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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Evaluation of Smap Passive Soil Moisture Products Using In-Situ Data from a Dense Observation Network
摘要： As a result of vital role of soil moisture in governing water and energy cycles of land-atmosphere, the remote sensing of soil moisture has become a key component of the observation and research programs involving water and energy cycles on the earth's surface. In addition, the accurate monitoring and prediction of soil moisture plays a crucial role in crop growth, flood and drought monitoring and prediction, research of hydrological and land surface process and global water cycle. The microwave is the optimal mean to obtain soil moisture in large scale due to its strong penetration capability and sensitivity to the change of surface soil moisture. And the L band microwave is considered to be the best band for monitoring soil moisture. The Soil Moisture Active Passive (SMAP) satellite with an L-band (1.26 GHz) radar and an L-band radiometer (1.41 GHz) was launched on January 31, 2015 by the NASA. The baseline science requirement for SMAP is to provide estimates of soil moisture in the top 5 cm of soil with an error of no greater than 0.04 cm3/cm3 at 10 km spatial resolution and 3-day average intervals over the global land area. The soil moisture baseline algorithm of SMAP is single-channel algorithm using horizontally polarized TB (SCA-H). In SCA-H, the emissivity model of bare land uses a semi-empirical Hp model and the value of H is determined by using empirical method for different land cover types; The vegetation model with zero-order radiative transfer model to describe the influence of vegetation on the surface emissivity; The dielectric constant model is one of the three models of Mironov model, Dobson model and Wang model. The SMAP mission generates 22 different distributable data products. Here, we intended to evaluate the SMAP_L3_SM_P (the descending and ascending, 36km) and SMAP_L3_SM_P_E (the descending and ascending, 9km).Many domestic and foreign papers suggested that SMAP passive microwave soil moisture products close to or reach the accuracy requirement. A.Colliander et al. validated the SMAP L2 SM product with 34 core validation sites, and its accuracy reached the expected goal 0.04 cm3/cm3; Chan et al. found that the RMSE of SMAP L2 SM product is 0.038 cm3/cm3 in Little Washita, TxSON and Little River; Mehrjardi et al., Pan et al., J.Zeng et al. also suggested that SMAP soil moisture estimates were approaching the expected accuracy. Moreover, C. Ma et al. validated the SMAP L3 soil moisture products in the Heihe River Basin in China, and the ubRMSE is about 0.03 cm3/cm3. However, because of the regional characteristics of soil moisture retrieved by SMAP, it was necessary to evaluate the performance of SMAP SM product in different regions. At present, SMAP products had not been validated in large areas of farmland in China. In this study, a soil moisture observation network was designed and built in the farmland area of Dehui in Jilin Province, and the point-scale SM data were upscaled to provide the 36-km and 9-km SM to validate the SMAP_L3_SM_P and SMAP_L3_SM_P_E products. Although some papers pointed out that RFI in Northeast China is more serious, the farm areas chose were far from the RFI region. We measured the distance from the closest airfield to the experimental area greater than 40km and the amplitude of RFI was less than 2K, which meaned the impact of RFI on validation results could be ignored. The main content of this paper is as follows. The second part describes the study area, the construction of soil moisture observation network and data source. The third part illustrates the up-scaling methods and the evaluation indices of soil moisture products. The fourth part describes the validation results of SMAP passive microwave soil moisture products. Finally, the fifth part summarizes the main conclusions of this study.
关键词： validation,SMAP,soil moisture,upscaling,passive microwave
更新于2025-09-10 09:29:36
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Introducing a Framework of Self-Organizing Maps for Regression of Soil Moisture with Hyperspectral Data
摘要： In this paper, we introduce a framework to solve regression problems based on high-dimensional and small datasets. This framework involves two self-organizing maps (SOM) and combines unsupervised with supervised learning. We investigate the impacts of SOM hyperparameters on the regression performance and compare the results of the SOM framework with two established regressors on a measured dataset. The derived results reveal the potential of the SOM framework. Finally, we propose further research aspects for the SOM framework to analyze its capabilities and limitations. We have published our dataset in [1] to ensure the reproducibility of the results.
关键词： machine learning,regression,hyperspectral data,soil moisture,Self-organizing maps
更新于2025-09-10 09:29:36
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Semi-Physical Integration of Scattering Models for Microwaves and Optical Wavelengths
摘要： Various approaches exist to model scattering of a vegetation canopy above ground in terms of optical and radar wavelengths. Due to the different scattering properties these two spectral regions are modelled separately for visible/ infrared bands and for microwave regions. The newly developed RadOptics model (RO-M) integrates these two spectral regions semi-physically into one radiative transfer (RT)-based model framework, resting on the law of Beer-Bougert-Lambert. Due to the integrative nature of RO-M, it can calculate/simulate the canopy and soil reflectances for the optical and radar spectrum using a single unified model architecture. By Applying RO-M in radar domain (ROR-M) it is shown that the observed dependence of Backscattering coefficient on Leaf Area Index (LAI), soil moisture content and frequency can be simulated consistently with results in literature. The results of the RO-M within the optical domain (ROO-M) present an equivalent trend of reflectance and band ratio values with LAI compared to studies in literature.
关键词： physics,optics,vegetation,modeling,microwaves,soil
更新于2025-09-10 09:29:36
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Irrigation Mapping Using Statistics of Sentinel-1 Time Series
摘要： This paper presents the methodology for irrigation mapping using the Sentinel-1 SAR data. The study is performed using VV polarization over an agricultural site in Urgell, Catalunya (Spain). From the time series for each field, the indices including the mean value and variance of the signal, the correlation length, the fractal dimension which are derived from the backscatter time series are analyzed. The classification of irrigated and nonirrigated fields is done with the indices vector formed by the parameters analyzed. The result is compared with the supervised classification from Sentinel-2 multi-band data. The accuracy is 77%. The methodology uses only SAR data, which makes it usable for all areas even with cloud cover most times of the year.
关键词： Sentinel-1,SAR,Soil moisture,irrigation,classification
更新于2025-09-10 09:29:36
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Visible-Near-Infrared Spectroscopy Prediction of Soil Characteristics as Affected by Soil-Water Content
摘要： Soil physical characteristics are important drivers for soil functions and productivity. Field applications of near-infrared spectroscopy (NIRS) are already deployed for in situ mapping of soil characteristics and therefore, fast and precise in situ measurements of the basic soil physical characteristics are needed at any given water content. Visible-near-infrared spectroscopy (vis–NIRS) is a fast, low-cost technology for determination of basic soil properties. However, the predictive ability of vis–NIRS may be affected by soil-water content. This study was conducted to quantify the effects of six different soil-water contents (full saturation, pF 1, pF 1.5, pF 2.5, pF 3, and air-dry) on the vis–NIRS predictions of six soil physical properties: clay, silt, sand, water content at pF 3, organic carbon (OC), and the clay/OC ratio. The effect of soil-water content on the vis–NIR spectra was also assessed. Seventy soil samples were collected from five sites in Denmark and Germany with clay and OC contents ranging from 0.116 to 0.459 and 0.009 to 0.024 kg kg-1, respectively. The soil rings were saturated and successively drained/dried to obtain different soil–water potentials at which they were measured with vis–NIRS. Partial least squares regression (PLSR) with leave-one-out cross-validation was used for estimating the soil properties using vis–NIR spectra. Results showed that the effects of water on vis–NIR spectra were dependent on the soil–water retention characteristics. Contents of clay, silt, and sand, and the water content at pF 3 were well predicted at the different soil moisture levels. Predictions of OC and the clay/OC ratio were good at air-dry soil condition, but markedly weaker in wet soils, especially at saturation, at pF 1 and pF 1.5. The results suggest that in situ measurements of spectroscopy are precise when soil-water content is below field capacity.
关键词： Visible-Near-Infrared Spectroscopy,Soil Physical Properties,Soil Characteristics,Soil-Water Content,Partial Least Squares Regression
更新于2025-09-09 09:28:46
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Extending the SCOPE model to combine optical reflectance and soil moisture observations for remote sensing of ecosystem functioning under water stress conditions
摘要： A radiative transfer and process-based model, called Soil-Canopy-Observation of Photosynthesis and Energy fluxes (SCOPE), relates remote sensing signals with plant functioning (i.e., photosynthesis and evapotranspiration). Relying on optical remote sensing data, the SCOPE model estimates photosynthesis and evapotranspiration, but these ecosystem-level fluxes may be significantly overestimated if water availability is the primary limiting factor for vegetation. Remedying this shortcoming, additional information from extra sources is needed. In this study, we propose considering water stress in SCOPE by incorporating soil moisture data in the model, besides using satellite optical reflectance observations. A functional link between soil moisture, soil surface resistance, leaf water potential and carboxylation capacity is introduced as an extra element in SCOPE, resulting in a soil moisture integrated version of the model, SCOPE-SM. The modified model simulates additional state variables: (i) vapor pressure (ei), both in the soil pore space and leaf stomata in equilibrium with liquid water potential; (ii) the maximum carboxylation capacity (Vcmax) by a soil moisture dependent stress factor; and (iii) the soil surface resistance (rss) through approximation by a soil moisture dependent hydraulic conductivity. The new approach was evaluated at a Fluxnet site (US-Var) with dominant C3 grasses and covering a wet-to-dry episode from January to August 2004. By using the original SCOPE (version 1.61), we simulated half-hourly time steps of plant functioning via locally measured weather data and time series of Landsat (TM and ETM) imagery. Then, SCOPE-SM was similarly applied to simulate plant functioning for three cases using Landsat imagery: (i) with modeled ei; (ii) with modeled ei and Vcmax; and (iii) with modeled ei, Vcmax, and rss. The outputs of all four simulations were compared to flux tower plant functioning measurements. The results indicate a significant improvement proceeding from the first to the fourth case in which we used both Landsat optical imagery and soil moisture data through SCOPE-SM. Our results show that the combined use of optical reflectance and soil moisture observations has great potential to capture variations of photosynthesis and evapotranspiration during drought episodes. Further, we found that the information contained in soil moisture observations can describe more variations of measured evapotranspiration compared to the information contained in thermal observations.
关键词： SCOPE-SM model,Landsat,Evapotranspiration,Vegetation properties,Water stress,Remote sensing,Soil moisture,Vegetation functioning,Vapor pressure,Photosynthesis,Maximum carboxylation capacity,Soil surface resistance,Reflectance
更新于2025-09-09 09:28:46
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Evaluating Leaf and Canopy Reflectance of Stressed Rice Plants to Monitor Arsenic Contamination
摘要： Arsenic contamination is a serious problem in rice cultivated soils of many developing countries. Hence, it is critical to monitor and control arsenic uptake in rice plants to avoid adverse effects on human health. This study evaluated the feasibility of using re?ectance spectroscopy to monitor arsenic in rice plants. Four arsenic levels were induced in hydroponically grown rice plants with application of 0, 5, 10 and 20 μmol¨ L′1 sodium arsenate. Re?ectance spectra of upper fully expanded leaves were acquired over visible and infrared (NIR) wavelengths. Additionally, canopy re?ectance for the four arsenic levels was simulated using SAIL (Scattering by Arbitrarily Inclined Leaves) model for various soil moisture conditions and leaf area indices (LAI). Further, sensitivity of various vegetative indices (VIs) to arsenic levels was assessed. Results suggest that plants accumulate high arsenic amounts causing plant stress and changes in re?ectance characteristics. All leaf spectra based VIs related strongly with arsenic with coef?cient of determination (r2) greater than 0.6 while at canopy scale, background re?ectance and LAI confounded with spectral signals of arsenic affecting the VIs’ performance. Among studied VIs, combined index, transformed chlorophyll absorption re?ectance index (TCARI)/optimized soil adjusted vegetation index (OSAVI) exhibited higher sensitivity to arsenic levels and better resistance to soil backgrounds and LAI followed by red edge based VIs (modi?ed chlorophyll absorption re?ectance index (MCARI) and TCARI) suggesting that these VIs could prove to be valuable aids for monitoring arsenic in rice ?elds.
关键词： SAIL model,spectral re?ectance,vegetative indices,arsenic uptake,leaf chlorophyll,red edge,plant stress,soil re?ectance,LAI,rice
更新于2025-09-09 09:28:46
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Quantification of Different Forms of Iron from Intact Soil Cores of Paddy Fields with Vis-NIR Spectroscopy
摘要： Iron (Fe) occurs in almost all soils and the analysis of various forms of Fe in the soil is of great pedological interest. Very little is known, however, about how visible and near-infrared (Vis-NIR) spectroscopy performs in intact soil cores of paddy fields for quantifying Fe concentrations. Our objective was to evaluate the feasibility of Vis-NIR spectroscopy of intact soil cores for rapid determination of the four Fe forms: total Fe (Fet), pyrophosphate-extractable Fe (Fep), dithionite-citrate-bicarbonate extractable Fe (Fed), and oxalate-extractable Fe (Feo). A total of 148 intact soil cores in Yujiang County, China, were sampled, and Vis-NIR spectra (350–2500 nm) were sectioned and scanned on four horizontal surfaces (5-cm depth intervals) of each soil core in the laboratory. Partial least squares regression (PLSR) and support vector machine regression (SVMR) models were compared using 70% of the section samples for calibration and 30% for independent validation. Results showed that the nonlinear SVMR models performed better than the PLSR models for the predictions of all Fe forms. The SVMR models produced the best predictions in the independent validation set for Fed (RMSEP = 2.223; R2 P = 0.85; RPDP = 2.59), Fet (RMSEP = 3.693; R2 P = 0.82; RPDP = 2.32), and Fep (RMSEP = 0.086; R2 P = 0.79; RPDP = 2.17). It was concluded that Vis-NIR spectroscopy coupled with SVMR is suitable for quantitatively determining different Fe forms in intact soil cores of paddy fields.
关键词： intact soil cores,iron forms,SVMR,Vis-NIR spectroscopy,paddy fields,PLSR
更新于2025-09-09 09:28:46
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Urban Land Use/Land Cover Discrimination Using Image-Based Reflectance Calibration Methods for Hyperspectral Data
摘要： Irrespective of substantial research in land use/land cover (LULC) monitoring of urban area, hyperspectral data is not yet exploited effectively because of lack of local spectral resources and a practical reflectance calibration method. The objective of this research is to develop an effective methodology for urban LULC classification using image-based reflectance calibration methods: especially Vegetation-Impervious-Soil classes (VIS), using hyperspectral data. We used EO-1 Hyperion image of Pune City, India and assessed the suitability of different land covers as reflectance calibration surfaces. Furthermore, we performed LULC classification using different reflectance calibration methods such as Internal Area Relative Reflectance, Flat Field Relative Reflectance, and 6S for comparative analysis. Urban VIS signatures extracted from Hyperion image show distinct spectral curves at broader level. Flat Field Relative Reflectance method provides above 90 percent average overall accuracy. An advanced physics-based method such as 6S does not provide any added advantage over image-based calibration methods.
关键词： urban LULC classification,hyperspectral data,Vegetation-Impervious-Soil classes,EO-1 Hyperion,reflectance calibration
更新于2025-09-09 09:28:46
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Inversion of Surface Soil Moisture from Radar Altimetry Backscattering in Semi-Arid Environments
摘要： Surface Soil Moisture (SSM) is a key parameter of water and energy balances in semi-arid areas. SSM is linearly related to the radar backscattering coefficients (σ0) over sand. Linear relationships are commonly used for inverting SSM in semi-arid areas from SAR and scatterometer data. Recent studies demonstrated that SSM can also be inversed from radar altimetry backscattering. An inversion method combining radar altimetry σ0 and land surface model (LSM) outputs is proposed here.
关键词： Soil moisture,backscattering,radar altimetry
更新于2025-09-09 09:28:46