Analysis of the Development Status and Prospect of China's Agricultural Sensor Market under Smart Agriculture.

Agricultural sensors are essential technologies for smart agriculture, which can transform non-electrical physical quantities such as environmental factors. The ecological elements inside and outside of plants and animals are converted into electrical signals for control system recognition, providing a basis for decision-making in smart agriculture. With the rapid development of smart agriculture in China, agricultural sensors have ushered in opportunities and challenges. Based on a literature review and data statistics, this paper analyzes the market prospects and market scale of agricultural sensors in China from four perspectives: field farming, facility farming, livestock and poultry farming and aquaculture. The study further predicts the demand for agricultural sensors in 2025 and 2035. The results reveal that China's sensor market has a good development prospect. However, the paper garnered the key challenges of China's agricultural sensor industry, including a weak technical foundation, poor enterprise research capacity, high importation of sensors and a lack of financial support. Given this, the agricultural sensor market should be comprehensively distributed in terms of policy, funding, expertise and innovative technology. In addition, this paper highlighted integrating the future development direction of China's agricultural sensor technology with new technologies and China's agricultural development needs.

Utilization of food waste for hydrogen-based power generation: Evidence from four cities in Ghana.

Hydrogen gas will be an essential energy carrier for global energy systems in the future. However, non-renewable sources account for 96% of the production. Food wastes have high hydrogen generation potential, which can positively influence global production and reduce greenhouse gas (GHG) emissions. The study evaluates the potential of food waste hydrogen-based power generation through biogas steam reforming and its environmental and economic impact in major Ghanaian cities. The results highlight that the annual hydrogen generation in Kumasi had the highest share of 40.73 kt, followed by Accra with 31.62 kt, while the least potential was in Tamale (3.41 kt). About 2073.38 kt was generated in all the major cities. Hydrogen output is predicted to increase from 54.61 kt in 2007 to 119.80 kt by 2030. Kumasi produced 977.54 kt of hydrogen throughout the 24-year period, followed by Accra with 759.76 kt, Secondi-Takoradi with 255.23 kt, and Tamale with 81.85 kt. According to the current study, Kumasi had the largest percentage contribution of hydrogen (47.15%), followed by Accra (36.60%), Secondi-Takoradi (12.31%), and Tamale (3.95%). The annual power generation potential in Kumasi and Accra was 73.24 GWh and 56.85 GWh. Kumasi and Accra could offset 8.19% and 6.36% of Ghana's electricity consumption. The total electricity potential of 3728.35 GWh could displace 17.37% of Ghana's power consumption. This electricity generated had a fossil diesel displacement capacity of 1125.90 ML and could reduce GHG emissions by 3060.20 kt CO2 eq. Based on the findings, the total GHG savings could offset 8.13% of Ghana's carbon emissions. The cost of power generation from hydrogen is $ 0.074/kWh with an annual positive net present value of $ 658.80 million and a benefit-to-cost ratio of 3.43. The study lays the foundation and opens policy windows for sustainable hydrogen power generation in Ghana and other African countries.

Estimation of land surface directional emissivity in mid-infrared channel around 4.0 microm from MODIS data.

This work addressed the estimate of the directional emissivity in the mid-infrared (MIR) channel around 4.0 microm from MODIS data. A series of bidirectional reflectances in MODIS channel 22 (3.97 mum) were retrieved using the method developed by Tang and Li (Int. J. Remote Sens. 29, 4907, 2008) and then were used to estimate the directional emissivity in this channel with the aid of the BRDF model modified by Jiang and Li (Opt. Express 16, 19310, 2008). To validate the estimated directional emissivity, a cross-comparison of MODIS derived emissivities in channel 22 using the proposed method were performed with those provided by the MODIS land surface temperature/emissivity product MYD11B1 data. The results show that the proposed method for estimating the directional emissivity in MIR channel gives results comparable to those of MYD11B1 product with a Mean Error of -0.007 and a Root Mean Square Error of 0.024.

A review of current methodologies for regional evapotranspiration estimation from remotely sensed data.

An overview of the commonly applied evapotranspiration (ET) models using remotely sensed data is given to provide insight into the estimation of ET on a regional scale from satellite data. Generally, these models vary greatly in inputs, main assumptions and accuracy of results, etc. Besides the generally used remotely sensed multi-spectral data from visible to thermal infrared bands, most remotely sensed ET models, from simplified equations models to the more complex physically based two-source energy balance models, must rely to a certain degree on ground-based auxiliary measurements in order to derive the turbulent heat fluxes on a regional scale. We discuss the main inputs, assumptions, theories, advantages and drawbacks of each model. Moreover, approaches to the extrapolation of instantaneous ET to the daily values are also briefly presented. In the final part, both associated problems and future trends regarding these remotely sensed ET models were analyzed to objectively show the limitations and promising aspects of the estimation of regional ET based on remotely sensed data and ground-based measurements.

Generalized Split-Window Algorithm for Estimate of Land Surface Temperature from Chinese Geostationary FengYun Meteorological Satellite (FY-2C) Data.

On the basis of the radiative transfer theory, this paper addressed the estimate ofLand Surface Temperature (LST) from the Chinese first operational geostationarymeteorological satellite-FengYun-2C (FY-2C) data in two thermal infrared channels (IR1,10.3-11.3 µ m and IR2, 11.5-12.5 µ m ), using the Generalized Split-Window (GSW)algorithm proposed by Wan and Dozier (1996). The coefficients in the GSW algorithmcorresponding to a series of overlapping ranging of the mean emissivity, the atmosphericWater Vapor Content (WVC), and the LST were derived using a statistical regressionmethod from the numerical values simulated with an accurate atmospheric radiativetransfer model MODTRAN 4 over a wide range of atmospheric and surface conditions.The simulation analysis showed that the LST could be estimated by the GSW algorithmwith the Root Mean Square Error (RMSE) less than 1 K for the sub-ranges with theViewing Zenith Angle (VZA) less than 30° or for the sub-rangs with VZA less than 60°and the atmospheric WVC less than 3.5 g/cm² provided that the Land Surface Emissivities(LSEs) are known. In order to determine the range for the optimum coefficients of theGSW algorithm, the LSEs could be derived from the data in MODIS channels 31 and 32 provided by MODIS/Terra LST product MOD11B1, or be estimated either according tothe land surface classification or using the method proposed by Jiang et al. (2006); and theWVC could be obtained from MODIS total precipitable water product MOD05, or beretrieved using Li et al.' method (2003). The sensitivity and error analyses in term of theuncertainty of the LSE and WVC as well as the instrumental noise were performed. Inaddition, in order to compare the different formulations of the split-window algorithms,several recently proposed split-window algorithms were used to estimate the LST with thesame simulated FY-2C data. The result of the intercomparsion showed that most of thealgorithms give comparable results.