Soil moisture estimation using MODIS and ground measurements in eastern China

Wang, L.; Qu, John J.; Zhang, S.; Hao, Xianjun; Dasgupta, Swarvanu

doi:10.1080/01431160601075525

Cited by 55 publications

(36 citation statements)

References 7 publications

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“…The study area is located in Iowa, on the central and northern plain of the United States (U.S. 31 August 2016. The study area was one of the major grain production areas in the U.S. Corn and soybean are the primary crops in this area, and the soil is dominated by loams and silty clay loams.…”

Section: Study Areamentioning

confidence: 99%

Downscaling of Surface Soil Moisture Retrieval by Combining MODIS/Landsat and In Situ Measurements

Hao

et al. 2018

Remote Sensing

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Soil moisture, especially surface soil moisture (SSM), plays an important role in the development of various natural hazards that result from extreme weather events such as drought, flooding, and landslides. There have been many remote sensing methods for soil moisture retrieval based on microwave or optical thermal infrared (TIR) measurements. TIR remote sensing has been popular for SSM retrieval due to its fine spatial and temporal resolutions. However, because of limitations in the penetration of optical TIR radiation and cloud cover, TIR methods can only be used under clear sky conditions. Microwave SSM retrieval is based on solid physical principles, and has advantages in cases of cloud cover, but it has low spatial resolution. For applications at the local scale, SSM data at high spatial and temporal resolutions are important, especially for agricultural management and decision support systems. Current remote sensing measurements usually have either a high spatial resolution or a high temporal resolution, but not both. This study aims to retrieve SSM at both high spatial and temporal resolutions through the fusion of Moderate Resolution Imaging Spectroradiometer (MODIS) and Land Remote Sensing Satellite (Landsat) data. Based on the universal triangle trapezoid, this study investigated the relationship between land surface temperature (LST) and the normalized difference vegetation index (NDVI) under different soil moisture conditions to construct an improved nonlinear model for SSM retrieval with LST and NDVI. A case study was conducted in Iowa, in the United States (USA) (Lat: 42.

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Section: Study Areamentioning

confidence: 99%

Downscaling of Surface Soil Moisture Retrieval by Combining MODIS/Landsat and In Situ Measurements

Hao

et al. 2018

Remote Sensing

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“…On the other hand, dead fuel moisture is primarily affected by soil moisture (which would also be affected by weather) near the dead fuel surface [17]. Some studies showed that surface soil moisture could be estimated using optical data for croplands, short grass lands [18,19], and forest-dominated areas (deciduous species) [20]. Given this information, vegetation indices could be used indirectly to estimate dead leaves covering the forest floor.…”

Section: Introductionmentioning

confidence: 99%

Estimating and Up-Scaling Fuel Moisture and Leaf Dry Matter Content of a Temperate Humid Forest Using Multi Resolution Remote Sensing Data

2016

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Vegetation moisture and dry matter content are important indicators in predicting the behavior of fire and it is widely used in fire spread models. In this study, leaf fuel moisture content such as Live Fuel Moisture Content (LFMC), Leaf Relative Water Content (RWC), Dead Fuel Moisture Content (DFMC), and Leaf Dry Matter Content (LDMC) (hereinafter known as moisture content indices (MCI)) were calculated in the field for different forest species at 32 sites in a temperate humid forest (Zaringol forest) located in northeastern Iran. These data and several relevant vegetation-biophysical indices and atmospheric variables calculated using Landsat 7 Enhanced Thematic Mapper Plus (ETM+) data with moderate spatial resolution (30 m) were used to estimate MCI of the Zaringol forest using Artificial Neural Network (ANN) and Multiple Linear Regression (MLR) methods. The prediction of MCI using ANN showed that ETM+ predicted MCI slightly better (Mean Absolute Percentage Error (MAPE) of 6%-12%)) than MLR (MAPE between 8% and 17%). Once satisfactory results in estimating MCI were obtained by using ANN from ETM+ data, these data were then upscaled to estimate MCI using MODIS data for daily monitoring of leaf water and leaf dry matter content at 500 m spatial resolution. For MODIS derived LFMC, LDMC, RWC, and DLMC, the ANN produced a MAPE between 11% and 29% for the indices compared to MLR which produced an MAPE of 14%-33%. In conclusion, we suggest that upscaling is necessary for solving the scale discrepancy problems between the indicators and low spatial resolution MODIS data. The scaling up of MCI could be used for pre-fire alert system and thereby can detect fire prone areas in near real time for fire-fighting operations.

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“…Using polynomial regression statistics based on Universal Triangle methods Wang et al [10,11] upscaled in situ soil moisture measurements using MODIS-based land surface temperature (LST) and normalized difference vegetation index (NDVI) data to map daily soil moisture products at 1 km resolution. By implementing a geostatistical algorithm, such as block kriging, researchers can compute the spatial semivariogram of surface soil moisture measurements at different stations in a local area and compute the surface soil moisture across the whole area [12,13].…”

Section: Introductionmentioning

confidence: 99%

Upscaling of Surface Soil Moisture Using a Deep Learning Model with VIIRS RDR

Zhang

Huang

et al. 2017

IJGI

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Abstract:In current upscaling of in situ surface soil moisture practices, commonly used novel statistical or machine learning-based regression models combined with remote sensing data show some advantages in accurately capturing the satellite footprint scale of specific local or regional surface soil moisture. However, the performance of most models is largely determined by the size of the training data and the limited generalization ability to accomplish correlation extraction in regression models, which are unsuitable for larger scale practices. In this paper, a deep learning model was proposed to estimate soil moisture on a national scale. The deep learning model has the advantage of representing nonlinearities and modeling complex relationships from large-scale data. To illustrate the deep learning model for soil moisture estimation, the croplands of China were selected as the study area, and four years of Visible Infrared Imaging Radiometer Suite (VIIRS) raw data records (RDR) were used as input parameters, then the models were trained and soil moisture estimates were obtained. Results demonstrate that the estimated models captured the complex relationship between the remote sensing variables and in situ surface soil moisture with an adjusted coefficient of determination of R 2 = 0.9875 and a root mean square error (RMSE) of 0.0084 in China. These results were more accurate than the Soil Moisture Active Passive (SMAP) active radar soil moisture products and the Global Land data assimilation system (GLDAS) 0-10 cm depth soil moisture data. Our study suggests that deep learning model have potential for operational applications of upscaling in situ surface soil moisture data at the national scale.

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Soil moisture estimation using MODIS and ground measurements in eastern China

Cited by 55 publications

References 7 publications

Downscaling of Surface Soil Moisture Retrieval by Combining MODIS/Landsat and In Situ Measurements

Downscaling of Surface Soil Moisture Retrieval by Combining MODIS/Landsat and In Situ Measurements

Estimating and Up-Scaling Fuel Moisture and Leaf Dry Matter Content of a Temperate Humid Forest Using Multi Resolution Remote Sensing Data

Upscaling of Surface Soil Moisture Using a Deep Learning Model with VIIRS RDR

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