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Land-use and land-cover (LULC) change analyses are useful in understanding the changes in our living environments and their driving factors. Modeling changes of LULC in the future, together with the driving factors derived through analyzing the trends of past LULC changes, bring the opportunity to assess and orientate the current and future land-use policies. As the entryway of Quang Ninh province, Vietnam, Dong Trieu locale has experienced significant LULC changes during the past two decades. In this study, the spatial distribution of six Level I LULC classes, forest, cropland, orchards, waterbody, built-up, and barren land, in Dong Trieu district at 2000, 2010, and 2019 were obtained from Landsat imageries by maximum likelihood technique. The most significant changes observed over the past twenty years are a decrease of barren land (9.1%) and increases of built-up (8.1%) and orchards (6.8%). Driving factor analysis indicated that the changes of cropland and built-up were dependent on distance from road (DFR), distance from main road (DFMR), distance from urban (DFU), distance from water (DFW), elevation, slope, and population density. The changes of forest were dependent on all the driving forces listed above, except DFMR. The orchards mainly appeared near the high-population-density area. The transformation of the waterbody was affected by geography (elevation and slope) and population density. The higher the population density, the less barren the land would appear.
Thi-Thu Vu; Yuan Shen. Land-Use and Land-Cover Changes in Dong Trieu District, Vietnam, during Past Two Decades and Their Driving Forces. Land 2021, 10, 798 .
AMA StyleThi-Thu Vu, Yuan Shen. Land-Use and Land-Cover Changes in Dong Trieu District, Vietnam, during Past Two Decades and Their Driving Forces. Land. 2021; 10 (8):798.
Chicago/Turabian StyleThi-Thu Vu; Yuan Shen. 2021. "Land-Use and Land-Cover Changes in Dong Trieu District, Vietnam, during Past Two Decades and Their Driving Forces." Land 10, no. 8: 798.
Precision nitrogen fertilizer application depends on accurate estimation of plant nitrogen content. However, the assessment of plant nitrogen content at early growth stages of paddy rice through remote sensed images is complicated by the compound effects of backgrounds (e.g. flood water, bare soil, algae, etc.) on the band reflectance. The rapid changing of plant nitrogen content during the vegetative phase makes the development of an operational prediction model very difficult. In this study, aerial images acquired by a quadcopter unmanned aerial vehicle (UAV) equipped with a multispectral sensor were used to estimate plant nitrogen content at vegetative phase of rice crops. The experiments were conducted at the experimental farm of Taiwan Agricultural Research Institute (TARI) from 2018 to 2020. A variable, N-index (ratio between N content of plants to be evaluated and plants not receiving N fertilizers), was introduced to resolve the issues related to rapid changing of plant N content during the vegetative phase. After removing the interference on band reflectance by background from the aerial images, the most appropriate vegetation indices and period that can capture the variations of N-index of rice plants were identified. It was found that a normalized difference red edge index (NDRI) and red edge chlorophyll index (RECI) based model correlated well with the N-index values from c.a. 30 days after transplanting (DAT) to 55 DAT (i.e., the most crucial period for rice yield and grain quality). The developed model was then used to display the spatial and temporal heterogeneity in plant nitrogen status within an experimental field as an example to illustrate how to use the model. In the example, soil plant analysis development (SPAD) meter values at locations of various levels of estimated N-index were collected as surrogates of plant nitrogen content at various DATs to build relationships for converting N-index maps to SPAD maps for potential variable rate fertilizer application management.
Yi-Ping Wang; Yu-Chieh Chang; Yuan Shen. Estimation of nitrogen status of paddy rice at vegetative phase using unmanned aerial vehicle based multispectral imagery. Precision Agriculture 2021, 1 -17.
AMA StyleYi-Ping Wang, Yu-Chieh Chang, Yuan Shen. Estimation of nitrogen status of paddy rice at vegetative phase using unmanned aerial vehicle based multispectral imagery. Precision Agriculture. 2021; ():1-17.
Chicago/Turabian StyleYi-Ping Wang; Yu-Chieh Chang; Yuan Shen. 2021. "Estimation of nitrogen status of paddy rice at vegetative phase using unmanned aerial vehicle based multispectral imagery." Precision Agriculture , no. : 1-17.
In this study, we aim to develop an inexpensive site-specific irrigation advisory service for resolving disadvantages related to using immobile soil moisture sensors and to the differences in irrigation needs of different tea plantations affected by variabilities in cultivars, plant ages, soil heterogeneity, and management practices. In the paper, we present methodologies to retrieve two biophysical variables, surface soil water content and canopy water content of tea trees from Sentinel-2 (S2) (European Space Agency, Paris, France) images and consider their association with crop water availability status to be used for making decisions to send an alert level. Precipitation records are used as auxiliary information to assist in determining or modifying the alert level. Once the site-specific alert level for each target plantation is determined, it is sent to the corresponding farmer through text messaging. All the processes that make up the service, from downloading an S2 image from the web to alert level text messaging, are automated and can be completed before 7:30 a.m. the next day after an S2 image was taken. Therefore, the service is operated cyclically, and corresponds to the five-day revisit period of S2, but one day behind the S2 image acquisition date. However, it should be noted that the amount of irrigation water required for each site-specific plantation has not yet been estimated because of the complexities involved. Instead, a single irrigation rate (300 t ha−1) per irrigation event is recommended. The service is now available to over 20 tea plantations in the Mingjian Township, the largest tea producing region in Taiwan, free of charge since September 2020. This operational application is expected to save expenditures on buying irrigation water and induce deeper root systems by decreasing the frequency of insufficient irrigation commonly employed by local farmers.
Yi-Ping Wang; Chien-Teh Chen; Yao-Chuan Tsai; Yuan Shen. A Sentinel-2 Image-Based Irrigation Advisory Service: Cases for Tea Plantations. Water 2021, 13, 1305 .
AMA StyleYi-Ping Wang, Chien-Teh Chen, Yao-Chuan Tsai, Yuan Shen. A Sentinel-2 Image-Based Irrigation Advisory Service: Cases for Tea Plantations. Water. 2021; 13 (9):1305.
Chicago/Turabian StyleYi-Ping Wang; Chien-Teh Chen; Yao-Chuan Tsai; Yuan Shen. 2021. "A Sentinel-2 Image-Based Irrigation Advisory Service: Cases for Tea Plantations." Water 13, no. 9: 1305.
The assessment of soil organic matter (SOM) content by proximal sensing using Visible and Near-Infrared (VNIR) reflectance spectroscopy of field soil samples is complicated by interactions with various soil constituents and moisture content. This study examined a total of 486 archived agricultural soil samples, covering a wide range of soil reflectance characteristics and soil textures. Spectral reflectance was measured with a spectrometer for samples with wetness ranges from air-dry to near saturation. Prediction models for soil water and SOM content were then developed using partial least square regressions (PLSR) combined with various reflectance spectrum pre-processing methods (standard normal variate transformation and detrend, as well as spectral derivatives). Our results indicate that the PLSR model based on spectra measured on sufficiently wet soil samples had slightly better accuracy for SOM predictions than models based on air-dried samples. The mechanisms of wetting to increase prediction accuracy were also explored. Important reflectance wavelengths associated with organic functional groups such as aromatics, aliphatics, and amides were identified through analysis of their variable importance in projections (VIP). Robustness of the developed models was tested against other two independent datasets (comprised of sample numbers 126 and 99 each), achieving prediction accuracies of mean bias difference (MBD) = 0.02%, root mean square difference (RMSD) = 0.99%, Ratio of Performance to Inter-Quartile (RPIQ) = 2.90 and MBD = −0.23%, RMSD = 1.35%, RPIQ = 1.44, respectively. These findings suggest that developing SOM prediction PLSR models based on sufficiently wetted soil samples may be a viable approach, particularly when developing models for operational use in the field.
Yi-Ping Wang; Che-Kuan Lee; Yi-Hao Dai; Yuan Shen. Effect of wetting on the determination of soil organic matter content using visible and near-infrared spectrometer. Geoderma 2020, 376, 114528 .
AMA StyleYi-Ping Wang, Che-Kuan Lee, Yi-Hao Dai, Yuan Shen. Effect of wetting on the determination of soil organic matter content using visible and near-infrared spectrometer. Geoderma. 2020; 376 ():114528.
Chicago/Turabian StyleYi-Ping Wang; Che-Kuan Lee; Yi-Hao Dai; Yuan Shen. 2020. "Effect of wetting on the determination of soil organic matter content using visible and near-infrared spectrometer." Geoderma 376, no. : 114528.
The soil properties, climate, type of management, and fermentation process critically affect the productivity and quality of tea. In this study, tender tea leaves were collected from central Taiwan, and organic components in their infusions as well as physical and chemical soil properties differentiated using aerial photographs where good (G) and bad (B) growth exhibitions were determined. Eleven physical and chemical soil properties as well as five compounds in tea infusions were analyzed to determine the main factor that affects the growth of these tea trees. The Fleiss’ kappa statistic results revealed that the wet aggregate stability, pH, and exchangeable potassium content exhibit the most significant effect, with scores of 0.86, 0.64, and 0.62, respectively. Soil quality calculated using the mean weight diameter based on 11 soil properties revealed that ~67% of the total score of G is greater than that of B. Generally, contents of total polyphenols (51.67%) and catechins (51.76%) in the infusions of B were greater than those of G. In addition, significant positive correlations between the free amino acids content and soil properties, including pH and copper content, were observed. However, a negative correlation between the free amino acids and flavone contents and most of the soil properties was observed. The survey data set obtained from this study can provide useful information for the improved management of tea plantations.
Prapasiri Tongsiri; Wen-Yu Tseng; Yuan Shen; Hung-Yu Lai. Comparison of Soil Properties and Organic Components in Infusions According to Different Aerial Appearances of Tea Plantations in Central Taiwan. Sustainability 2020, 12, 4384 .
AMA StylePrapasiri Tongsiri, Wen-Yu Tseng, Yuan Shen, Hung-Yu Lai. Comparison of Soil Properties and Organic Components in Infusions According to Different Aerial Appearances of Tea Plantations in Central Taiwan. Sustainability. 2020; 12 (11):4384.
Chicago/Turabian StylePrapasiri Tongsiri; Wen-Yu Tseng; Yuan Shen; Hung-Yu Lai. 2020. "Comparison of Soil Properties and Organic Components in Infusions According to Different Aerial Appearances of Tea Plantations in Central Taiwan." Sustainability 12, no. 11: 4384.
Soil provides crop with nutrients, water and root support. But, soils vary a great deal in terms of origin, appearance, characteristics and production capacity. Better understanding of the causality between yield and yield-limiting soil factor(s) is essential for site-specific crop management. The objectives of this study were deriving a spatiotemporal yield trend map of a 144 km2 paddy rice growing region located at an alluvial plain in southwestern Taiwan from satellite images and exploring the potential yield-limiting soil factor(s) in conjunction with general soil survey data. Due to the complexity of data sets, classification and regression trees analysis (CART) was used to relate soil characteristics to yield classes in the spatiotemporal yield trend map, and followed by comparisons of soil characteristics between those consistently-high and -low yielding areas to explore the interactions between yields and soil properties. Through the above data mining analysis, high soil pH, severe leaching loss of applied nitrogen fertilizers, and excessive reductive root environment were suspected to be the major soil related low-yielding mechanisms spread within studied region. Soil characteristics that induced these low-yielding mechanisms were identified and mapped. Error analysis indicated that 61.8 % of the consistently low-yield areas could be correctly identified by just a few soil characteristics. Improvements of management practices to alleviate the negative effects on yields were also proposed based on the identified low yielding mechanisms. Our study highlighted the pressing need and possible methodologies to adjust management strategies for narrowing yield variability and increasing crop production.
Yi-Ping Wang; Yuan Shen. Identifying and characterizing yield limiting soil factors with the aid of remote sensing and data mining techniques. Precision Agriculture 2014, 16, 99 -118.
AMA StyleYi-Ping Wang, Yuan Shen. Identifying and characterizing yield limiting soil factors with the aid of remote sensing and data mining techniques. Precision Agriculture. 2014; 16 (1):99-118.
Chicago/Turabian StyleYi-Ping Wang; Yuan Shen. 2014. "Identifying and characterizing yield limiting soil factors with the aid of remote sensing and data mining techniques." Precision Agriculture 16, no. 1: 99-118.
Identification and characterization of yield limiting factors based on multi-year yield maps is important for delineating field management zones. Multi-year yield maps were derived from satellite images of a paddy-rice (Oryza sativa L.) study site with a conventional two-cropping system in central Taiwan. Spatiotemporal yield-trend maps with consistently high, average and low yields, and inconsistent yield areas were delineated based on temporal variation and the means of the normalized yields on a per pixel basis. Soil and plant samples were collected and grouped for statistical analysis based on the derived yield-trend maps. Comparison of soil properties and rice yield components among yield classes indicated that differences in leaching loss of basal and top-dressed N fertilizers were the likely limiting factor affecting the spatial variation of yield within the study site.
Yi-Ping Wang; Shou-Hung Chen; Kuo-Wei Chang; Yuan Shen. Identifying and characterizing yield limiting factors in paddy rice using remote sensing yield maps. Precision Agriculture 2012, 13, 553 -567.
AMA StyleYi-Ping Wang, Shou-Hung Chen, Kuo-Wei Chang, Yuan Shen. Identifying and characterizing yield limiting factors in paddy rice using remote sensing yield maps. Precision Agriculture. 2012; 13 (5):553-567.
Chicago/Turabian StyleYi-Ping Wang; Shou-Hung Chen; Kuo-Wei Chang; Yuan Shen. 2012. "Identifying and characterizing yield limiting factors in paddy rice using remote sensing yield maps." Precision Agriculture 13, no. 5: 553-567.
Ability to make large-area yield prediction before harvest is important in many aspects of agricultural decision-making. In this study, canopy reflectance band ratios (NIR/RED, NIR/GRN) of paddy rice ( Oryza sativa L.) at booting stage, from field measurements conducted from 1999 to 2005, were correlated with the corresponding yield data to derive regression-type yield prediction models for the first and second season crop, respectively. These yield models were then validated with ground truth measurements conducted in 2007 and 2008 at eight sites, of different soil properties, climatic conditions, and various treatments in cultivars planted and N application rates, using surface reflectance retrieved from atmospherically corrected SPOT imageries. These validation tests indicated that root mean square error of predicting grain yields per unit area by the proposed models were less than 0.7 T ha −1 for both cropping seasons. Since village is the basic unit for national rice yield census statistics in Taiwan, the yield models were further used to forecast average regional yields for 14 selected villages and compared with officially reported data. Results indicate that the average yield per unit area at village scale can be forecasted with a root mean square error of 1.1 T ha −1 provided no damaging weather occurred during the final month before actual harvest. The methodology can be applied to other optical sensors with similar spectral bands in the visible/near-infrared and to different geographical regions provided that the relation between yield and spectral index is established. Keywords Paddy rice Yield forecasting County/village scale Remote sensing 1 Introduction Reliable and timely forecasting of crop yield over large areas are critical for national food security through decision-making on import/export policies and prices optimization ( Hutchinson, 1991; Thornton et al., 1997 ). Acreage of crop planted and yield per unit area are data required to estimate crop production. For the inventories of acreage, methods for national and subnational estimation from satellite data have already been developed ( Okamoto and Fukuhara, 1996; Fang, 1998; Xiao et al., 2006 ). However, accurate estimates on yield per unit area are hard to get in time because of the spatial and temporal changes in weather, soil, and cultural practices. Generally, estimates on yield per unit area are derived through traditional field survey. Visual assessment and sample harvesting at farm plots are traditional methods commonly used to estimate yield per unit area at regional scale, but are very time consuming and costly. In Taiwan, a village district is the basic unit for national rice yield census statistics. Yield data collected from approximately 1500 random plots across the nation's paddy fields, i.e. about one sample point per 300 ha and 5–10 samples per village district, are used to calculate the final yield. Plot yields were first aggregated with acreage of paddy rice grown within each village to project the rice production at village levels and then making up the statistics for county and national production. The official yield statistics are generally not available until months after harvest. This may also be true in many other countries as well. Remote sensing is an attractive option to supplement/substitute the traditional field survey for large-area crop production estimates because it provides spatial and temporal information of crops ( Kumar and Monteith, 1981; Moulin et al., 1998; Hatfield et al., 2008 ). Generally, three approaches have been proposed to estimate regional yield production with the aid of remote sensed imageries. The first approach was based on empirical regressions that relate crop yield to vegetation indices (VI) derived from remotely sensed surface reflectance ( Ma et al., 2001; Chang et al., 2005; Baez-Gonzalez et al., 2005 ). This approach is the least complicated as compared to other two approaches, but it is commonly criticized that the derived coefficients may not be universally suitable for all cultivars, environments and management practices. The second approach, more physiologically oriented than the first approach, was based on application of Monteith-based models ( Bastiaanssen and Ali, 2003; Lobell et al., 2003 ). This approach employed fractional absorbed photosynthetically active radiation (fAPAR), estimated from remote sensing data, and experimentally derived constants of radiation conversion efficiency and harvest index to estimate yield. The third approach was the most sophisticated among three, which combined remote sensing data and dynamic crop simulation models ( Bach, 1998; Serrano et al., 2000 ). Remotely sensed data were used to derive biophysical information inputs to the crop model, such as leaf area index (LAI), to provide accurate and regionally representative measurements that could be efficiently modeled in a spatial environment. This approach is capable of predicting time-dependent processes of growth that are critical for real-time yield forecasting. However, it requires not only mechanistic and physiologically sound crop growth models, but also complete sets of inputs to account for the inhomogeneous data of soils, cultivated varieties, and management practices within the studied region. From the standpoint of operational routine use, the method to forecast regional yield should neither be very complicated, nor require lots of auxiliary information not derivable from remote sensed imageries. Because grain yield is closely linked to crop growth, and reflectance-based VI respond to canopy variables, such as LAI and fAPAR that largely determine crop growth, all three approaches mentioned above depend on empirical relations to derive required biophysical parameters from spectral data. The normalized difference vegetation index (NDVI) is the most commonly used VI to retrieve required biophysical variables because NDVI relates the...
Yi-Ping Wang; Kuo-Wei Chang; Rong-Kuen Chen; Jeng-Chung Lo; Yuan Shen. Large-area rice yield forecasting using satellite imageries. International Journal of Applied Earth Observation and Geoinformation 2010, 12, 27 -35.
AMA StyleYi-Ping Wang, Kuo-Wei Chang, Rong-Kuen Chen, Jeng-Chung Lo, Yuan Shen. Large-area rice yield forecasting using satellite imageries. International Journal of Applied Earth Observation and Geoinformation. 2010; 12 (1):27-35.
Chicago/Turabian StyleYi-Ping Wang; Kuo-Wei Chang; Rong-Kuen Chen; Jeng-Chung Lo; Yuan Shen. 2010. "Large-area rice yield forecasting using satellite imageries." International Journal of Applied Earth Observation and Geoinformation 12, no. 1: 27-35.
Spatial distribution of canopy N status is the primary information needed for precision management of N fertilizer. This study demonstrated the feasibility of a simple spectral index (SI) using the first derivative of canopy reflectance spectrum at 735 nm (dR/dλ|735) to assess N concentration of rice (Oryza sativa L.) plants, and then validated the applicability of a simplified imaging system based on the derived spectral model from the dR/dλ|735 relationship in mapping canopy N status within field. Results showed that values of dR/dλ|735 were linearly related to plant N concentrations measured at the panicle formation stage. The leaf N accumulation per unit ground area was better fitted than other ratio-based SIs, such as simple ratio vegetation index (SRVI), normalized difference vegetation index (NDVI), R810/R560, and (R1100 − R660)/(R1100 + R660), and remained valid when pooling more data from different cropping seasons in varied years and locations. A simplified imaging system was assembled and mounted on a mobile lifter and a helicopter to take spectral imageries for mapping canopy N status within fields. Results indicated that the imaging system was able to provide field maps of canopy N status with reasonable accuracy (r = 0.465–0.912, root mean standard error [RMSE] = 0.100–0.550) from both remote sensing platforms. Copyright © 2008. American Society of Agronomy. Copyright © 2008 by the American Society of Agronomy
Yuh-Jyuan Lee; Chwen-Ming Yang; Kuo-Wei Chang; Yuan Shen. A Simple Spectral Index Using Reflectance of 735 nm to Assess Nitrogen Status of Rice Canopy. Agronomy Journal 2008, 100, 205 -212.
AMA StyleYuh-Jyuan Lee, Chwen-Ming Yang, Kuo-Wei Chang, Yuan Shen. A Simple Spectral Index Using Reflectance of 735 nm to Assess Nitrogen Status of Rice Canopy. Agronomy Journal. 2008; 100 (1):205-212.
Chicago/Turabian StyleYuh-Jyuan Lee; Chwen-Ming Yang; Kuo-Wei Chang; Yuan Shen. 2008. "A Simple Spectral Index Using Reflectance of 735 nm to Assess Nitrogen Status of Rice Canopy." Agronomy Journal 100, no. 1: 205-212.
Yuh-Jyuan Lee; Chwen-Ming Yang; Kuo-Wei Chang; Yuan Shen. A Simple Spectral Index Using Reflectance of 735 nm to Assess Nitrogen Status of Rice Canopy. Agronomy Journal 2008, 100, 205 -212.
AMA StyleYuh-Jyuan Lee, Chwen-Ming Yang, Kuo-Wei Chang, Yuan Shen. A Simple Spectral Index Using Reflectance of 735 nm to Assess Nitrogen Status of Rice Canopy. Agronomy Journal. 2008; 100 (1):205-212.
Chicago/Turabian StyleYuh-Jyuan Lee; Chwen-Ming Yang; Kuo-Wei Chang; Yuan Shen. 2008. "A Simple Spectral Index Using Reflectance of 735 nm to Assess Nitrogen Status of Rice Canopy." Agronomy Journal 100, no. 1: 205-212.
An inexpensive imaging system able to take various narrow‐band images and placed on platforms of various heights can be very useful to many remote‐sensing studies, particularly for researchers in precision agriculture areas. A handy imaging system, composed of an Electrim EDC‐1000L monochrome camera, a Canon PHF6 1.4 lens, a set of Andover bandpass filters, and an Advantech PCA6751 single board computer, was built up and installed with corresponding self‐developed application software. The system had been deployed on platforms such as a mobile high‐lift crane and helicopter to acquire various narrow‐band images. This simplified imaging system may help greatly in performing validation tests on many stress‐identification indices and related algorithms derived from ground spectroradiometer measurements.
Y. J. Lee; K. W. Chang; Y. Shen; T. M. Huang; H. L. Tsay. A handy imaging system for precision agriculture studies. International Journal of Remote Sensing 2007, 28, 4867 -4876.
AMA StyleY. J. Lee, K. W. Chang, Y. Shen, T. M. Huang, H. L. Tsay. A handy imaging system for precision agriculture studies. International Journal of Remote Sensing. 2007; 28 (21):4867-4876.
Chicago/Turabian StyleY. J. Lee; K. W. Chang; Y. Shen; T. M. Huang; H. L. Tsay. 2007. "A handy imaging system for precision agriculture studies." International Journal of Remote Sensing 28, no. 21: 4867-4876.
Abilities to estimate rice (Oryza sativa L.) yields within fields from remote sensing images is not only fundamental to applications of precision agriculture, but can also be very useful to food provisions management. Major objectives of this study were to identify spectral characteristics associated with rice yield and to establish their quantitative relationships. Field experiments were conducted at Shi-Ko experimental farm of TARI's Chiayi Station during 1999–2001. Rice cultivar Tainung 67, the major cultivar grown in Taiwan, was used in the study. Various levels of rice yield were obtained via N application treatments. Canopy reflectance spectra were measured during entire growth period, and dynamic changes of characteristic spectrum were analyzed. Relationships among rice yields and characteristic spectrum were studied to establish yield estimation models suitable for remote sensing purposes. Spectrum analysis indicated that the changes of canopy reflectance spectrum were least during booting stages. Therefore, the canopy reflectance spectra during this period were selected for model development. Two multiple regression models, constituting of band ratios (NIR/RED and NIR/GRN), were then constructed to estimate rice yields for first and second crops separately. Results of the validation experiments indicated that the derived regression equations successfully predicted rice yield using canopy reflectance measured at booting stage unless other severe stresses occurred afterward. Copyright © 2005. American Society of Agronomy. American Society of Agronomy
Kuo-Wei Chang; Yuan Shen; Jeng-Chung Lo. Predicting Rice Yield Using Canopy Reflectance Measured at Booting Stage. Agronomy Journal 2005, 97, 872 -878.
AMA StyleKuo-Wei Chang, Yuan Shen, Jeng-Chung Lo. Predicting Rice Yield Using Canopy Reflectance Measured at Booting Stage. Agronomy Journal. 2005; 97 (3):872-878.
Chicago/Turabian StyleKuo-Wei Chang; Yuan Shen; Jeng-Chung Lo. 2005. "Predicting Rice Yield Using Canopy Reflectance Measured at Booting Stage." Agronomy Journal 97, no. 3: 872-878.