Crop Growth Status Research Articles

Measurements of evapotranspiration from eddy-covariance systems and large aperture scintillometers in the Hai River Basin, China

Summary Evapotranspiration (ET) observations were made for 3 years (2008–2010), using eddy covariance (EC) systems and large aperture scintillometers (LAS), in typical underlying surfaces across the Hai River Basin: orchards (Miyun, MY), cropland in the suburbs (Daxing, DX), and cropland in the plains (Guantao, GT). Reliable data were obtained after carefully data processing, and the seasonal and interannual variability in ET was quantitatively analyzed. The annual ET during 2008–2010 ranged from 510–730 mm for the EC measurements and 430–560 mm for the LAS measurements. The differences in ET among the years and sites were connected with differences in soil moisture and crop growing conditions. The difference in the source areas of EC and LAS measurements and the heterogeneity in their source areas are the primary causes of the discrepancy between EC and LAS measurements. The EC and LAS measurements are compared to the field water balance method calculation and MOD16 ET (the MODIS ET product from the MODIS Global Evapotranspiration Project), respectively. The average difference was 0.85% (mean relative error) and 33.80 mm (root mean square error) between the EC measurements and field water balance method calculations, and 7.72% and 47.08 mm between LAS measurements and MOD16 ET from 2008 to 2010 at the three sites. We found a decreasing tendency for ET in the past 15 years across the Hai River Basin, especially after the year of 2005.

Adapting crop management practices to climate change: Modeling optimal solutions at the field scale

Climate change will alter the environmental conditions for crop growth and require adjustments in management practices at the field scale. In this paper, we analyzed the impacts of two different climate change scenarios on optimal field management practices in winterwheat and grain maize production with case studies from Switzerland. Management options included nitrogen fertilization (amount, timing and allocation) as well as irrigation. Optimal solutions that maximize the farmer’s utility were sought with the help of a bioeconomic modeling system that integrated the process-based crop growth model CropSyst into an economic decision model. The latter accounted not only for the crop specific average profit margins, but also for production risks, reflecting the utility (expressed as the certainty equivalent) of a risk-averse farmer’s management decisions at field scale. In view of the non-linearity and complexity of the problem, we used a genetic algorithm as optimization technique. For grain maize, our results showed that climate change will foster the use of irrigation, not only at sites prone to water limitation already under current climatic conditions, but more in general for climate change scenarios projecting a substantial decrease in summer precipitation. For winterwheat, irrigation was never identified as an optimal management option. For both crops and sites, climate change reduced the optimum nitrogen fertilization amount and decreased for winterwheat the number of fertilization applications. In all cases, the farmer’s certainty equivalent decreased between 7% and 25% under climate change, implying negative impacts on winterwheat and grain maize production even under the assumption of an adjustment of the optimum management practices.

Remote Sensing Technology Contributes Towards Food Security of Bangladesh

Remote Sensing as well as Space Technology has a spin off benefit in the socio-economic uplift of the countries that has it. It promotes the quality of life of the whole society. Food security means that everyone should get enough food throughout the year to satisfy their nutritional needs. Bangladesh is an agricultural country .Its development and economy depends predominantly on agriculture as well as food security. Rice is the main crop of the country. The main rice is Aus, Aman and Boro which is produced in every part of Bangladesh. The crop monitoring using the Remote Sensing Technology and Geographic Information System (GIS) has a great potential to provide the near real time information on crop growth condition, production and damage of crops due to natural disasters like cyclone, flood, drought etc. Timely and accurate information about the natural resources like crops as well as environmental conditions play a significant role towards food security .It helps the decision-aids of disaster management and national food security policy for the sustainable development of the country. Space Research and Remote Sensing Organization (SPARRSO) conducts monitoring of crop and weather in this regard regularly. In this paper, crop monitoring using Remote Sensing Technology towards food security of Bangladesh, will be discussed.

Evaluation of an Active Remote Sensor for Monitoring Winter Wheat Growth Status

The usefulness of a ground-based sensor embedded on tractor for monitoring the growth of winter wheat using a two plant nutrition active sensors (CropSpec) and RTK-GPS was investigated in this study. In order to consider the reliability of the results, the winter wheat was cultivated in farm land of Hokkaido University. Four levels of N fertilizer were applied to make differences in crop conditions, and growth information was acquired at four growth stages. A strong correlation (R2 > 0.70) for Nitrogen Sufficiency Index (S1 value) and ground truth data was found in the second stage of data collection. Also, a strong relationship (R2 > 0.90) between in-season estimation (INSE) of S1 value and other vegetation indices acquired using a spectroradiometer as a passive sensor demonstrated good performance of CropSpec. However, for diagnosis of crop growth condition for variable rate application, use of the S1 value without elimination of different measurement times is recommended.

Adding Organic Matter Enhanced the Effectiveness of Silicate Rock Fertilizer for Food Crops Grown on Nutritionally Disorder Soils: A Glasshouse Assessment

A glasshouse experiment was carried to identify effects of the application rate of ground silicate rock as a multinutrientfertilizer (SRF) with and without organic matter (OM) on growth and nutrient status of food crops (rice,corn, and soybean). Those crops were grown on 3 different soils in 2 cropping patterns, i.e., rice – soybean and corn– soybean, providing 6 experimental sets. A completely randomized design was applied in each experimental set.The treatment in each set consisted of 3 rates of SRF (5, 10, and 15 g kg-1), those 3 rates + 5 g kg-1 of OM, and acontrol (without adding SRF or OM). The first crops (rice and corn) were grown up to 65 days, while the secondcrop (soybean) was up to 40 days. Results indicated that for crops grown on less fertile soils, the application of SRFonly slightly increased growth of crops, mainly of the 2nd crops, and adding OM greatly increased the growth ofboth the 1st and 2nd crops. In those experimental sets, about 60 – 80% of the variation of crop growth was significantlydetermined by concentration of Cu and several other essential nutrients in crop tissue. In contrast, the growth forcrops grown on more fertile soils was not affected by the application of SRF or/and OM. It was concluded thatadding OM enhanced the effectiveness of SRF as a multi-nutrient fertilizer, and that may be used as an appropriatemulti-nutrient fertilizer or general ameliorant to sustain soil quality and remediate the nutritionally disorder soils.

Image Reduction Method for Rice Leaf Disease Based on Visual Attention Model

Analyzing the crop growth status through leaf disease image is one of the hottest issues in agriculture and forestry fields currently. But the size of image gathered by digital camera is too large, the focus of this research is to zooming-out image at the condition of ensuring the main information which carried by the image to distort lower. Based on the further study of visual attention model proposed by Itti and Ma YF. This paper establishes visual attention and visual saliency map of rice blast and brown spot disease image, whose size is 4272*2878 pixels. Finally, determines the reduction scale of the corresponding effective target collection and provide a new way to reduce the plant leaf images.

Simple weighing lysimeters for measuring evapotranspiration and developing crop coefficients

Knowledge of cotton crop evapotranspiration (ET) is important in scheduling irrigations, optimizing crop production, and modeling ET and crop growth. The ability to measure, estimate, and predict ET and cotton crop water requirements can result in better satisfying the crop' s water needs and improving water use efficiency. Weighing lysimeters have been used for many years to measure and study water use, and to develop crop-coefficient functions necessary in estimating ET. Electronic weighing lysimeters, consisting of a steel outer tank and inner tank, electronic loadcell assemblies, and a PVC drain system, were designed, constructed, and installed. Each lysimeter cost approximately US$1700 (in 2001) in materials, required two people and 40 hours of labor to construct, and were installed by two people using minimal excavation and hand tools. Daily ET data for cotton were collected from 2003 to 2006 to quantify cotton water-use and to develop crop coefficient functions. Seasonal water use ranged generally from 2 to 8 mm/d. Seasonal water-use patterns varied considerably among growing seasons due to variable environmental and crop-growth conditions, making determination of an average crop-coefficient

Fractional vegetation cover estimation in arid and semi-arid environments using HJ-1 satellite hyperspectral data

This paper evaluates the usefulness of the hyperspectral imager (HSI) onboard Chinese HJ-1-A small satellite in vegetation mapping. Fractional vegetation cover (FVC) is an important surface microclimate parameter for characterizing land surface vegetation cover as well as the most effective indicator for assessing desertification and crop growth condition. The HJ-1/HSI data were used to calculate the narrow band vegetation index by using the in situ plot FVC data, which was then applied in sub-pixel de-composition model for the FVC estimation, namely the dimidiate pixel model. The FVC information in the Shihezi Area, Xinjiang, China was retrieved based on the dimidiate pixel model. Cross-checked with the in situ measured FVC data, a correlation coefficient square of 0.86, and the root mean square error of 10.9% is statistically achieved. The verification indicates that the FVC result retrieved from the HJ-1/HSI data is well correlated with the in situ measurements, demonstrating that the HJ-1/HSI data are promising for studying the potential impacts of global climate change on the arid and semi-arid landscapes.

Current and future irrigation water requirements in pan-Europe: An integrated analysis of socio-economic and climate scenarios

Irrigated cropland plays a crucial role for agricultural production but has also been identified as a major user of global freshwater resources. The amount of irrigation water needed depends on climate conditions and socio-economic factors (e.g. amount of crop production and technology). In this study we analyze the development of irrigated area and the corresponding net irrigation water requirements for pan-Europe using a spatially explicit land-use model (LandSHIFT) together with a hydrology and water-use model (WaterGAP3). Four simulation experiments for the assessment of irrigation water requirements were conducted: (1) irrigated area for the base year 2000 under climate normal conditions, (2) irrigated area for 2000 with two climate scenarios for 2050, (3) two scenarios for changing irrigated area until 2050 under climate normal conditions and (4) the two irrigated area scenarios in combination with the climate scenarios. Results show that under baseline conditions yearly irrigation water requirements sum up to 53billion m3. The expansion of irrigated area strongly depends on the combination of socio-economic drivers and climate change. Generally, the two analyzed climate scenarios lead to an expansion of irrigated area which we explain by negative effects of increasing air temperatures on crop growth conditions. Analysing the irrigated area scenarios and climate scenarios separately, we find that the effect of land-use change on net irrigation water requirements is larger than the climate effect. Combined analysis of socio-economic and climate drivers reveal that the expansion of irrigated area is not directly correlated to the changes of irrigation water requirements as the adaptation of sowing dates to the changing climatic conditions might help to overcome seasonal water deficits. The study underlines the importance of considering both the change of irrigated area and agricultural management as well as hydrology aspects in regional water use analysis and shows that simulation models can serve as tools to provide this information to the development of regional water management strategies.

Estimating crop biophysical properties from remote sensing data by inverting linked radiative transfer and ecophysiological models

Remote sensing technology can rapidly provide spatial information on crop growth status, which ideally could be used to invert radiative transfer models or ecophysiological models for estimating a variety of crop biophysical properties. However, the outcome of the model inversion procedure will be influenced by the timing and availability of remote sensing data, the spectral resolution of the data, the types of models implemented, and the choice of parameters to adjust. Our objective was to investigate these issues by inverting linked radiative transfer and ecophysiological models to estimate leaf area index (LAI), canopy weight, plant nitrogen content, and yield for a durum wheat (Triticum durum) study conducted in central Arizona over the winter of 2010–2011. Observations of crop canopy spectral reflectance between 268 and 1095nm were obtained weekly using a GER 1500 spectroradiometer. Other field measurements were regularly collected to describe plant growth characteristics and plant nitrogen content. Linkages were developed between the DSSAT Cropping System Model (CSM) and the PROSAIL radiative transfer model (CSM-PROSAIL) and between the DSSAT-CSM and an empirical model relating NDVI to LAI (CSM-Choudhury). The PEST parameter estimation algorithm was implemented to adjust the leaf area growth parameters of the CSM by minimizing error between measured and simulated NDVI or canopy spectral reflectance. A genetic algorithm was implemented to identify the optimum combination of remote sensing observations required to optimize simulations of LAI through model inversion. The relative root mean squared error (RRMSE) between measured and simulated LAI was 24.1% for the CSM-PROSAIL model, whereas the stand-alone PROSAIL and CSM models simulated LAI with RRMSEs of 40.7% and 27.8%, respectively. Wheat yield was simulated with RRMSEs of 12.8% and 10.0% for the lone CSM model and the CSM-PROSAIL model, respectively. Optimized leaf area growth parameters for CSM-PROSAIL were different among cultivars (p<0.05), while those for CSM-Choudhury were not. Only two observations, one at mid-vegetative growth and one at maximum vegetative growth, were required to optimize LAI simulations for CSM-PROSAIL, whereas CSM-Choudhury required four observations. Inverting CSM-PROSAIL using hyperspectral data offered several advantages as compared to the CSM-Choudhury inversion using a simple vegetation index, including better estimates of crop biophysical properties, different leaf area growth parameter estimates among cultivars (p<0.05), and fewer required remote sensing observations for optimum LAI simulation.

Evaluation of desert management and rehabilitation by petroleum mulch base on temporal spectral analysis and field study (case study: Ahvaz, Iran)

In past decades, Iranian desert areas have undergone widespread land use transformation. Sand dune stabilizing projects and anti-desertification programs were initiated in 1965 and now cover about one million hectares of degraded land in Iran. This project aimed to stabilize sand dunes, to induce afforestation, to establish windbreaks and to apply mulch. Currently, more than three million hectares of arid land has been reclaimed across the country. In this study, maximum likelihood supervised classification and post-classification change detection techniques were applied to Landsat and TM/ETM images acquired in 1991 and 2002, respectively to map the anti-desertification process in southwestern Iran. A supervised classification was carried out for each of the two images. The results of this classification were refined using auxiliary data, visual interpretation and expert knowledge of the area. A post-classification change detection technique was used to produce change image through a cross module. Changes were assessed according to different classes. Observations made during the course of this study demonstrate that an obvious, positive change has taken place as a result of the petroleum mulch project. Results show that sand dune areas decreased from 66.4% to 56%, and in this case afforestation areas increased from 2.1% to 7.3%. However afforestation increased soil organic matter and recuperation of soil moisture storage conditions and chemical reactions such as dissolution of minerals and established more suitable conditions for crop growth.

Effects Silver Nanoparticles and Magnetic Field on Growth of Fodder Maize (Zea mays L.)

Two experiments were done in 2008 and 2009 to study the effects of magnetic field and silver nanoparticles on fodder maize (Zea mays L.). These experiments were done with seven treatments based on a randomized complete block design in four replications. The treatments were as follows: magnetic field and silver nanoparticles + Kemira fertilizer (T1), magnetic field and silver nanoparticles + Humax fertilizer (T2), magnetic field and silver nanoparticles (T3), Kemira fertilizer (T4), Librel fertilizer (T5), Humax fertilizer (T6), and a control (T7). Results showed that fresh yield was higher in treatments T3 and T4. Treatments T3 and T4 had increased maize fresh yields of 35 and 17.5 % in comparison to the control, respectively. The dry matter yield of those plants exposed to magnetic field and silver nanoparticles was significantly higher than that from any of the other treatments. Magnetic field and silver nanoparticle treatments (T3 and T1) showed higher percentages for ears, and the lowest percentages were found in treatments T7 and T5. In general, the soil conditions for crop growth were more favorable in 2009 than in 2008, which caused the maize to respond better to treatments tested in the study; therefore, treatments had more significant effects on studied traits in 2008 than in 2009.

Predicting how adaptation to climate change could affect ecological conservation: secondary impacts of shifting agricultural suitability

AbstractAim: Ecosystems face numerous well‐documented threats from climate change. The well‐being of people also is threatened by climate change, most prominently by reduced food security. Human adaptation to food scarcity, including shifting agricultural zones, will create new threats for natural ecosystems. We investigated how shifts in crop suitability because of climate change may overlap currently protected areas (PAs) and priority sites for PA expansion in South Africa. Predicting the locations of suitable climate conditions for crop growth will assist conservationists and decision‐makers in planning for climate change.Location: South Africa.Methods: We modelled climatic suitability in 2055 for maize and wheat cultivation, two extensively planted, staple crops, and overlaid projected changes with PAs and PA expansion priorities.Results: Changes in winter climate could make an additional 2 million ha of land suitable for wheat cultivation, while changes in summer climate could expand maize suitability by up to 3.5 million ha. Conversely, 3 million ha of lands currently suitable for wheat production are predicted to become climatically unsuitable, along with 13 million ha for maize. At least 328 of 834 (39%) PAs are projected to be affected by altered wheat or maize suitability in their buffer zones.Main conclusions: Reduced crop suitability and food scarcity in subsistence areas may lead to the exploitation of PAs for food and fuel. However, if reduced crop suitability leads to agricultural abandonment, this may afford opportunities for ecological restoration. Expanded crop suitability in PA buffer zones could lead to additional isolation of PAs if portions of newly suitable land are converted to agriculture. These results suggest that altered crop suitability will be widespread throughout South Africa, including within and around lands identified as conservation priorities. Assessing how climate change will affect crop suitability near PAs is a first step towards proactively identifying potential conflicts between human adaptation and conservation planning.

Plant adaptation to climate change—opportunities and priorities in breeding

Climate change in Australia is expected to influence crop growing conditions through direct increases in elevated carbon dioxide (CO2) and average temperature, and through increases in the variability of climate, with potential to increase the occurrence of abiotic stresses such as heat, drought, waterlogging, and salinity. Associated effects of climate change and higher CO2 concentrations include impacts on the water-use efficiency of dryland and irrigated crop production, and potential effects on biosecurity, production, and quality of product via impacts on endemic and introduced pests and diseases, and tolerance to these challenges. Direct adaptation to these changes can occur through changes in crop, farm, and value-chain management and via economically driven, geographic shifts where different production systems operate. Within specific crops, a longer term adaptation is the breeding of new varieties that have an improved performance in ‘future’ growing conditions compared with existing varieties. In crops, breeding is an appropriate adaptation response where it complements management changes, or when the required management changes are too expensive or impractical. Breeding requires the assessment of genetic diversity for adaptation, and the selection and recombining of genetic resources into new varieties for production systems for projected future climate and atmospheric conditions. As in the past, an essential priority entering into a ‘climate-changed’ era will be breeding for resistance or tolerance to the effects of existing and new pests and diseases. Hence, research on the potential incidence and intensity of biotic stresses, and the opportunities for breeding solutions, is essential to prioritise investment, as the consequences could be catastrophic. The values of breeding activities to adapt to the five major abiotic effects of climate change (heat, drought, waterlogging, salinity, and elevated CO2) are more difficult to rank, and vary with species and production area, with impacts on both yield and quality of product. Although there is a high likelihood of future increases in atmospheric CO2 concentrations and temperatures across Australia, there is uncertainty about the direction and magnitude of rainfall change, particularly in the northern farming regions. Consequently, the clearest opportunities for ‘in-situ’ genetic gains for abiotic stresses are in developing better adaptation to higher temperatures (e.g. control of phenological stage durations, and tolerance to stress) and, for C3 species, in exploiting the (relatively small) fertilisation effects of elevated CO2. For most cultivated plant species, it remains to be demonstrated how much genetic variation exists for these traits and what value can be delivered via commercial varieties. Biotechnology-based breeding technologies (marker-assisted breeding and genetic modification) will be essential to accelerate genetic gain, but their application requires additional investment in the understanding, genetic characterisation, and phenotyping of complex adaptive traits for climate-change conditions.

Extraction of Planting Areas of Major Crops and Crop Growth Monitoring in Northeast China

Abstract This paper presents a method used in China Agriculture Remote Sensing Monitoring System (CHARMS) for automatically identifying crop planting areas and monitoring crop growth conditions at a large scale based on time-series of MODIS NDVI Datasets. In doing that, the characteristics of NDVI time series of spring wheat, spring corn, soybean and rice in Northeastern China were firstly analyzed to determine the threshold values used for extracting different crops. Then using these thresholds, extraction models for above-mentioned four major crops were established and applied to obtain the spatial distribution of these four crops in Northeastern China in 2009. In comparison with the average statistic data of several years, the total extraction accuracy is over 87%, which suggests its feasibility to extract planting areas of different crops at a large scale using MODIS data. Based on the extracted crop planting areas, the same MODIS NDVI time series data were used to monitor crop growth conditions in 20...

A Sun Tracking PV System Equipped Greenhouse Control System for Educational Purpose

The greenhouse control system for educational purpose is designed by personal computer. This system should observe and control the growth conditions for crops in both plastic film and glass greenhouses. In this contribution puts emphasis on construction of greenhouse control system by personal computer under the aim of creating safer, more effective and more economical services. This system is developed for the requirements of the intelligent greenhouse control system and is powered by solar energy. A photovoltaic system with sun tracking module is used for the greenhouse control system.

Salicylic acid-induced glutathione status in tomato crop and resistance to root-knot nematode, Meloidogyne incognita (Kofoid &amp; White) Chitwood

Salicylic acid-(SA) is a plant defense stimulator. Exogenous application of SA might influence the status of glutathione-(GSH). GSH activates and SA alters the expression of defense genes to modulate plant resistance against pathogens. The fate of GSH in a crop following SA treatment is largely unknown. The SA-induced profiles of free reduced-, free oxidized-(GSSG) and protein bound-(PSSG) glutathione in tomato crop following foliar treatment of transplant at 5.0-10.0 &amp;mu;g mL&amp;ndash;1 were measured by liquid chromatography. Resistance to root-knot nematode, &lt;em&gt;Meloidogyne incognita&lt;/em&gt; damaging tomato and crop performance were also evaluated. SA treatment at 5.0-10.0 &amp;mu;g mL&amp;ndash;1 to tomato transplants increased GSH, GSSG and PSSG in plant leaf and root, more so in leaf, during crop growth and development. As the fruits ripened, GSH and PSSG increased and GSSG declined. SA reduced the root infection by &lt;em&gt;M. incognita&lt;/em&gt;, nematode reproduction and thus, improved the resistance of tomato var. Pusa Ruby, but reduced crop growth and redox status. SA at 5.0 &amp;mu;g mL&amp;ndash;1 improved yield and fruit quality. The study firstly linked SA with activation of glutathione metabolism and provided an additional dimension to the mechanism of induced resistance against obligate nematode pathogen. SA increased glutathione status in tomato crop, imparted resistance against &lt;em&gt;M. incognita&lt;/em&gt;, augmented crop yield and functional food quality. SA can be applied at 5.0 &amp;mu;g mL&amp;ndash;1 for metabolic engineering of tomato at transplanting to combine host-plant resistance and health benefits in formulating a strategic nematode management decision.

Hardware Design of Soil Moisture Wireless Monitoring System

Proper soil moisture content is one of the necessary conditions for crop growth and steady yield. The soil moisture real time measurement is the basis of reasonable irrigation and an effective way for saving water resources. This paper presented a real-time monitoring system of volumetric soil moisture content. The system consisted of field data collection terminal and monitor center. The data is transmitted by wireless communication between the measurement field and monitor center. The soil moisture content is obtained by measuring the sensor frequency variation with the soil dielectric constant. With solar cell, solar energy was collected and stored in accumulator cell to provide power supply for field data collection terminal. The experimental result shows the system is operating good and working stably, it is promising to be used for real time measurement of soil moisture.

A comparison of harvest index estimation methods of winter wheat based on field measurements of biophysical and spectral data

Crop harvest index (HI) is a key parameter in grain yield simulation from indirect biomass estimates and varies with crop growth conditions. How to access the HI still remains an issue to be addressed. Several HI estimation techniques for wheat have been proposed based on the fraction of water transpired after anthesis (theta(E) or theta(Ee)), the fraction of biomass accumulation after anthesis (f(G)) and the ratio of the mean normalized difference vegetation index (NDVI) after anthesis to that of the pre-anthesis period (NDVI(post)/NDVI(pre)). In this paper, efforts were made to test the performances of the above HI estimation methods based on field measurements of winter wheat at different nitrogen (N) levels. Results showed that the HI changed significantly from low-N to high-N treatments, while the theta(E) or theta(Ee) was not sensitive to the N deficiency. In addition, no consistent effect of N fertilizer on f(G) was observed. Using the canopy NDVI to estimate HI seemed workable since strong correlations were found between HI, the term 'NDVI(post)/NDVI(pre)' and the fraction of cumulative NDVI after anthesis. Further analysis suggested a good estimation of HI can be obtained just using the NDVI dataset from anthesis to maturity, especially via binary regression relations of HI, NDVI at anthesis and cumulative NDVI after anthesis. The proposed method in this study may be well oriented to practical application as its short-term dataset is relatively easy to access from satellite platforms. (C) 2011 IAgrE. Published by Elsevier Ltd. All rights reserved.

Serial Crop Growth Status Diagnosis by Using Image Data Obtained with Different Sensors in Different Seasons: Case Study for the Diagnosis of Wheat Growth Status

本研究では，限られた地域における入手可能な多時期，多センサ画像データを活用した時系列的な作物生育診断を可能にするべく，アスファルトの地上での反射率を基準として波長帯毎にディジタルナンバー（以下DNと略す）を正規化して多時期，多センサ画像データの比較を可能にする手法を提案する．ここでいう「正規化」とは，各画像データのDNを地上反射率に換算することである．センサが異なる画像データのDNは，反射率に換算するためのゲイン及びオフセットの値が異なり，そのままでは画像データ間での比較に用いることができない．多時期に撮影された画像データは大気補正も必要である．そこで，衛星データの分解能でも位置を目で特定できる広さを持ち，かつ時期による反射率の変動が少ないアスファルトの地上での反射率を基準として波長帯毎にDNを正規化することによりこの両者を同時に補正し，多時期，多センサ画像データの比較を可能にした．その上で，茨城県筑西市田谷川地区を対象とし，2006～2009年にかけて秋播きコムギ茎立ち期～成熟期におけるALOS/AVNIR-2，航空機/ADS-40及び航空写真用フィルムの画像データに本手法を適用し，有効性を検証した．DNの正規化による地上反射率から計算したNDVIの経時変化はいずれの画像データでも同様の値及び傾向を示し，正規化の適正さを確認した．また，コムギ茎立ち期のNDVIは，いずれの年次でも収量と有意な相関関係を持ち，生育診断の指標となることが示された．