Wheat growth profile: satellite monitoring and crop yield modelling

The most important advantage of the low resolution National Oceanic and Atmospheric Administration’s Advanced Very High Resolution Radiometer (NOAA AVHRR) data is its high temporal frequency and high radiometric sensitivity which helps in vegetation detection in the visible and near-infrared spectral regions. In areas where most of the crop cultivation is in large contiguous areas, and if the AVHRR data are selected for time period such that the crop of interest is well discriminated from other crops, these data can be used for monitoring vegetative growth and condition very effectively. The present study deals with the application of AVHRR data for the monitoring of the wheat crop in its seventeen main growing districts of the Rajasthan state. The fourteen date AVHRR data covering the entire growth period have been used to generate the normalized difference vegetation index (NDV1) growth profile for the crop by masking the non-crop pixels following the two-date NDVI change method. The growth profile parameters and other derived parameters, such as post-anthesis senescence rate and areas under the entire growth profile or under selected growth periods have been related to the district average wheat yield through statistical regression models. Various methods adopted for wheat pixels masking have been critically evaluated. It is found that the wheat yield can be predicted well by the area under the profile in different growth periods.     

Use of spectral data in Markov chain model for crop yield forecasting

Crop yield is mainly dependent on weather, soil and technological inputs. Yield forecasting models have been developed mainly using multiple regression techniques based on biometrical characters of the plants and/or weather parameters. Matiset al. (1985) proposed another approach of crop yield modelling using Markov Chain theory based on biometrical characters. The integration of remote sensing with other technologies has provided an immense scope to improve upon the existing crop yield models. In the present study, multi date spectral data during crop growth period was used in Markov Chain Model to forecast wheat yield. The results indicate that the use of spectral data near the maximum vegetative growth of wheat crop improves the efficiency and reliability of yield forecast about a month before its actual harvest.     

Yield estimation of summer crops grown in a mixed cropping area,a spectral growth curve profile approach

Estimation of crop production in advance of the harvest has been an intensively researched field in agriculture. Spectral parameters derived from the spectral growth profile being indicator of growth and development characteristics of the crop have a direct utility in crop-yield modeling. The present study is undertaken in a mixed cropping area of Karveer taluka, Kolhapur district, Maharashtra, to assess feasibility of multi-date moderately coarse WiFS data in developing spectral growth curves following Badhwar model (1980) for summer groundnut and paddy. The analysis highlighted potential of moderately coarse resolution WiFS data in discriminating the crops grown in fragmented conditions, provided detailed and adequate ground truth is used. The regression models using spectral parameters explained 94 % variation in paddy yield. However, model using ground information as peak LAI in addition to spectral variables, could explain 91 % variation in groundnut yield; thus for prediction of low-yielding and poorly managed crop a convergent model is essential. Vegetative growth rate during the pre-heading phase and total growing season absorbed photosynthetically active radiation (APAR) indicated by the area under the curve are the main predictors.     

Evaluation of potential usefulness of irs-1c wifs simulated data for dryland rabi sorghum crop discrimination

Two band simulad WiFS data for five dates correspfonding to rabi sorghun growing season of 1993-94 has been generated for Aurangabad district of Maharashtra. Ground truth data has been used for supervised classificatioa of one date raw image and five date NDVI of simulated WiFS data and the results were compared with those derived from single date IRS LISS I data. Analysis of classification accuracies indicate that single date WIFS data gives slightly lower accuracy of 79 per cent against 81 per cent obtained for single date LISS I data. Overall accuracy for 5-date WiFS data is 96 per cent which shows that classification performance of five date WiFS NDVI data is far superior to the single date data of the IRS-IC WiFS as well as the IRS LISS I. The study thus shows the importance of temporal domain of data acquisition in sorghum crop discrimination, Growth profile for sorghum and other crop classes were generated from multidate WiFS derived NDVI data. Differences in growth profiles of sorghum vigour classes as well as amongst different crop types and forests corroborate the premise of better discrimination of crop types and their vigour on multidate remotely sensed data.     

District wise yield models of rice in Bihar based on water requirement and meteorological data

Pre-harvest crop production forecast has been successfully provided by remote sensing technique. However, the probability to get cloud-free optical remote sensing data during kharif season is poor. Microwave data having the capability to penetrate cloud is used in the absence of cloud free optical remote sensing data. Yield models in broad band frequency range are in development stage. Meteorological yield models are developed and predicted yield is combined with area estimated by remote sensing data to provide rice production forecast. This paper describes the methodology adopted for improving the predictability of rice yield before harvest of the crop in Bihar province by taking into consideration meteorological parameters during its growth cycle upto October. Models developed using fortnightly meteorological data have been found to give reasonably fair indications of expected yield of rice in advance of harvest. The yield predictions have been made based on meteorological data and effective rainfall based on water requirement calculations representing a group of districts under similar agro-climatic zones, which could be further improved by incorporating meteorological data of individual districts within each group.     

Spectral wheat growth profile in Punjab using IRS WiFS data

A functional form of crop spectral profile suggested by Badhwar was applied to district-wise wheat Normalised Difference Vegetation Index (NDVI) values relatively normalised by Pseudo-Invariant Feature (urban and built-up) NDVI values, derived from Wide Field Sensor (WiFS) onboard Indian Remote Sensing Satellites (IRS) for 17 dates during 1999–2000 rabi season. The goodness of overall profile fitting and the three basic parameters i.e., crop emergence date (T_o), and crop specific parameters (a and P) was found to be statistically significant. While a corresponds to profile progressive growth rate, β corresponds to profile decay rate. A comparison with earlier studies in Punjab using NOAA-AVHRR indicated improvement in relation between peak NDVI and wheat yield. The estimated time of spectral emergence and profile-derived peak NDVI follow the observed behaviour of shortened crop pre-anthesis period with delayed sowing.     

陆面温度反演算法——劈窗算法的敏感度分析

首先给出ＣＯ２ 倍增下遥感光合作物产量的概念模型,之后分析未受ＣＯ２ 倍增的遥感光合作物产量估测模型;在考虑ＣＯ２ 倍增对作物产量的影响后,对影响干物质累积的作物光合速率的模型进行修正,进而修正遥感光合作物产量估测模型。建立ＣＯ２ 倍增下作物产量响应模型,求取各参数,并在ＣＯ２ 倍增下对我国华北地区冬小麦产量响应进行填图,表明模型的估测结果有良好的可比性。     

农作物单产预测的运行化方法

提出了适于运行化农作物单产预测的方法。即以农作物单产区划为基础 ,通过搜集不同地区不同作物的单产预测模型 ,分析每个模型的空间适用范围 ,并从模型参数等角度筛选模型 ,然后利用这些模型进行气象站点的作物单产预测 ,并以NDVI分布图为参考数据将点上的单产数据空间外推到区域尺度。借助耕地分布估计区域水平的农作物单产。最后以 2 0 0 3年冬小麦为例 ,进行了全国 10个省的冬小麦平均单产估算 ,花费了较少的人力和时间 ,符合运行化遥感估产要求     

应用粒子群算法的遥感信息与水稻生长模型同化技术

在研究遥感信息和水稻生长模型的同化过程中, 最小化遥感反演与生长模型(RiceGrow)输出的水稻生长 信息差值绝对值时引入了一种新的优化算法-粒子群算法(PSO), 并对比了其与模拟退火算法(SA)的优缺点; 探讨 了叶面积指数(LAI)和叶片氮积累量(LNA)分别作为同化参数时的同化效果。结果表明, PSO 无论是从同化效率还是 反演精度上都要好于SA, 粒子群优化算法是一种可靠的遥感与模型同化算法; LAI 和LNA 作为外部同化参数时各 有优势, LAI 作为同化参数可获得较准确的播期及播种量, 而LNA 作为同化参数可获得更为准确的施氮量信息。但 是LAI 作为外部同化参数时的反演结果总体要优于利用LNA 作为同化参数时的反演结果。利用试验资料对该技术 进行了测试和检验, 结果显示反演的模型初始参数的平均值与真实值的相对误差(RE)均小于2.5%, 均方根误差 (RMSE)为0.7—2.2, 产量模拟值与实测值之间的相对误差为5%左右, 模拟与实测相关指标值吻合度较高, 该同化 技术具有较好的适用性。从而为生长模型从单点扩展到区域尺度应用奠定了基础。     

Assessment of crop growth parameters of wheat under stress condition through ground based spectral data

A field experiment was conducted on wheat crop during rabi seasons of 1995–96, 1996–97 and 1997–98 to study the spectral response of wheat crop (between 490 to 1080 nm) under water and nutrient stress condition. An indigenously developed ground truth radiometer having narrow band in visible and near infrared region (490 – 1080 nm) was used. Vegetation indices derived using different band combinations and related to crop growth parameters. The near infrared spectral region of 710 – 1025 nm was found most important for monitoring stress condition. Relationship has been developed between crop growth parameters and vegetation indices. Leaf Area Index (LAI) and chlorophyll could be predicted by knowing different reflectance ratios at milking stage of crop with R² value of 0.78 and 0.89, respectively. Dry biomass (DBM), Plant Water Content (PWC) and grain yield are also significantly related with reflectance ratios at flowering stage of crop with R² value of 0.90, 0.98 and 0.74, respectively.     

Development and validation of spectrometeorological yield models of mustard oil seed crop in Rajasthan state

Attempt has been made to develop spectro meteorological yield models using normalized difference vegetation index (NDVI) derived from NOAA AVHRR data over the crop growth period and monthly rainfall data for predicting yield of mustard crop. The AVHRR data spanning seven crop growing seasons, the rain gauze station-level rainfall data and crop yield data determined from crop cutting experiments (CCE) conducted by state Directorate of Economics and Statistics (DES) are the basic input data. A methodology has been developed to normalize the multi-temporal NDVIs for the minimisation of atmospheric effects, which is found to reduce the noise in NDVI due to varying atmospheric conditions from season to season and improve the predictability of statistical multiple linear regression yield models developed for nine geographically large districts of Rajasthan state. The spectro meteorological yield models had been validated by comparing the predicted district level yields with those estimated from the crop cutting experiments.     

Multiple forecasts of kharif rice in orissa state-four year experience of fasal pilot study

Considering the requirement of multiple pre-harvest crop forecasts, the concept of Forecasting Agricultural output using Space, Agrometeorology and Land based observations (FASAL) has been formulated. Development of procedure and demonstration of this technique for four in-season forecasts for kharif rice has been carried out as a pilot study in Orissa State since 1998. As the availability of cloud-free optical remote sensing data during kharif season is very poor for Orissa state, multi-date RADARSAT SCANSAR data were used for acreage estimation of kharif rice. Meteorological models have been developed for early assessment of acreage and prediction of yield at mid and late crop growth season. Four in-season forecasts were made during four kharif seasons (1998-2001); the first forecast of zone level rice acreage at the beginning of kharif crop season using meteorological models, second forecast of district level acreage at mid growth season using two-date RADARSAT SCANSAR data and yield using meteorological models, third forecast at late growth season of district level acreage using three-date RADARSAT SCANSAR data and yield using meteorological models and revised forecast incorporating field observations at maturity. The results of multiple forecasts have shown rice acreage estimation and yield prediction with deviation up to 14 and 11 per cent respectively. This study has demonstrated the potential of FASAL concept to provide inseason multiple forecasts using data of remote sensing, meteorology and land based observations.     

Derivation of crop phenological parameters using multi-date SPOT-VGT-NDVI data: A case study for Punjab

Crop phenological parameters, such as the start and end time of the crop growth, the total length of the growing season, time of peak vegetation and rate of greening and senescence are important for planning crop management and crop diversification/intensification. Multi-temporal remote sensing data provides opportunity to characterize the crop phenology at regional level. This study was conducted during the kharif season of the year 2001–02 for Punjab. The ten-day Normalised Difference Vegetation Index (NDVI) composite products, with 1 km spatial resolution, available from the Vegetation sensor onboard SPOT4 were used for the study. Twenty-one temporal datasets from May 1, 2001 to November 21, 2001 were used. Logical modelling approach was followed to compute the minimum and maximum NDVI, the amplitude of NDVI, the threshold NDVI during sowing and harvest, the crop duration, integrated NDVI and skewness of profile. The analysis showed that before July beginning, in the whole of Punjab, sowing/planting was over. It was found that the crop emergence in the eastern part of the state started earlier than the western region. The maximum NDVI, which represented peak vegetative stage, was above 0.7 and occurred mostly during August. The duration of crops ranged between 90–140 days, with majority between 110–120 days. Total integrated NDVI in Punjab was generally above 60. Using principal component analysis and divergence analysis seven best metrics were selected for crop discrimination.     

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.     

On the prognostics of growth profile based yield modelling

In order to improve the prognostics of yield forecasts two approaches have been explored using NDVI-based growth profiles for wheat crop of 1987-88 and 1990-91 seasons for some districts of Punjab and Haryana. Correlation of yield with variables based on profile area segments and with product of profile segment area and time to peak occurrence of growth cycle have been investigated. While the correlations are low and inconsistent for area variables, the îndex time product moment (IIPM) variable shows consistent and significant correlations and advances the date of forecast by 45-50 days over other approaches.     

Utility of satellite data for land evaluation in land use planning for a part of Northern Karnataka

The study reported herein deals with the utility of satellite remote sensing techniques for land evaluation for agricultural land use planning. False colour composite of Landsat imagery in the scale of 1:250,000 was visually interpreted for physiography that formed the base for mapping soil and land resources in the field. The small-scale soil map thus prepared has thirteen map units with association of soil families. Soil and land resource units shown on these small-scale maps were evaluated for their suitability for growing sorghum crop by matching the relevant land qualities against the land requirements for sorghum. The land evaluation carried out for growing sorghum crop in the study area revealed that about 38.6 per cent is highly suitable (S1), 31.5 per cent moderately suitable (S2) and 24.5 per cent marginally suitable (S3). An area of about 5.4 per cent is not suitable, of which 3.0 per cent is currently not suitable (N1) and 2.4 per cent permanently not suitable for growing sorghum crop.     

Wheat crop production estimation using satellite data

This paper reports acreage, yield and production forecasting of wheat crop using remote sensing and agrometeorological data for the 1998–99 rabi season. Wheat crop identification and discrimination using Indian Remote Sensing (IRS) ID LISS III satellite data was carried out by supervised maximum likelihood classification. Three types of wheat crop viz. wheat-1 (high vigour-normal sown), wheat-2 (moderate vigour-late sown) and wheat-3 (low vigour-very late sown) have been identified and discriminated from each other. Before final classification of satellite data spectral separability between classes were evaluated. For yield prediction of wheat crop spectral vegetation indices (RVI and NDVI), agrometeorological parameters (ET_max and TD) and historical crop yield (actual yield) trend analysis based linear and multiple linear regression models were developed. The estimated wheat crop area was 75928.0 ha. for the year 1998–99, which sowed ?2.59% underestimation with land record commissioners estimates. The yield prediction through vegetation index based and vegetation index with agrometeorological indices based models were 1753 kg/ha and 1754 kg/ha, respectively and have shown relative deviation of 0.17% and 0.22%, the production estimates from above models when compared with observed production show relative deviation of ?2.4% and ?2.3% underestimations, respectively.     

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。     

Relation of wheat yield with parameters derived from a spectral growth profile

An attempt has been made to generate crop growth profiles using multi-date NOAA AVHRR data of wheat-growing season of 1987–88 for the districts of Punjab and Haryana states of India. A profile model proposed by Badhwar was fitted to the multi-date Normalised Difference Vegetation Index (NDVI) values obtained from geographically referenced samples in each district. A novel approach of deriving a set of physiologically meaningful profile parameters has been outlined and the relation of these parameters with district wheat yields has been studied in order to examine the potential of growth profiles for crop-yield modelling. The parameter ‘area under the profile’ is found to be the best estimator of yield. However, with such a parameter time available for prediction gets reduced. Combination of different profile parameters shows improvement in correlation but lacks the consistency for individual state data.     

Spectral signatures of moisture-stressed wheat

One of the important parameters affecting crop yield is the availability of soil moisture to the crop. Lack of it may bring about moisture stress in plants which manifests itself in terms of changes in the spectral reflectance and emittance properties of plants. An experiment involving radiometric measurements over six wheat plots subjected to different irrigation schedules was conducted to test this hypothesis. Vegetation index defined in terms of crop reflectances in 0.6 to 0.7 and 0.8 to 1,1 micrometer bands was found to be a sensitive parameter to distinguish normal plants from moisture-stressed plants. The optimum period for the discrimination of such plants through remote sensing techniques has been indicated to be 45–80 days after sowing. The experiment also demonstrates that yield per unit area is linearly related to the maximum leaf-area index of the crop thus providing a possible method of crop yield prediction.