Remote Sensing and Geographical Information System for canopy cover mapping

Availability of remote sensing data from earth observation satellites has made it convenient to map and monitor land use/land cover at regional to local scales. A land cover map is very critical for a various planning activities including watershed planning. The spectral and spatial resolutions are major constraints for mapping the crop resources at microlevel. The cropping pattern zones have been mapped using the false color composite, physiography, irrigation and toposheets. The IRS LISS-III data is classified into various categories depending on spectral reflectance from crop canopy and are overlaid on cropping zones map. The re-classified resultant map provides land use/land cover information including dominant cropping systems. The canopy cover is estimated monthly considering the crop calendar for the area.     

Concurrent use of active and passive microwave remote sensing data for monitoring of rice crop

Estimation of crop area, growth and phenological information is very important for monitoring of agricultural crops. However, judicious combination of spatial and temporal data from different spectral regions is necessary to meet the requirement. This study highlights the use of active microwave QuikSCAT Ku-band scatterometer and Special Sensor Microwave/Imager (SSM/I) passive microwave radiometer data to derive information on important phenological phases of rice crop. The wetness index, a weekly composite product derived using brightness temperatures from 19, 37 and 85 GHz channels of SSM/I, was used to identify the puddling period. Ku-band scatterometer data provided the signal of transplanted rice seedlings since they acts as scatterers and increases the backscattering. Dual peak nature of temporal backscatter curve around the heading stage of rice crop was observed in Ku-band. The decrease of backscatter after first peak was associated with the threshold value of 60% crop canopy cover. The symmetric (Gaussian) and asymmetric (lognormal) curve fits were attempted to derive the date of initiation of the heading phase. The temporal signature from each of these sensors was found to complement each other in crop growth monitoring. Image showing pixel-wise timings of heading stage revealed the differences exists in various parts of the study area.     

Viewing Angle Effect on the Remote Sensing Monitoring of Wheat and Rice Crops

Abstract

Bidirectional reflectance characteristics of a wheat and a rice canopy were studied using radiometric measurements over the two canopies with an Exotech Radiometer Model 100AX. For both rice and wheat, the measurements exhibited significant variations in reflectance amplitude for varying viewing zenith angles and their values decreased from backward to forward scattering direction. Reflectance obtained its maximum value in the backscatter direction for the viewing angle that corresponds approximately to the solar zenith angle at the time of measurements. Canopy background condition (irrigated or non‐irrigated) was an important determining factor of canopy reflectance particularly in the near‐infrared region of the solar spectrum. Furthermore, bidirectional response characteristics of a vegetation canopy were influenced by the amount of vegetation cover.     

Fully constrained linear spectral unmixing based global shadow compensation for high resolution satellite imagery of urban areas

Shadows commonly exist in high resolution satellite imagery, particularly in urban areas, which is a combined effect of low sun elevation, off-nadir viewing angle, and high-rise buildings. The presence of shadows can negatively affect image processing, including land cover classification, mapping, and object recognition due to the reduction or even total loss of spectral information in shadows. The compensation of spectral information in shadows is thus one of the most important preprocessing steps for the interpretation and exploitation of high resolution satellite imagery in urban areas. In this study, we propose a new approach for global shadow compensation through the utilization of fully constrained linear spectral unmixing. The basic assumption of the proposed method is that the construction of the spectral scatter plot in shadows is analogues to that in non-shadow areas within a two-dimension spectral mixing space. In order to ensure the continuity of land covers, a smooth operator is further used to refine the restored shadow pixels on the edge of non-shadow and shadow areas. The proposed method is validated using the WorldView-2 multispectral imagery collected from downtown Toronto, Ontario, Canada. In comparison with the existing linear-correlation correction method, the proposed method produced the compensated shadows with higher quality.     

Crop cover and crop phenological information from red and infrared spectral responses

Possibility of utilizing the red and infrared spectral information for assessing status of vegetation cover and consequential crop phenological information are discussed. The experiment was conducted in a potential agricultural area around Mandya town of Karnataka State and airborne spectral information was obtained through modular multispectral scanner from a height of 1000 meters above the ground level. The spectral information of red (0.66–0.70 urn) and infrared (0.77–0.86 urn) bands was extracted with the aid of an interactive computer system : the multispectral data analysis system. Based on the spectral information, the data was analysed and interpreted with the support of ground information. Crop fields without vegetation were observed to have infrared/red ratio in the range of 0.70 to 0.97 and also it was possible to distinguish wet and dry paddy field. Crop fields covered with vegetation exhibited higher infrared/red ratio depending on the nature of crop growth. For instance, rice crop exhibited spectral ratio of 0.78 at the time of planting, 3.52 at the time of maximum vegetation growth and 2.04 during the maturation phase. In case of sugarcane crop, the increase and decrease in spectral ratio were gradual because of its longer duration. From infrared and red band information it was possible to distinguish crop species based on rate of change of vegetation cover which corresponded with the change in spectral ratios. The temporal information expressed in two dimensional space for red and infrared band also enabled clearly to distinguish between rice and sugarcane.     

Comparison of ERS-SAR and spot data for sugar beet crop cover assessment

The present work was aimed to compare the abilities of radar and optical satellite data to estimate crop canopy cover, which is a key component of productivity estimates. Three ERS-1 SAR images were obtained of East Anglia (UK) in 1995 and one ERS-2 SAR image in 1996. The images covered a study area around the IACR Brooms Barn Sugar Beet Research Institute. Field data comprising radiometric and biophysical measurements of the crop canopy were collected in two fields from June 22 to August 3, 1995 to coincide with ERS-1 SAR overpass dates. In 1996, field data were collected in two fields from June 11 to July 29 on a weekly basis. A previously calibrated version of the water cloud model was inverted to estimate Leaf Area Index (LAI) from ERS-1 and ERS-2 SAR backscatter and soil moisture samples. Canopy cover was estimated from the radar-estimated LAI using a standard exponential relationship that has a well-established coefficient for sugar beet. Radio-metrically and atmospherically corrected data from three SPOT images in 1995 and one SPOT image in 1996 were used to calculate the Optimised Soil Adjusted Vegetation Index (OSAVI), from which crop canopy cover was estimated using a relationship determined previously by canopy modelling. The crop cover values estimated by satellite were in good agreement with those measured on ground with the Parkinson radiometer. Radar data may be able to provide useful estimates of canopy cover for crop production modelling, especially in the case of loss of optical data due to cloud.     

基于多项式表达模型的多角度覆盖率反演研究

冠层光谱的多项式表达模型是理解叶片／土壤系统内的光线散射过程，描述植被的冠层光谱与组分光谱间非线性关系的一种新的方法。冠层光谱的多项式表达模型用高阶多项式的形式表达了系统内的各组分对入射光的散射过程，多项式中每一项系数表达了各组分对入射光多次散射的可能性有多少，这与冠层的结构，包括作物的覆盖率、叶倾角、垄行结构等有关。因此，多项式系数具有反演冠层结构参数的潜力。本次研究首先使用SAILH模型得到玉米全生长期的BRDF波谱以及模型计算过程的中间变量冠层多角度覆盖率，建立多项式系数与多角度覆盖率之间的经验关系。最后，设计了一次野外BRDF观测试验，对经验关系进行验证。     

Inferred leaf senescence of rice crop and its relationship with grain yield

The possibility of detecting change in green cover of rice crop and its relationship with the grain yield was studied using the spectral data collected by an airborne scanner. The spectral data in the form of difference in vegetation index over a 35 day interval, from the time of flowering, was observed to show good relationship between change of green cover (a measure of degree of senescence) and grain yield.     

Studies on spectral reflectance under normal and nitrogen,phosphorus and pest and disease stress condition in soybean (<Emphasis Type="Italic">Glycine max</Emphasis> L.)

The results emerged out of the studies on spectral reflectance under normal and nitrogen and phosphorus stress condition in soybean (Glycine max L.) conducted at Marathwada Agricultural University experimental farm, Parbhani duringkharif 2004–05 showed that crop growth and bio-physiological parameters viz., Height, chlorophyll, leaf area index and total biomass influenced by pest and disease and nutrient stress resulted in detectable spectral reflectance variation. Poor crop growth, reduced canopy cover, chlorophyll content and biomass production are the effects observed in nutrient deficient crops. These above changes in soybean crop were related to spectral indices (RVI and NDVI) that are resulted in discrimination of stressed and normal (non-stressed) soybean crop.     

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.     

农作物冠层光谱分析及反演技术综述

农作物的冠层光谱反射率与作物的氮含量、叶绿素含量及叶面积指数等参数之间具有很强的相关性,通过对作物冠层光谱进行分析可反演出作物的生物物理参数,并应用在长势分析、产量预测、病虫害预警等领域。本文首先阐述了作物冠层反射率采集方法,对地面、机载及遥感卫星3个采集层面的优缺点进行了对比;其次给出了植被指数构建原理及常用植被指数,分析了物理模型反演法和统计反演法的复杂度和性能;最后提出了农作物冠层光谱分析及反演技术的下一步发展方向及面临的挑战。     

Study of fractional vegetation cover using high spectral resolution data

A field experiment was conducted to study the effect of vegetation cover on soil spectra and relationship of spectral indices with vegetation cover. Multi-date spectral measurements were carried out on twelve wheat fields. Five sets of measurements were taken during the growth period of wheat crop. Field reflectance data were collected in the range 350 to 1800 nm using ASD spectroradiometer. Analysis of data was done to select narrow spectral bands for estimation of ground cover. The ratio of reflectance from vegetation covered soil and reflectance from bare soil indicated that spectral reflectance at 670 and 710 nm are the most sensitive bands. Two bands in visible (670 and 560 nm), three bands in near infrared (710, 870 and 1100 nm) and three bands in middle infrared (1480, 1700 and 1800 nm) were found highly correlated with fractional cover. Vegetation indices developed using narrow band spectral data have been found to be better than those developed using broad- band data for estimation of ground cover.     

Deducing Rice Crop Dynamics and Cultural Types of Bangladesh Using Geospatial Techniques

Rice crop occupies an important aspect of food security and also contributes to global warming via GHGs emission. Characterizing rice crop using spatial technologies holds the key for addressing issues of global warming and food security as different rice ecosystems respond differently to the changed climatic conditions. Remote sensing has become an important tool for assessing seasonal vegetation dynamics at regional and global scale. Bangladesh is one of the major rice growing countries in South Asia. In present study we have used remote sensing data along with GIS and ancillary map inputs in combination to derive seasonal rice maps, rice phenology and rice cultural types of Bangladesh. The SPOT VGT S10 NDVI data spanning Aus, Aman and Boro crop season (1^st May 2008 to 30^th April 2009) were used, first for generating the non-agriculture mask through ISODATA clustering and then to generate seasonal rice maps during second classification. The spectral rice profiles were modelled and phenological parameters were derived. NDVI growth profiles were modelled and crop calendar was derived. To segregate the rice cultural types of Bangladesh into IPCC rice categories, we used elevation, irrigated area, interpolated rainfall maps and flood map through logical modelling in GIS. The results indicated that the remote sensing derived rice area was 9.99 million ha as against the reported area of 11.28 million ha. The wet and dry seasons accounted for 64% and 36 % of the rice area, respectively. The flood prone, drought prone and deep water categories account for 7.5%, 5.56% and 2.03%, respectively. The novelty of current findings lies in the spatial outcome in form of seasonal and rice cultural type maps of Bangladesh which are helpful for variety of applications.     

高光谱反演水稻叶面积指数的主成分分析法

为了通过水稻冠层反射光谱来提取水稻叶面积指数信息，尝试利用辐射传输模型PROSPECT+SAIL来模拟水稻冠层反射光谱， 比较了各植被指数中叶面积指数(LAI)和叶绿素浓度的相关性。在观察光谱曲线后发现，红边位置光谱可以较好地区分LAI和叶绿素 浓度二者引起光谱变化的差异。由此提出对700 nm~750 nm区间内的反射光谱做主成分变换，并利用第2主成分与LAI建立反演模型( 即主成分分析法)，取得了较好效果，表明在植被指数趋近于饱和以至于无法区分二者相关性时，主成分分析法可以作为一种简单 而有效提取水稻叶面积指数信息的补充手段。     

Remotely-sensed assessment of the impact of century-old biochar on chicory crop growth using high-resolution UAV-based imagery

In recent years, special attention has been given to the long-term effects of biochar on the performance of agro-ecosystems owing to its potential for improving soil fertility, harvested crop yields, and aboveground biomass production. The present experiment was set up to identify the effects on soil-plant systems of biochar produced more than 150 years ago in charcoal mound kiln sites in Wallonia (Belgium). Although the impacts of biochar on soil-plant systems are being increasingly discussed, a detailed monitoring of the crop dynamics throughout the growing season has not yet been well addressed. At present there is considerable interest in applying remote sensing for crop growth monitoring in order to improve sustainable agricultural practices. However, studies using high-resolution remote sensing data to focus on century-old biochar effects are not yet available. For the first time, the impacts of century-old biochar on crop growth were investigated at canopy level using high-resolution airborne remote sensing data over a cultivated field. High-resolution RGB, multispectral and thermal sensors mounted on unmanned aerial vehicles (UAVs) were used to generate high frequency remote sensing information on the crop dynamics. UAVs were flown over 11 century-old charcoal-enriched soil patches and the adjacent reference soils of a chicory field. We retrieved crucial crop parameters such as canopy cover, vegetation indices and crop water stress from the UAV imageries. In addition, our study also provides in-situ measurements of soil properties and crop traits. Both UAV-based RGB imagery and in-situ measurements demonstrated that the presence of century-old biochar significantly improved chicory canopy cover, with greater leaf lengths in biochar patches. Weighted difference vegetation index imagery showed a negative influence of biochar presence on plant greenness at the end of the growing season. Chicory crop stress was significantly increased by biochar presence, whereas the harvested crop yield was not affected. The main significant variations observed between reference and century-old biochar patches using in situ measurements of crop traits concerned leaf length. Hence, the output from the present study will be of great interest to help developing climate-smart agriculture practices allowing for adaptation and mitigation to climate.     

高光谱数据与水稻叶面积指数及叶绿素密度的相关分析

分析了北京大屯科技站水稻叶面积指数 (LAI)、叶绿素密度 (CH .D)与高光谱分辨率遥感数据在整个生育期内的变化过程。利用微分技术处理水稻群体反射光谱以减少土壤等低频背景光谱噪音的影响。通过单相关分析和逐步回归方法研究水稻LAI、CH .D分别与光谱反射率、反射率的一阶微分光谱的相关关系 ,并建立预测回归方程。结果表明 ,微分技术能够改善光谱数据与LAI、CH .D的相关性 ,CH .D与光谱数据的相关明显优于同LAI的。     

高光谱数据与水稻指数及叶绿素密度的相关分析

分析了北京大屯科技站不稻叶面积指数（ＬＡ）、叶绿素密度（ＣＨ．Ｄ）与高光谱分辨率遥感数据在整个生育期内的变化过程。利用微分技术处理不稻群体反射光谱以减少土壤等低频背景光谱噪音的影响。通过单相关分析和逐步回归方法研究不稻ＬＡＩ、ＣＨ．Ｄ分别与光谱反射率、反射率的一阶微分光谱的相关关系,并建立预测回归方程。结果表明,微分技术能够改善数据与ＬＡＩ、ＣＨ．Ｄ的相关性,ＣＨ．Ｄ与光谱数据的相关明显优于同ＬＡ     

植被冠层立体结构与叶片倾角的偏振光效应

植被偏振特性研究对于植被监测与组分定量反演具有极其重要的作用。植被冠层的反射辐射具有偏振特性,这种特性与入射辐射和植被冠层结构相关。本文分析了偏振对光子—叶片—冠层之间细微相互作用及其变化的有效探测能力,并利用研究型扫描式偏振辐射仪RSP(Research Scanning Polarimeter)数据系统对比分析了偏振对不同叶倾角分布的估测。通过上述研究得出以下结论:(1)偏振观测能够对光线在冠层立体结构中的透射反射再出射过程给出精细刻画,若不用偏振手段对这一过程进行甄别并去除,则直接测算的植被散射系数会产生高达140%的误差;(2)利用偏振手段可以为高精度大倾角、多时相遥感观测提供可能,以此可改变目前光学遥感小角度、垂直观测的较严格约束;(3)偏振辐射呈现出随波长的稳定特性(相关系数0.96),使得利用偏振手段可以更好地研究冠层结构;(4)不同叶倾角分布对入射辐射存在不同的偏振反射,为利用多角度偏振信息进行遥感植被精细分类提供了新的途径。本文详细描述冠层结构和植被偏振特性的相互作用,通过对冠层立体结构与叶倾角的研究,刻画了植被定量遥感的方向性信息与高精度实现,为高分辨率遥感定量化的有效信息挖掘提供了新手段。     

面向地理国情监测的房屋建筑区树冠覆盖多特征协同提取

针对全国地理国情监测工作新增树冠覆盖提取这一全新的工作任务,本文通过深入分析房屋建筑区主要地物光谱特征和纹理特征,确定以光谱特征归一化植被指数（NDVI）,以及对比度（contrast）信息熵（entropy）两个纹理特征作为判断规则,按照面向对象的思路,设计了一种综合应用高分辨率遥感影像光谱特征和纹理特征的房屋建筑区树冠覆盖范围提取方法。试验结果表明,该方法能够自动提取房屋建筑区树冠覆盖范围,大幅降低了当前常用的目视解译方法的工作量,与采用单一影像特征的提取方法相比,本文方法能够有效地区分房屋建筑区内与树冠覆盖光谱特征相近的地物要素。     

光谱指数用于叶绿素含量提取的评价及一种改进的农作物冠层叶绿素含量提取模型

对目前提出的光谱指数用以提取叶片叶绿素含量的适应性进行了分析和评价。通过分析,解释了为什么研究者得出这些指数与他们的观测样本叶绿素含量有显著的相关的结论以及为什么某个研究者提出的某个指数和叶绿素含量间的关系用于其他样本时会失效。此外,改进了一个农作物冠层叶绿素含量的提取模型,通过独立实测数据验证,效果较好,认为是可以用于其他地区农作物叶绿素含量提取的模型。