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Timely and accurate estimation of the winter wheat planting area and its spatial distribution is essential for the implementation of crop growth monitoring and yield estimation, and hence for the development of national agricultural production and food security. In remotely sensed winter wheat mapping based on spectral similarity, the reference curve is obtained by averaging multiple standard curves, which limits mapping accuracy. We propose a spectral reconstruction method based on singular value decomposition (SR-SVD) for winter wheat mapping based on the unique growth characteristics of crops. Using Sentinel-2 A/B satellite data, we tested the SR-SVD method in Puyang County, and Shenzhou City, China. Performance was increased, with the optimal overall accuracy and the Kappa of Puyang County and Shenzhou City were 99.52% and 0.99, and 98.26% and 0.97, respectively. We selected the spectral angle mapper (SAM) and Euclidean Distance (ED) as the similarity measures. Compared to spectral similarity methods, the SR-SVD method significantly improves mapping accuracy, as it avoids excessive extraction, can identify more detailed information, and is advantageous in distinguishing non-winter wheat pixels. Three commonly used supervised classification methods, support vector machine (SVM), maximum likelihood (ML), and minimum distance (MD) were used for comparison. Results indicate that SR-SVD has the highest mapping accuracy and greatly reduces the number of misidentified pixels. Therefore, the SR-SVD method can achieve high-precision crop mapping and provide technical support for monitoring regional crop planting structure information. [ABSTRACT FROM AUTHOR]

During the past century Chinese agriculture has been struggling to produce more food to support the ever growing population, while dealing with the increased degradation of air, water and soil quality in the agricultural regions. Lessons learnt from the long-term efforts indicated that scientifically sound methodology could play a crucial role in understanding the complex interactions among climate, soil, water and farming management practices, all of which collectively control the agroecosystem services including crop yield, greenhouse gas emissions, nutrient loading and other environmental issues. Therefore, the development of a more process-based approach is needed. The process-based model, DNDC (Denitrification-Decomposition) has been widely used internationally to simulate detailed carbon and nitrogen biogeochemical cycles occurring in agricultural systems. However, this model is not fully suitable for China as it lacks a number of features which are crucial for representing Chinese agro-ecosystems, including paddy rice cultivation, complex and multiple cropping systems, and intensive management practices, etc. Recently a new version of DNDC, China-DNDC, was developed. The new model improved its capability of predicting the fluxes of all three terrestrial greenhouse gases: nitrous oxide (N 2 O), carbon dioxide (CO 2 ), and methane (CH 4 ), as well as other important indicators such as crop phenology, soil C sequestration, and nutrient leaching from different cropping systems across all the major agricultural regions in China. This paper reported how China-DNDC was developed, tested and applied for sustaining Chinese agriculture. And then, it identified the weaknesses and potential improvements for the model. [ABSTRACT FROM AUTHOR]

Abstract: Nitrogen (N) loss through nitrate leaching in arable cropping systems in China has been recognized as one of the most common agricultural sources of groundwater contamination. The Denitrification–Decomposition or DNDC model, equipped with detailed soil hydrological and biogeochemical processes, was adopted in the study to quantify nitrate leaching for an intensively cultivated region in northern China. Several key parameters embedded in DNDC were calibrated against leaching data measured at a typical field with winter wheat–corn rotation within the target domain region. Five more sites representing the predominant cropping systems in the study region were selected for validating the modified model. In comparison with the original version of DNDC, the revised version yielded better results in simulated soil water and N leaching losses. To upscale the model simulation to regional level, we linked the validated DNDC to a regional database containing meteorological data, soil properties, vegetation types, and management practices for the target domain. Results from the regional simulation indicated that the total potential nitrate leaching load from the simulated 16.31millionha croplands (sown area) ranged from 1.5 to 2.15TgN per year, with an average of 1.8TgN, which was equivalent to 26.1% of the total amount of N fertilizer applied in the region in 2009. The modeled results showed clear spatial patterns of nitrate leaching rates across the region due to the spatially differentiated fertilizer application rates as well as the soil water regimes. Alternative water management practices were suggested to effectively reduce nitrate leaching losses from the agricultural region in northern China. [Copyright &y& Elsevier]

The influence of adjacency effect on high-spatial-resolution satellite imagery in the visible and near infrared region has been studies since the 1980s. However, the adjacency effect is usually neglected in the thermal infrared (TIR) region. The conventional TIR radiative transfer equation (RTE) only takes into account radiance from target pixel and neglects radiance contribution from surrounding background pixels. In this study, two TIR RTEs taking into account the adjacency effect for uniform and non-uniform Lambertian surfaces were deduced based on the radiative transfer theory. In terms of the TIR RTEs, we analyze the dominating influence factors of the adjacency effect and quantify the magnitude of the adjacency effect in the TIR region using simulation and actual satellite data. Two different definitions of the adjacency effect are presented in this study: (1) the effect due to radiance from background pixels scattered into sensor's instantaneous field of view, and (2) the effect due to apparent thermal contrast between target and background pixels. The results show that atmospheric visibility, water vapor content, sensor spectral band, and background pixel land surface temperature (LST) have significant influence on the adjacency effect. For a specific simulation scene, the magnitude of the first definition of the adjacency effect is larger than 0.5 K when atmospheric visibility is lower than 23 km. This magnitude is as much as 2 K under haze weather conditions with atmospheric visibility of 5 km. The results indicate that the adjacency effect should be taken into account in the radiative transfer simulation. The magnitude of the second definition of the adjacency effect is less than 0.3 K, even at atmospheric visibility of 5 km. The results reveal that the accuracy of the TIR RTE for a uniform Lambertian surface is enough for the development of LST retrieval algorithm under relatively haze weather conditions. In situ LST measurements collected at the Hailar, Urad Front Banner, and Wuhai sites in China were used to validate the accuracies of the LST retrieved by the RTE-based single channel algorithm with/without adjacency effect correction. There are nearly no discrepancies between the LST retrieved with/without adjacency effect correction when aerosol optical depth (AOD) is low than 0.3. However, the RMSE of the differences between the retrieved LST and the in situ LST decreases from approximately 1.4 K for the LST retrieved without adjacency effect correction to approximately 0.6 K for the LST retrieved with adjacency effect correction when AOD is high than 0.3. • Two TIR RTEs taking into account adjacency effect were deduced. • Dominating influence factor of adjacency effect in the TIR region was analyzed. • The magnitude of adjacency effect in the TIR region was quantified. • RMSE of LST derived with adjacency effect correction reduces 0.8 K for AOD > 0.3. [ABSTRACT FROM AUTHOR]