Estimation of root zone soil moisture using passive microwave remote sensing: A case study for rice and wheat crops for three states in the Indo-Gangetic basin. (15th March 2019)

• Record Type: Journal Article
• Authors: Sure, Anudeep; Dikshit, Onkar
• Is Part Of: Journal of environmental management. Volume 234(2019)
• Page Start: 75
• Page End: 89
• Publication Date: 2019-03-15
• Subjects: Kharif and Rabi crops -- Soil wetness index -- Soil moisture studies -- AMSR-2 and SMAP -- Basin level study -- Exponential filter; Environmental policy -- Periodicals; Environmental management -- Periodicals; Environment -- Periodicals; Ecology -- Periodicals; 363.705
• DOI: 10.1016/j.jenvman.2018.12.109
• Languages: English
• ISSNs: 0301-4797

Abstract

Abstract: This paper investigates estimation of root zone soil moisture using two passive microwave remote sensing datasets, Advanced Microwave Scattering Radiometer – 2 and Soil Moisture Active Passive satellites sensors. The study is focused on two crops, namely rice and wheat for the Indo-Gangetic basin, India, having a dynamic crop and soil type and land use land cover. A total of 21 rice crop and 23 wheat crop locations are chosen from the states of Uttar Pradesh, Madhya Pradesh and Bihar falling in the basin. The root zone soil moisture information is derived by estimating soil wetness index from surface soil moisture at 10 and 40 cm depths using a recursive exponential filter. The soil wetness index based algorithm is implementable even in the absence of ground information for a basin level study. The reference soil moisture dataset is obtained from the Global Land Database Assimilation System – NOAH at 10 and 40 cm depth. The research has also demonstrated significant potential of GLDAS-NOAH soil moisture data in the absence of ground (in-situ) soil moisture data. Of the various factors affecting surface and root zone soil moisture, this work evaluates the control of soil constituents on root zone soil moisture. The Spearman rank correlation coefficient is estimated for characteristic time delay with sand, silt and clay percentage at different locations. Coupling between and trends of surface and root zone soil moisture for rice and wheat crop locations are studied. The accuracy of estimated soil wetness index at 10 and 40 cm from two different satellite sensors at two different acquisition times (ascending and descending passes) is investigated by calculating the coefficient of determination, mean absolute error and mean biased error. This work highlights the significant difference in surface soil moisture estimation by two satellite sensors to derive root zone soil moisture for rice and wheat crops. Coefficient of determination is more (∼0.9) for SMAP derived soil wetness index whereas it is lower (∼0.65) for AMSR-2 derived soil wetness index for both crops. Characteristic time delay variation is observed at two different times and at both the depths, with characteristic time delay increasing with depth. Also, at the descending pass characteristic time delay is lower as compared to the ascending pass. A strong relationship between root zone soil moisture and soil texture is observed. For rice crop, a positive correlation with sand and clay is observed for Uttar Pradesh, Madhya Pradesh and Bihar locations having loam and sandy loam as the major soil class. And, for wheat locations, a positive correlation is observed for silt and clay for Uttar Pradesh locations and sand for Madhya Pradesh locations having loam and clay (light) soil texture. This work delivers essential information in understanding sustainable irrigation scheduling and increasing irrigation potential for rice and wheat crop locations. Having the knowledge of all the factors influencing crop cultivation and the derived root zone soil moisture, crop production can be optimized. Graphical abstract: Image 10347690 Highlights: Estimating root zone soil moisture from AMSR-2 and SMAP surface soil moisture data. Correlation between derived root zone soil moisture and soil texture for rice and wheat crops. Error of estimated soil moisture is higher at morning times than afternoon. Precise root zone soil moisture estimation (higher R 2 ) from SMAP than AMSR-2 sensor. Understanding the potential of GLDAS-NOAH as the reference data in absence of in-situ data.