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[IEEE 2018 7th International Conference on Agro-geoinformatics (Agro-geoinformatics) - Hangzhou (2018.8.6-2018.8.9)] 2018 7th International Conference on Agro-geoinformatics (Agro-geoinformatics) - Improved Estimation of Leaf Chlorophyll Content from Non-Noon Reflectance Spectra of Wheat Canopies by Avoiding the Effect of Soil Background
摘要： Crop leaf chlorophyll content (LCC) is a valuable indicator for agronomists to make fertilization recommendation and can be estimated from canopy reflectance spectra. However, the estimation accuracy of LCC is often influenced by soil background. To alleviate the adverse effect of soil background, this study proposed to collect spectral measurements at non-noon (such as 14:00-16:00 local time) hours and evaluated the performance of these spectral measurements with experimental data and radiative transfer model. The results from the wheat experiment conducted at Rugao demonstrated that the canopy spectra measured at non-noon were less sensitive to the soil background compared with those collected at midday (such as 12:00), which improved the estimation accuracy (R2) for LCC from 0.71 to 0.77. A canopy radiative transfer model called 4SAIL-RowCrop was also used to validate the performance and feasibility of the non-noon measurement scheme. One thousand spectra with different combinations of LCC, soil reflectance, and canopy structure were simulated at three observation times (12:00, 14:00 and 16:00). The CIred-edge calculated from the canopy reflectance spectra simulated for 16:00 exhibited a higher correlation to LCC (R2 = 0.76) than that for 12:00 (R2 = 0.43). These consistent findings from experimental and modeled datasets suggested that the effect of soil background can be alleviated and the estimation accuracy of LCC can be improved by determining a proper timing of spectral observation.
关键词： Remote sensing,Wheat,Leaf chlorophyll content,Non-noon observation,Soil Background
更新于2025-09-11 14:15:04
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[IEEE 2018 7th International Conference on Agro-geoinformatics (Agro-geoinformatics) - Hangzhou (2018.8.6-2018.8.9)] 2018 7th International Conference on Agro-geoinformatics (Agro-geoinformatics) - Hyperspectral Inversion of Soil Moisture Content Based on SOILSPECT Model
摘要： Soil moisture content (SMC) is the important information for the crop land irrigation management and drought warning. The study of SMC quantitative inversion based on hyperspectral remote sensing technology has become the hot spot. Because of using statistical modeling methods and without considering soil bidirectional reflection characteristics, the SMC inversion accuracy and model applicable scope were limited. The purpose of this study was to use the soil radiation transfer model (SOILSPECT) to invert SMC in order to not only improve the SMC hyperspectral inversion accuracy, but also make the model applying widely. Taking the black soil and chernozem in Gongzhuling city of Jilin province as the study object, spectrometry measurement for different SMC from 5% to 45% (5% interval) to get measured soil reflectance by using the ASD Fieldspec Pro spectrometer under the indoor condition. The influences of the light source zenith angle, observing zenith angle, azimuth angle and SMC on soil bidirectional reflectance analyzed. Then distribution SOILSPECT model parameters were obtained by using Particle Swarm Optimization (PSO) method under the different SMC gradients, and SMC inversion by using SOILSPECT model was conducted. The results showed that soil BRDF declined with SMC rising in the range of 400~1400 nm wavelength, when SMC was less than the field water holding capacity, and soil BRDF rose with SMC rising when SMC was larger than the field water holding capacity. In the range of 1400~2400nm wavelength and under different observing zenith angles, there was no rules for soil BRDF changing. SMC inversion accuracy based on SOILSPECT model was higher. R2 value was above 0.98 compared the estimated values with the measured values. The inversion accuracy for 15% and 30% of SMC were higher at every sensitive band, but that for other SMC was unsteadiness. SMC inversion accuracy based on SOILSPECT model increased with the decreasing of the observational zenith angle. The vertical observation can get the highest SMC inversion accuracy.
关键词： BRDF,SOILSPECT model,hyperspectral inversion,soil moisture content
更新于2025-09-11 14:15:04
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A Coupling Model for Soil Moisture Retrieval in Sparse Vegetation Covered Areas Based on Microwave and Optical Remote Sensing Data
摘要： Soil moisture is an important component of natural water cycles and plays a crucial role for the healthy development of ecological environment in arid and semiarid areas. Microwave remote sensing techniques are promising to rapidly monitor regional soil moisture. However, major difficulties associated with retrieving soil moisture via microwave remote sensing are attributed to effects of surface roughness and vegetation cover. The objective of this paper is to investigate the potentials of combined roughness parameters and develop a model that mostly relies on the satellite data and requires minimum a priori information for soil moisture inversion over sparse vegetation covered areas through combing Radarsat-2 synthetic aperture radar (SAR) and GF-1 data. For the purpose, the impacts of vegetation on the radar backscattering coefficient were removed by the water-cloud model (WCM) and a new roughness parameter Zs (Zs = S3/L is defined as a combined roughness parameter) was proposed by simulating relationships between surface roughness parameters, soil moisture, and backscattering coefficients using advanced integral equation model (AIEM). On the basis, a coupling model of soil moisture inversion for blown-wind areas of the Uxin Banner in Inner Mongolia, China, was in turn developed with Radarsat-2 HH and VV polarization data, and the soil moisture content (SMC) values for sparse vegetation covered surfaces were retrieved with limited use of in situ roughness parameters. The in situ measurements and satellite data set were used to validate the reliability of the developed model. The results showed that the retrieved soil moisture levels fell below in situ soil moisture levels before the vegetation effect was removed, and the precision of retrieved soil moisture was effectively improved when the vegetation effect was corrected by the WCM, with the root-mean-square error and mean absolute error decreasing from 7.45% and 6.24% to 5.12% and 3.44%, respectively. According to the map of retrieved soil moisture levels generated for the study area, most of SMCs were below 35% in accordance with field observations. These study results can serve as a foundation for monitoring soil moisture and water environments in arid and semiarid regions.
关键词： Combined roughness parameter,sparse vegetation,coupling model,radiative transfer model,soil moisture retrieval
更新于2025-09-11 14:15:04
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Transfer Learning for Soil Spectroscopy Based on Convolutional Neural Networks and Its Application in Soil Clay Content Mapping Using Hyperspectral Imagery
摘要： Soil spectra are often measured in the laboratory, and there is an increasing number of large-scale soil spectral libraries establishing across the world. However, calibration models developed from soil libraries are difficult to apply to spectral data acquired from the field or space. Transfer learning has the potential to bridge the gap and make the calibration model transferrable from one sensor to another. The objective of this study is to explore the potential of transfer learning for soil spectroscopy and its performance on soil clay content estimation using hyperspectral data. First, a one-dimensional convolutional neural network (1D-CNN) is used on Land Use/Land Cover Area Frame Survey (LUCAS) mineral soils. To evaluate whether the pre-trained 1D-CNN model was transferrable, LUCAS organic soils were used to fine-tune and validate the model. The fine-tuned model achieved a good accuracy (coefficient of determination (R2) = 0.756, root-mean-square error (RMSE) = 7.07 and ratio of percent deviation (RPD) = 2.26) for the estimation of clay content. Spectral index, as suggested as a simple transferrable feature, was also explored on LUCAS data, but did not performed well on the estimation of clay content. Then, the pre-trained 1D-CNN model was further fine-tuned by field samples collect in the study area with spectra extracted from HyMap imagery, achieved an accuracy of R2 = 0.601, RMSE = 8.62 and RPD = 1.54. Finally, the soil clay map was generated with the fine-tuned 1D-CNN model and hyperspectral data.
关键词： hyperspectral imagery,soil spectroscopy,CNNs,deep learning,transfer learning
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 - How does the Spatial Scale Mismatch Between in Situ and Smos Soil Moisture Evolve Through Timescales?
摘要： The SMOS (Soil Moisture and Ocean Salinity) mission, together with other passive microwave based missions (AMSR, SMAP), provides soil moisture estimates at resolutions ranging from 30 to 55 km. These estimates are validated by direct comparison to in situ measurements that typically measure over an area of a few centimeters. There exist a spatial scale mismatch between the satellite (large support) and the in situ measurements (point support), which contributes to the differences observed. Their magnitude depends on the spatial representativeness of the in situ measurements, which varies in time and with the selected location. This communication will show how the spatial scale mismatch evolves through timescales. It is characterized by using modeled, in situ and satellite soil moisture time series. Timescales, from 0.5 to 128 days, are obtained using wavelet transforms and the spatial representativeness is assessed with a new approach that uses wavelet-based correlations (WCor).
关键词： time scales,satellite validation,wavelet decomposition,soil moisture,spatial representativeness,spatial scales
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 - Soil Moisture Retrieval Using full Wave Simulations of 3-D Maxwell Equations for Compensating Vegetation Effects
摘要： In this paper we introduce an approach to enhance the precision of the vegetation parameterization in soil moisture retrieval using passive microwave observations. We present an algorithm that utilizes conventionally defined scattering (S) parameters to represent the propagation of the electromagnetic radiation through the vegetation layer over soil. The S-parameters can be determined with full wave simulations of 3-D Maxwell Equations enabled by recent advances in numerical simulation techniques. Traditional retrieval algorithms have relied on approximation of the radiative transfer equation in order to find simple parameterization for the equation. This results in added uncertainty when the attenuation and scattering within the vegetation layer increases. The presented method provides a way to model the radiative transfer accurately while preserving a reasonable complexity and number of parameters in the algorithm. The results show that the presented method improves the brightness temperature modelling accuracy significantly when vegetation water content reaches about 5 kg/m2, depending on the scattering properties of the vegetation type.
关键词： Soil Moisture,Vegetation,S-parameters,Scattering,NMM3D
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 - Analysis of CYGNSS Data for Soil Moisture Applications
摘要： An analysis of CYGNSS data is presented, with the objective of assessing the potentials of these data for land applications. The GPS-Reflections over land acquired by the CYGNSS observatories are exploited to detect properties of the land soil moisture. A land reflectivity observable, derived from CYGNSS Delay/Doppler Maps, is computed using a calibration approach suitable for land reflections. The sensitivity of the reflectivity observable to the soil moisture parameter is investigated through a comparison with soil moisture data from the SMAP satellite. Some preliminary results on the correlation between the CYGNSS reflectivity and the SMAP soil moisture are presented. This work is being conducted within the framework of the European Space Agency Project “Potential of Spaceborne GNSS-R for Land Applications”.
关键词： Delay-Doppler Map,soil moisture,GPS-Reflectometry
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 - Modelling Forest Decline Using Smos Soil Moisture and Vegetation Optical Depth
摘要： Global change is increasing the risk of forest decline worldwide, impacting carbon and water cycles. Hence, there is an urgent need for predicting forest decline occurrence. To that purpose, this study links forest decline events in Catalonia, detected by the DEBOSCAT forest monitoring program, with information from the Soil Moisture and Ocean Salinity (SMOS) satellite. Firstly, this study reviews the role of the SMOS soil moisture in a previous forest decline episode occurred in 2012, where the authors concluded that dry soils increased the probability of observing decline in broadleaved forests. Secondly, the present study detects that forest decline in 2012 and 2016 was linked to very dry soil conditions (generally with SM<0.06 m3·m-3). A similar analysis is proposed using SMOS Vegetation Optical Depth (VOD) data, which is a proxy of vegetation hydric status. Results and preliminary models will be presented at IGARSS 2018.
关键词： SMOS,Soil Moisture,Climate Change,Vegetation Optical Depth,Forest Decline
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 - Improving Gpm Precipitation Data Over Yarlung Zangbo River Basin Using Smap Soil Moisture Retrievals
摘要： Precipitation plays an essential role in land surface processes, as a vital forcing variable of hydrology and ecosystem models. Satellite remote sensing is able to provide precipitation estimations at regional and global scales with high spatiotemporal resolutions. However, the accuracy of these products still need improvement especially at daily scale. In this study, we proposed a new method which can improve the rainfall product of the Global Precipitation Mission by using the soil moisture product of the Soil Moisture Active Passive mission over the Yarlung Zangbo River basin in the Tibetan Plateau. Compared to rain gauge observation, our method improve rainfall estimation at 97 of 108 GPM grids. Overall, the average correlation between improved GPM and in situ observation increased from 0.12 to 0.31, while the RMSE and RRMSE decreased by 1.16 and 0.42, respectively. It indicates that this approach can be used in a large scale to improve satellite-based rainfall products over the Tibetan Plateau.
关键词： Yarlung Zangbo River basin,GPM,rainfall estimation,SMAP,soil moisture
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 - Soil Moisture Estimation by Linear Regression from Smap Polarimetric Radar Data with Aquarius Derived Coefficients
摘要： Algorithms for soil moisture estimation from radars conventionally use substantial amounts of ancillary data to parametrize complex electromagnetic models. In contrast, we describe radar data of a vegetated scene as a linear function of soil moisture. This eliminates the dependence on ancillary data while providing reasonable global soil moisture estimates. We derive two polarization dependent coefficients of a linear model on the basis of spatial and temporal similarity at a global scale from nearly 4 years of L-band Aquarius radar and radiometer derived soil moisture data. These global coefficients are then used to derive soil moisture from 2.5 months of L-band SMAP radar data. The resulting soil moisture estimates are evaluated with the SMAP Level 2 radiometer-only soil moisture product.
关键词： Soil moisture,synthetic aperture radar (SAR),time series,polarimetric radar
更新于2025-09-10 09:29:36