Retrieval and Multi-scale Validation of Soil Moisture from Multi-temporal SAR Data in a Semi-Arid Tropical Region

Tomer, Sat Kumar; Bitar, Ahmad Al; Sekhar, M.; Zribi, Mehrez; Bandyopadhyay, Samir Kumar; Sreelash, K.; Sharma, Amit Kumar; Corgne, Samuel; Kerr, Yann H.

doi:10.3390/rs70608128

Cited by 79 publications

(63 citation statements)

References 63 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The Kabini Critical Zone Observatory [52] has been monitored since 2002 by the Indo-French Cell for Water Science The Berambadi watershed has been developed as a calibration (CAL) and validation (VAL) site for various satellite mission like Radarsat2 [53], Soil Moisture and Ocean Salinity (SMOS) [53,54] The soil type in the Berambadi watershed is categorized in seven major soils ( Figure 1) at 1:50,000 scale by National Bureau of Soil Survey and Land Use Planning (NBSSLUP) [53]. Major LULC of the watershed is agriculture (approx.…”

Section: Study Areamentioning

confidence: 99%

Irrigation History Estimation Using Multitemporal Landsat Satellite Images: Application to an Intensive Groundwater Irrigated Agricultural Watershed in India

et al. 2018

Self Cite

View full text Add to dashboard Cite

Groundwater has rapidly evolved as a primary source for irrigation in Indian agriculture. Over-exploitation of the groundwater substantially depletes the natural water table and has negative impacts on the water resource availability. The overarching goal of the proposed research is to identify the historical evolution of irrigated cropland for the post-monsoon (rabi) and summer cropping seasons in the Berambadi watershed (Area = 89 km 2 ) of Kabini River basin, southern India. Approximately five-year interval irrigated area maps were generated using 30 m spatial resolution Landsat satellite images for the period from 1990 to 2016. The potential of Support Vector Machine (SVM) was assessed to discriminate irrigated and non-irrigated croplands. Three indices, Normalized Difference Vegetation Index (NDVI), Normalized Difference Moisture Index (NDMI) and Enhanced Vegetation Index (EVI), were derived from multi-temporal Landsat satellite images. Spatially distributed intensive ground observations were collected for training and validation of the SVM models. The irrigated and non-irrigated croplands were estimated with high classification accuracy (kappa coefficient greater than 0.9). At the watershed scale, this approach allowed highlighting the contrasted evolution of multiple-cropping (two successive crops in rabi and summer seasons that often imply dual irrigation) with a steady increase in the upstream and a recent decrease in the downstream of the watershed. Moreover, the multiple-cropping was found to be much more frequent in the valleys. These intensive practices were found to have significant impacts on the water resources, with a drastic decline in the water table level (more than 50 m). It also impacted the ecosystem: Groundwater level decline was more pronounced in the valleys and the rivers are no more fed by the base flow.

show abstract

Section: Study Areamentioning

confidence: 99%

Irrigation History Estimation Using Multitemporal Landsat Satellite Images: Application to an Intensive Groundwater Irrigated Agricultural Watershed in India

et al. 2018

Self Cite

View full text Add to dashboard Cite

show abstract

“…Though the impact of RFI is sensibly different between ascending and descending overpasses, all data is used together to enhance the temporal availability. A good temporal behaviour between average RADARSAT-2 soil moisture and SMOS soil moisture was observed with an RMSE of approximately 0.06 m 3 /m 3 and Pearson's correlation coefficient of approximately 0.9 [30]. …”

Section: Satellite Soil Moisture Datasetsmentioning

confidence: 81%

“…For the active microwave, 30 RADARSAT-2 retrieved soil moisture maps were available over the study area based on the previous study from [30]. Details of the RADARSAT-2 maps are given in Table 1.…”

Section: Satellite Soil Moisture Datasetsmentioning

confidence: 99%

“…They approximately cover the entire study area. Soil moisture was retrieved using the Cumulative Density Function (CDF) transformation at a spatial resolution of 20 m. The details of the CDF transformation along with the multi-scale validation of the retrieved soil moisture using measured soil moisture in 50 plots (1262 data) and SMOS soil moisture are given in [30]. Comparison of the retrieved soil moisture with the field data showed a Root Mean Squared Error (RMSE) ranging from 0.02 to 0.06 m 3 /m 3 for the majority of plots.…”

Section: Satellite Soil Moisture Datasetsmentioning

confidence: 99%

“…SMOS provides soil moisture at a temporal resolution of 3 days at the equator and at a nominal spatial resolution of 40 km [26]. Several validation studies provided insights into the reliability and robustness of the soil moisture estimates from SMOS [2,[27][28][29] and over India [30]. RADAR SATellite-2 (RADARSAT-2) is an active microwave satellite mission operating in C-band, capable of retrieving surface soil moisture at a spatial resolution of less than 100 m and with a temporal resolution of 24 days [31].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

MAPSM: A Spatio-Temporal Algorithm for Merging Soil Moisture from Active and Passive Microwave Remote Sensing

et al. 2016

Self Cite

View full text Add to dashboard Cite

Simple Summary: -A novel algorithm delivering high resolution soil moisture maps is developed by merging active (SAR) and passive microwave.-MAPSM is based on the concept of Water Change Capacity. -A case study using MAPSM is presented by using the RADARSAT-2 and SMOS retrieved soil moisture data products over Berambadi watershed, Karnataka, India. -The algorithm parameters show scalability from the spatial resolution of 20 m to 2000 m.Abstract: Availability of soil moisture observations at a high spatial and temporal resolution is a prerequisite for various hydrological, agricultural and meteorological applications. In the current study, a novel algorithm for merging soil moisture from active microwave (SAR) and passive microwave is presented. The MAPSM algorithm-Merge Active and Passive microwave Soil Moisture-uses a spatio-temporal approach based on the concept of the Water Change Capacity (WCC) which represents the amplitude and direction of change in the soil moisture at the fine spatial resolution. The algorithm is applied and validated during a period of 3 years spanning from 2010 to 2013 over the Berambadi watershed which is located in a semi-arid tropical region in the Karnataka state of south India. Passive microwave products are provided from ESA Level 2 soil moisture products derived from Soil Moisture and Ocean Salinity (SMOS) satellite (3 days temporal resolution and 40 km nominal spatial resolution). Active microwave are based on soil moisture retrievals from 30 images of RADARSAT-2 data (24 days temporal resolution and 20 m spatial resolution). The results show that MAPSM is able to provide a good estimate of soil moisture at a spatial resolution of 500 m with an RMSE of 0.025 m 3 /m 3 and 0.069 m 3 /m 3 when comparing it to soil moisture from RADARSAT-2 and in-situ measurements, respectively. The use of Sentinel-1 and RISAT products in MAPSM algorithm is envisioned over other areas where high number of revisits is available. This will need an update of the algorithm to take into account the angle sampling and resolution of Sentinel-1 and RISAT data.

show abstract

A conceptually superior variant of Shepard's method with modified neighbourhood selection for precipitation interpolation

Yeggina

Teegavarapu

Sekhar

2019

Intl Journal of Climatology

View full text Add to dashboard Cite

The accuracy of gridded precipitation data depends on the availability of a uniformly spaced rain gauge network and an appropriate spatial interpolation method that considers the rainfall variability and other factors that influence the precipitation patterns in the region of interest. In the current study, conceptually superior variants of a widely used spatial interpolation algorithm, Shepard's method, are proposed, formulated and evaluated to overcome one of the major limitations in neighbourhood selection, that is, arbitrary selection of rain gauges. The variants provide mechanisms to objectively select the rain gauges (control points) based on correlation (variant 1), distribution similarity (variant 2) and a combination of both (variant 3). The improved variants were used in the development of gridded rainfall data at a resolution of 5 km over the Kabini River basin in south India, and in the state of Kentucky, United States. Results from multiple experiments using the original Shepard's method and its variants indicate improvements in the accuracy of precipitation estimates. Also, these variants have preserved the site‐specific statistics and distributional characteristics of the rainfall data. A variant 1 that uses a correlation‐based neighbourhood selection criterion performed better for daily and monthly data compared to others and is suitable for generation of gridded rainfall data. The variant 1 when used with information from clustering of sites for selection of the neighbours has led to improvement in gridded precipitation data estimates. The proposed variant 1 can also be used for point data estimation useful for filling missing data at any site.

show abstract

Retrieval and Multi-scale Validation of Soil Moisture from Multi-temporal SAR Data in a Semi-Arid Tropical Region

Cited by 79 publications

References 63 publications

Irrigation History Estimation Using Multitemporal Landsat Satellite Images: Application to an Intensive Groundwater Irrigated Agricultural Watershed in India

Irrigation History Estimation Using Multitemporal Landsat Satellite Images: Application to an Intensive Groundwater Irrigated Agricultural Watershed in India

MAPSM: A Spatio-Temporal Algorithm for Merging Soil Moisture from Active and Passive Microwave Remote Sensing

A conceptually superior variant of Shepard's method with modified neighbourhood selection for precipitation interpolation

Contact Info

Product

Resources

About