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1.
Monitoring winter wheat growth in North China by combining a crop model and remote sensing data 总被引：6，自引：0，他引：6
Ma Yuping Wang Shili Zhang Li Hou Yingyu Zhuang Liwei He Yanbo Wang Futang 《International Journal of Applied Earth Observation and Geoinformation》2008,10(4):426
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. 相似文献
2.
V. K. Sehgal C. V. S. Sastri N. Kalra V. K. Dadhwal 《Journal of the Indian Society of Remote Sensing》2005,33(1):131-136
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). 相似文献
3.
多光谱多角度遥感数据综合反演叶面积指数方法研究 总被引：10，自引：2，他引：10
叶面积指数是陆地生态系统的一个十分重要的结构参数。用遥感数据求取叶面积指数可以利用光谱的信息,比如通过植被指数来拟合一个经验关系,但很多植被指数明显受土壤背景的影响,对于有明显行结构的农作物,土壤的影响很难消除,植被指数的方法误差较大。多角度遥感包含了大量的地面目标的立体结构信息,具备求解植被特征参数的潜力,但通常多角度遥感反演对光谱信息的利用不足。与以往的反演方法相区别,该文利用行播作物二向反射模型,将多角度与多光谱数据结合进行行播作物LAI反演实验,并对反演算法进行了详细的敏感性分析实验,结果表明采用多角度、多光谱遥感数据相结合的方法可以有效反演行播作物的叶面积指数。 相似文献
4.
作物LAI的遥感尺度效应与误差分析 总被引：7，自引：2，他引：5
以黑河中游盈科绿洲为研究区, 利用Hyperion高光谱数据, 采用双层冠层反射率模型(ACRM)迭代运算反演LAI; 通过LAI的均值化(LAImean)以及Hyperion数据反射率线性累加反演LAI(LAIp), 定量分析LAI反演的尺度效应; 从模型的非线性和地表景观结构的空间异质性2个方面分析引起反演误差的原因, 并在LAI-NDVI回归方程的基础上利用泰勒展开的方法对低分辨率数据反演结果进行了误差纠正。结果表明, 地表景观结构的空间异质性是造成多尺度LAI反演误差的关键因素, 通过泰勒展开式能很好地实现大尺度数据LAI反演结果的误差纠正。 相似文献
5.
基于PROSPECT＋SAIL模型的遥感叶面积指数反演 总被引：4，自引：1，他引：4
以PROSPECT+SAIL模型为基础，从物理机理角度反演植被叶面积指数(LAI)。首先，通过FLAASH模型进行大气校正，使得图像像元值表达植被冠层反射率； 然后，根据LOPEX 93数据库和JHU光谱数据库选择植物生化参数和光谱数据，以PROSPECT模型模拟出的植物叶片反射率和透射率作为SAIL模型的输入参数，得到植被冠层反射率，将结果与遥感影像的植被冠层反射率对应，回归出植被LAI； 最后，以地面实测数据对遥感反演数据进行验证，并分析了误差的可能来源。 相似文献
6.
应用粒子群算法的遥感信息与水稻生长模型同化技术 总被引：4，自引：0，他引：4
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。 相似文献
7.
由于地表空间异质性的普遍存在,遥感反演模型的非线性必然会导致不同分辨率观测的遥感结果不一致,从而产生遥感产品尺度效应。本文研究了遥感产品尺度效应概念、模拟方法和定量计算模型,并利用锡林浩特草原研究区的实测数据,对尺度效应模型和方法进行了定量计算与验证分析。首先,基于不同升尺度方法与多尺度遥感成像机理之间的机理联系,通过“先反演再平均”与“先平均再反演”之间的差异,可计算“高”分辨率与“低”分辨率之间的遥感产品尺度差异。其次,分别以红光、近红外两波段反射率和归一化植被指数(NDVI)为自变量,对叶面积指数(LAI)非线性遥感模型进行泰勒展开,研究了模型非线性、遥感数据空间异质性对LAI遥感产品尺度差异的影响,发现高阶项可忽略,利用二阶导数项和遥感数据方差项可定量计算遥感产品尺度差异,经过二阶导数项纠正后的尺度差异相对偏差从5.6%分别降低到0.78%和1.45%。最后,分析了LAI遥感产品尺度效应的特征规律,得出以下结论:随着植被覆盖的增大,同等遥感空间异质性的LAI遥感产品尺度差异越大,且红光波段比近红外波段的尺度差异敏感性高近2个数量级;对于绝大部分陆地植被区域,存在“低分辨率低估”尺度效应,且遥感产品尺度差异的主导要素为LAI模型非线性,NDVI变量自身非线性对尺度效应贡献占23.5%;对于湿地类植被与水体混合情形,NDVI变量非线性的贡献为主导贡献,出现“低分辨率高估”尺度效应,必须利用红光、近红外两波段的二阶导数项非线性尺度差异,才能解释这一类型的LAI遥感产品尺度效应。本文建立了具有一定普适意义的遥感产品尺度效应定量模拟与尺度纠正方法,对推动定量遥感的尺度问题研究有一定参考价值。 相似文献
8.
9.
光学与微波植被指数协同反演农作物叶面积指数的可行性分析 总被引：1，自引：0，他引：1
光学遥感是目前反演植被叶面积指数LAI（Leaf Area Index）的主要手段,但是当叶面积指数较大时存在光学遥感信息饱和、反演精度显著降低的问题。叶面积指数和平均叶倾角对光学、微波波段范围内反射和散射特性都有重要影响,主要表现在植被结构参数的变化可以引起冠层孔隙率和消光截面大小的改变。本文以典型农作物玉米为例,通过构建统一的PROSAIL和MIMICS模型输入参数,生成一套玉米全生长期光学二向反射率和全极化微波后向散射系数模拟库和冠层参数库。通过对模拟数据与LAI敏感性和相关性分析得出：（1）光学植被指数MNDVI(800 nm,2000 nm),在LAI为0—3时敏感,基于MNDVI与LAI的回归模型可以估算LAI变化 0.4的情况,RMSE是0.33,R2是0.958。（2）微波植被指数SARSRVI(1.4 GHz HH,9.6 GHz HV),在LAI为3—6时敏感,基于SARSRVI与LAI的回归模型可以估算LAI变化1的情况,RMSE为0.22,R2是0.9839。研究表明,采用分段敏感的植被指数,协同光学和微波遥感反演玉米全生长期叶面积指数是可行的。 相似文献
10.
Mahender Singh Ram Niwas M. L. Khichar Manoj K. Yadav 《Journal of the Indian Society of Remote Sensing》2006,34(1):1-5
Field experiments were conducted during 1998–99 and 1999–2000 at research farm of the Department of Agricultural Meteorology,
CCS Haryana Agricultural University, Hisar. Five wheat cultivars: WH 542, PBW 343, UP 2338, Raj 3765 and Sonak were sown on
25th November, 10th and 25th December with four nitrogen levels viz., no nitrogen. 50, 100 and 150% of recommended dose. Leaf area index, dry matter at
anthesis, final dry biomass and grain yield were recorded in all the treatments. Chlorophyll and wax contents of wheat leaves
were estimated at different growth stages. Multiband spectral reflectance was measured using hand-held radiometer. Spectral
indices such as simple ratio, normalized difference, transformed vegetation index, perpendicular vegetation index and greenness
index were computed using the multiband spectral data. Values of all the spectral indices were maximum in 25 November sown
crop with maximum dose of nitrogen (180 kg N ha-1). PBW 343 showed higher values of all the spectral indices in comparison with other cultivars. The spectral indices recorded
during maximum leaf area index stage were correlated with crop parameters. Using stepwise regression, empirical models for
chlorophyll, leaf area index, dry biomass and yield prediction were developed. The ’R2’ values of these models ranged between 0.87 and 0.95. 相似文献
11.
基于波谱知识库的MODIS叶面积指数反演及验证 总被引：2，自引：0，他引：2
目前用物理模型反演叶面积指数普遍存在缺少先验知识的状况，如何获得准确的先验知识是遥感走向应用的一个关键环节。中国典型地物标准波谱数据库就是结合国家重大行业中的应用需求，研究制定地物波谱获取与分析的技术规范和数据标准，建立典型地物标准波谱数据库。从波谱数据库提取模型反演所需要的先验知识，实现了基于SAIL模型的MODIS数据（经过几何纠正与大气纠正）叶面积指数的反演。另外，基于TM数据，对MODIS混合像元进行了分解，用纯像元的叶面积指数与实测数据进行对比验证，同时，反演结果与NASA的LAI产品也进行了对比，结果表明基于波谱库的先验知识可以有效的提高叶面积指数的反演精度。 相似文献
12.
基于主成分分析的植被指数与叶面积指数相关性研究 总被引：1，自引：0，他引：1
综合分析了玉米叶面积指数与几种常见光谱植被指数相关性,确定主成分分析方法在反演叶面积指数中的作用。首先,借助MATLAB编程软件,以植被指数与玉米叶面积指数相关性最高为原则,选出遥感影像上各种植被指数,其波段组合为NDVI(752.4/701.5),RVI(752.4/701.5),MSR(752.4/701.5),SAVI(823.7/701.5),MSAVI(823.7/701.5),然后,对这5种植被指数进行主成分分析,建立LAI-VI多元逐步回归模型,并对模型精度进行验证,总体估测精度为96.237%。经实验验证,利用主成分分析方法在反演植被叶面积指数时能够起到较好的效果,具有广泛的应用前景。 相似文献
13.
U. P. Shahi Suman Kumar N. P. Singh A. K. Chaubey Yogesh Kumar 《Journal of the Indian Society of Remote Sensing》2007,35(1):53-65
The potential usefulness of spectral properties and vegetation indices in varietal discrimination of potato genotypes was
studied in the field experiment. Spectral measurements were recorded in different bands in blue (450–520 nm), green (520–590
nm), red (620–680 nm) and infrared (770–860 nm) of the electromagnetic spectrum at different stages during crop growth period.
A ground based hand held multiband radiometer (Model/041) was used for the purpose. The mean per cent green reflectance value
among different genotypes was lowest in genotype MS/86-89, while it was observed highest in genotype JX-216. Significant difference
among these genotypes was found at all growth stages except 6 week after planting. Consequent to variation in spectral reflectance
the vegetation indices like, NDVI, RVI, TVI and DVI showed significant difference among genotypes at all growth stages except
at 8th week after planting. The vegetation indices are good indicators of crop growth and condition. Similarly, fresh weight, dry
weight, and leaf area index were also highest in MS/86-89, followed by KUFRI Bahar and KUFRI Sutlej while in case of leaf
area index it was followed by Kufri Sutlej and Kufri Bahar. JX-23 was highest in chlorophyll content and tuber yield followed
by MS/86-89 and JW-160, while lowest chlorophyll content was seen in MS/89-1095 and poorest tuber yield in MS/89-60. Most
of the genotypes exhibited considerable variation in their spectral response and vegetation indices thereby indicating the
possibility of their discrimination through remote sensing technique. 相似文献
14.
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 (ETmax 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. 相似文献
15.
山地叶面积指数反演理论、方法与研究进展 总被引：2，自引：0，他引：2
叶面积指数LAI（Leaf Area Index）是表征叶片疏密程度和冠层结构特征的重要植被参数，在气候变化、作物生长模型以及碳、水循环研究中发挥着重要作用。遥感是获取区域及全球尺度LAI的一个重要手段，当前LAI产品主要基于遥感数据反演得到，但是多数LAI产品算法并未考虑地形特征的影响，导致山地LAI遥感反演精度不确定性大。提高山地LAI遥感反演精度亟需考虑地形因子对冠层反射率的影响，其中山地冠层反射率模型和遥感数据地形校正是提升山地LAI遥感反演精度的关键。本文围绕山地LAI遥感反演理论与方法，综合分析了国内外山地冠层反射率模型和地形校正模型的研究进展，总结了目前山地LAI遥感反演存在的问题，并讨论了未来研究的发展趋势。 相似文献
16.
Ishfaq Ahmad Umer Saeed Muhammad Fahad Asmat Ullah M. Habib ur Rahman Ashfaq Ahmad Jasmeet Judge 《Journal of the Indian Society of Remote Sensing》2018,46(10):1701-1711
Real time, accurate and reliable estimation of maize yield is valuable to policy makers in decision making. The current study was planned for yield estimation of spring maize using remote sensing and crop modeling. In crop modeling, the CERES-Maize model was calibrated and evaluated with the field experiment data and after calibration and evaluation, this model was used to forecast maize yield. A Field survey of 64 farm was also conducted in Faisalabad to collect data on initial field conditions and crop management data. These data were used to forecast maize yield using crop model at farmers’ field. While in remote sensing, peak season Landsat 8 images were classified for landcover classification using machine learning algorithm. After classification, time series normalized difference vegetation index (NDVI) and land surface temperature (LST) of the surveyed 64 farms were calculated. Principle component analysis were run to correlate the indicators with maize yield. The selected LSTs and NDVIs were used to develop yield forecasting equations using least absolute shrinkage and selection operator (LASSO) regression. Calibrated and evaluated results of CERES-Maize showed the mean absolute % error (MAPE) of 0.35–6.71% for all recorded variables. In remote sensing all machine learning algorithms showed the accuracy greater the 90%, however support vector machine (SVM-radial basis) showed the higher accuracy of 97%, that was used for classification of maize area. The accuracy of area estimated through SVM-radial basis was 91%, when validated with crop reporting service. Yield forecasting results of crop model were precise with RMSE of 255 kg ha?1, while remote sensing showed the RMSE of 397 kg ha?1. Overall strength of relationship between estimated and actual grain yields were good with R2 of 0.94 in both techniques. For regional yield forecasting remote sensing could be used due greater advantages of less input dataset and if focus is to assess specific stress, and interaction of plant genetics to soil and environmental conditions than crop model is very useful tool. 相似文献
17.
锡林浩特草原区域MODIS LAI产品真实性检验与误差分析 总被引：2，自引：0，他引：2
本文研究了LAI产品真实性检验的指标和方法,建立了LAI产品真实性检验的流程,将遥感产品真实性检验误差分解为模型误差、数据定量化差异和尺度效应3个方面。以内蒙古锡林浩特草原为研究区,结合实测数据和Landsat TM数据建立NDVI-LAI模型,得到LAI验证参考"真值",据此"真值"按照本文的流程对MODIS LAI产品进行验证,分析了研究区MODIS LAI产品真实性检验的误差来源。研究表明,该研究区的MODIS LAI(MOD15A2)产品相对高估约25%。各个误差因素中,LAI遥感模型差异对于结果影响最大,MODIS LAI模型高估了该区域草地LAI(高估约44.2%);数据定量差异的影响也比较大,MODIS地表反射率数据与Landsat TM地表反射率数据的差异造成了约16.2%的低估;尺度效应的影响较小,造成约3.1%的低估,其中NDVI-LAI模型的尺度效应带来2.4%的低估,NDVI数据的尺度效应造成约0.7%的低估。 相似文献
18.
R. B. Gajjar A. M. Shekh A. J. Dave C. T. Patel R. S. Parmar N. K. Patel J. G. Talati 《Journal of the Indian Society of Remote Sensing》2005,33(1):147-153
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. 相似文献
19.
A simple method for determining leaf area index (LAI), absorbed photosynthetically active radiation (APAR), dry biomass and yield in pearl millet (Pennisetum glaucum (L.) R. Br.) crop from remote sensing data is presented. The dry matter and yield can be simulated with maximum accuracy by normalised difference (ND) data during milking stage using the following models: $$\begin{array}{l} DM = - 435.45 + 786.86ND R^2 = 0.90 \\ Yield = - 95.43 + 180.00ND R^2 = 0.62 \\ \end{array}$$ 相似文献
20.
大范围、及时、准确的灾害损失评估与制图对防灾减灾、农业保险和粮食安全等至关重要。针对传统灾害损失评估方法空间尺度单一、泛化能力差、时效性低，可操作性弱等问题，本文建立了一种遥感产品耦合作物模型的多尺度的灾害损失评估方法MDLA （a Multiscale Disaster Loss Assessment）。该方法利用作物模型的多情景模拟产生大量的灾害样本，结合对应日期的遥感指标构建灾害脆弱性模型，依托Google Earth Engine（GEE）平台将其应用到高分辨率遥感影像和格点灾害指标进行逐象元评估。以鄂伦春自治旗玉米为例，基于精细校准的CERES-Maize模型的模拟，利用两个生长季窗口的LAI和冷积温（CDD）建立统计模型来刻画低温对最终产量的影响，结合Sentinel-2数据逐格点计算完成高精度损失制图。结果显示，校准后的CERES-Maize模拟物候和产量的NRMSE 分别为3.3%和8.9%。冷害情景模拟结果表明不同类型和生育期的低温冷害对玉米产量的影响不尽相同，其中生长峰值期（出苗—吐丝和吐丝—灌浆）最为敏感。回代检验显示，MDLA方法估算精度为11.4%，与历史冷害年份的实际损失相吻合。经评估，鄂伦春2018-08-09的冷害导致玉米减产23.7%，受灾面积1.86×104 ha，其中高海拔地区损失较重（减产率>25%），低温冷害对该区玉米生产构成了严重的威胁。与现有的统计回归、作物模型模拟以及同化等技术相比，其优势在于：（1）结合遥感观测和作物模型模拟技术能更好地刻画了灾害对产量的影响过程；（2）利用GEE平台快速处理海量遥感数据，提高了灾害损失评估的时效性；（3）不受地面实测数据的限制，易操作，可实现动态、多尺度（象元、田块、村，县等）的损失评估，这为防灾减损、维持粮食丰产稳产提供了保障，也为农业保险的业务化运行提供了思路。 相似文献