Disaggregating and mapping crop statistics using hypertemporal remote sensing

Governments compile their agricultural statistics in tabular form by administrative area, which gives no clue to the exact locations where specific crops are actually grown. Such data are poorly suited for early warning and assessment of crop production. 10-Daily satellite image time series of Andalucia, Spain, acquired since 1998 by the SPOT Vegetation Instrument in combination with reported crop area statistics were used to produce the required crop maps. Firstly, the 10-daily (1998–2006) 1-km resolution SPOT-Vegetation NDVI-images were used to stratify the study area in 45 map units through an iterative unsupervised classification process. Each unit represents an NDVI-profile showing changes in vegetation greenness over time which is assumed to relate to the types of land cover and land use present. Secondly, the areas of NDVI-units and the reported cropped areas by municipality were used to disaggregate the crop statistics. Adjusted R-squares were 98.8% for rainfed wheat, 97.5% for rainfed sunflower, and 76.5% for barley. Relating statistical data on areas cropped by municipality with the NDVI-based unit map showed that the selected crops were significantly related to specific NDVI-based map units. Other NDVI-profiles did not relate to the studied crops and represented other types of land use or land cover. The results were validated by using primary field data. These data were collected by the Spanish government from 2001 to 2005 through grid sampling within agricultural areas; each grid (block) contains three 700 m × 700 m segments. The validation showed 68%, 31% and 23% variability explained (adjusted R-squares) between the three produced maps and the thousands of segment data. Mainly variability within the delineated NDVI-units caused relatively low values; the units are internally heterogeneous. Variability between units is properly captured. The maps must accordingly be considered “small scale maps”. These maps can be used to monitor crop performance of specific cropped areas because of using hypertemporal images. Early warning thus becomes more location and crop specific because of using hypertemporal remote sensing.     

多源遥感与作物模型同化模拟作物物生长参数时空域连续变化

本文将遥感信息与作物模型同化实现作物生长参数的时空域连续模拟,进而监测生长参数的时空域变化.首先将作物模型WOFOST(World food studies) 与冠层辐射传输模型PROSAIL 耦合构建WOPROSAIL 模型,利用微粒群算法(PSO) 通过最小化从CCD 数据获取的土壤调节植被指数观测值SAVI(soil adjusted vegetation index) 与耦合模型得到的模拟值SAVI’之间差值优化作物模型初始参数.通过MODIS 数据反演实现参数的区域化,并将区域参数作为优化后作物模型输入参数驱动模型逐像元计算生长参数,实现生长参数的时空域连续模拟与监测,最终建立区域尺度遥感-作物模拟同化框架模型RS-WOPROSAIL .结果表明：同化模型解决了作物模型模拟空间域和遥感信息时间域的不连续问题.模型模拟的叶面积指数(LAI) 、穗重(WSO) 、地上总生物量(TAGP) 等生长参数较好地体现了水稻生长状况时空域变化,研究区水稻模拟产量与实际产量的误差为27.4% .     

Evaluation of irrigation management in a Canal Command area based on Agrometeorology and Remote Sensing

Efficacy of irrigation management of wheat and mustard crops grown in Western Yamuna Canal Command area was determined in the present study from agro-climatic data merged with Maximum Likelihood Classified (MXL) satellite image and from irrigation scheduling efficiencies obtained through FAO model CROPWAT. For computing irrigation scheduling efficiencies, amount of water supplied at different growth stages, soil water depletion and crop water need have been taken into account. Agro-meteorological data in combination with MXL classified crop map approximated the deficiency of applied irrigation amount compared to requirement. Irrigations at 35-80 Days After Sowing (DAS) for two times of applications, 30-60-90 DAS for three, 21-50-80-110 DAS in case of four and 20-45-70-90-120 DAS in case of five irrigations have yielded better scheduling efficiencies for wheat than other times of applications in all soil associations.     

Village level crop inventory using remote sensing and field survey data

This paper presents results of a pilot study in six villages located in the states of Haryana, Rajasthan and Madhya Pradesh, to evaluate accuracy of crop area at village level estimated by IRS - LISS-I1I data with respect to detailed field survey carried out by National Sample Survey Organization. The selected villages were located in Karnal, Kota and Bhopal districts which represented single dominant wheat crop as well as wheat-mustard and wheat-gram situation, respectively. Accuracy assessment of remote sensing based estimate with field survey of NSSO showed relative deviation in wheat estimate ranging from 3.72 percent for Mainmati village in Karnal district in Haryana to 22.65 percent fo Ranpur village in Kota district of Rajasthan. It was found that relative deviation in area estimation is inversely poportional to the crop proportion in that village. Observations of over estimation at low crop proportion and underestimation at higher crop proportion was explained by simple budgeting of relative proportion of ommision and commision errors. The study demonstrates that on the average, 90 percent crop area accuracy is possible with LISS-II1 data and the adopted approach.     

Monitoring winter wheat growth in North China by combining a crop model and remote sensing data

Both of crop growth simulation models and remote sensing method have a high potential in crop growth monitoring and yield prediction. However, crop models have limitations in regional application and remote sensing in describing the growth process. Therefore, many researchers try to combine those two approaches for estimating the regional crop yields. In this paper, the WOFOST model was adjusted and regionalized for winter wheat in North China and coupled through the LAI to the SAIL–PROSPECT model in order to simulate soil adjusted vegetation index (SAVI). Using the optimization software (FSEOPT), the crop model was then re-initialized by minimizing the differences between simulated and synthesized SAVI from remote sensing data to monitor winter wheat growth at the potential production level. Initial conditions, which strongly impact phenological development and growth, and which are hardly known at the regional scale (such as emergence date or biomass at turn-green stage), were chosen to be re-initialized. It was shown that re-initializing emergence date by using remote sensing data brought simulated anthesis and maturity date closer to measured values than without remote sensing data. Also the re-initialization of regional biomass weight at turn-green stage led that the spatial distribution of simulated weight of storage organ was more consistent to official yields. This approach has some potential to aid in scaling local simulation of crop phenological development and growth to the regional scale but requires further validation.     

Assessing environmental impacts of urban growth using remote sensing

ABSTRACT

This paper provides a study of the changes in land use in urban environments in two cities, Wuhan, China and western Sydney in Australia. Since mixed pixels are a characteristic of medium resolution images such as Landsat, when used for the classification of urban areas, due to changes in urban ground cover within a pixel, Multiple Endmember Spectral Mixture Analysis (MESMA) together with Super-Resolution Mapping (SRM) are employed to derive class fractions to generate classification maps at a higher spatial resolution using an Artificial Neural Network (ANN) predicted Wavelet method. Landsat images over the two cities for a 30-year period, are classified in terms of vegetation, buildings, soil and water. The classifications are then processed using Indifrag software to assess the levels of fragmentation caused by changes in the areas of buildings, vegetation, water and soil over the 30 years. The extents of fragmentation of vegetation, buildings, water and soil for the two cities are compared, while the percentages of vegetation are compared with recommended percentages of green space for urban areas for the benefit of health and well-being of inhabitants. Changes in Ecosystem Service Values (ESVs) resulting from the urbanization have been assessed for Wuhan and Sydney. The UN Sustainable Development Goals (SDG) for urban areas are being assessed by researchers to better understand how to achieve the sustainability of cities.     

Estimating fractional cover of crop,crop residue,and soil in cropland using broadband remote sensing data and machine learning

The fractional vegetation cover (FVC), crop residue cover (CRC), and bare soil (BS) are three important parameters in vegetation–soil ecosystems, and their correct and timely estimation can improve crop monitoring and environmental monitoring. The triangular space method uses one CRC index and one vegetation index to create a triangular space in which the three vertices represent pure vegetation, crop residue, and bare soil. Subsequently, the CRC, FVC, and BS of mixed remote sensing pixels can be distinguished by their spatial locations in the triangular space. However, soil moisture and crop-residue moisture (SM-CRM) significantly reduce the performance of broadband remote sensing CRC indices and can thus decrease the accuracy of the remote estimation and mapping of CRC, FVC, and BS. This study evaluated the use of broadband remote sensing, the triangular space method, and the random forest (RF) technique to estimate and map the FVC, CRC, and BS of cropland in which SM-CRM changes dramatically. A spectral dataset was obtained using: (1) from a field-based experiment with a field spectrometer; and (2) from a laboratory-based simulation that included four distinct soil types, three types of crop residue (winter-wheat, maize, and rice), one crop (winter wheat), and varying SM-CRM. We trained an RF model [designated the broadband crop-residue index from random forest (CRRF)] that can magnify spectral features of crop residue and soil by using the broadband remote sensing angle indices as input, and uses a moisture-resistant hyperspectral index as the target. The effects of moisture on crop residue and soil were minimized by using the broadband CRRF. Then, the CRRF-NDVI triangular space method was used to estimate and map CRC, FVC, and BS. Our method was validated by using both laboratory- and field-based experiments and Sentinel-2 broadband remote-sensing images. Our results indicate that the CRRF-NDVI triangular space method can reduce the effect of moisture on the broadband remote-sensing of CRC, and may also help to obtain laboratory and field CRC, FVC, and BS. Thus, the proposed method has great potential for application to croplands in which the SM-CRM content changes dramatically.     

Water logging and drainage Assessment in Ravi-Tawi irrigation command (J&K) using remote sensing approach

Water logging is one of the major land degradation processes that restricts the economic and efficient utilisation of soil and land resources in command areas. Since independence, various irrigation schemes, for providing water for agriculture and drinking have been taken up by Central and State agencies in India. In most of these schemes very little efforts have been made for proper drainage. Obstruction of natural drainage by way of construction of roads, railways, aerodrome, various structures, etc., causes the ponding of monsoon runoff on the upstream of the structures. Periodic monitoring of command areas helps in analysing the extent of water logging, and should help in taking suitable remedial measures. Remote sensing and GIS are powerful tools, which could be effectively used to study the dynamic behaviour of waterlogged areas. In this study, waterlogged and salt-affected areas have been estimated in the command area of Ravi-Tawi Irrigation Complex in Jammu region. About 14% of the total command area is water logged/ salt-affected. Being a new project, this area is likely to grow in future when the project runs with its installed capacity, and as the distributaries expand in the command area. Plausible causes of water logging have been discussed, and remedial measures suggested for reclaiming operations.     

Farm-Level Yield mapping for precision crop management by linking remote sensing inputs and a crop simulation model

A study aimed at generating wheat yield maps of farmer’s fields by using remote sensing (RS) inputs was undertaken during the rabi season of 1998-99 in six villages of Alipur Block of Delhi State. RS derived leaf area index (LAI) were linked to wheat simulation model WTGROWS by adopting a strategy christened “Modified Corrective Approach”. This essentially uses an empirical relation of grain yield and LAI, which was derived from WTGROWS simulation model by running model for a combination of input resources, management practices and soil types occurring in the area. This biometric relationship was applied to all the wheat fields of the study area for which the LAI was derived from single acquisition of IRS LISS-III data (Jan 27, 99). The LAI-NDVI relation adopted was logarithmic in nature (R²=0.83) and was based on ground measurements of LAI in farmer’s fields in the same area. A comparison of predicted grain yield by the modified corrective approach and actual observed yield for the 22 farmer’s fields showed high correlation coefficient of 0.8 and a root mean square error (RMSE) of 597 kg ha^-1 which was 17% of the observed mean yield. Thus linking of RS information and crop simulation model provides an alternative for mapping and forecasting crop yield under highly variable cropping environment of Indian farms, which is a pre-requisite for implementing Precision Crop Management (PCM).     

Study of irrigation and crop water requirements and growth of two <Emphasis Type="Italic">Rabi</Emphasis> crops grown in a semi arid region using agrometeorology and remote sensing

Irrigation water requirements of wheat and mustard crops grown in Western Yamuna Canal Command area were estimated using FAO model CROPWAT with the help of agrometeorological and remote sensing data (1986–1998 and 2008). The variations in irrigation water requirements of these two crops were judged by calculating coefficient of Variations (CVs) of yearly data. Crop coefficient values were obtained through FAO (1993) method. Supervised Maximum Likelihood Classification (MXL) of IRS 1B image was done to estimate area under wheat and mustard in the canal command. Water need was calculated from amount of supply and water requirement for the whole area. Results showed that ET_crop values of both wheat and mustard varied very little over different years (CVs 4.7% and 5.6% respectively). Irrigation water requirements of both these crops were having relatively large variations (CVs 14.1% and 22.6% respectively) which were mainly because of high variations of their effective rainfall (CVs 61.1% and 69.2% respectively). In general, increase in amount of irrigation enhanced the growth performance of the wheat crop. Increase in distribution equity within soil associations slightly improved the growth performance of the wheat crop. Agro-climatic data merged with satellite image approximated the deficiency of applied irrigation amount (549.5 ha-m for wheat and 692.7 ha-m for mustard) as compared to requirement.     

Modeling the spatial distribution of above-ground carbon in Mexican coniferous forests using remote sensing and a geostatistical approach

Forest conservation is considered an option for mitigating the effect of greenhouse gases on global climate, hence monitoring forest carbon pools at global and local levels is important. The present study explores the capability of remote-sensing variables (vegetation indices and textures derived from SPOT-5; backscattering coefficient and interferometric coherence of ALOS PALSAR images) for modeling the spatial distribution of above-ground biomass in the Environmental Conservation Zone of Mexico City. Correlation and spatial autocorrelation coefficients were used to select significant explanatory variables in fir and pine forests. The correlation for interferometric coherence in HV polarization was negative, with correlations coefficients r = −0.83 for the fir and r = −0.75 for the pine forests. Regression-kriging showed the least root mean square error among the spatial interpolation methods used, with 37.75 tC/ha for fir forests and 29.15 tC/ha for pine forests. The results showed that a hybrid geospatial method, based on interferometric coherence data and a regression-kriging interpolator, has good potential for estimating above-ground biomass carbon.     

Monitoring growth of built-up areas in indo-gangetic plain using multi-sensor remote sensing data

This paper provides an approach for rapid and accurate estimation of built-up areas on a per pixel-basis using a integration of two coarse spatial resolution remote sensing data namely DMSP-OLS and MODIS NDVI. The DMSP-OLS data due to its free availability, high temporal resolution and wide swath was used for regional level mapping of built-up areas. However, due to its low radiometric resolution, the built-up areas cannot be estimated accurately from the DMSP-OLS data. In present study, the DMSP-OLS data was combined with MODIS NDVI data to develop an Human Settlement Index (HSI) image, which estimated the fraction of built-up area on a per pixel basis. The resultant HSI image conveys more information than both the individual datasets. These temporal HSI images were then used for monitoring urban growth in Indo-Gangetic plains during the 2001–2007 time period. Thus, the present research can be very useful for regional level monitoring of built-up areas from coarse resolution data within limited time and minimal cost.     

基于遥感和GIS的矿产资源多目标监测方法

长期以来,矿产资源开发利用比较粗放是一个普遍存在的现象,在造成矿产资源严重浪费的同时还引发一系列环境地质问题.遥感技术用高空鸟瞰的形式进行探测,可以跨越交通的阻隔和视野的限制,洞察地面调查的禁区和死角,对大面积的环境状况进行全面彻底的调查;同时它远离观察对象,不损害研究对象及其环境条件,保证了获取的信息资料的客观性、可靠性;而且能对同一目标进行多次遥感,提供客观现象在时间维上的演化轨迹.     

遥感技术在主要粮食作物估产中的应用

文章分析了国内外遥感技术在主要粮食作物估产中应用现状,探讨了遥感技术在作物估产领域的研究进展,研究了作物气候产量预报模型、遗传算法结合神经网络模型、基于人机交互的反演模型、基于决策树分类的县域估产模型、单产估测模型、基于SCE_UA算法的CERES_Wheat模型、雷达遥感估产模型等在我国主要农作物估产中的应用;分析表明遥感关键技术及模型选择为农作物估产精度的提高提供了重要的技术支持.最后对作物估产遥感技术发展趋势及农业信息化相关技术做了展望,指出综合遥感与计算机技术开发自动化系统、推进物联网与遥感技术结合等问题,是进一步的研究趋势.     

Non-point source pollution in Indian agriculture: Estimation of nitrogen losses from rice crop using remote sensing and GIS

The paper presents a detailed understanding of nitrogenous fertilizer use in Indian agriculture and estimation of seasonal nitrogen loosses from rice crop in Indo-Gangetic plain region, the ‘food bowl’ of the Indian sub-continent. An integrated methodology was developed for quantification of different forms of nitrogen losses from rice crop using remote sensing derived inputs, field data of fertilizer application, collateral data of soil and rainfall and nitrogen loss coefficients derived from published nitrogen dynamics studies. The spatial patterns of nitrogen losses in autumn or ‘kharif’ and spring or ‘rabi’ season rice at 1 × 1 km grid were generated using image processing and GIS. The nitrogen losses through leaching in form of urea-N, ammonium-N (NH₄-N) and nitrate-N (NO₃-N) are dominant over ammonia volatilization loss. The study results indicate that nitrogen loss through leaching in kharif and rabi rice is of the order of 34.9% and 39.8% of the applied nitrogenous fertilizer in the Indo-Gangetic plain region. This study provides a significant insight to the role of nitrogenous fertilizer as a major non-point source pollutant from agriculture.     

农作物种植面积遥感估算的影响因素研究

针对不同的农作物种植结构区,研究影响遥感影像分类各因素与农作物种植面积估算精度的定性和定量关系是十分必要的。以Rapid Eye影像提取的早稻种植信息为研究对象,从农作物的种植成数、种植破碎度和地块形状指数3个角度进行了不同空间分辨率下各因素对农作物面积监测的影响研究。结果表明:随着农作物种植成数的降低,种植结构越来越破碎,种植地块趋于狭长分布,各分辨率下农作物面积估算精度均呈递减趋势;要达到85%以上的面积估算精度,当作物种植成数在50%以上时,可选取高于150 m分辨率的遥感数据;当作物种植较为破碎时,需要在提高影像空间分辨率的同时融入其他技术手段;当作物种植地块为狭长分布时,提高影像的空间分辨率并不能保证面积估算精度,必须通过其他技术手段达到精度要求;并最终得到了4种影响因素对面积估算精度的定量评估模型。研究结果为解决不同农作物种植结构区遥感数据的选择、面积估算精度的提高,以及在特定研究区和数据源条件下可达到的面积估算水平等问题提供了理论基础。     

Landslide susceptibility mapping in a hilly terrain using remote sensing and GIS

Remote sensing and Geographic Information System (GIS) are well suited to landslide studies. The aim of this study is to prepare a landslide susceptibility map of a part of Ooty region, Tamil Nadu, India, where landslides are common. The area of the coverage is approximately 10 × 14 km in a hilly region where planting tea, vegetables and cash crops are in practice. Hence, deforestation, formation of new settlements and changing land use practices are always in progress. Land use and land cover maps are prepared from Indian Remote Sensing Satellite (IRS 1C - LISS III) imagery. Digital Elevation Model (DEM) was developed using 20 m interval contours, available in the topographic map. Field studies such as local enquiry, land use verification, landslide location identification were carried out. Analysis was carried out with GIS software by assigning rank and weights for each input data. The output shows the possible landslide areas, which are grouped for preparation of landslide susceptibility maps.     

作物病虫害高光谱遥感进展与展望

作物病虫害作为影响农作物品质、产量及威胁粮食安全的主要因素,仅依靠人工田间调查对其进行监测已不能满足当下农业生产精准高效的需求。高光谱遥感作为能够获取地表物体连续波谱信息的遥感技术,已经成为当下作物病虫害监测识别的重要手段。本文对作物病虫害高光谱遥感监测识别的研究进展进行综述,通过对该领域发表文献的统计以及对主要机构、团队、数据源的分析,明确了病虫害高光谱遥感监测的研究热点和趋势;在此基础上,分析高光谱技术及其作物病虫害的监测识别机理,从病虫害胁迫探测、分类识别、危害严重度定量分析及早期检测四个方面综述相关技术及研究现状;通过探讨当下高光谱遥感病虫害监测识别面临的挑战,提出作物病虫害标准图谱库的建立、星载高光谱传感器的完善以及星空地一体化监测平台的搭建是当前作物病虫害高光谱遥感监测识别技 术落到实处的关键,也是未来发展的重点方向。     

Hydromorphogeological investigations in a typical khondalitic terrain using remote sensing data

In order to examine the influence of tectonic and morphological characteristics on the occurrence and movement of ground water in Khondalitic (garnetiferrous sillimanite gneiss) suite of rocks, hydromorphogeological studies were carried out in a typical Khondalitic terrain situated in Viziangaram district of Andhra Pradesh, India. Different land forms have been identified with the aid of visual interpretation of Landsat imagery together with ground truth data in order to prepare hydromorphogeological and lineament maps. Drainage map and topographic slope map have been prepared using toposheets. These maps and other collateral data like well yields and geophysical data have been analysed to evaluate the ground water prospective geomorphic units. Ground water prospect areas are located in shallow buried pediplains and wash plains in such a way that they are identified on gently sloping uplands situated between the lineaments. Non potential areas are those, which are, low-lying areas near the streams and high slope areas near the residual hills. It is found near low lying areas i.e., beneath the streams that the khondalite must have transformed itself into kaoline and acting as barrier evidently preventing lateral movement of ground water forcing it to accumulate in flat upland areas between two streams or lineaments. From the lithologic cross sections it is found that there are four distinct subsurface layers namely (1) top soil, (2) highly weathered khondalite (kaolinised layer), (3) moderately weathered and fractured khondalite (aquifer layer) and (4) basement of granitic gneiss.