卫星遥感技术在秸秆焚烧监测业务中的应用

农村秸秆焚烧常造成灰烬飞扬，烟雾迷漫，形成空气污染。常规的秸秆焚烧监测难度较大。本文介绍了河南省利用卫星遥感技术开展秸秆焚烧监测业务、服务的方法，包括卫星遥感技术监测秸秆焚烧火点的基本原理，以及秸秆焚烧火点遥感监测业务服务的流程、相关业务服务系统的组成及服务方式等。     

秸秆焚烧对区域城市空气质量影响的模拟分析

利用融合火点排放源、人为源和生物源的WRF-Chem(Weather Research and Forecasting Model coupled with Chemistry)模式,模拟2015年9月30日08:00(北京时间)起的72 h发生在淮河流域的一次农作物秸秆大面积露天焚烧过程,研究了农作物秸秆焚烧释放的气态污染物和颗粒物对区域城市空气质量的影响。通过有无火点两组试验分析了此次秸秆焚烧对流域内河南、山东、江苏和安徽四省83座城市CO、PM10(空气动力学当量直径小于等于10μm的颗粒物,即可吸入颗粒物)、PM2.5(空气动力学当量直径小于等于2.5μm的颗粒物,即细颗粒物)和O3浓度的定量影响,结果表明:(1)融合NCAR-FINN(Fire Inventory from NCAR)火点排放资料的WRF-Chem模式较好地再现了此次秸秆焚烧及火点烟羽扩散过程。同时结合EDGAR-HTAP(Emission Database for Global Atmospheric Research on Hemispheric Transport of Air Pollution)人为源和MEGAN(Model of Emission of Gases and Aerosols from Nature)生物源的WRF-FIRE(考虑火点排放试验)对流域内城市大气污染物的模拟效果较为理想,尤其对秸秆焚烧释放的污染物CO、PM10和PM2.5和产生的二次污染物O3浓度的模拟。(2)秸秆焚烧所释放的污染物造成流域内城市一次污染物CO、PM10和PM2.5浓度的增加,火点中心和下风向城市增幅最为明显,最大小时浓度增幅达到3倍标准差。气态污染物CO和相比PM10粒径更小的PM2.5可随风扩散至更远的地区,对城市浓度影响更大。(3)此外,秸秆焚烧也使得火点中心城市和下风向城市二次污染物O3浓度增加,但小时浓度增幅极值区分布在火点下风向烟羽末端太阳光照充足的地区,最大小时浓度增幅接近3倍标准差。秸秆焚烧对区域城市空气质量的影响存在明显的空间分布差异且对城市各大气污染成分的影响也不相同。     

Assessment of impact of climate change and adaptation strategies on maize production in Uganda

Globally, various climatic studies have estimated a reduction of crop yields due to changes in surface temperature and precipitation especially for the developing countries which is heavily dependent on agriculture and lacks resources to counter the negative effects of climate change. Uganda's economy and the wellbeing of its populace depend on rain-fed agriculture which is susceptible to climate change. This study quantified the impacts of climate change and variability in Uganda and how coping strategies can enhance crop production against climate change and/or variability.The study used statistical methods to establish various climate change and variability indicators across the country, and uses the FAO AquaCrop model to simulate yields under possible future climate scenarios with and without adaptation strategies. Maize, the most widely grown crop was used for the study. Meteorological, soil and crop data were collected for various districts representing the maize growing ecological zones in the country.Based on this study, it was found that temperatures have increased by up to 1 °C across much of Uganda since the 1970s, with rates of warming around 0.3 °C per decade across the country. High altitude, low rainfall regions experience the highest level of warming, with over 0.5 °C/decade recorded in Kasese. Rainfall is variable and does not follow a specific significant increasing or decreasing trend. For both future climate scenarios, Maize yields will reduce in excess of 4.7% for the fast warming-low rainfall climates but increase on average by 3.5% for slow warming-high rainfall regions, by 2050. Improved soil fertility can improve yields by over 50% while mulching and use of surface water management practices improve yields by single digit percentages. The use of fertilizer application needs to go hand in hand with other water management strategies since more yields as a result of the improved soil fertility leads to increased water stress, especially for the dry climates.     

Effects of Activated Irrigation Water on Soil Physicochemical Properties and Crop Growth and Analysis of the Probable Pathway

With the problem of shortage of water resources becoming increasingly prominent, the improvement of production efficiency of agricultural water resources in China has become an important research content of modern irrigated agriculture. The technology of activated irrigation water provides a new way to excavate the physiological production potential of irrigation water and improve the comprehensive efficacy of irrigation water in agro-ecosystems. In this study, the research progress of the variable characteristics of physicochemical properties of activated irrigation water, the transport and transformation of soil material by activated irrigation water, and the promotion of crop growth by activated irrigation water were comprehensively analyzed. On this basis, according to the basic theories of soil physics, crop physiology, and material transport dynamics, the effects of activated irrigation water on soil material transport, soil material transformation, water and nutrition uptake by root, and crop yield formation and the probable pathway were analyzed. The key problems of basic science and applied technology in the future research are put forward to provide reference for the scientific and reasonable utilization of activated irrigation water technology.     

Integrating age in the detection and mapping of incongruous patches in coffee (Coffea arabica) plantations using multi-temporal Landsat 8 NDVI anomalies

The development of cost-effective, reliable and easy to implement crop condition monitoring methods is urgently required for perennial tree crops such as coffee (Coffea arabica), as they are grown over large areas and represent long term and higher levels of investment. These monitoring methods are useful in identifying farm areas that experience poor crop growth, pest infestation, diseases outbreaks and/or to monitor response to management interventions. This study compares field level coffee mean NDVI and LSWI anomalies and age-adjusted coffee mean NDVI and LSWI anomalies in identifying and mapping incongruous patches across perennial coffee plantations. To achieve this objective, we first derived deviation of coffee pixels from the global coffee mean NDVI and LSWI values of nine sequential Landsat 8 OLI image scenes. We then evaluated the influence of coffee age class (young, mature and old) on Landsat-scale NDVI and LSWI values using a one-way ANOVA and since results showed significant differences, we adjusted NDVI and LSWI anomalies for age-class. We then used the cumulative inverse distribution function (α ≤ 0.05) to identify fields and within field areas with excessive deviation of NDVI and LSWI from the global and the age-expected mean for each of the Landsat 8 OLI scene dates spanning three seasons. Results from accuracy assessment indicated that it was possible to separate incongruous and healthy patches using these anomalies and that using NDVI performed better than using LSWI for both global and age-adjusted mean anomalies. Using the age-adjusted anomalies performed better in separating incongruous and healthy patches than using the global mean for both NDVI (Overall accuracy = 80.9% and 68.1% respectively) and for LSWI (Overall accuracy = 68.1% and 48.9% respectively). When applied to other Landsat 8 OLI scenes, the results showed that the proportions of coffee fields that were modelled incongruent decreased with time for the young age category and while it increased for the mature and old age classes with time. We concluded that the method could be useful for the identification of anomalous patches using Landsat scale time series data to monitor large coffee plantations and provide an indication of areas requiring particular field attention.     

Hyperspectral-based predictive modelling of grapevine water status in the Portuguese Douro wine region

In this study, hyperspectral reflectance (HySR) data derived from a handheld spectroradiometer were used to assess the water status of three grapevine cultivars in two sub-regions of Douro wine region during two consecutive years. A large set of potential predictors derived from the HySR data were considered for modelling/predicting the predawn leaf water potential (Ψ_pd) through different statistical and machine learning techniques. Three HySR vegetation indices were selected as final predictors for the computation of the models and the in-season time trend was removed from data by using a time predictor. The vegetation indices selected were the Normalized Reflectance Index for the wavelengths 554 nm and 561 nm (NRI_554;561), the water index (WI) for the wavelengths 900 nm and 970 nm, and the D1 index which is associated with the rate of reflectance increase in the wavelengths of 706 nm and 730 nm. These vegetation indices covered the green, red edge and the near infrared domains of the electromagnetic spectrum. A large set of state-of-the-art analysis and statistical and machine-learning modelling techniques were tested. Predictive modelling techniques based on generalized boosted model (GBM), bagged multivariate adaptive regression splines (B-MARS), generalized additive model (GAM), and Bayesian regularized neural networks (BRNN) showed the best performance for predicting Ψ_pd, with an average determination coefficient (R²) ranging between 0.78 and 0.80 and RMSE varying between 0.11 and 0.12 MPa. When cultivar Touriga Nacional was used for training the models and the cultivars Touriga Franca and Tinta Barroca for testing (independent validation), the models performance was good, particularly for GBM (R² = 0.85; RMSE = 0.09 MPa). Additionally, the comparison of Ψ_pd observed and predicted showed an equitable dispersion of data from the various cultivars. The results achieved show a good potential of these predictive models based on vegetation indices to support irrigation scheduling in vineyard.     

Changes in crop type distribution in Zhangye City of the Heihe River Basin,China

With rapid economic development in China, crops have undergone remarkable changes in both their type and spatial pattern. Timely and accurate information of crop type distribution will help government and agricultural producers quickly understand regional agricultural production conditions to better facilitate appropriate adjustments in planting patterns and policies. Another benefit of acquiring such knowledge of crops is that it should enhance regional agricultural competitiveness, optimize resource allocations, and further guarantee national food security. Towards this end, and using the Zhangye City in the Heihe River Basin as a study area, the present research elaborated upon a methodology to classify crop type distribution based on multi-temporal Thematic Mapper and Enhanced Thematic Mapper Plus (TM/ETM+) images. Using this methodology we achieved the spatial distributions of crop types in Zhangye City in 2007 and 2012, and analyzed changes in their distributions over this period. In addition, some landscape indices were calculated to clarify the landscape pattern of crops. The crop conversion potentials in 2017 were modeled using four conversion sub-models of the Multi-Layer Perceptron (MLP) neural network. Generally, the overall accuracy of crop classification in Zhangye was high, at 89.38%. From 2007 to 2012, the cultivated land area in Zhangye increased from 463.81 × 10³ ha to 493.89 × 10³ ha. The sowing area of corn and oilseed rape increased by 39.21 × 10³ ha and 5.99 × 10³ ha, respectively, while for wheat and barley the sowing area decreased by 3.61 × 10³ ha and 9.14 × 10³ ha, respectively. Considering other crop types as a group, their sowing area decreased by only 2.37 × 10³ ha. The increase in corn sowing area mainly came from the conversion of other crops to corn, which accounted for 43.09% of its total sowing area in 2012. Furthermore, corn and oilseed rape showed a tendency of intensive sowing, whereas for wheat and barley the tendency was towards scattered sowing. For the future, corn has high conversion potential in Linze and Gaotai counties of Zhangye, while wheat, barley and oilseed rape have high conversion potentials in Minle and Shandan counties.     

Quantifying winter wheat residue biomass with a spectral angle index derived from China Environmental Satellite data

Quantification of crop residue biomass on cultivated lands is essential for studies of carbon cycling of agroecosystems, soil-atmospheric carbon exchange and Earth systems modeling. Previous studies focus on estimating crop residue cover (CRC) while limited research exists on quantifying crop residue biomass. This study takes advantage of the high temporal resolution of the China Environmental Satellite (HJ-1) data and utilizes the band configuration features of HJ-1B data to establish spectral angle indices to estimate crop residue biomass. Angles formed at the NIR_IRS vertex by the three vertices at R, NIR_IRS, and SWIR (ANIR_IRS) of HJ-1B can effectively indicate winter wheat residue biomass. A coefficient of determination (R²) of 0.811 was obtained between measured winter wheat residue biomass and ANIR_IRS derived from simulated HJ-1B reflectance data. The ability of ANIR_IRS for quantifying winter wheat residue biomass using HJ-1B satellite data was also validated and evaluated. Results indicate that ANIR_IRS performed well in estimating winter wheat residue biomass with different residue treatments; the root mean square error (RMSE) between measured and estimated residue biomass was 0.038 kg/m². ANIR_IRS is a potential method for quantifying winter wheat residue biomass at a large scale due to wide swath width (350 km) and four-day revisit rate of the HJ-1 satellite. While ANIR_IRS can adequately estimate winter wheat residue biomass at different residue moisture conditions, the feasibility of ANIR_IRS for winter wheat residue biomass estimation at different fractional coverage of green vegetation and different environmental conditions (soil type, soil moisture content, and crop residue type) needs to be further explored.     

Assessing crop yield benefits from in situ rainwater harvesting through contour ridges in semi-arid Zimbabwe

Rainwater harvesting through modified contour ridges known as dead level contours has been practiced in Zimbabwe in the last two decades. Studies have shown marginal soil moisture retention benefits for using this technique while results on crop yield benefits are lacking. This paper presents results from a field study for assessing the impact of dead level contours on soil moisture and crop yield carried out from 2009 to 2011 within the Limpopo River Basin. The experiments were carried out on two study sites; one containing silt loam soil and another containing sandy soil. Three treatments constituting dead level contoured plots, non-contoured plots and plots with the traditional graded contours were used on each site. All the three treatments were planted with a maize crop and managed using conventional farming methods. Planting, weeding and fertiliser application in the three treatments were done at the same time. Crop monitoring was carried out on sub plots measuring 4 m by 4 m established in every treatment. The development of the crop was monitored until harvesting time with data on plant height, leaf moisture and crop yield being collected. An analysis of the data shows that in the site with silt loam soil more soil moisture accumulated after heavy rainfall in dead level contour plots compared to the control (no contours) and graded contour plots (P < 0.05). However the maize crop experienced an insignificantly (P > 0.05) higher yield in the dead level contoured treatment compared to the non-contoured treatment while a significantly (P < 0.05) higher yield was obtained in the dead level contoured treatment when compared with a graded contoured treatment. Different results were obtained from the site with sandy soil where there was no significant difference in soil moisture after a high rainfall event of 60 mm/day between dead level contour plots compared to the control and graded contour plots. The yield from the dead level contoured treatment and that from the graded contoured treatment were comparable and both not significantly (P > 0.05) higher than that from the non-contoured treatment. This suggests that adopting dead level contours as an in situ rainwater harvesting technique results in crop yield benefits in fields with soil type conditions that enable runoff generation but is not likely to have benefit in soils with low runoff generation.     

Determining crop acreage estimates for specific winter crops using shape attributes from sequential MODIS imagery

There are increasing societal and plant industry demands for more accurate, objective and near real-time crop production information to meet both economic and food security concerns. The advent of the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite platform has augmented the capability of satellite-based applications to monitor large agricultural areas at acceptable pixel scale, cost and accuracy. Fitting parametric profiles to growing season vegetation index time series reduces the volume of data and provides simple quantitative parameters that relates to crop phenology (sowing date, flowering). In this study, we modelled various Gaussian profiles to time sequential MODIS enhanced vegetation index (EVI) images over winter crops in Queensland, Australia. Three simple Gaussian models were evaluated in their effectiveness to identify and classify various winter crop types and coverage at both pixel and regional scales across Queensland's main agricultural areas. Equal to or greater than 93% classification accuracies were obtained in determining crop acreage estimates at pixel scale for each of the Gaussian modelled approaches. Significant high to moderate correlations (log-linear transformation) were also obtained for determining total winter crop (R² = 0.93) areas as well as specific crop acreage for wheat (R² = 0.86) and barley (R² = 0.83). Conversely, it was much more difficult to predict chickpea acreage (R² ≤ 0.26), mainly due to very large uncertainties in survey data. The quantitative approach utilised here further had additional benefits of characterising crop phenology in terms of length of growing season and providing regression diagnostics of how well the fitted profiles matched the EVI time series. The Gaussian curve models utilised here are novel in application and therefore will enhance the use and adoption of remote sensing technologies in targeted agricultural application. With innate simplicity and accuracies comparable to other more convoluted multi-temporal approaches it is a good candidate in determining total and specific crop acreage estimates in future national and global food security frameworks.