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Variability in soil moisture using AMSR-E product- A regional case study in the province of Marathwada division, India

Variability in soil moisture using AMSR-E product- A regional case study in the province of... GEOLOGY, ECOLOGY, AND LANDSCAPES INWASCON https://doi.org/10.1080/24749508.2021.1943811 RESEARCH ARTICLE Variability in soil moisture using AMSR-E product- A regional case study in the province of Marathwada division, India a b c c a Shubham A. Gade , Jyoti V. Yadav , Sachin P. Shinde , Dnyaneshwar D. More , Komal R. Gadekar and Vikrant Nikam a b Department of Irrigation and Drainage Engineering, Mahatma Phule Agriculture University, Rahuri, India; Department of Soil and Water Conservation Engineering, Sahyadri College of Agricultural Engineering, Karad, India; Department of Soil and Water Conservation Engineering, Mahatma Phule Agriculture University, Rahuri, India; Project Associate, Albedo Foundation, Nashik ABSTRACT ARTICLE HISTORY Received 3 November 2020 Soil moisture plays a crucial role in the assessment of weather patterns and analyzing the Accepted 11 June 2021 precipitation. The moisture analysis in the soil surface has been most efficient with passive remote sensing. The study attempts extraction of soil moisture and quantifying it with rainfall. KEYWORDS The Marathwada division was selected as the study area, the region corresponds to low rainfall Q-GIS; passive microwave and is a drought-prone area. The data used were AMSR-E/Aqua Daily L3 gridded soil moisture remote sensing; PERSIANN- and CHRS rainfall data, the daily product of 25 km×25 km resolution. The methodology was CDR; spatial analysis; demonstrated for the years 2003, 2004, and 2005. A cumulative 122 AMSR-E daily ascending temporal variation and descending scenes were retrieved and analyzed for the rainy season. The spatial analysis depicts peak soil moisture of 25% for the year 2005, greater than 20%, and 18% during 2003 and 2004, respectively. Peak moisture corresponding to all years is 20% in July compared to 13%, 17%, and 16% in June, August, and September, respectively. The temporal analysis revealed that during the onset of monsoon, soil moisture is 4% with 1.5 mm rainfall and it increases by 25% at peak rainfall of 39 mm. The soil moisture variation is in line with seasonal changes and rainfall variations. 1. Introduction satisfy the community needs for worldwide reach (daily to weekly) at a higher temporal resolution The volume fraction of water retained in the soil is (Kolassa et al., 2017). Banerjee & Kumar (2018) iden- known as soil moisture and is considered tified and estimated trend in surface soil moisture by a fundamental concept in the hydrological cycle AMSR-E dataset in Godavari and Krishna river basins (Berwal et al. 2016). It is considered a significant para- and found better correlation with rainfall. meter in numerous applications such as agriculture, Thiruvengadam & Rao (2016) analysed spatio- water management, climate studies, and environmen- temporal dynamics of soil moisture in India and tal risk prediction (Suri, 2013). The variability of soil observed that soil moisture trend has dynamic pattern moisture at a given location is primarily determined and positive correlation with the precipitation data. by the amount of precipitation measured in the area The comparison between soil moisture and mon- (Karthikeyan & Kumar, 2014). Soil moisture is soon patterns in India usually followed rainfall pat- a crucial climatic parameter of higher importance in terns and very good agreement between in situ and the rainfall scenario, and recognizing its spatial- soil moisture goods (Sathyanadh et al., 2016). The temporal variability across the Indian region is of dynamic analysis of soil moisture over any region, greater relevance (Sathyanadh et al., 2016). country, or continent is an outdating task. The soil moisture mapping has been performed Knowledge of existence soil moisture of a particular adopting optical, thermal infrared, and microwaves region creates awareness regarding various aspects remote sensing (Kingra et al., 2016). Remotely sensed such as drought, rainfall patterns, average annual rain- soil surface moisture is immensely beneficial in the fall, etc. fields of irrigation, hydrogeology, and meteorology. Researchers from all over the world have recently used and explored remotely sensed satellite data 2. Materials and methods items, obtained in the microwave region of the elec- 2.1. Study area description tromagnetic spectrum (Singh et al., 2015; Xiao et al., 2019). Significant efforts have been made to procure The Marathwada division of Maharashtra state, India, goods soil moisture from satellite data in order to is selected for the present study (Figure 1). It is CONTACT Shubham A. Gade shubhamgade66@gmail.com Department of Irrigation and Drainage Engineering, Mahatma Phule Agriculture University, Rahuri 413 722, India © 2021 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group on behalf of the International Water, Air & Soil Conservation Society(INWASCON). This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 2 S. A. GADE ET AL. Figure 1. Study area of Marathwada division. bounded by Karnataka and Telangana states on the (Dong et al., 2006; E. Njoku, 1999). The sensor detects south-east side and lies to the east of Vidarbha and geophysical parameters of land surface, surface soil Khandesh regions of Maharashtra. Marathwada cor- moisture, and vegetation water content. The sun- responds eight districts viz., Aurangabad, Jalna, Beed, synchronous satellite functioned in polar orbits, with Osmanabad, Nanded, Latur, Parbhani, and Hingoli the equator passing through at 1:30 A.M. (descending) with 76 tehsils located at 70°5′-78°5′ E longitude and and 1:30 P.M. (ascending). As a result, the data attrib- 17°5′-20°5′ N latitude. The region has a population of uted two files in a hierarchical data format (.hdf) as about 1.87 crores and has geographical area of ascending and descending scenes. 2 2 64,590 km , with only 57,000 km area suitable for The Center for Hydrometeorology and Remote agriculture. Of the total geographical area, the net Sensing (CHRS) portal was used for extracting gridded sowing area contributes only 75%. rainfall data (Nguyen et al., 2019). The CHRS devel- oped the PERSIANN-CDR which is Precipitation Estimation from Remotely Sensed Information using 2.2. Data acquisition and software used Artificial Neural Networks – Climate Data Record (http://apdrc.soest.hawaii.edu/datadoc/persiann_cdr. In the present study, AMSR-E/Aqua Daily L3 surface php). The output provides daily, monthly, and yearly soil moisture, daily 25 km × 25 km (gridded) dataset rainfall estimated at 0.25º × 0.25º resolution. Rainfall has been used (https://nsidc.org/data/AE_Land3). The product covers 60º S to 60º N globally. The data are data product corresponds to a 56 km mean spatial available from January – 1983 to December – 2015. resolution (Table 1). A passive microwave radiometer, The PERSIANN algorithm is developed by modifying AMSR-E (Advanced Microwave Scanning Radiometer the monthly Global Precipitation Climatology Project for the Earth Observing System), is updated from (GPCP) product and GridSat-B1 infrared data ADEOS-II, the Advanced Earth Observing Satellite-II (Ashouri et al., 2015; Levizzani et al., 2020). The R language was used for extraction and averaging of Table 1. Specifications and properties of AMSR-E. soil moisture data. Furthermore, the Q-GIS was used Sr. No. Properties Specifications for analysis and visualization. It is free and open- 1. Satellite AMSR-E source software. 2. Spatial Resolution 56 km (25 km × 25 km) 3. Swath 1445 km 4. Frequencies (GHz) 6.925, 10.65, 18.7, 23.8, 36.5, 89 2.2.1. Scientific rationale for soil moisture retrieval 5. Incidence angle 55º using passive microwave sensors 6. Mass 324 ± 15 kg The radiometers which are referred as passive sensors 7. Data Rate Average:- 87.4 kbps; Peak:- 125 kbps (Source: https://nsidc.org/data/AE_Land3/versions/2 ) generally detect the radiated energy. The Rayleigh- GEOLOGY, ECOLOGY, AND LANDSCAPES 3 Jeans approximation of Planck’s law is the scientific Initially, AMSR-E dataset is archived from NSIDC principle that functions in the process of detection by to obtain the value of soil moisture. The soil moisture the radiometers (Singh et al., 2015). The activity is data is accessible at 6.9 GHz for horizontal and vertical driven by the electromagnetic emission of the black polarization in ascending and descending modes. The body at a given temperature of T (°K) as prescribed by AMSR-E is a daily data generating a single file for Planck’s law. each day. Therefore, 122 days of monsoon corre- Planck’s rule is known as Rayleigh-Jeans approxi- sponded to a total of 244 Hierarchical Data Format (. mation for the black body, when it is estimated for f/ hdf) files with both ascending and descending passes. T⋘2� 10 given by: The HDF format has to be converted to a compatible image format, specifically either in .img or .tiff for 2KT further processing. The downloaded soil moisture Bðλ; TÞ ¼ (1) dataset comprises total of 135 rows and 175 columns with a total of 23,355 pixels (Singh et al., 2015). The The tendency to absorb or release radiation is fairly measure of soil moisture is the signed integer value of determined by Kirchhoff’s law as follows, 16 bit contributing to the single pixel. The ascending � � 2KT and descending soil moisture datasets provide a soil Bðλ; TÞ ¼ εðλÞ (2) moisture at resolution of 10.7 GHz, and data in pixels varies between 0 and 500. Consequently, for soil Where, emissivity “ε” is the ratio of the emission 3 moisture in g/cm , the data values were multiplied between the object and the blackbody governed at the by 0.001. Furthermore, it is multiplied by 100 to gen- similar temperature. The emissivity relies upon various erate soil moisture maps in percentage (%). parameters such as polarization, angle of incidence, The retrieval of surface soil moisture attribute from temperature, and the physical properties of the surface. AMSR-E data is followed by conversion (from .hdf to . The atmospheric attenuation and emission of signal are tiff) for further processing (Chakraborty et al., 2012). expressed as Eq. 3 for radiometers functioning in The averaging of data to create soil moisture map is shorter wavelength ranges (Engman, 1991; E. G. Njoku primarily done in R language. In downloaded datasets, et al., 2003; Sharma et al., 2018; Singh et al., 2015). the ‘9999ʹ fill value is allocated for pixels that are void/ � � null of any retrieval, where no data is recorded because T ¼ tðHÞ rT þð1 rÞT þð1 tðHÞÞT B sky soil atm of inherent differences between available swaths. (3) Figures 3 and 4 represent the ascending and descend- ing pass of a satellite from 1 to 7 July 2003. Both passes Here, T , t(H), and “r” are the brightness temperature, indicate that the entire study area (Marathwada divi- atmospheric transmission, and surface reflectivity. son) is not covered in a single pass. Whereas, T , T , and T corresponded to the sky soil atm temperatures of sky, soil, and atmosphere respectively (Schmugge et al., 1992). For standard soil moisture 2.4. Retrieval of soil moisture from the applications using longer microwave wavelengths, the AMSR-E data atmosphere is clear in most environmental conditions (t(H)~1 K) and T is much less (~3.5 K); hence these sky The Graphical modeler in processing plugin of Q-GIS is terms can be neglected. used for retrieval of soil moisture values by building model, as shown in Figure 5. The merge attribute inte- T ¼ ð1 rÞT ¼ eT (4) B soil soil grates ascending and descending separate dataset files of each day. The raster calculator is utilized to convert In above Eq. 4, e ¼ ð1 rÞ represents the emissivity values in gm/cm into percent soil moisture. As the out- depending upon medium dielectric constant put raster has no Coordinate Reference System (CRS), it (Engman, 1991; Singh et al., 2015). has given Source Reference System (SRS) as NSIDC EASE-Grid Global (EPSG: 3410) and is converted into target reference as WGS-84 (EPSG: 4326) using re- 2.3. Preparation and pre-processing of the projecting. Lastly, enhancing the zonal statistics attribute datasets and Marathwada grid shapefile, the mean statistic values The current research mostly relies on the estimation of of the pixels corresponding the raster layer is calculated. soil moisture through AMSR-E data product and As a result, a daily raster of soil moisture gets generated. retrieval of PERSIANN-CDR rainfall data from the CHRS Data Portal, which is available in the public domain. The study is conducted for three years 2.5. Soil moisture map generation (2003, 2004, and 2005) during rainy season (June to Weekly, monthly, and seasonal maps are prepared, September). The flowchart of the overall methodology calculating its averages as per requirement. is represented in Figure 2. 4 S. A. GADE ET AL. 2.6. Retrieval of rainfall data from PERSIANN-CDR The PERSIANN is centered on geostationary long- wave infrared imagery to generate global rainfall. Using a gridded shapefile of the study area and select- ing the required date images were downloaded. The downloaded data is in Arc-Grid format as shown in Figure 6. The map signified that the central part of Marathwada has good rainfall, whereas low rainfall along the northern region. This phenomenon is further explain in comparison to soil moisture. 3. Results and discussion This study has been made an attempt to fulfil the primary objective of retrieving the soil moisture pro- duct of AMSR-E and do the spatial as well as temporal soil moisture variation analysis for a Marathwada region during monsoon season. 3.1. Spatial analysis of soil moisture The variation of soil moisture in the Marathwada region is categorized into three types, (1) Good soil moisture – More than 18% (2) Moderate soil moisture – Between 10% to 18% (3) Poor soil moisture – Below 10% Figure 2. Flow chart illustrating overall methodology. The soil moisture is odd throughout the study area and didn’t follow a unique pattern. The variation in 2.5.1. Weekly average soil moisture map soil moisture content over the Marathwada division is generation primarily controlled by rainfall, seasons, and agricul- Seven raster files were averaged using the developed tural practices adopted by farmers. The overall soil R code, which gives a single output file as a weekly moisture showed maximum variation utmost to 25%. average soil moisture map. A total of 12 weekly average The estimated soil moisture values might differ from maps of July were composed for 2003, 2004, and 2005. observed soil moisture values due to the coarse resolu- tion of the AMSR-E sensor of the order of 56 km, which is resampled to 25 km resolution. 2.5.2. Monthly average soil moisture map Figure 7 demonstrated weekly soil moisture in July generation st (2003, 2004, and 2005) for the Marathwada. The 1 For the monthly average soil moisture map, a similar week of July 2003, 2004, and 2005 observed poor soil procedure explained earlier was incorporated for June, moisture (6–12%) in the southern province of July, August, and September month of the year 2003, Marathwada, while moderate and good soil moisture 2004, and 2005 to generate a single map for respective (14–22%) was observed for the remaining area. The months depicting average soil moisture. nd 2 week of 2005 showed a drastic increase in moisture from moderate to a good condition that might be due rd th 2.5.3. Seasonal average soil moisture map to the onset of monsoon. The 3 and 4 weeks of 2003 generation and 2004 resembled almost similar variation with To generate seasonal average soil moisture maps, 122 a slight rise in soil moisture. In 2005 as proceeded nd rd rd th files considering June, July, August, and September sequentially from 2 to 3 and 3 to 4 week, the th month, are averaged using the similar code. The four soil moisture increased phenomenally with 4 week months average monthly soil moisture maps for incorporating good soil moisture pattern over entire the year 2003, 2004, and 2005 generate final seasonal area. average soil moisture, which forms three seasons for Overall it was envisioned that the soil moisture the respective years. resembled similar variation in 2003 and 2004 with no GEOLOGY, ECOLOGY, AND LANDSCAPES 5 Figure 3. Ascending pass scenes depicting swaths of soil moisture data of India recorded from 1 July 2003 to 7 July 2003. major changes except the northern and central part of fairly good soil moisture during the year 2005, fol- Marathwada. While in 2005, soil moisture in July lowed by 2004 and 2003. The majority portion of the consistently increased with maximum soil moisture study area in July 2005 showed moderate and good soil th observed in the 4 week of 2005. moisture (18–22%). The August month corresponded a similar trend in soil moisture variation as July for all 3.1.1. Monthly soil moisture analysis three years. The south-east region had a slight edge Figure 8 illustrated the month-wise spatial pattern in and better soil moisture (10–18%) as compared to the soil moisture in the Marathwada region from June to overall study area for August 2003, 2004, and 2005. September, during 2003, 2004, and 2005. The data The variation for September 2003, revealed poor soil analysis revealed that for June (2003, 2004, and moisture (6–10%), whereas poor and moderate soil 2005), soil moisture was approximately less than 10% moisture during 2004. During September 2005, the considering the total geographical area. The utmost soil moisture was moderate and varied in the range area corresponded to soil moisture less than 6%, which of 10–14%. specifies the area falling under poor soil moisture From the overall monthly soil moisture maps, it condition. The spatial maps derived for July indicated was analysed that the entire southern and northern 6 S. A. GADE ET AL. Figure 4. Descending pass scenes depicting swaths of soil moisture data of India recorded from 1 July 2003 to 7 July 2003. regions of Marathwada fall under poor soil moist- region of Marathwada had minimum soil moisture ure except the July month of 2003, 2004, 2005, and as compared to the rest of the area for 2003 and 2004. August 2005. Extreme poor soil moisture was In 2005 the entire study area had moderate soil recorded in June 2005, as compared to the other moisture (10–14%), which contributes to the fact maps of various months. The June and September that year 2005 had appreciable rainfall followed by months had the least soil moisture as compared to 2004 and 2003. July and August months. The July month had max- imum soil moisture as compared to June, August, 3.2. Temporal analysis of soil moisture with and September in 2003, 2004, and 2005 that might rainfall be due to the initiation of rainfall. Time series plot depicts merged soil moisture with 3.1.2. Seasonal soil moisture analysis seasonal rainfall for the study area during year 2005 Figure 9 depicts seasonal soil moisture variation from (Figure 10). The illustrated graph demonstrates that June to September during 2003, 2004, and 2005. It rainfall patterns had a direct impact on soil moist- was noticed that soil moisture pattern showed an ure variation, i.e., with an increment in rainfall, increasing trend from 2003 to 2005. The southern there is a significant increase in soil moisture. GEOLOGY, ECOLOGY, AND LANDSCAPES 7 Figure 5. Graphical modeler in QGIS for soil moisture raster formation. Figure 6. Daily rainfall map for 15 July 2005. During the onset of monsoon, soil moisture is 4%, 16%. Hence, it was remarked that soil moisture and it increases by 21% as rainfall reaches its peak and rainfall follows an identical behavior. rate. In June, due to the initiation of rainfall soil moisture is minimum. With an increase in rainfall th th 4. Conclusions from 13 June to 19 July, soil moisture gradually th th rose by 15%. Later from 19 July to 28 August, The soil moisture derived from AMSR-E product was rainfall decreased from 40 mm to 3 mm, which used to analyze variation in Marathwada region for the eventually leads to a decline in soil moisture by monsoon season of 2003, 2004, and 2005. It was 8 S. A. GADE ET AL. Figure 7. Weekly average soil moisture for July (2003–2005). observed that the soil moisture patterns were highly conclude the least soil moisture in June and September dynamic and AMSR-E-derived soil moisture will help as compared to July and August month. The southern to figure out drought and flooding conditions. and northwest province of the study area falls under Amongst the three years, the maximum soil moisture poor soil moisture conditions as compared to the east- was observed in 2005 which is 25%, whereas minimum ern and central parts. The seasonal soil moisture maps soil moisture was envisioned for 2003 (1%). The foretell least soil moisture in 2003 covering 60% of weekly soil moisture maps of July specified that the total area whereas, moderate soil moisture in 2005 southwest region of Marathwada is always in poor soil which covers 80% of the study area for the moisture conditions. The monthly soil moisture maps Marathwada region in accordance to 2003, 2004, 2005. GEOLOGY, ECOLOGY, AND LANDSCAPES 9 Figure 8. Monthly average soil moisture for June – September (2003–2005). 10 S. A. GADE ET AL. Figure 9. Seasonal soil moisture (2003–2005). Figure 10. Soil moisture variation with time and rainfall from (June – September) in 2005. For the accuracy purpose, the estimated soil Monthly soil moisture maps produced using the sug- moisture should be cross-verified with ground gested approach successfully explain seasonal fluctua - truth soil moisture data. It should be reanalyzed tion in soil moisture. The similar procedure can be with land use land cover for soil moisture utiliza- incorporated in the analysis of different parameters of tion and the percentage of errors should be AMSR E sensor viz., precipitation rate, cloud water, counted in accordance to ground truth and land water vapor, sea surface winds, sea surface tempera- use land cover. The AMSR-E sensor has the coarse ture, ice, and snow in global context. resolution. Due to this reason, it is difficult to get pure pixels in agricultural areas. This can be over- come by incorporating vegetation/roughness para- Acknowledgments meters in the AMSR-E sensor. We are extremely thankful to NSIDC for providing Aqua One of the key benefits of the proposed methodol- AMSR-E soil moisture data and CHRS for providing ogy is that a user can produce average soil moisture PERSIANN-CDR rainfall data free of cost. We appreciate maps at chosen intervals viz., weekly/monthly/season- the editors and the reviewers for their constructive sugges- tions and insightful comments, which helped us greatly to ally/yearly, etc.; this could substantially reduce the improve this manuscript. time and effort of managers and decision makers. GEOLOGY, ECOLOGY, AND LANDSCAPES 11 from AMSR-E and ASCAT microwave observation Disclosure statement synergy. Part 2: Product evaluation. Remote Sensing of The authors declare that they have no known competing Environment, 195, 202–217. https://doi.org/10.1016/j.rse. financial interests or personal relationships that could have 2017.04.020 appeared to influence the work reported in this paper. Levizzani, V., Kidd, C., Kirschbaum, D. B., Kummerow, C. D., Nakamura, K., & Turk, F. J. (2020). PERSIANN-CDR for hydrology and hydro-climatic applications. Advances in Global Change Research, 69 ORCID (April), 993–1012. https://doi.org/10.1007/978-3-030- 35798-6_26 Shubham A. Gade http://orcid.org/0000-0003-1671-8394 Nguyen, P., Shearer, E. J., Tran, H., Ombadi, M., Sachin P. Shinde http://orcid.org/0000-0002-5039-2992 Hayatbini, N., Palacios, T., Huynh, P., Braithwaite, D., Dnyaneshwar D. More http://orcid.org/0000-0001-9818- Updegraff, G., Hsu, K., Kuligowski, B., Logan, W. S., & Sorooshian, S.. (2019). The CHRS data portal, an easily Komal R. Gadekar http://orcid.org/0000-0001-6354- accessible public repository for PERSIANN global satel- 293X lite precipitation data. Scientific Data, 6,1-10. https://doi. org/10.1038/sdata.2018.296 Njoku, E. (1999). AMSR land surface parameters. Algorithm References theoretical basis document, Version 3., Jet Propulsion Laboratory California, Institute of Technology Ashouri, H., Hsu, K. 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Variability in soil moisture using AMSR-E product- A regional case study in the province of Marathwada division, India

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GEOLOGY, ECOLOGY, AND LANDSCAPES INWASCON https://doi.org/10.1080/24749508.2021.1943811 RESEARCH ARTICLE Variability in soil moisture using AMSR-E product- A regional case study in the province of Marathwada division, India a b c c a Shubham A. Gade , Jyoti V. Yadav , Sachin P. Shinde , Dnyaneshwar D. More , Komal R. Gadekar and Vikrant Nikam a b Department of Irrigation and Drainage Engineering, Mahatma Phule Agriculture University, Rahuri, India; Department of Soil and Water Conservation Engineering, Sahyadri College of Agricultural Engineering, Karad, India; Department of Soil and Water Conservation Engineering, Mahatma Phule Agriculture University, Rahuri, India; Project Associate, Albedo Foundation, Nashik ABSTRACT ARTICLE HISTORY Received 3 November 2020 Soil moisture plays a crucial role in the assessment of weather patterns and analyzing the Accepted 11 June 2021 precipitation. The moisture analysis in the soil surface has been most efficient with passive remote sensing. The study attempts extraction of soil moisture and quantifying it with rainfall. KEYWORDS The Marathwada division was selected as the study area, the region corresponds to low rainfall Q-GIS; passive microwave and is a drought-prone area. The data used were AMSR-E/Aqua Daily L3 gridded soil moisture remote sensing; PERSIANN- and CHRS rainfall data, the daily product of 25 km×25 km resolution. The methodology was CDR; spatial analysis; demonstrated for the years 2003, 2004, and 2005. A cumulative 122 AMSR-E daily ascending temporal variation and descending scenes were retrieved and analyzed for the rainy season. The spatial analysis depicts peak soil moisture of 25% for the year 2005, greater than 20%, and 18% during 2003 and 2004, respectively. Peak moisture corresponding to all years is 20% in July compared to 13%, 17%, and 16% in June, August, and September, respectively. The temporal analysis revealed that during the onset of monsoon, soil moisture is 4% with 1.5 mm rainfall and it increases by 25% at peak rainfall of 39 mm. The soil moisture variation is in line with seasonal changes and rainfall variations. 1. Introduction satisfy the community needs for worldwide reach (daily to weekly) at a higher temporal resolution The volume fraction of water retained in the soil is (Kolassa et al., 2017). Banerjee & Kumar (2018) iden- known as soil moisture and is considered tified and estimated trend in surface soil moisture by a fundamental concept in the hydrological cycle AMSR-E dataset in Godavari and Krishna river basins (Berwal et al. 2016). It is considered a significant para- and found better correlation with rainfall. meter in numerous applications such as agriculture, Thiruvengadam & Rao (2016) analysed spatio- water management, climate studies, and environmen- temporal dynamics of soil moisture in India and tal risk prediction (Suri, 2013). The variability of soil observed that soil moisture trend has dynamic pattern moisture at a given location is primarily determined and positive correlation with the precipitation data. by the amount of precipitation measured in the area The comparison between soil moisture and mon- (Karthikeyan & Kumar, 2014). Soil moisture is soon patterns in India usually followed rainfall pat- a crucial climatic parameter of higher importance in terns and very good agreement between in situ and the rainfall scenario, and recognizing its spatial- soil moisture goods (Sathyanadh et al., 2016). The temporal variability across the Indian region is of dynamic analysis of soil moisture over any region, greater relevance (Sathyanadh et al., 2016). country, or continent is an outdating task. The soil moisture mapping has been performed Knowledge of existence soil moisture of a particular adopting optical, thermal infrared, and microwaves region creates awareness regarding various aspects remote sensing (Kingra et al., 2016). Remotely sensed such as drought, rainfall patterns, average annual rain- soil surface moisture is immensely beneficial in the fall, etc. fields of irrigation, hydrogeology, and meteorology. Researchers from all over the world have recently used and explored remotely sensed satellite data 2. Materials and methods items, obtained in the microwave region of the elec- 2.1. Study area description tromagnetic spectrum (Singh et al., 2015; Xiao et al., 2019). Significant efforts have been made to procure The Marathwada division of Maharashtra state, India, goods soil moisture from satellite data in order to is selected for the present study (Figure 1). It is CONTACT Shubham A. Gade shubhamgade66@gmail.com Department of Irrigation and Drainage Engineering, Mahatma Phule Agriculture University, Rahuri 413 722, India © 2021 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group on behalf of the International Water, Air & Soil Conservation Society(INWASCON). This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 2 S. A. GADE ET AL. Figure 1. Study area of Marathwada division. bounded by Karnataka and Telangana states on the (Dong et al., 2006; E. Njoku, 1999). The sensor detects south-east side and lies to the east of Vidarbha and geophysical parameters of land surface, surface soil Khandesh regions of Maharashtra. Marathwada cor- moisture, and vegetation water content. The sun- responds eight districts viz., Aurangabad, Jalna, Beed, synchronous satellite functioned in polar orbits, with Osmanabad, Nanded, Latur, Parbhani, and Hingoli the equator passing through at 1:30 A.M. (descending) with 76 tehsils located at 70°5′-78°5′ E longitude and and 1:30 P.M. (ascending). As a result, the data attrib- 17°5′-20°5′ N latitude. The region has a population of uted two files in a hierarchical data format (.hdf) as about 1.87 crores and has geographical area of ascending and descending scenes. 2 2 64,590 km , with only 57,000 km area suitable for The Center for Hydrometeorology and Remote agriculture. Of the total geographical area, the net Sensing (CHRS) portal was used for extracting gridded sowing area contributes only 75%. rainfall data (Nguyen et al., 2019). The CHRS devel- oped the PERSIANN-CDR which is Precipitation Estimation from Remotely Sensed Information using 2.2. Data acquisition and software used Artificial Neural Networks – Climate Data Record (http://apdrc.soest.hawaii.edu/datadoc/persiann_cdr. In the present study, AMSR-E/Aqua Daily L3 surface php). The output provides daily, monthly, and yearly soil moisture, daily 25 km × 25 km (gridded) dataset rainfall estimated at 0.25º × 0.25º resolution. Rainfall has been used (https://nsidc.org/data/AE_Land3). The product covers 60º S to 60º N globally. The data are data product corresponds to a 56 km mean spatial available from January – 1983 to December – 2015. resolution (Table 1). A passive microwave radiometer, The PERSIANN algorithm is developed by modifying AMSR-E (Advanced Microwave Scanning Radiometer the monthly Global Precipitation Climatology Project for the Earth Observing System), is updated from (GPCP) product and GridSat-B1 infrared data ADEOS-II, the Advanced Earth Observing Satellite-II (Ashouri et al., 2015; Levizzani et al., 2020). The R language was used for extraction and averaging of Table 1. Specifications and properties of AMSR-E. soil moisture data. Furthermore, the Q-GIS was used Sr. No. Properties Specifications for analysis and visualization. It is free and open- 1. Satellite AMSR-E source software. 2. Spatial Resolution 56 km (25 km × 25 km) 3. Swath 1445 km 4. Frequencies (GHz) 6.925, 10.65, 18.7, 23.8, 36.5, 89 2.2.1. Scientific rationale for soil moisture retrieval 5. Incidence angle 55º using passive microwave sensors 6. Mass 324 ± 15 kg The radiometers which are referred as passive sensors 7. Data Rate Average:- 87.4 kbps; Peak:- 125 kbps (Source: https://nsidc.org/data/AE_Land3/versions/2 ) generally detect the radiated energy. The Rayleigh- GEOLOGY, ECOLOGY, AND LANDSCAPES 3 Jeans approximation of Planck’s law is the scientific Initially, AMSR-E dataset is archived from NSIDC principle that functions in the process of detection by to obtain the value of soil moisture. The soil moisture the radiometers (Singh et al., 2015). The activity is data is accessible at 6.9 GHz for horizontal and vertical driven by the electromagnetic emission of the black polarization in ascending and descending modes. The body at a given temperature of T (°K) as prescribed by AMSR-E is a daily data generating a single file for Planck’s law. each day. Therefore, 122 days of monsoon corre- Planck’s rule is known as Rayleigh-Jeans approxi- sponded to a total of 244 Hierarchical Data Format (. mation for the black body, when it is estimated for f/ hdf) files with both ascending and descending passes. T⋘2� 10 given by: The HDF format has to be converted to a compatible image format, specifically either in .img or .tiff for 2KT further processing. The downloaded soil moisture Bðλ; TÞ ¼ (1) dataset comprises total of 135 rows and 175 columns with a total of 23,355 pixels (Singh et al., 2015). The The tendency to absorb or release radiation is fairly measure of soil moisture is the signed integer value of determined by Kirchhoff’s law as follows, 16 bit contributing to the single pixel. The ascending � � 2KT and descending soil moisture datasets provide a soil Bðλ; TÞ ¼ εðλÞ (2) moisture at resolution of 10.7 GHz, and data in pixels varies between 0 and 500. Consequently, for soil Where, emissivity “ε” is the ratio of the emission 3 moisture in g/cm , the data values were multiplied between the object and the blackbody governed at the by 0.001. Furthermore, it is multiplied by 100 to gen- similar temperature. The emissivity relies upon various erate soil moisture maps in percentage (%). parameters such as polarization, angle of incidence, The retrieval of surface soil moisture attribute from temperature, and the physical properties of the surface. AMSR-E data is followed by conversion (from .hdf to . The atmospheric attenuation and emission of signal are tiff) for further processing (Chakraborty et al., 2012). expressed as Eq. 3 for radiometers functioning in The averaging of data to create soil moisture map is shorter wavelength ranges (Engman, 1991; E. G. Njoku primarily done in R language. In downloaded datasets, et al., 2003; Sharma et al., 2018; Singh et al., 2015). the ‘9999ʹ fill value is allocated for pixels that are void/ � � null of any retrieval, where no data is recorded because T ¼ tðHÞ rT þð1 rÞT þð1 tðHÞÞT B sky soil atm of inherent differences between available swaths. (3) Figures 3 and 4 represent the ascending and descend- ing pass of a satellite from 1 to 7 July 2003. Both passes Here, T , t(H), and “r” are the brightness temperature, indicate that the entire study area (Marathwada divi- atmospheric transmission, and surface reflectivity. son) is not covered in a single pass. Whereas, T , T , and T corresponded to the sky soil atm temperatures of sky, soil, and atmosphere respectively (Schmugge et al., 1992). For standard soil moisture 2.4. Retrieval of soil moisture from the applications using longer microwave wavelengths, the AMSR-E data atmosphere is clear in most environmental conditions (t(H)~1 K) and T is much less (~3.5 K); hence these sky The Graphical modeler in processing plugin of Q-GIS is terms can be neglected. used for retrieval of soil moisture values by building model, as shown in Figure 5. The merge attribute inte- T ¼ ð1 rÞT ¼ eT (4) B soil soil grates ascending and descending separate dataset files of each day. The raster calculator is utilized to convert In above Eq. 4, e ¼ ð1 rÞ represents the emissivity values in gm/cm into percent soil moisture. As the out- depending upon medium dielectric constant put raster has no Coordinate Reference System (CRS), it (Engman, 1991; Singh et al., 2015). has given Source Reference System (SRS) as NSIDC EASE-Grid Global (EPSG: 3410) and is converted into target reference as WGS-84 (EPSG: 4326) using re- 2.3. Preparation and pre-processing of the projecting. Lastly, enhancing the zonal statistics attribute datasets and Marathwada grid shapefile, the mean statistic values The current research mostly relies on the estimation of of the pixels corresponding the raster layer is calculated. soil moisture through AMSR-E data product and As a result, a daily raster of soil moisture gets generated. retrieval of PERSIANN-CDR rainfall data from the CHRS Data Portal, which is available in the public domain. The study is conducted for three years 2.5. Soil moisture map generation (2003, 2004, and 2005) during rainy season (June to Weekly, monthly, and seasonal maps are prepared, September). The flowchart of the overall methodology calculating its averages as per requirement. is represented in Figure 2. 4 S. A. GADE ET AL. 2.6. Retrieval of rainfall data from PERSIANN-CDR The PERSIANN is centered on geostationary long- wave infrared imagery to generate global rainfall. Using a gridded shapefile of the study area and select- ing the required date images were downloaded. The downloaded data is in Arc-Grid format as shown in Figure 6. The map signified that the central part of Marathwada has good rainfall, whereas low rainfall along the northern region. This phenomenon is further explain in comparison to soil moisture. 3. Results and discussion This study has been made an attempt to fulfil the primary objective of retrieving the soil moisture pro- duct of AMSR-E and do the spatial as well as temporal soil moisture variation analysis for a Marathwada region during monsoon season. 3.1. Spatial analysis of soil moisture The variation of soil moisture in the Marathwada region is categorized into three types, (1) Good soil moisture – More than 18% (2) Moderate soil moisture – Between 10% to 18% (3) Poor soil moisture – Below 10% Figure 2. Flow chart illustrating overall methodology. The soil moisture is odd throughout the study area and didn’t follow a unique pattern. The variation in 2.5.1. Weekly average soil moisture map soil moisture content over the Marathwada division is generation primarily controlled by rainfall, seasons, and agricul- Seven raster files were averaged using the developed tural practices adopted by farmers. The overall soil R code, which gives a single output file as a weekly moisture showed maximum variation utmost to 25%. average soil moisture map. A total of 12 weekly average The estimated soil moisture values might differ from maps of July were composed for 2003, 2004, and 2005. observed soil moisture values due to the coarse resolu- tion of the AMSR-E sensor of the order of 56 km, which is resampled to 25 km resolution. 2.5.2. Monthly average soil moisture map Figure 7 demonstrated weekly soil moisture in July generation st (2003, 2004, and 2005) for the Marathwada. The 1 For the monthly average soil moisture map, a similar week of July 2003, 2004, and 2005 observed poor soil procedure explained earlier was incorporated for June, moisture (6–12%) in the southern province of July, August, and September month of the year 2003, Marathwada, while moderate and good soil moisture 2004, and 2005 to generate a single map for respective (14–22%) was observed for the remaining area. The months depicting average soil moisture. nd 2 week of 2005 showed a drastic increase in moisture from moderate to a good condition that might be due rd th 2.5.3. Seasonal average soil moisture map to the onset of monsoon. The 3 and 4 weeks of 2003 generation and 2004 resembled almost similar variation with To generate seasonal average soil moisture maps, 122 a slight rise in soil moisture. In 2005 as proceeded nd rd rd th files considering June, July, August, and September sequentially from 2 to 3 and 3 to 4 week, the th month, are averaged using the similar code. The four soil moisture increased phenomenally with 4 week months average monthly soil moisture maps for incorporating good soil moisture pattern over entire the year 2003, 2004, and 2005 generate final seasonal area. average soil moisture, which forms three seasons for Overall it was envisioned that the soil moisture the respective years. resembled similar variation in 2003 and 2004 with no GEOLOGY, ECOLOGY, AND LANDSCAPES 5 Figure 3. Ascending pass scenes depicting swaths of soil moisture data of India recorded from 1 July 2003 to 7 July 2003. major changes except the northern and central part of fairly good soil moisture during the year 2005, fol- Marathwada. While in 2005, soil moisture in July lowed by 2004 and 2003. The majority portion of the consistently increased with maximum soil moisture study area in July 2005 showed moderate and good soil th observed in the 4 week of 2005. moisture (18–22%). The August month corresponded a similar trend in soil moisture variation as July for all 3.1.1. Monthly soil moisture analysis three years. The south-east region had a slight edge Figure 8 illustrated the month-wise spatial pattern in and better soil moisture (10–18%) as compared to the soil moisture in the Marathwada region from June to overall study area for August 2003, 2004, and 2005. September, during 2003, 2004, and 2005. The data The variation for September 2003, revealed poor soil analysis revealed that for June (2003, 2004, and moisture (6–10%), whereas poor and moderate soil 2005), soil moisture was approximately less than 10% moisture during 2004. During September 2005, the considering the total geographical area. The utmost soil moisture was moderate and varied in the range area corresponded to soil moisture less than 6%, which of 10–14%. specifies the area falling under poor soil moisture From the overall monthly soil moisture maps, it condition. The spatial maps derived for July indicated was analysed that the entire southern and northern 6 S. A. GADE ET AL. Figure 4. Descending pass scenes depicting swaths of soil moisture data of India recorded from 1 July 2003 to 7 July 2003. regions of Marathwada fall under poor soil moist- region of Marathwada had minimum soil moisture ure except the July month of 2003, 2004, 2005, and as compared to the rest of the area for 2003 and 2004. August 2005. Extreme poor soil moisture was In 2005 the entire study area had moderate soil recorded in June 2005, as compared to the other moisture (10–14%), which contributes to the fact maps of various months. The June and September that year 2005 had appreciable rainfall followed by months had the least soil moisture as compared to 2004 and 2003. July and August months. The July month had max- imum soil moisture as compared to June, August, 3.2. Temporal analysis of soil moisture with and September in 2003, 2004, and 2005 that might rainfall be due to the initiation of rainfall. Time series plot depicts merged soil moisture with 3.1.2. Seasonal soil moisture analysis seasonal rainfall for the study area during year 2005 Figure 9 depicts seasonal soil moisture variation from (Figure 10). The illustrated graph demonstrates that June to September during 2003, 2004, and 2005. It rainfall patterns had a direct impact on soil moist- was noticed that soil moisture pattern showed an ure variation, i.e., with an increment in rainfall, increasing trend from 2003 to 2005. The southern there is a significant increase in soil moisture. GEOLOGY, ECOLOGY, AND LANDSCAPES 7 Figure 5. Graphical modeler in QGIS for soil moisture raster formation. Figure 6. Daily rainfall map for 15 July 2005. During the onset of monsoon, soil moisture is 4%, 16%. Hence, it was remarked that soil moisture and it increases by 21% as rainfall reaches its peak and rainfall follows an identical behavior. rate. In June, due to the initiation of rainfall soil moisture is minimum. With an increase in rainfall th th 4. Conclusions from 13 June to 19 July, soil moisture gradually th th rose by 15%. Later from 19 July to 28 August, The soil moisture derived from AMSR-E product was rainfall decreased from 40 mm to 3 mm, which used to analyze variation in Marathwada region for the eventually leads to a decline in soil moisture by monsoon season of 2003, 2004, and 2005. It was 8 S. A. GADE ET AL. Figure 7. Weekly average soil moisture for July (2003–2005). observed that the soil moisture patterns were highly conclude the least soil moisture in June and September dynamic and AMSR-E-derived soil moisture will help as compared to July and August month. The southern to figure out drought and flooding conditions. and northwest province of the study area falls under Amongst the three years, the maximum soil moisture poor soil moisture conditions as compared to the east- was observed in 2005 which is 25%, whereas minimum ern and central parts. The seasonal soil moisture maps soil moisture was envisioned for 2003 (1%). The foretell least soil moisture in 2003 covering 60% of weekly soil moisture maps of July specified that the total area whereas, moderate soil moisture in 2005 southwest region of Marathwada is always in poor soil which covers 80% of the study area for the moisture conditions. The monthly soil moisture maps Marathwada region in accordance to 2003, 2004, 2005. GEOLOGY, ECOLOGY, AND LANDSCAPES 9 Figure 8. Monthly average soil moisture for June – September (2003–2005). 10 S. A. GADE ET AL. Figure 9. Seasonal soil moisture (2003–2005). Figure 10. Soil moisture variation with time and rainfall from (June – September) in 2005. For the accuracy purpose, the estimated soil Monthly soil moisture maps produced using the sug- moisture should be cross-verified with ground gested approach successfully explain seasonal fluctua - truth soil moisture data. It should be reanalyzed tion in soil moisture. The similar procedure can be with land use land cover for soil moisture utiliza- incorporated in the analysis of different parameters of tion and the percentage of errors should be AMSR E sensor viz., precipitation rate, cloud water, counted in accordance to ground truth and land water vapor, sea surface winds, sea surface tempera- use land cover. The AMSR-E sensor has the coarse ture, ice, and snow in global context. resolution. Due to this reason, it is difficult to get pure pixels in agricultural areas. This can be over- come by incorporating vegetation/roughness para- Acknowledgments meters in the AMSR-E sensor. We are extremely thankful to NSIDC for providing Aqua One of the key benefits of the proposed methodol- AMSR-E soil moisture data and CHRS for providing ogy is that a user can produce average soil moisture PERSIANN-CDR rainfall data free of cost. We appreciate maps at chosen intervals viz., weekly/monthly/season- the editors and the reviewers for their constructive sugges- tions and insightful comments, which helped us greatly to ally/yearly, etc.; this could substantially reduce the improve this manuscript. time and effort of managers and decision makers. GEOLOGY, ECOLOGY, AND LANDSCAPES 11 from AMSR-E and ASCAT microwave observation Disclosure statement synergy. Part 2: Product evaluation. Remote Sensing of The authors declare that they have no known competing Environment, 195, 202–217. https://doi.org/10.1016/j.rse. financial interests or personal relationships that could have 2017.04.020 appeared to influence the work reported in this paper. Levizzani, V., Kidd, C., Kirschbaum, D. 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Journal

Geology Ecology and Landscapes – Taylor & Francis

Published: Apr 3, 2023

Keywords: Q-GIS; passive microwave remote sensing; PERSIANN-CDR; spatial analysis; temporal variation

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