Soil Moisture Retrieval from Microwave (RADARSAT-2) and Optical Remote Sensing (MODIS) Data Using Artificial Intelligence Techniques

Research in different agricultural sectors, including in crop loss estimation during flood and yield estimation, substantially rely on inundation information. Spaceborne remote sensing has widely been used in the mapping and monitoring of floods. However, the inability of optical remote sensing to cloud penetration and the scarcity of fine temporal resolution SAR data hinder the application of flood mapping in many cases. Soil Moisture Active Passive (SMAP) level 4 products, which are model-driven soil moisture data derived from SMAP observations and are available at 3-h intervals, can offer an intermediate but effective solution. This study maps flood progress in croplands by incorporating SMAP surface soil moisture, soil physical properties, and national floodplain information. Soil moisture above the effective soil porosity is a direct indication of soil saturation. Soil moisture also increases considerably during a flood event. Therefore, this approach took into account three conditions to map the flooded pixels: a minimum of 0.05 m3m−3 increment in soil moisture from pre-flood to post-flood condition, soil moisture above the effective soil porosity, and the holding of saturation condition for the 72 consecutive hours. Results indicated that the SMAP-derived maps were able to successfully map most of the flooded areas in the reference maps in the majority of the cases, though with some degree of overestimation (due to the coarse spatial resolution of SMAP). Finally, the inundated croplands are extracted from saturated areas by Spatial Hazard Zone areas (SHFA) of Federal Emergency Management Agency (FEMA) and cropland data layer (CDL). The flood maps extracted from SMAP data are validated with FEMA-declared affected counties as well as with flood maps from other sources.

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Estimation of spatially enhanced soil moisture combining remote sensing and artificial intelligence approaches

International Journal of Remote Sensing ◽

10.1080/01431161.2016.1244366 ◽

2016 ◽

Vol 37 (23) ◽

pp. 5605-5631 ◽

Cited By ~ 8

Author(s):

Vahid Moosavi ◽

Ali Talebi ◽

Mohammad Hossein Mokhtari ◽

Mohammad Reza Hadian

Keyword(s):

Artificial Intelligence ◽

Remote Sensing ◽

Soil Moisture

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APPLICATIONS OF ARTIFICIAL INTELLIGENCE TECHNIQUES TO REMOTE SENSING∗

The Professional Geographer ◽

10.1111/j.0033-0124.1986.00133.x ◽

1986 ◽

Vol 38 (2) ◽

pp. 133-141 ◽

Cited By ~ 15

Author(s):

John E. Estes ◽

Charlene Sailer ◽

Larry R. Tinney

Keyword(s):

Artificial Intelligence ◽

Remote Sensing ◽

Artificial Intelligence Techniques

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Soil Moisture Inversion Based on AMSR-E and MODIS Data Fusion: A Case Study of Huaihe River Basin

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.716-717.1064 ◽

2014 ◽

Vol 716-717 ◽

pp. 1064-1067

Author(s):

Jing Wen Xu ◽

Yu Peng Wang ◽

Jun Fang Zhao ◽

Fei Yu Pu ◽

Peng Wang

Keyword(s):

Remote Sensing ◽

Soil Moisture ◽

Data Fusion ◽

River Basin ◽

Surface Condition ◽

Remote Sensing Data ◽

Huaihe River Basin ◽

Huaihe River ◽

Sensing Data ◽

Modis Data

In this paper, the correlation between fused data and original data, the measured soil and the precipitation data over Huaihe river basin by exploring the inversion of soil moisture from the time and space based on the method of multi-source remote sensing data fusion has been studied. In order to fuse the AMSR-E data which is all-day and all-weather and can penetrate the earth surface to some extent, with the MODIS data that can reflect the surface condition and temperature characteristics, the method of wavelet fusion was carried out in MATLAB. The conclusions of this study are listed as follows: (1) the inversion result of the fused data based on AMSE-E and MODIS is much better than a single remote sensing data inversion; (2) the fused data based on AMSE-E and MODIS is sensitive to soil moisture change trend when the seasons alternated every year, especially in the spring, summer and autumn.

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ESTIMATION OF PHYSICAL PARAMETERS OF A MULTILAYERED MULTI-SCALE VEGETATED SURFACE

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsarchives-xli-b1-453-2016 ◽

2016 ◽

Vol XLI-B1 ◽

pp. 453-460

Author(s):

I. Hosni ◽

L. Bennaceur Farah ◽

M. S. Naceur ◽

I. R. Farah

Keyword(s):

Remote Sensing ◽

Soil Moisture ◽

Cloud Model ◽

Optical Data ◽

Vegetation Coverage ◽

Physical Parameters ◽

Atmospheric Effects ◽

Optical Remote Sensing ◽

Statistical Parameters ◽

The Impact

Soil moisture is important to enable the growth of vegetation in the way that it also conditions the development of plant population. Additionally, its assessment is important in hydrology and agronomy, and is a warning parameter for desertification. Furthermore, the soil moisture content affects exchanges with the atmosphere via the energy balance at the soil surface; it is significant due to its impact on soil evaporation and transpiration. Therefore, it conditions the energy transfer between Earth and atmosphere. Many remote sensing methods were tested. For the soil moisture; the first methods relied on the optical domain (short wavelengths). Obviously, due to atmospheric effects and the presence of clouds and vegetation cover, this approach is doomed to fail in most cases. Therefore, the presence of vegetation canopy complicates the retrieval of soil moisture because the canopy contains moisture of its own. This paper presents a synergistic methodology of SAR and optical remote sensing data, and it’s for simulation of statistical parameters of soil from C-band radar measurements. Vegetation coverage, which can be easily estimated from optical data, was combined in the backscattering model. The total backscattering was divided into the amount attributed to areas covered with vegetation and that attributed to areas of bare soil. Backscattering coefficients were simulated using the established backscattering model. A two-dimensional multiscale SPM model has been employed to investigate the problem of electromagnetic scattering from an underlying soil. The water cloud model (WCM) is used to account for the effect of vegetation water content on radar backscatter data, whereof to eliminate the impact of vegetation layer and isolate the contributions of vegetation scattering and absorption from the total backscattering coefficient.

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Pemetaan Wilayah Lahan Basah Berdasarkan Data Penginderaan Jauh Optik Dan Radar

Jurnal Rekayasa Hijau ◽

10.26760/jrh.v4i2.1-14 ◽

2020 ◽

Vol 4 (2) ◽

pp. 48-61

Author(s):

Rian Nurtyawan ◽

Ervan Muktamar Hendarna

Keyword(s):

Remote Sensing ◽

Soil Moisture ◽

Surface Temperature ◽

Land Surface Temperature ◽

Land Surface ◽

Satellite Image ◽

Soil Surface ◽

Landsat 8 ◽

Optical Remote Sensing ◽

Radar Sensing

ABSTRAKPada umumnya lahan basah dikelola menjadi area pertanian ataupun perkebunan. Fungsi lahan basah memiliki fungsi ekologis seperti pengendali banjir, pencegah intrusi air laut, erosi, pencemaran, dan pengendali iklim global. Data pengindraan jauh yang digunakan pengelolaan lahan basah yaitu pengindraan jauh optik dan radar. Tujuan dari penelitian ini adalah mengeksplorasi korelasi potensial dari data optik dan radar untuk mengamati dinamika pada kawasan lahan basah tersebut dan melakukan pemetaan. Metode yang digunakan pada pengindraan jauh optik yaitu LST (Land Surface Temperature) berdasarkan Citra Satelit Landsat-8 dan metode yang digunakan pada pengindraan jauh radar yaitu estimasi kelembaban tanah berdasarkan Citra Satelit Sentinel-1A. Hasil pengamatan dinamika dan pemetaan pada wilayah Kabupaten Bandung Raya memiliki nilai kelembaban tanah tertinggi pada Bulan Mei dengan nilai kelembapan tanah tanah rata-rata sebesar 20,9 % pada polarisasi VH. Suhu permukaan tanah terendah terjadi pada bulan Mei dengan nilai suhu rata-rata sebesar 19.5 °C. Kolerasi antara nilai kelembapan tanah tanah dan suhu permukaan tanah pada wilayah Kabupaten Bandung Raya berdasarkan metode koefisien determinasi sebesar R2=0.705 didapatkan bahwa semakin tinggi nilai kelembapan tanah tanah maka nilai suhu permukaan tanah akan semakin rendah.Kata kunci: Kawasan lahan basah, Pengindraan Jauh Optik, Pengindraan Jauh Radar, Pengamatan Dinamika, Pemetaan. ABSTRACTIn general wetlands managed become an area of agriculture or plantations. The extent of wetland that has been used can be damaged if it is not managed properly and integrated.. The purpose of this research is to explore the potential correlations between several parameters of optical and radar data to observe the dynamics of wetlands area and mapping the wetlands area. The methodology that was used in optical remote sensing is LST (Land Surface Temperature) based on Landsat-8 Satellite Image and the method used in remote radar sensing is estimation of soil moisture based on Sentinel-1A Satellite Image. The result of the observation in the area and mapping the dynamics in Bandung Raya District had the highest soil moisture values in May with 27% of soil water level in VH polarization and 78.1% in VV polarization and the lowest value in each month is 11.8% and the highest soil surface temperature in August with a value 37.9 ° C and the minimum value 19 ° C..Keywords: Wetland Area, Optical Remote Sensing, Remote Radar Sensing, Dynamics Observation, Mapping.

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