Remote. Sens..

[22]  Piamchan Doungmanee,et al.  The nexus of agricultural water use and economic development level , 2016 .

[23]  L. S. Davis,et al.  An assessment of support vector machines for land cover classi(cid:142) cation , 2002 .

[24]  Rui Sun,et al.  Mapping Irrigated and Rainfed Wheat Areas Using Multi-Temporal Satellite Data , 2016, Remote. Sens..

[25]  Mehrez Zribi,et al.  Synergic Use of Sentinel-1 and Sentinel-2 Images for Operational Soil Moisture Mapping at High Spatial Resolution over Agricultural Areas , 2017, Remote. Sens..

[26]  Prasad S. Thenkabail,et al.  Ganges and Indus river basin land use/land cover (LULC) and irrigated area mapping using continuous streams of MODIS data , 2005 .

[27]  Mehrez Zribi,et al.  Retrieval of Both Soil Moisture and Texture Using TerraSAR-X Images , 2015, Remote. Sens..

[28]  Kamal Sarabandi,et al.  An empirical model and an inversion technique for radar scattering from bare soil surfaces , 1992, IEEE Trans. Geosci. Remote. Sens..

[29]  Tomas Norton,et al.  Advanced Monitoring and Management Systems for Improving Sustainability in Precision Irrigation , 2017 .

[30]  Yann Kerr,et al.  CAROLS: A New Airborne L-Band Radiometer for Ocean Surface and Land Observations , 2011, Sensors.

[31]  N. Baghdadi,et al.  Soil Moisture Profile Effect on Radar Signal Measurement , 2008, Sensors.

[32]  Vimal Mishra,et al.  Descriptor : Remotely sensed high resolution irrigated area mapping in India for 2000 to 2015 , 2017 .

[33]  Mark A. Friedl,et al.  Using prior probabilities in decision-tree classification of remotely sensed data , 2002 .

[34]  A. Kuusk.  Monitoring of vegetation parameters on large areas by the inversion of a canopy reflectance model , 1998 .

[35]  Mehrez Zribi,et al.  Calibration of the Water Cloud Model at C-Band for Winter Crop Fields and Grasslands , 2017, Remote. Sens..

[36]  Lars Eklundh,et al.  An Optical Sensor Network for Vegetation Phenology Monitoring and Satellite Data Calibration , 2011, Sensors.

[37]  Mehrez Zribi,et al.  A new soil roughness parameter for the modelling of radar backscattering over bare soil , 2014, 2014 IEEE Geoscience and Remote Sensing Symposium.

[38]  K. S. Hari Prasad,et al.  Estimation of water cloud model vegetation parameters using a genetic algorithm , 2012 .

[39]  Jungho Im,et al.  ISPRS Journal of Photogrammetry and Remote Sensing , 2022 .

[40]  F. Ulaby,et al.  Vegetation modeled as a water cloud , 1978 .

[41]  Paul M. Mather,et al.  Support vector machines for classification in remote sensing , 2005 .

[42]  Prasad S. Thenkabail,et al.  Irrigated areas of India derived using MODIS 500 m time series for the years 2001–2003 , 2010 .

[43]  Bernard Pinty,et al.  Evaluation of the performance of various vegetation indices to retrieve vegetation cover from AVHRR data , 1994 .

[44]  N. Baghdadi,et al.  Soil moisture retrieval over irrigated grassland using X-band SAR data , 2016 .

[45]  V. Wuwongse,et al.  Discrimination of irrigated and rainfed rice in a tropical agricultural system using SPOT VEGETATION NDVI and rainfall data , 2005 .

[46]  G. Gutman,et al.  The derivation of the green vegetation fraction from NOAA/AVHRR data for use in numerical weather prediction models , 1998 .

[47]  Nicolas Baghdadi,et al.  Remote-sensing data as an alternative input for the ‘STREAM’ runoff model , 2005 .

[48]  L. Jarlan,et al.  Assessment of Equity and Adequacy of Water Delivery in Irrigation Systems Using Remote Sensing-Based Indicators in Semi-Arid Region, Morocco , 2013, Water Resources Management.

[49]  Martha C. Anderson,et al.  Development of soil moisture profiles through coupled microwave–thermal infrared observations in the southeastern United States , 2017, Hydrology and Earth System Sciences.

[50]  Mehrez Zribi,et al.  Calibration of the Integral Equation Model for SAR data in C‐band and HH and VV polarizations , 2006 .

[51]  Emanuele Santi,et al.  Generation of soil moisture maps from ENVISAT/ASAR images in mountainous areas: a case study , 2010 .

[52]  C. Ottlé,et al.  Water and energy budgets simulation over the AMMA-Niger super-site spatially constrained with remote sensing data , 2009 .

[53]  B. Duchemin,et al.  Combined use of optical and radar satellite data for the monitoring of irrigation and soil moisture of wheat crops , 2011 .

[54]  Jun Qin,et al.  Estimation of Surface Soil Moisture from Thermal Infrared Remote Sensing Using an Improved Trapezoid Method , 2015, Remote. Sens..

[55]  Pascale C. Dubois,et al.  Measuring soil moisture with imaging radars , 1995, IEEE Trans. Geosci. Remote. Sens..

[56]  Ahmad Al Bitar,et al.  Retrieval and Multi-scale Validation of Soil Moisture from Multi-temporal SAR Data in a Semi-Arid Tropical Region , 2015, Remote. Sens..

[57]  Pawan K. Joshi,et al.  Decision tree classification of land use land cover for Delhi, India using IRS-P6 AWiFS data , 2011, Expert Syst. Appl..

[58]  A. Chehbouni,et al.  Soil surface moisture estimation over a semi-arid region using ENVISAT ASAR radar data for soil evaporation evaluation , 2011 .

[59]  Adrian K. Fung,et al.  Backscattering from a randomly rough dielectric surface , 1992, IEEE Trans. Geosci. Remote. Sens..