Methodology to classify rice cultural types based on water regimes using multi-temporal RADARSAT-1 data

This study presents a methodology to classify rice cultural types based on water regimes using multi-temporal synthetic aperture radar (SAR) data. The methodology was developed based on the theoretical understanding of radar scattering mechanisms with rice crop canopy, considering crop phenology and variation in water depth in the rice field, emphasizing the sensitivity of SAR to crop geometry and water. The logic used was the characteristic decrease in SAR backscatter that is associated with the puddled or transplanted field due to specular reflection for little exposure of crop, with increase in backscatter as the crop growth progresses due to volume scattering. Besides, the multiple interactions between SAR and vegetation/water also lead to an increase in backscatter as the crop growth progresses. Classification thresholds were established based on the information provided by each pixel in each image, the pixel's typical temporal behaviour due to crop phenology and changing water depth in rice field and their corresponding SAR signature. Based on this logic, the study site (i.e. South 24 Paraganas district, West Bengal) was classified into three major rice cultural types, namely shallow water rice (SWR; 5 cm ≤ water depth ≤ 30 cm), intermediate water rice (IWR; 30 cm ≤ water depth ≤ 50 cm) and deep water rice (DWR; water depth > 50 cm) during the kharif season. These three types represent most of the traditional rice-growing areas of India. The methodology was validated with the field data collected synchronously with the satellite passes. Classification results showed an overall accuracy of 98.5% (95.5% kappa coefficient) compared with a maximum-likelihood classifier (MLC) with an overall accuracy of 95.5% (84.2% of kappa coefficient) with 95% confidence interval. The relationship between field parameters, especially exposed plant height and water depth with SAR backscatter, was explored to design empirical models for each of the three rice classes. Significant relationships were observed in all the rice classes (coefficient of determination, R ², value more than 0.85) even though they had similar growth profiles but varied with water depth. The two main conclusions drawn from this study are (i) the importance of multi-temporal SAR data for the classification of rice culture types based on water regimes and (ii) the advantages and flexibility of the knowledge-based classifier for classification of RADARSAT-1 data. However, being empirical, the approach needs modification according to the current rainfall pattern and rice-growing practice.     

Satellite derived crop calendar for canal operation schedule in Bhadra project command area,India

Spatial information on crop calendars in the command areas of irrigation systems is useful to irrigation engineers in order to achieve chronological synchronization between water delivery pattern and crop life cycle. Such synchronization is vital for efficient use of water. The capability of satellite remote sensing technology to generate spatial crop calendar information in an irrigated command area and its usefulness in the evaluation of water delivery patterns are demonstrated in this paper. The study pertains to the major crop paddy during the rabi season (December to June) of 1992-93, in the Bhadra project command area of Karnataka state, India. Analyses of multidate Normalized Difference Vegetation Index (NDVI) profiles of paddy crop generated from Indian Remote Sensing (IRS) satellite data for each distributary command reveal three distinct growth patterns in the study area with each pattern characterized by a particular crop calendar. The spatial variability in crop calendar over the total command area has thus been derived. The ground truth data obtained in crop cutting experiments (CCEs) validate the satellite derived crop calendar. Distributary wise, water delivery data have been studied in conjunction with the satellite derived crop calendar to determine whether the existing pattern of water delivery covers the required length of crop life cycle in the command area. It was found that the water supply was stopped about 30 days before harvesting in some distributaries and in some about 20-30 days before harvesting. A list of distributaries with greater lags between cessation of water supply and crop harvest was provided to irrigation system engineers to aid their plans for providing a reliable and predictable irrigation service. This is possible either through reorganization of canal operation schedule or through educating farmers about the need for adjusting their agricultural activities to match water supply patterns.     

Crop type identification by integration of high-spatial resolution multispectral data with features extracted from coarse-resolution time-series vegetation index data

Crop type identification is the basis of crop acreage estimation and plays a key role in crop production prediction and food security analysis. However, the accuracy of crop type identification using remote-sensing data needs to be improved to support operational agriculture-monitoring tasks. In this paper, a new method integrating high-spatial resolution multispectral data with features extracted from coarse-resolution time-series vegetation index data is proposed to improve crop type identification accuracy in Hungary. Four crop growth features, including peak value, date of peak occurrence, average rate of green-up, and average rate for the senescence period were extracted from time-series Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) profiles and spatially enhanced to 30 m resolution using resolution merge tools based on a multiplicative method to match the spatial resolution of Landsat Thematic Mapper (TM) data. A maximum likelihood classifier (MLC) was used to classify the TM and merged images. Independent validation results indicated that the average overall classification accuracy was improved from 92.38% using TM to 94.67% using the merged images. Based on the classification results using the proposed method, acreages of two major summer crops were estimated and compared to statistical data provided by the United States Department of Agriculture (USDA). The proposed method was able to achieve highly satisfactory crop type identification results.     

An optimal estimation technique for increasing the accuracy of crop forecasts by combining remotely sensed and conventional forecast results.

Abstract

An optimal estimation (OE) technique has been used to increase the accuracy of crop acreage and yield estimates by combining results from remotely sensed (RS) data and conventional models. For crop acreage estimation the OE increased the accuracy of wheat acreage estimation when the first forecasts of the Directorate of Economics and Statistics (DES) were combined with state level RS estimates over the states of Haryana and Punjab in India.

To increase the accuracy of wheat yield forecasts an autoregressive (AR) model was developed. Results of AR model were optimally combined with RS-based estimates for Hisar and Karnal districts in Haryana, India. The OE results for a total of eight forecasts had a lower mean absolute per cent deviation than the forecasts using RS and AR approaches. The power of OE was further demonstrated by combining weather-based wheat yield model results for the state of Punjab (India) with first order AR model results, suggesting an increase in accuracy of forecasts by optimally combining results from two or more algorithms.     

基于光谱和时相特征的夏玉米遥感识别

精确提取作物种植面积一直是农业遥感关注的主要问题之一。综合运用低分辨率的时相变化特征和中分辨率的光谱特征,提出一种夏玉米识别方法。首先基于MODIS NDVI时间序列曲线,分析夏玉米在时相变化上的识别特征,构建识别模型。夏玉米纯像元利用识别模型识别,而耕地和非耕地类型的植被产生的混合像元,则基于像元分解办法获取耕地组分的NDVI时序特征,再利用识别模型判定,然后结合土地利用数据根据空间关系得到中分辨率结果;玉米与其他作物的混合像元则利用中分辨率尺度光谱差异加以区分。研究结果表明,在伊洛河流域主要农业区,识别精度达到90.33%,为作物类型识别提供了新的思路。     

Towards an operational system for regional-scale rice yield estimation using a time-series of Radarsat ScanSAR images

This paper demonstrates that Radarsat ScanSAR data can be an important data source of radar remote sensing for monitoring crop systems and estimation of rice yield in large areas in tropical and sub-tropical regions. Experiments were carried out to show the effectiveness of Radarsat ScanSAR data for rice yield estimation in the whole province of Guangdong, South China. A methodology was developed to deal with a series of issues in extracting rice information from the ScanSAR data, such as topographic influences, levels of agro-management, irregular distribution of paddy fields and different rice cropping systems. A model was provided for rice yield estimation based on the relationship between the backscatter coefficient of multi-temporal SAR data and the biomass of rice. The study indicates that the whole procedure can become a low-cost and convenient operational system for large-scale rice yield estimation which is difficult for conventional methods.     

NOAA-AVHRR NDVI decomposition and subpixel classification using linear mixing in the Argentinean Pampa

Crop Normalized Difference Vegetation Index (NDVI) time profiles and crop acreage estimates were derived from the application of linear mixture modelling to Advanced Very High Resolution Radiometer (AVHRR) data over a test area in the southern part of the Pampa region, Argentina. Bands 1 and 2 from seven AVHRR scenes (June to January 1991) were combined to produce fraction images of winter crops, summer crops and pastures. A Landsat Thematic Mapper (TM) scene of the region was classified and superimposed to the AVHRR Local Area Coverage (LAC) data by means of a correlation technique. Each class signature was extracted by regressing the AVHRR response on the cover types proportions, estimated from Landsat-TM data, over sets of calibration windows. The crop NDVI profiles were hence derived from the class signatures in bands 1 and 2. These profiles appeared consistent with the cover types, but variability depending on the set of windows was noted. The assessment of the class signatures was indirectly accomplished through the subpixel classifications of the AVHRR data, performed using the different sets of class spectra. Although some discrepancies between AVHRR and Landsat–TM estimates were observed at the individual window level, the classification results compared quite well on a regional scale with Landsat–TM estimates: crop acreage was estimated to an overall accuracy ranging from 89 to 95 per cent according to the spectra used in the classification. Definitely, the proposed methodology should permit a better exploitation of the temporal resolution of AVHRR data in both the areas of yield prediction and vegetation classification. Furthermore, the perational application of such a methodology for crop monitoring will undoubtedlybe facilitated with the coming sensor systems such as the ModerateResolution Imaging Spectroradiometer (MODIS), the SPOT Vegetation Monitoring Instrument or the ‘Satelite Argentino Cientifico’ (SAC–C).     

基于MODIS时间序列数据的作物季相信息提取

基于MODIS NDVI时间序列数据对浙北平原单季稻区进行作物季相一致性分析。对NDVI时间序列数据进行离散傅立叶变换去除噪声,再利用土地利用现状图提取耕地区的NDVI影像图,根据时间序列曲线的最大值研究作物的季相。结果表明:水稻生长期对NDVI时间序列曲线的响应和季相一致性均较小麦和油菜好;8 d合成的数据较16 d合成的数据可以更详细地反映作物季相信息。研究证实了MODIS NDVI时间序列曲线对区域作物季相分析的意义。     

Assessment of millet yields and production in northern Burkina Faso using integrated NDVI from the AVHRR.

Abstract

It is possible to assess crop yields at the end of the growing season in a semi-arid environment using data from meteorological satellites. This is the result of a work carried out in northern Burkina Faso. The technique used is based on linear correlation between millet yield and the time integral of the Normalized Difference Vegetation Index (iNDVI) derived from NOAA AVHRR data. In contrast to earlier related studies, the correlation has been established using satellite data extracted exclusively within the agricultural domain. The integration period for the iNDVI correponds to the reproductive phase only of the growing period of millet. Furthermore, iNDVI can also be used to estimate the acreage or the agricultural domain, by the application of a suitable threshold to classify areas into agricultural and non-agricultural domains.

It is therefore possible to assess the yield and the acreage of the agricultural domain and to derive an estimate of the millet production of the area by the end of the season, on the basis of NOAA AVHRR data alone.     

Discrimination of irrigated and rainfed rice in a tropical agricultural system using SPOT VEGETATION NDVI and rainfall data

The classification of irrigated crops by remote sensing requires the use of time series data, since the timing, cropping intensity and duration of cropping is quite variable over the course of a year. Rice is the dominant irrigated crop in tropical and sub‐tropical Asia, where rainfall is high, but is seasonal and often uni‐modal. Existing crop classification methods for rice are not able to distinguish between rainfed and irrigated crops, leading to errors in classification and estimated irrigated area. This paper describes a technique, a ‘peak detector algorithm’, to successfully discriminate between rainfed and irrigated rice crops in Suphanburi province, Thailand. The methodology uses a three‐year time series of Satellite pour l'Observation de la Terre (SPOT) VEGETATION S10 Normalized Difference Vegetation Index (NDVI) data (10 day composites) to identify cropping intensity (number, timing and peak values). Peak NDVI is then lag‐correlated with long term average rainfall data. There is a high correlation at a 40–50 day lag, between a peak rainfall and a ‘single’ peak NDVI of rainfed rice. In irrigated areas, there are multiple peaks, and multiple correlations with low values for at least 90 days after peak rainfall. The methodology currently uses a mask to remove un‐cropped and non‐rice areas, which is derived from existing Geographical Information Systems (GIS). The method achieves a classification accuracy of 89% or better against independent groundtruth data. The procedure is designed as a second level of analysis to refine classifications using other techniques of mapping irrigated area at global and regional scales.     

Wheat production forecasting for a predominantly unirrigated region in Madhya Pradesh )India)

The area under wheat was estimated and a forecast of production made in a predominantly un-irrigated region (36 per cent irrigated wheal crop, geographical area 5-61 Mha) of Madhya Pradesh (India) using digital data from LISS-I (Linear Imaging Self Scanner) onboard Indian Remote Sensing Satellite (IRS-IB), for the crop season 1991-92. A stratified sampling approach based on 5 km by 5 km sample segments, 10 per cent sampling fraction in conjunction with supervised maximum likelihood (MXL) classification was used for wheat acreage estimation. Yield forecasts were based on an optimal combination of forecasts from two different methodologies, viz., wheat yield-spectral relationship and time series analysis using ARIMA (Auloregressive Integrated Moving Average) approach. In the former, a two-year (1989-90, 1990-91) pooled regression relating LISS-I derived Near Infrared/Red (NIR/R) radiance ratio to district wheat yields was developed and used to forecast wheat yields for the year 1991-92 based on classified wheat pixels. In the latter case, historical district-wise wheat yield data of 35 years was used to develop appropriate ARIMA models and used to forecast 1991-92 yields. The relative deviation of remotely-sensed-based forecasted production, acreage and yield from the post-harvest estimates released later by the State Department of Agriculture were — 15.8, — 1002 and — 601 per cent, respectively. The acreage and yield meet the accuracy of 85 per cent at 90 and 95 per cent confidence levels, respectively.     

Remote sensing of regional yield assessment of wheat in Haryana,India

Regional estimates of crop yield are critical for a wide range of applications, including agricultural land management and carbon cycle modelling. Remotely sensed images offer great potential in estimating crop extent and yield over large areas owing to their synoptic and repetitive coverage. Over the last few decades, the most commonly used yield–vegetation index relationship has been criticized because of its strong empirical character. Therefore, the present study was mainly focused on estimating regional wheat yield by remote sensing from the parametric Monteith's model, in an intensive agricultural region (Haryana state) in India. Discrimination and area estimates of wheat crop were achieved by spectral classification of image from AWiFS (Advanced Wide Field Sensor) on‐board the IRS‐P6 satellite. Remotely sensed estimates of the fraction of absorbed photosynthetically active radiation (fAPAR) and daily temperature were used as input to a simple model based on light‐use efficiency to estimate wheat yields at the pixel level. Major winter crops (wheat, mustard and sugarcane) were discriminated from single‐date AWiFS image with an accuracy of more than 80%. The estimates of wheat acreage from AWiFS had less than 5% relative deviation from official reports, which shows the potential of single‐date AWiFS image for estimating wheat acreage in Haryana. The physical range of yield estimates from satellites using Monteith's model was within reported yields of wheat for both methods of fAPAR, in an intensive irrigated wheat‐growing region. Comparison of satellite‐based and official estimates indicates errors in regional yields within 10% for 78% and 68% of cases with fAPAR_M1 and fAPAR_M2, respectively. However, wheat yields in general are over‐ and underestimated by the fAPAR_M1 and fAPAR_M2 methods, respectively. The validation with district level wheat yields revealed a root mean square error of 0.25 and 0.35 t ha^?1 from fAPAR_M1 and fAPAR_M2, respectively, which shows the better performance of the fAPAR_M1 method for estimating regional wheat yields. Future work should address improvement in crop identification and field‐scale yield estimation by integration of high and coarse resolution satellite sensor data.     

Evaluation of Ku‐band QuikSCAT scatterometer data for rice crop growth stage assessment

A study has been carried out to analyse the high temporal Ku‐band scatterometer data from QuikSCAT with 4.45 km resolution for regional assessment of rice crop phenology. Four‐day composite data were used covering the two predominant rice‐growing states in India during the monsoon season of 2004. These data were registered with reference to a rice map derived from RADARSAT SAR data of the same season in order to select predominant rice sites. Analysis shows the dual peak backscatter profile of rice crop (at tillering stage and another at maturity). Minima of the backscatter profile were found to coincide with the heading. The derived heading stage using a lognormal curve fitting matched well with the observed dates. The slope of the second peak varied with crop variety, and shows the potential of correlation with the panicle characteristic.     

Mapping of rice-cropping pattern and cultural type using remote-sensing and ancillary data: a case study for South and Southeast Asian countries

A geospatial database on the spatial distribution of rice areas and rice cultural types of major rice-producing countries of South and Southeast Asia has been developed in this study using remote-sensing and ancillary data sets. Multitemporal SPOT VGT normalized difference vegetation index (NDVI) data for the period 2009–2010 were used for the analysis. The classification was performed adopting ISODATA clustering to build a non-agricultural area mask followed by rice area mapping. The derived rice area was stratified by logical modelling of ancillary data sets into five rice cultural types: irrigated wet, upland, flood-prone, drought-prone, and deep-water. The uniqueness of this study is a synergistic approach based solely on single-source, high-temporal remote-sensing data coupled with ancillary data, which demonstrate the application of SPOT VGT NDVI data in building a geospatial database for rice crops over a wide spatial extent. This approach was adopted for cost effectivity as the study extent was vast and thus lacking ground truth information. Comparison of the derived rice area against the reported literature values for validation yielded a good correlation (linear coefficient of determination, R² = 0.95–0.99). The high-temporal resolution NDVI data enabled effective characterization of vegetation phenology. The derived spatial outputs can be used in various studies associated with the assessment of greenhouse gas emissions from paddy fields, change detection, and inputs to crop simulation models, which are significantly related to different rice cultural types.     

用模糊聚类法研究中药成分特征谱

为了寻求和评价不同产地中药裂解气相色谱图之间的相似关系和差异程度,建立指纹图谱.选取了10个郁金样品的裂解气相色谱图,提取图谱中反映不同样本在化学成分和含量上有差异的信息特征(保留时间、相对浓度、峰面积等),采用模糊聚类6种不同分析法在16个峰参数上进行分类,得到简洁明了的模糊聚类图.实验结果反映了图谱之间的相似关系,并能快速准确地选出具有代表性的图谱.     

The relationships between electrical conductivity of soil and reflectance of canopy,grain, and leaf of rice in northeastern Thailand

Soil salinity is a global environmental problem and the most widespread land degradation problem that reduces crop yields and agricultural productivity. The characteristic of soil salinity is conventionally measured by the electric conductivity (EC) of soil while remote-sensing techniques have been extensively applied to detect the presence of salts indirectly through the vegetation using crop spectral reflectance. This study aims primarily to investigate whether salt stress the rice can be detected by field reflectance or not, and second, to search the significant bands of vegetation indices that can indicate the relationships between the EC of soil and field hyperspectral reflectance of canopy, grain, and leaf of rice, using the normalized difference spectral index (NDSI). Field investigations on various paddy fields in northeastern Thailand were carried out in late November 2010 during the ripening season just before harvest in an attempt to realize the applications of the field hyperspectral technique for monitoring the spread of saline soils and estimation of the effects of soil salinity on rice plants. Jasmine rice and glutinous rice were two different rice species selected for this study. Rice plant investigations were conducted by collecting data on crop length, panicle length, canopy openness, leaf area index, and digital photographs of plant conditions from each site. The statistical analysis revealed that the changes in soil EC were significantly sensitive to the ripening stages of both jasmine rice and glutinous rice planted on different levels of soil salinity. Among reflectance measurements, canopy reflectance was highly correlated with soil EC. However, the estimated accuracies of the relationship between soil EC and reflectance of glutinous rice were relatively lower than those of jasmine rice.     

Efficiency Research of Planted Rice Area Estimation From NOAA一AVHRR Data in Taihu Domain

运用NOAA-AVHRR资料估算水稻种植面积，是遥感应用领域中一个新的研究方向，结合国家“八五”攻关项目“太湖地区遥感话产”的要求，在太湖地区进行了初步的尝试:(1)根据估产精度要求和NOAA一AVHRR资料校正精度，探讨了运用NOAA一AVHRR资料估产所需的最小区域范围。(2)针对太湖地区的具休地理环境设计了提取水稻种植曲积的技术方案，并在试验区取得了初步成果。     

Acreage estimation of wheat and barley fields in the province of Adana,Turkey

In this study, the wheat (triticum) and barley (hordeurn) planted areas in the province of Adana were determined by using Landsat-5 TM data in 1991. To classify the wheat and barley fields in this region, Landsat bands 3, 4 and 5 were used. Reflectance distribution in these bands has been expected to have an ellipsoidal shape, and a method was developed to make classification for such distribution. To check the accuracy of the classification, test areas in the province were selected and the classification results were compared with ground-truth. Consequently, it was found that the error estimated wheat and barley planted areas was around 15% and the results of the acreage estimation for wheat and barley fields were 218000 ±32000 hectare in 1991.     

Use of RADARSAT‐1 data and a digital elevation model to assess flood damage and improve rice production in the lower part of the Chi River Basin,Thailand

In Thailand, flooding due to seasonal monsoon conditions frequently destroys a substantial amount of rice production, the most important agricultural activity of the country. Taking the 2001 monsoon flooding that hit the Lower Chi River Basin as an example, we developed a new method for accurately assessing damage to flood‐affected paddies. A RADARSAT‐1 image acquired during peak flooding was combined with a 30‐m digital elevation model (DEM) to develop a ‘flood‐level‐determination’ algorithm for estimating floodwater depth. Based on the elongation capability of the rice varieties, a water depth of 80 cm was used to separate ‘non‐damaged’ from ‘damaged’ paddy areas, indicating that about 60% of the paddy fields in the flooded areas were non‐damaged paddies. To minimize the loss of rice and maximize farmers' incomes, a map of rice varieties appropriate for the damaged paddy areas was produced, combining the flood‐affected paddy map with the flood frequency map. Our results demonstrate the potential of using single‐date RADARSAT‐1 data and a DEM to provide accurate and economic means of assessing flood damage to rice fields that can be used to improve rice production.     

Large-area crop mapping using time-series MODIS 250 m NDVI data: An assessment for the U.S. Central Great Plains

Improved and up-to-date land use/land cover (LULC) data sets that classify specific crop types and associated land use practices are needed over intensively cropped regions such as the U.S. Central Great Plains, to support science and policy applications focused on understanding the role and response of the agricultural sector to environmental change issues. The Moderate Resolution Imaging Spectroradiometer (MODIS) holds considerable promise for detailed, large-area crop-related LULC mapping in this region given its global coverage, unique combination of spatial, spectral, and temporal resolutions, and the cost-free status of its data. The objective of this research was to evaluate the applicability of time-series MODIS 250 m normalized difference vegetation index (NDVI) data for large-area crop-related LULC mapping over the U.S. Central Great Plains. A hierarchical crop mapping protocol, which applied a decision tree classifier to multi-temporal NDVI data collected over the growing season, was tested for the state of Kansas. The hierarchical classification approach produced a series of four crop-related LULC maps that progressively classified: 1) crop/non-crop, 2) general crop types (alfalfa, summer crops, winter wheat, and fallow), 3) specific summer crop types (corn, sorghum, and soybeans), and 4) irrigated/non-irrigated crops. A series of quantitative and qualitative assessments were made at the state and sub-state levels to evaluate the overall map quality and highlight areas of misclassification for each map.The series of MODIS NDVI-derived crop maps generally had classification accuracies greater than 80%. Overall accuracies ranged from 94% for the general crop map to 84% for the summer crop map. The state-level crop patterns classified in the maps were consistent with the general cropping patterns across Kansas. The classified crop areas were usually within 1-5% of the USDA reported crop area for most classes. Sub-state comparisons found the areal discrepancies for most classes to be relatively minor throughout the state. In eastern Kansas, some small cropland areas could not be resolved at MODIS' 250 m resolution and led to an underclassification of cropland in the crop/non-crop map, which was propagated to the subsequent crop classifications. Notable regional areal differences in crop area were also found for a few selected crop classes and locations that were related to climate factors (i.e., omission of marginal, dryland cropped areas and the underclassification of irrigated crops in western Kansas), localized precipitation patterns (overclassification of irrigated crops in northeast Kansas), and specific cropping practices (double cropping in southeast Kansas).