共查询到20条相似文献，搜索用时 984 毫秒
1.
Dailiang Peng Alfredo R. Huete Jingfeng Huang Fuming Wang Huasheng Sun 《International Journal of Applied Earth Observation and Geoinformation》2011
In this paper, we developed a more sophisticated method for detection and estimation of mixed paddy rice agriculture from Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data. Previous research demonstrated that MODIS data can be used to map paddy rice fields and to distinguish rice from other crops at large, continental scales with combined Enhanced Vegetation Index (EVI) and Land Surface Water Index (LSWI) analysis during the flooding and rice transplanting stage. Our approach improves upon this methodology by incorporating mixed rice cropping patterns that include single-season rice crops, early-season rice, and late-season rice cropping systems. A variable EVI/LSWI threshold function, calibrated to more local rice management practices, was used to recognize rice fields at the flooding stage. We developed our approach with MODIS data in Hunan Province, China, an area with significant flooded paddy rice agriculture and mixed rice cropping patterns. We further mapped the aerial coverage and distribution of early, late, and single paddy rice crops for several years from 2000 to 2007 in order to quantify temporal trends in rice crop coverage, growth and management systems. Our results were validated with finer resolution (2.5 m) Satellite Pour l’Observation de la Terre 5 High Resolution Geometric (SPOT 5 HRG) data, land-use data at the scale of 1/10,000 and with county-level rice area statistical data. The results showed that all three paddy rice crop patterns could be discriminated and their spatial distribution quantified. We show the area of single crop rice to have increased annually and almost doubling in extent from 2000 to 2007, with simultaneous, but unique declines in the extent of early and late paddy rice. These results were significantly positive correlated and consistent with agricultural statistical data at the county level (P < 0.01). 相似文献
2.
Large-scale crop yield prediction is critical for early warning of food insecurity, agricultural supply chain management, and economic market. Satellite-based Solar-Induced Chlorophyll Fluorescence (SIF) products have revealed hot spots of photosynthesis over global croplands, such as in the U.S. Midwest. However, to what extent these satellite-based SIF products can enhance the performance of crop yield prediction when benchmarking against other existing satellite data remains unclear. Here we assessed the benefits of using three satellite-based SIF products in yield prediction for maize and soybean in the U.S. Midwest: gap-filled SIF from Orbiting Carbon Observatory 2 (OCO-2), new SIF retrievals from the TROPOspheric Monitoring Instrument (TROPOMI), and the coarse-resolution SIF retrievals from the Global Ozone Monitoring Experiment-2 (GOME-2). The yield prediction performances of using SIF data were benchmarked with those using satellite-based vegetation indices (VIs), including normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), and near-infrared reflectance of vegetation (NIRv), and land surface temperature (LST). Five machine-learning algorithms were used to build yield prediction models with both remote-sensing-only and climate-remote-sensing-combined variables. We found that high-resolution SIF products from OCO-2 and TROPOMI outperformed coarse-resolution GOME-2 SIF product in crop yield prediction. Using high-resolution SIF products gave the best forward predictions for both maize and soybean yields in 2018, indicating the great potential of using satellite-based high-resolution SIF products for crop yield prediction. However, using currently available high-resolution SIF products did not guarantee consistently better yield prediction performances than using other satellite-based remote sensing variables in all the evaluated cases. The relative performances of using different remote sensing variables in yield prediction depended on crop types (maize or soybean), out-of-sample testing methods (five-fold-cross-validation or forward), and record length of training data. We also found that using NIRv could generally lead to better yield prediction performance than using NDVI, EVI, or LST, and using NIRv could achieve similar or even better yield prediction performance than using OCO-2 or TROPOMI SIF products. We concluded that satellite-based SIF products could be beneficial in crop yield prediction with more high-resolution and good-quality SIF products accumulated in the future. 相似文献
3.
High spectral resolution spectroscopy enables to have detailed information on chemical and morphological status of crop. An attempt of using space platform for detecting red edge shift during different growth stages of wheat crop is reported. Study was conducted during rabi 1996–97 season using Modular Opto-Electronic Scanner MOS-B Imaging data onboard IRS-P3 satellite. Inverted Gaussian model was fitted for satellite derived reflectances between 650 and 870 nm to derive inflection wavelength and its subsequent change with crop stages i.e. red shift. Red shift of 10 nm observed from crown root initiation stage (703.8 nm) to peak vegetative stage (714.2 nm). A comparative study on temporal behaviour of vegetative indices like NDVI and ARVI with Red edge showed that latter is more atmospherically stable parameter. It is concluded that red edge shift which hitherto has been observed from ground and airborne sensors, can also be detected from space. 相似文献
4.
地上生物量能够有效反映作物的生长状态,其信息的实时估算对产量预测和农田生产管理都有重要意义。作物生长模型因其详尽的生理生化基础和对生长过程数字化描述能力,成为生物量估算的理想模型。近年来,研究人员利用数据同化算法将时间序列遥感数据同化到作物生长模型中,实现了作物模型由基于气象站的点模拟到区域尺度面模拟的外推,使生物量模拟结果同时具备大范围和机理性两个方面的特点。这一模式下,时间序列的遥感数据质量将对生物量模拟精度产生直接影响,作物生长后期受到光谱饱和的影响,遥感数据的作物冠层信息获取能力会出现明显下降,因此有必要对该阶段遥感数据和作物模型的结合方式进行优化,提升生物量模拟精度。本文针对东北地区春玉米生物量遥感估算存在的问题,提出了利用WOFOST作物模型结合无人机（UAV）遥感数据实现作物生长后期生物量准确估算的新思路。新思路首先利用多光谱遥感数据获取WOFOST模型具备较高空间异质性的土壤速效养分参数以提升模型的空间信息模拟能力,使其能在一定程度上摆脱点尺度模拟的限制。同时,结合集合卡尔曼滤波算法将生长前期无人机（UAV）遥感数据同化到模型中,以缩短模型单独运行时间,减少模型运行过程中的参数误差累积,实现无遥感数据参与下的短期作物生长模拟,并输出生长后期相应的生物量模拟结果。最后,本文利用地面实测数据对新方法的生物量模拟精度进行了评价。结果表明,与全生育期数据同化相比,新方法的生物量估算精度有了明显的提升（全生育期同化：R2 = 0.45,RMSE = 4254.30 kg/ha;新方法：R2= 0.86,RMSE = 2216.79 kg/ha）。 相似文献
5.
近年来,卫星大地测量技术的快速发展为精确测量地壳形变和断层行为提供了前所未有的多维观测数据。它与地震学的结合使得地震周期形变监测的时空分辨率大大提升,为更加深入地研究地震周期过程和机理提供了一个窗口,地震大地测量学应运而生。它能够对地壳运动进行定量描述、对断层活动进行精准建模,从而为洞悉整个地震周期过程的应力应变演化提供科学依据,同时为评估地震危险性、实现地震预测预警提供科学指导。以卫星大地测量观测探究断层形变为主线,分析了断层处于地震周期不同阶段的运动学特征（震间、同震和震后）,回顾了地震大地测量学在震源物理方面的一些重要发现。研究表明,利用卫星大地测量数据判定断层所处地震周期的阶段是实现地震预测的可行思路。 相似文献
6.
Monitoring agricultural drought effectively and timely is important to support drought management and food security. Effective drought monitoring requires a suite of drought indices to capture the evolution process of drought. Thermal infrared signals respond rapidly to vegetation water stress, thus being regarded useful for drought monitoring at the early stage. Several temperature-based drought indices have been developed considering the role of land surface temperature (LST) in surface energy and water balance. Here, we compared the recently proposed Temperature Rise Index (TRI) with several agricultural drought indices that also use thermal infrared observations, including Temperature Condition Index (TCI), Vegetation Health Index (VHI) and satellite-derived evapotranspiration ratio anomaly (ΔfRET) for a better understanding of these thermal infrared drought indices. To do so, we developed a new method for calculating TRI directly from the top-of-atmosphere brightness temperatures in the two split-window channels (centered around ∼11 and 12 μm) rather than from LST. TRI calculated using the Himawari-8 brightness temperatures (TRI_BT) and LST retrievals (TRI_LST), along with the other LST-based indices, were calculated for the growing season (July–October) of 2015−2019 over the Australian wheatbelt. An evaluation was conducted by spatiotemporally comparing the indices with the drought indices used by the Australian Bureau of Meteorology in the official drought reports: the Precipitation Condition Index (PCI) and the Soil Moisture Condition Index (SMCI). All the LST-based drought indices captured the wet conditions in 2016 and dry conditions in 2019 clearly. Ranking of Pearson correlations of the LST-based indices with regards to PCI and SMCI produced very similar results. TRI_BT and TRI_LST showed the best agreement with PCI and SMCI (r > 0.4). TCI and VHI presented lower consistency with PCI and SMCI compared with TRI_BT and TRI_LST. ΔfRET had weaker correlations than the other LST-based indices in this case study, possibly because of outliers affecting the scaling procedure. The capability of drought early warning for TRI was demonstrated by comparing with the monthly time series of the greenness index Vegetation Condition Index (VCI) in a case study of 2018 considering the relatively slow response of the greenness index to drought. TRI_BT and TRI_LST had a lead of one month in showing the changing dryness conditions compared with VCI. In addition, the LST-based indices were correlated with annual wheat yield. Compared to wheat yields, all LST-based indices had a peak correlation in September. TRI_BT and TRI_LST had strong peak and average correlations with wheat yield (r ≥ 0.8). We conclude that TRI has promise for agricultural drought early warning, and TRI_BT appears to be a good candidate for efficient operational drought early warning given the readily accessible inputs and simple calculation approach. 相似文献
7.
应用面向对象的决策树模型提取橡胶林信息 总被引：4，自引：0，他引：4
橡胶林的无序和不合理种植引发了一系列的生态问题,快速监测橡胶林空间分布及动态变化,对橡胶的合理种植、区域生态环境保护以及有关部门的规划决策有重要的指导意义。以MODIS归一化植被指数NDVI时间序列数据和多时相的Landsat TM数据为基础分析橡胶林的季相和光谱特征,确定橡胶识别的关键时期和特征参数,构建面向对象的决策树分类模型,开展橡胶信息提取研究。结果表明,多时相的遥感数据可反映橡胶的季相特征,以TM数据为基础计算得到的陆表水分指数LSWI和归一化植被指数NDVI可作为橡胶识别的光谱特征参数,橡胶休眠期是利用遥感方法进行橡胶提取的最佳时期。相比于单时相数据,利用包含橡胶关键物候期的多时相遥感数据能得到更高的橡胶林提取精度。 相似文献
8.
Abstract Recent investigations demonstrated that inter‐year NOAA‐AVHRR NDVI variations at the middle of the rainy season can provide information on annual crop yields in Sahelian countries. This line of research is presently extended to the consideration of multitemporal NDVI data for several years (1986-1991) pre‐processed by a proven methodology. The investigation was conducted using NDVI and crop yield data from the sahelian sub‐districts of Niger. The results confirm that geographically standardized NDVI data are efficient for crop yield forecasting, but notable differences exist in this prediction capability depending on the beginning of the season. Late beginnings of the growing (rainy) season (after the end of June) allow optimum forecasting only after mid‐August, while early beginnings lead to anticipate the forecasting capability but also to decrease its accuracy. The importance of these findings in the context of an early warning system is finally discussed. 相似文献
9.
Water stress during crop cultivation due to inconsistent rainfall is a common phenomenon in maize growing area of Shanmuganadi watershed, located in the semi-arid region of southern peninsular India. The objective is to estimate the supplementary irrigation required to improve the crop productivity during water stress period. Spatial hydrological model, Soil and Water Assessment Tool, has been applied to simulate the watershed hydrology and crop growth for rabi season (October–February) considering the rainfed and irrigated scenarios. The average water stress days of rainfed maize was 60 days with yield of 1.6 t/ha. Irrigated maize with supplementary irrigation of 93–126 mm was resulted in improved yield of 3.8 t/ha with 28 water stress days. The results also suggest that supplemental irrigation can be obtained from groundwater reserves and by adopting early sowing strategy can provide opportunities for improving water productivity in rainfed farming. 相似文献
10.
针对地基SAR异常数据难以识别、潜在滑坡体发展特征及规律不明显、临滑面积估算难度大的问题,本文以甘肃某矿山滑坡数据为基础,介绍了地基SAR形变数据的处理方法,通过Matlab软件有效地识别、筛选、剔除了异常形变数据,并以色彩差异进行区分,进一步突显了不同阶段潜在灾变体面积的演化规律。基于地基雷达监测原理,提出一种多边形面域估算方法,实现潜在危险区域面积的估算。研究不等周期处理的速度倒数曲线组,发现速度倒数平方法能够进一步放大特征趋势,预测结果准确、预报效果好,为实现矿山滑坡超前预警预报提供了新的思路。 相似文献
11.
松毛虫灾害的TM影像监测技术 总被引：31，自引：2，他引：29
中国森林病虫害日趋严重 ,每年都造成巨大损失 ,其主要原因之一就是不能实现森林病虫害的及时准确监测与中长期预测预报 ,以便把灾害控制在萌芽状态 ,虽然目前科学技术和研究水平还不能准确预测森林病虫害的发生发展 ,但可以及时监测早期灾害点 ,尽力把损失降到最低限度。该文主要介绍了利用陆地卫星TM数据开展早期灾害点 (或虫源地 )监测的方法和利用航天遥感数据对“虫源地”实施的有效监测 ,为航天遥感技术用于重大森林病虫害的宏观监测和预警提供了实例 相似文献
12.
Secondary tropical dry forests (TDFs) provide important ecosystem services such as carbon sequestration, biodiversity conservation, and nutrient cycle regulation. However, their biogeophysical processes at the canopy-atmosphere interface remain unknown, limiting our understanding of how this endangered ecosystem influences, and responds to the ongoing global warming. To facilitate future development of conservation policies, this study characterized the seasonal land surface temperature (LST) behavior of three successional stages (early, intermediate, and late) of a TDF, at the Santa Rosa National Park (SRNP), Costa Rica. A total of 38 Landsat-8 Thermal Infrared Sensor (TIRS) data and the Surface Reflectance (SR) product were utilized to model LST time series from July 2013 to July 2016 using a radiative transfer equation (RTE) algorithm. We further related the LST time series to seven vegetation indices which reflect different properties of TDFs, and soil moisture data obtained from a Wireless Sensor Network (WSN). Results showed that the LST in the dry season was 15–20 K higher than in the wet season at SRNP. We found that the early successional stages were about 6–8 K warmer than the intermediate successional stages and were 9–10 K warmer than the late successional stages in the middle of the dry season; meanwhile, a minimum LST difference (0–1 K) was observed at the end of the wet season. Leaf phenology and canopy architecture explained most LST variations in both dry and wet seasons. However, our analysis revealed that it is precipitation that ultimately determines the LST variations through both biogeochemical (leaf phenology) and biogeophysical processes (evapotranspiration) of the plants. Results of this study could help physiological modeling studies in secondary TDFs. 相似文献
13.
Early stress detection in crop plants is highly relevant, but hard to achieve. We hypothesize that close range hyperspectral imaging is able to uncover stress related processes non-destructively in the early stages which are invisible to the human eye. We propose an approach which combines unsupervised and supervised methods in order to identify several stages of progressive stress development from series of hyperspectral images. Stress of an entire plant is detected by stress response levels at pixel scale. The focus is on drought stress in barley (Hordeum vulgare). Unsupervised learning is used to separate hyperspectral signatures into clusters related to different stages of stress response and progressive senescence. Whereas all such signatures may be found in both, well watered and drought stressed plants, their respective distributions differ. Ordinal classification with Support Vector Machines (SVM) is used to quantify and visualize the distribution of progressive stages of senescence and to separate well watered from drought stressed plants. For each senescence stage a distinctive set of most relevant Vegetation Indices (VIs) is identified. The method has been applied on two experiments involving potted barley plants under well watered and drought stress conditions in a greenhouse. Drought stress is detected up to ten days earlier than using NDVI. Furthermore, it is shown that some VIs have overall relevance, while others are specific to particular senescence stages. The transferability of the method to the field is illustrated by an experiment on maize (Zea mays). 相似文献
14.
《ISPRS Journal of Photogrammetry and Remote Sensing》1999,54(4):254-262
The Canadian satellite RADARSAT launched in November 1995 acquires C-band HH polarisation Synthetic Aperture Radar (SAR) data in various incident angles and spatial resolutions. In this study, the Standard Beam S7 SAR data with 45°–49° incidence angle has been used to discriminate rice and potato crops grown in the Gangetic plains of West Bengal state. Four-date data acquired in the 24-day repeat cycle between January 2 and March 15, 1997 was used to study the temporal backscatter characteristics of these crops in relation to the growth stages. Two, three and four-date data were used to classify the crops. The results show that the backscatter was the lowest during puddling of rice fields and increased as the crop growth progressed. The backscatter during this period changed from −18 dB to −8 dB. This temporal behaviour was similar to that observed in case of ERS-SAR data. The classification accuracy of rice areas was 94% using four-date data. Two-date data, one corresponding to pre-field preparation and the other corresponding to transplantation stage, resulted in 92% accuracy. The last observation is of particular interest as one may estimate the crop area as early as within 20–30 days of transplantation. Such an early estimate is not feasible using optical remote sensing data or ERS-SAR data. The backscatter of potato crop varied from −9 dB to −6 dB during the growth phase and showed large variations during early vegetative stage. Two-date data, one acquired during 40–45 days of planting and another at maturing stage, resulted in 93% classification accuracy for potato. All other combinations of two-date data resulted in less than 90% classification accuracy for potato. 相似文献
15.
D. R. Fastring 《国际地球制图》2013,28(3):207-221
The primary objective of this research was to determine if the remotely-sensed metric, Normalised Difference Vegetation Index (NDVI) and ground-collected dekadal climatological variables were useful predictors of future malaria outbreaks in an epidemic-prone area of Nairobi, Kenya. Data collected consisted of 36 dekadal (10-day) periods for the variables rainfall, temperature and NDVI along with yearly documented malaria admissions in 2003 for Nairobi, Kenya. Linear regression models were built for malaria cases reported in Nairobi, Kenya, as the dependent variable and various time-based groupings of temperature, rainfall and NDVI data from the dekads in both the current and the previous month as the independent variables. Data from 2003 show that malaria incidence in any given month is best predicted (R2 = 0.881, p < 0.001) by the average NDVI for the 30 days including the final two dekads of the previous month and first dekad of the current month, and by the average rainfall for the 30 days including the three dekads of rainfall data from the prior month. Forecasting an outbreak in an epidemic zone would allow public health entities to plan for and disseminate resources to the general public such as antimalarials and insecticide impregnated bed nets. In addition, vector control measures could be implemented to slow the rate of transmission in the impacted population. 相似文献
16.
17.
Assessment of digital camera-derived vegetation indices in quantitative monitoring of seasonal rice growth 总被引：4，自引：0，他引：4
Toshihiro Sakamoto Michio Shibayama Akihiko Kimura Eiji Takada 《ISPRS Journal of Photogrammetry and Remote Sensing》2011,66(6):872-882
A commercially available digital camera can be used in a low-cost automatic observation system for monitoring crop growth change in open-air fields. We developed a prototype Crop Phenology Recording System (CPRS) for monitoring rice growth, but the ready-made waterproof cases that we used produced shadows on the images. After modifying the waterproof cases, we repeated the fixed-point camera observations to clarify questions regarding digital camera-derived vegetation indices (VIs), namely, the visible atmospherically resistant index (VARI) based on daytime normal color images (RGB image) and the nighttime relative brightness index (NRBINIR) based on nighttime near infrared (NIR) images. We also took frequent measurements of agronomic data such as plant length, leaf area index (LAI), and aboveground dry matter weight to gain a detailed understanding of the temporal relationship between the VIs and the biophysical parameters of rice. In addition, we conducted another nighttime outdoor experiment to establish the link between NRBINIR and camera-to-object distance. The study produced the following findings. (1) The customized waterproof cases succeeded in preventing large shadows from being cast, especially on nighttime images, and it was confirmed that the brightness of the nighttime NIR images had spatial heterogeneity when a point light source (flashlight) was used, in contrast to the daytime RGB images. (2) The additional experiment using a forklift showed that both the ISO sensitivity and the calibrated digital number of the NIR (cDNNIR) had significant effects on the sensitivity of NRBINIR to the camera-to-object distance. (3) Detailed measurements of a reproductive stem were collected to investigate the connection between the morphological feature change caused by the panicle sagging process and the downtrend in NRBINIR during the reproductive stages. However, these agronomic data were not completely in accord with NRBINIR in terms of the temporal pattern. (4) The time-series data for the LAI, plant length, and aboveground dry matter weight could be well approximated by a sigmoid curve based on NRBINIR and VARI. The results confirmed that NRBINIR was more sensitive to all of the agronomic data for overall season, including the early reproductive stages. VARI had an especially high correlation with LAI, unless yellow panicles appeared in the field of view. 相似文献
18.
Several methods have been proposed to delineate management zones in agricultural fields, which can guide interventions of the farmers to increase crop yield. In this study, we propose a new approach using remote sensing data to delineate management zones at three farm sites located in southern Brazil. The approach is based on the hypothesis that the measured aboveground biomass (AGB) of the cover crops is correlated with the measured cash-crop yield and can be estimated from surface reflectance and/or vegetation indices (VIs). Therefore, we used seven different statistical models to estimate AGB of three cover crops (forage turnip, white oats, and rye) in the season prior to cash-crop planting. Surface reflectance and VIs were used as predictors to test the performance of the models. They were obtained from high spatial and temporal resolution data of the PlanetScope (PS) constellation of satellites. From the time series of 30 images acquired in 2017, we used the PS data that matched the dates of the field campaigns to build the models. The results showed that the satellite AGB estimates of the cover crops at the date of maximum VI response at the beginning of the flowering stage were useful to delineate the management zones. The cover-crop AGB models that presented the highest coefficient of determination (R2) and the lowest root mean square (RMSE) in the validation and test datasets were Support Vector Machine (SVM), Cubist (CUB) and Stochastic Gradient Boosting (SGB). For most models and cover crops, the Enhanced Vegetation Index (EVI) and the Normalized Difference Vegetation Index (NDVI) were the two most important AGB predictors. At the date of maximum VI at the beginning of the flowering stage, the correlation coefficients (r) between the cover-crop AGB and the cash-crop yield (soybean and maize) ranged from +0.70 for forage turnip to +0.78 for rye. The fuzzy unsupervised classification of the cover-crop AGB estimates delineated two management zones, which were spatially consistent with those obtained from cash-crop yield. The comparison between both maps produced overall accuracies that ranged from 61.20% to 68.25% with zone 2 having higher cover-crop AGB and cash-crop yield than zone 1 over the three sites. We conclude that satellite AGB estimates of cover crops can be used as a proxy for generating management zone maps in agricultural fields. These maps can be further refined in the field with any other type of method and data, whenever necessary. 相似文献
19.
T. N. Hajare N. G. Patil K. S. Verma 《Journal of the Indian Society of Remote Sensing》2008,36(3):267-272
Spectral indices as an indicator of physiological traits affecting safflower yield in relation to soil variability were evaluated
in a two year experiment (1997–1999). Reflectance, biometric and phonological data were collected. Two indices namely normalized
differential vegetation index (NDVI) and ratio of spectral reflectance in infrared region to red region (1R/R) were derived
from radiometric observation. Yield data indicated significant difference in different soils. Temporal NDVI behaviour as a
function of soil type was not prominent especially in early stages of crop growth. However NDVI at 75 days after sowing (DAS)
was found to be relatively better indicator of plant status and yield. IR/R was relatively less effective in indicating the
differential response of crop to soil types. Effect of soil and crop interaction on spectral indices was significant at 75
and 90 DAS, which was attributed to attainment of maximum leaf area and leaf area at these stages of growth. Regression analysis
showed strong positive relationship between NDVI and leaf area, dry matter and yield. IR/R and leaf area had the strongest
and consistent relationship (r = 0.96). A single regression equation accounted for yield variability in the dataset. Thus
possible transformation of NDVI maps (satellite data) to LAI units and consequently applications like yield forecasting was
indicated. Utility of spectra-temporal data as a pointer of plant development status and yield was also demonstrated. 相似文献
20.
Y. Nagaraja Rao Y. V. Subbarao K. S. Sundara Sarma R. L. Karale K. K. Narula Naveen Kalra 《Journal of the Indian Society of Remote Sensing》1987,15(1):41-47
Maize crop was sown at weekly intervals on six dates in a randomized replicated trial under nonlimiting moisture conditions. The different dates of sowing represent different growth stages in the same given environment. Spectral data were collected using a portable radiometer at different wavelengths, ranging form visible to infrared on two different dates. The spectral reflectance data in the red and infrared region were analysed for their sensitivity to leaf area index and leaf dry biomass. During active crop growth period significant correlations existed between leaf area index and ratio of infrared to red as well as the normalized differences. Similar relationships were also observed between dry biomass and spectral data. However, these relationships were found to be valid upto the crop growth stage when the leaf area index has reached its maximum, corresponding to flowering. Beyond this stage, the spectral reflectances were found to be not related to LAI. The relsults suggest the possibility of obtaining crop phenological information from the spectral response data. 相似文献