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Land surface temperature (LST) is a key parameter in investigating environmental, ecological processes and climate change at various scales, and is also valuable in the studies of evapotranspiration, soil moisture conditions, surface energy balance, and urban heat islands. However, it is difficult to acquire satellite LSTs with both high spatial and temporal resolutions due to tradeoffs between these parameters. The Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) algorithm was initially designed to predict surface reflectance and is based on the assumption that MODIS and Landsat surface reflectance are highly consistent over homogeneous areas. However, the ESTARFM method prediction results degrade somewhat when the method is used for heterogeneous fine-grained landscapes. This research extended the ESTARFM model from reflectivity range to thermal infrared for estimation of daily temperature at 90 m resolution combined MODIS and ASTER. The implementation of ESTARFM requires input of the search window size, selection of spectrally similar pixels, determination of the weight of similar pixels, and computation of the correction coefficient and temporal weight. The calculation of weights for spectrally similar pixels involves weighing the contribution of neighboring pixels to the computation of a central pixel. Using a local moving window, neighboring spectrally similar pixels were included for the computation of the LST corresponding to a central pixel with the temporal weights of the two dates. In this study, we used multiple bands, i.e., red, NIR, and LST bands, as the input variables and generated high spatial-temporal resolution land surface temperature, combining temporal change information from multi-temporal MODIS with high-resolution spatial resolution from ASTER. The objective of this paper was to evaluate the ESTARFM method using ground measurements coordination with ASTER LST products collected in an arid region of Northwest China during the first thematic Multi-Scale Observation Experiment on Evapotranspiration (MUSOEXE) over heterogeneous land surfaces in 2012, as part of the Heihe Watershed Allied Telemetry Experimental Research (HiWATER) .We didn't modified the model even if the linear hypothesis was directly applied in LST prediction, which may result in uncertainty and errors. The remote sensing data were acquired with from Jun to September in 2012. The results showed that ESTARFM was positively and linearly related with the actual measured TIR. The correlation coefficient values were all found to be greater than 0.71. The mean absolute error and root mean square error were all below 2.00 K and 2.60 K, respectively. From the feature of scattering plots between the predicted and observed LST, the data points fell close to the diagonal line in each panel, indicated that the predictions were all in good agreement with the observations. Overall, the values of mean absolute error and root mean square error between the predicted and the observed LST were quiet small; whereas the correlation coefficient values between the predicted LSTs and ASTER LST products were all found to be greater than 0.95.It should be noted that some pixels in the scatter plots showed differences between the predicted and observed LSTs. These discrepancies revealed a major limitation to the method; i.e., ESTARFM does not capture land cover that has been altered between two imaging dates. Thus, changes in land cover or other surface conditions can lead to prediction errors. In addition, the fusion results showed that value of correlation coefficient was better in non-vegetation area than vegetation and water area, and up to 0.91 especially in the August 27, 2012. However, the application of the ESTARFM and its variants to LST prediction is immature in terms of methodology. Many critical issues have not been solved, especially with respect to the determination of the search window size, conversion coefficient improvement, and thermal landscape heterogeneity. [ABSTRACT FROM AUTHOR]

Photosynthetically active radiation (PAR) is an important parameter in agricultural applications. Some researchers showed that many parameters, such as leaf area index (LAI), leaf angle distribution (LAD) and the heterogeneity of vegetation, were concerned with the distribution of PAR. Many models were used to simulate the distribution of PAR effectors, such as the structure of canopy and sun zenith angle. This paper simulated the vertical distribution of PAR in the canopy and analyzed the relationships between PAR and some parameters, such as solar zenith angle, LAD, LAI, maize canopy structure, special for the heterogeneous canopies such as that crop with width and narrow ridges. It took account the effects of structural features of different type canopies. In this study, the distributions of solar radiation at different heights in maize canopy were simulated based on the radiation transfer model. The row structure model was used to simulate the vertical distribution of PAR in canopies. It accounted for direct radiation including radiation paths going through one or more rows. Leaf angle distribution was generated by using this model to measure leaf angle distributions in canopies. Intensive investigation was made on the effect of these canopy architecture on the penetration of total visible radiation into the canopy at various solar zenith angle. The simulation result of PAR in maize canopy was validated with the measured data, and it appeared good accuracy. By simulation with box model, the regularity of vertical distribution of PAR in the canopy showed that: 1) the transmittance of solar incidence was affected by the effective light path; 2) the attenuation of light in the canopy was diminishing exponentially along the light path. It could be reflected by the vertical distribution of light extinction coefficient (K); 3) the solar altitude angle varying from 60° to 45° or 30° solar altitude angle with the RMSE value of 0.07 or 0.08, it could improve PAR estimation accuracy. The vertical distribution of leaf area was affected by the light attenuation through the canopy. In this paper, we proposed the method based on the regularity of vertical distribution of PAR by using Beer-Lambert law for inversion of the vertical distribution of LAI in maize canopy. The relationships between the vertical distribution of the leaf area index and solar zenith angle was analyzed, and the results were validated with the measured data. Results showed that the algorithm of Bonhomme&Chartier was proved to be effective for inversion of the vertical distribution of LAI. There were differences in inversion results with different solar zenith angles. In the upper canopy, the solar altitude angle varying from 30° to 45° solar altitude angle could improve LAI estimation accuracy with the RMSE of 0.18, and from 45 to 30 solar altitude angle with the RMSE of 0.30 in the middle canopy, 30° and 45° with the RMSE value of 0.11 and 0.09 in the under canopy. The result showed that it had a fairly good agreement between calculated and observed data, which proved the validity of the theoretical model. [ABSTRACT FROM AUTHOR]

Nitrogen has significant effect on the growth and development in crop, the formation of yield and quality. Precision diagnosis and dynamic regulation of crop is the important content and scientific basis of precision agriculture. Thus, predicting crop N status accurately and applying appropriate rate N to crop are the focus for many studies in agricultural sciences. The crop canopy nitrogen status estimation based on spectroscopy is important tool for crop nitrogen management, but its accuracy of estimation is often affected by other factors such as canopy structures. The nitrogen nutrition index (NNI) was sensitive to nitrogen status because it combined the information of plant nitrogen content, the individual character of crop and biomass, and the group character of crop. The traditional methods for calculation of plant N concentration and aboveground biomass are done manually and they are time consuming. Thus, it is hard to apply NNI in precise farming. Recently, it has been documented remote sensing technology can be used to assess many biophysical and biochemical variable of crops, especially through spectral indices. NNI was considered a good indicator of crop nitrogen status and provided new opportunities for hyperspectral applications. The objective of this study was on the NNI estimation through remote sensing spectral parameters sensitive to leaf N content and canopy nitrogen density (CND). Based on the field experiments of different N rates and varieties of winter wheat from booting to filling stages, the relationships between spectral indices and leaf N and CND status in wheat were analyzed to determine the key spectral indices for assessment of leaf N content and CND. These relationships can help accurately quantitative diagnosis of nitrogen status, and provide the reference for the estimation of fertilizer rate and crop yield and quality. Upon the analysis the empirical model for NNI estimation based on the optimal parameters of leaf N and CND was established and evaluated. The results showed that, Red edge position based on linear interpolation method (REPLI), modified red edge simple ratio index (mSR705), ratio index-1dB (RI-1dB), simple ratio pigment index (SRPI), Vogelman red edge index (VOG) and other indicators had a good correlation with winter wheat nitrogen nutrition (r⩾0.85), and this correlation was not affected by growing period. Therefore, they can be used to evaluate the nutritional status of canopy nitrogen inversion. Then the optimal spectral parameters were selected and the nitrogen index regression models were established. Independent experimental data was used for model validation. The results showed that REPLI in the nitrogen nutrition index estimation performed better (r=0.927, p<0.01), and the model estimation accuracy was high (R2=0.859, RMSE=0.078). Our research indicated that NNI had advantages in the field crop nitrogen nutrition diagnosis, and it had potential in qualitative and quantitative diagnosis of nitrogen nutrition status by hyperspectral inversion. [ABSTRACT FROM AUTHOR]

Arable land is the foundation of the national economy. How to make the best of arable land resources has become a focus problem of modern science and technology information. The rapid development of agricultural condition remote sensing monitoring technology provides more scientific ways and information technology for monitoring the arable land in real-time. In order to obtain the information of Shaanxi Province agricultural condition monitoring for managing arable land more efficiently, this thesis aimed to study the crop planting patterns and types of arable land, and took the main crops (wheat, spring maize, summer maize, rice and rape) of arable land in Shaanxi Province as the research object. Firstly, the remote sensing datasets of 250 m MOD09Q1 time series during 2003-2012 were used, and the Savitzky-Golay filtering method of TIMESAT software was used to reconstruct the NDVI time series datasets. Secondly, combined with the agricultural meteorological station datasets, TM 30 m land cover classification data, and the main crops' information and crop phenological information in Shaanxi Province, we extracted the change trends of typical terrain feature and determined the interannual dynamic thresholds. According to the threshold of a peak and crop growth period and other information, the crop planting patterns and crop types were identified. Thirdly, owing to the mixed pixel that the major factor affected the classification accuracy of the low spatial resolution remote sensing, therefore, the IDL optimization function (CONSTRAINED_MIN) was used to obtain each crop types' abundance figure by the method of non-negative least squares. Two kinds of precision validation methods of spatial and quantitative were adopted in this paper. The total classification accuracy and Kappa coefficient were 88.18% and 59.64% respectively according to spatial comparative analysis. The classification results were revised by the crop types' abundance figure, and the overall consistency of classification was 87.56% according to quantitative analysis, and the validation results for the rice and other grains had good consistency (93.74%, 92.36%), while the winter wheat and maize followed (83.68%, 84.61%). Through the analysis of mixed pixels, the overall consistency of estimating crop acreage increased by 6.23%, the consistency of winter wheat, maize, rice and other grains increased by 6.35%, 8.01%, 7.26% and 4.85%. The results indicate that using the Savitzky-Golay filtering method to reconstruct NDVI time series datasets could meet the requirement of the classification. Combining phonological information with time series datasets and using the classification method presented in this thesis could identify the crop planting patterns and crop types effectively in large scale. Using the IDL optimization function (CONSTRAINED_MIN) to analyze the mixed pixels, the crop acreages were calculated accurately. [ABSTRACT FROM AUTHOR]

Canopy structural property is a noticeable factor that exerts a certain influence on canopy bidirectional reflectance, thus it is primarily significant to detect a crop structure signature to refer to quantified remote sensing. This paper analyzed the variations in bidirectional reflectance of erective wheat J411 and loose wheat Z9507 under different solar zenith angles and viewed zenith angles in a solar principal plane. What's more BRDF (Bidirectional Reflectance Distribution Function) features for the two varieties were explored by exploiting six BRDF shape indictors in a red band (680 nm) and NIR (Near Infrared) band (860 nm) based on the semi-empirical BRDF kernel model and 25 field-measured multi-angle datasets from three years during the year 2003-2004, 2004-2005, and 2008-2009. The results showed that during the jointing stage, the sensitive stage to identify canopy geometry, Z9507 conceived a larger bi-NDVI (Bidirectional Normalized Difference Vegetation Index) than J411 in a solar principal plane, that means the loose wheat held a larger coverage than the erective variety. Performances of six anisotropic indices differed in varieties and bands. For the erective variety, both ANIF and ANIX in the red band were slightly higher than the loose wheat, while in the NIR band the values for loose wheat exceeded the erective variety. Meanwhile, a similar variation trend presented for the two varieties was that the ANIX was always higher than the ANIF in two bands and values of the two indices in NIR band were larger than that in the red band for both two varieties. Referring to AFX, it displayed a different variation trend compared with ANIF and ANIX. Its values fluctuated around 1. In the red band, the AFX for two varieties were almost less than 1, and in the NIR band were beyond 1, which explicitly depicted the dominance of canopy geometric scattering in the red band and volumetric scattering in the NIR band. Moreover, in the red band the AFX of erective wheat was slightly higher than the loose wheat, while in the NIR band the AFX of loose wheat outweighed erective wheat. Therefore, it is concluded that the geometric weights of an erective variety in the red band are more significant than the loose variety, indicating a stronger geometric effect in the red band for erective wheat; and in the NIR band higher volumetric weights are for the loose variety, suggesting a stronger volumetric effect in the NIR band for loose wheat. Finally, it was illustrated that the anisotropic indices are more capable and applicable to extract crop canopy structural information. [ABSTRACT FROM AUTHOR]

Wheat lodging is one of the most popular agriculture disaster. It has a great influence on the yield formation of wheat and causes huge loss every year. However, available means for monitoring crop lodging in large area is very limited. The potential capability of radar remote sensing for monitoring wheat lodging was explored in this study. After the backscattering coefficient of wheat field parcels were extracted at different polarization mode, radar backscattering behavior of typical lodging wheat parcels and the one of typical normal wheat parcels was dynamically compared at different growth stages and at different polarization mode, based on 5 multi-temporal Radarsat-2 images that covered an entire wheat growth cycle from sowing to near harvest. Results showed that there were distinct differences between lodging and normal wheat parcels in all polarization channels. When compared to normal wheat, the backscattering coefficient at HH (H, horizontal polarization) polarization decreased remarkably, while the one at VV (V, vertical polarization) polarization increased and the one at HV polarization increased slightly for lodging wheat. It was also found that polarimetric feature from synthetic aperture radar (SAR) data was very sensitive to wheat lodging and the sensibility was caused by unique structural characteristics of wheat vegetation and inherent polarization characteristics of SAR observation. Then a method based on radar polarimetric index, which took the full advantage of this sensibility, was put forward to monitoring wheat lodging. And the method was validated by in-situ data collected in 28 wheat parcels in Shangkuli Farmland in Inner Mongolia, China, at heading and filling stages of spring wheat. The result revealed that the polarimetric index, especially based on the ratio of dual-polarization backscattering coefficient (HH/VV and HH/HV), had excellent performance for distinguishing lodging from normal wheat parcels: all the 11 lodging wheat parcels with different severity were detected successfully from 28 parcels. In addition, the results were compared with the ones by single polarization channel data. It showed that the polarimetric index method had good anti-inference ability such as resisting influence of wheat growth difference, and can better reflect the intrinsic feature of lodging. It should be mentioned that the monitoring result may be influenced by harvest situation and vegetation water content etc. Moreover, while optical remote sensing relied on its spectral features to monitor crop lodging, radar remote sensing utilized polarimetric features to monitor crop lodging since SAR observation had advantage in reflecting the structural variation of lodging. Therefore, radar remote sensing have great potential for crop lodging monitoring, and the study presented a simple and effective method for monitoring wheat lodging in large area. [ABSTRACT FROM AUTHOR]