Estimating wheat spike-leaf composite indicator(SLI)dynamics by coupling spectral indices and machine learning

The contribution of spike photosynthesis to grain yield(GY)has been overlooked in the accurate spectral prediction of yield.Thus,it’s essential to construct and estimate a yield-related phenotypic trait considering spike photosynthesis.Based on field and spectral reflectance data from 19 wheat cultivars under two nitrogen fertilization conditions in two years,our objectives were to(i)construct a yield-related phenotypic trait(spike–leaf composite indicator,SLI)accounting for the contribution of the spike to photosynthesis,(ii)develop a novel spectral index(enhanced triangle vegetation index,ETVI3)sensitive to SLI,and(iii)establish and evaluate SLI estimation models by integrating spectral indices and machine learning algorithms.The results showed that SLI was sensitive to nitrogen fertilizer and wheat cultivar variation as well as a better predictor of yield than the leaf area index.ETVI3 maintained a strong correlation with SLI throughout the growth stage,whereas the correlations of other spectral indices with SLI were poor after spike emergence.Integrating spectral indices and machine learning algorithms improved the estimation accuracy of SLI,with the most accurate estimates of SLI showing coefficient of determination,root mean square error(RMSE),and relative RMSE values of 0.71,0.047,and 26.93%,respectively.These results provide new insights into the role of fruiting organs for the accurate spectral prediction of GY.This high-throughput SLI estimation approach can be applied for wheat yield prediction at whole growth stages and may be assisted with agronomical practices and variety selection.

Detection of Burned Areas through Spectral Indices Analysis of Sentinel-2A Satellite Images in the Abokouamékro Wildlife Reserve (Central, Côte D’Ivoire)

In Côte d’Ivoire, the recurring and unregulated use of bushfires, which cause ecological damage, presents a pressing concern for the custodians of protected areas. This study aims to enhance our comprehension of the dynamics of burnt areas within the Abokouamékro Wildlife Reserve (AWR) by employing the analysis of spectral indices derived from satellite imagery. The research methodology began with the calculation of mean indices and their corresponding spectral sub-indices, including NDVI, SAVI, NDWI, NDMI, BAI, NBR, TCW, TCG, and TCB, utilizing data from the Sentinel-2A satellite image dated January 17, 2022. Subsequently, a fuzzy classification model was applied to these various indices and sub-indices, guided by the degree of membership α, with the goal of effectively distinguishing between burned and unburned areas. Following the classification, the accuracies of the classified indices and sub-indices were validated using the coordinates of 100 data points collected within the AWR through GPS technology. The results revealed that the overall accuracy of all indices and sub-indices declines as the degree of membership α decreases from 1 to 0. Among the mean spectral indices, NDVI-mean, SAVI-mean, NDMI-mean exhibited the highest overall accuracies, achieving 97%, 95%, and 90%, respectively. These results closely mirrored those obtained by sub-indices using band 8 (NDVI-B8, SAVI-B8, and NDMI-B8), which yield respective overall accuracies of 93%, 92%, and 89%. At a degree of membership α = 1, the estimated burned areas for the most effective indices encompassed 2144.38 hectares for NDVI-mean, 1932.14 hectares for mean SAVI-mean, and 4947.13 hectares for mean NDMI-mean. A prospective approach involving the amalgamation of these three indices could have the potential to yield improved outcomes. This study could be a substantial contribution to the discrimination of bushfires in Côte d’Ivoire.

Assessing fire severity in Turkey's forest ecosystems using spectral indices from satellite images

Fire severity classifications determine fire damage and regeneration potential in post-fire areas for effective implementation of restoration applications.Since fire damage varies according to vegetation and fire characteristics,regional assessment of fire severity is crucial.The objectives of this study were:(1)to test the performance of different satellite imagery and spectral indices,and two field—measured severity indices,CBI(Composite Burn Index)and GeoCBI(Geometrically structured Composite Burn Index)to assess fire severity;(2)to calculate classification thresholds for spectral indices that performed best in the study areas;and(3)to generate fire severity maps that could be used to determine the ecological impact of forest fires.Five large fires in Pinus brutia(Turkish pine)and Pinus nigra subsp.pallasiana var.pallasiana(Anatolian black pine)—dominated forests during 2020 and 2021 were selected as study sites.The results show that GeoCBI provided more reliable estimates of field—measured fire severity than CBI.While Sentinel-2 and Landsat-8/OLI images performed similarly well,MODIS performed poorly.Fire severity classification thresholds were determined for Sentinel-2 based RdNBR,dNBR,dSAVI,dNDVI,and dNDMI and Landsat-8/OLI based dNBR,dNDVI,and dSAVI.Among several spectral indices,the highest accuracy for fire severity classification was found for Sentinel-2 based RdNBR(72.1%)and Landsat-8/OLI based dNBR(69.2%).The results can be used to assess and map fire severity in forest ecosystems similar to those in this study.

Quantitative estimation of photosynthetic pigments using new spectral indices

Foliar chlorophylls are the most important pigments related to the physiological function of plants. Quantitative estimation of photosynthetic pigments can provide important information about relationships between plants and their environmental conditions. In this study, new spectral indices were designed to enhance spectral resistance to noise, using the area of the spectral curve and axis. The specific area was around the red edge （Rdaa）, instead of the sum of the first derivative of the spectrum, specifically the area of red edge （Rda）. Meanwhile, three reference indices were also introduced as non-sensitive bands of chlorophylls. The results show that by dividing spectral references, a kind of re-projection, the spectral indices can be calibrated to allow direct and reasonable comparisons of the results. The sensitivity of these reference indices to chlorophylls was also evaluated in this study. The regression results show that Rdaa and its derivates are highly related to chlorophylls and resistant to noise.

Estimation of rock Fe content based on hyperspectral indices

Information on the Fe content of bare rocks is needed for implementing geochemical processes and identifying mines.However,the influence of Fe content on the spectra of bare rocks has not been thoroughly analyzed in previous studies.The Saur Mountain region within the Hoboksar of the Russell Hill depression was selected as the study area.Specifically,we analyzed six hyperspectral indices related to rock Fe content based on laboratory measurements(Dataset I)and field measurements(Dataset II).In situ field measurements were acquired to verify the laboratory measurements.Fe content of the rock samples collected from different fresh and weathered rock surfaces were divided into six levels to reveal the spatial distributions of Fe content of these samples.In addition,we clearly displayed wavelengths with obvious characteristics by analyzing the spectra of these samples.The results of this work indicated that Fe content estimation models based on the fresh rock surface measurements in the laboratory can be applied to in situ field or satellite-based measurements of Fe content of the weathered rock surfaces.It is not the best way to use only the single wavelengths reflectance at all absorption wavelengths or the depth of these absorption features to estimate Fe content.Based on sample data analysis,the comparison with other indices revealed that the performance of the modified normalized difference index is the best indicator for estimating rock Fe content,with R2 values of 0.45 and 0.40 corresponding to datasets I and II,respectively.Hence,the modified normalized difference index(the wavelengths of 2220,2290,and 2370 nm)identified in this study could contribute considerably to improve the identification accuracy of rock Fe content in the bare rock areas.The method proposed in this study can obviously provide an efficient solution for large-scale rock Fe content measurements in the field.

Principal polar spectral indices for mapping mangroves forest inSouth East Asia: study case Indonesia

Identification and monitoring of species composition and richness isneeded to formulate effective mangrove management and conservationpriorities. Prior studies have used commercial satellite images which arecost prohibitive for national and global applications. Here, we usedfreely available Landsat satellite data and new indices to discriminatemangrove species in Maros Regency, South Sulawesi, Indonesia andSegara Anakan, West Java, Indonesia. We use sensitive algorithm of theprincipal polar spectral (PPS) indices to discriminate mangroves species.PPS Indices were produced from a set of 3-dimensional Landsat 8Operational Land Imager (OLI) spectral indices (PPS Brightness, PPSGreenness, and PPS Wetness) determined by a polar change of theprincipal component axes of a spectral image of reference scene. Wequalitatively compare this set of PPS indices with the set of conventionalRGB multi-bands image composition and conventional NormalizedDifference Vegetation Indices (NDVI) for mangroves speciesdiscrimination. The comparisons indicate that the set of PPS indiceshave the potential for regional and possibly global applications inmangroves species mapping and monitoring.

An automatic procedure for generating burn severity maps from the satellite images-derived spectral indices

Fire,especially wildfire,which can be considered as one of the main threats to vegetation cover and animals’life,has attracted lots of attention from environmental researchers.To better manage the fire crisis and take the necessary measures to compensate for its damages,it is essential to have detailed information about the burn severity levels.Accordingly,satellite images and their spectral indices have been widely considered in the literature as powerful tools in producing burn severity information.Despite the efficiency of the previously proposed methods,the necessity of ground reference data for their thresholding step faces them with serious challenges.To address this problem,in this study,an automatic procedure based on the change-point analysis is presented for thresholding differenced normalized burn ratio(dNBR)and its another version,dNBR2.In this procedure,a mean-shift based change-point analysis is performed on the dNBR and dNBR2 images for classifying them into burn severity levels.Experiments,conducted on some parts of Alaska and California in the United States,illustrated the high efficiency of the proposed method.Moreover,as an applied experiment,the severity of the fires,occurred in 2020 in the Khaeiz protected area in Iran,was estimated and compared with local reports.

Soil Salinity Detection in Semi-Arid Region Using Spectral Unmixing, Remote Sensing and Ground Truth Measurements

Soil salinity is one of the serious environmental problems ravaging the soils of arid and semi-arid region, thereby affecting crop productivity, livestock, increase level of poverty and land degradation. Hyperspectral remote sensing is one of the important techniques to monitor, analyze and estimate the extent and severity of soil salt at regional to local scale. In this study we develop a model for the detection of salt-affected soils in arid and semi-arid regions and in our case it’s Ghannouch, Gabes. We used fourteen spectral indices and six spectral bands extracted from the Hyperion data. Linear Spectral Unmixing technique (LSU) was used in this study to improve the correlation between electrical conductivity and spectral indices and then improve the prediction of soil salinity as well as the reliability of the model. To build the model a multiple linear regression analysis was applied using the best correlated indices. The standard error of the estimate is about 1.57 mS/cm. The results of this study show that hyperion data is accurate and suitable for differentiating between categories of salt affected soils. The generated model can be used for management strategies in the future.

Development of fragility curves by incorporating new spectral shape indicators and a weighted damage index:case study of steel braced frames in the city of Mashhad,Iran

In this study, strong ground motion record （SGMR） selection based on Eta （~/） as a spectral shape indicator has been investigated as applied to steel braced flame structures. A probabilistic seismic hazard disaggregation analysis for the definition of the target Epsilon （ε） and the target Eta （η） values at different hazard levels is presented, taking into account appropriately selected SGMR＇s. Fragility curves are developed for different limit states corresponding to three representative models of typical steel braced frames having significant irregularities in plan, by means of a weighted damage index. The results show that spectral shape indicators have an important effect on the predicted median structural capacities, and also that the parameter r/is a more robust predictor of damage than searching for records with appropriate c values.

Mapping rapeseed planting areas using an automatic phenology-and pixel-based algorithm(APPA) in Google Earth Engine

The timely and rapid mapping of rapeseed planting areas is desirable for national food security. Most current rapeseed mapping methods depend strongly on images with good observations obtained during the flowering stages. Although vegetation indices have been proposed to identify the rapeseed flowering stage in some areas, automatically mapping rapeseed planting areas in large regions is still challenging.We developed an automatic phenology-and pixel-based algorithm(APPA) by integrating Landsat 8 and Sentinel-1 satellite data. We found that the Normalized Rapeseed Flowering Index shows unique spectral characteristics during the flowering and post-flowering periods, which distinguish rapeseed parcels from other land-use types(urban, water, forest, grass, maize, wheat, barley, and soybean). To verify the robustness of APPA, we applied APPA to seven areas in five rapeseed-producing countries with flowering images unavailable. The rapeseed maps by APPA showed consistently high accuracies with producer accuracies of 0.87–0.93 and F-scores of 0.92–0.95 based on 4503 verification samples. They showed high spatial consistency at the pixel level with the land cover Scientific Expertise Centres(SEC) map in France,Crop Map of England in United Kingdom, national-scale crop-and land-cover map of Germany, and Annual Crop Inventory in Canada at the pixel level. We propose APPA as a highly promising method for automatically and efficiently mapping rapeseed areas.

Modeling the Spatial Distribution of Soil Heavy Metals Using Random Forest Model—A Case Study of Nairobi and Thirirka Rivers’ Confluence

Modeling the spatial distribution of soil heavy metals is important in determining the safety of contaminated soils for agricultural use. This study utilized 60 topsoil samples (0 - 30 cm), multispectral images (Sentinel-2), spectral indices, and ancillary data to model the spatial distribution of heavy metals in the soils along the Nairobi River. The model was generated using the Random Forest package in R. Using R2 to assess the prediction accuracy, the Random Forest model generated satisfactory results for all the elements. It also ranked the variables in order of their importance in the overall prediction. Spectral indices were the most important variables within the rankings. From the predicted topsoil maps, there were high concentrations of Cadmium on the easterly end of the river. Cadmium is an impurity in detergents, and this section is in close proximity to the Nairobi water sewerage plant, which could be a direct source of Cadmium. Some farms had Zinc levels which were above the World Health Organization recommended limit. The Random Forest model performed satisfactorily. However, the predictions can be improved further if the spatial resolutions of the various variables are increased and through the addition of more predictor variables.

Above Ground Biomass Assessment from Combined Optical and SAR Remote Sensing Data in Surat Thani Province, Thailand

Today the carbon content in the atmosphere is predominantly increasing due to greenhouse gas emission and deforestation. Forest plays a key role in absorbing carbon dioxide from atmosphere by process of sequestration through photosynthesis and stores in form of wood biomass which contains nearly 70% - 80% of global carbon. Different forms of biomass in the environment include agricultural products, wood, renewable energy and solid waste. Therefore, it is essential to estimate the biomass content in the environment. In olden days, biomass is estimated by forest inventory techniques which consume lot of time and cost. The spatial distribution of biomass cannot be obtained by traditional inventory forest techniques so the application of remote sensing in biomass assessment is introduced to solve the problem. Overall accuracy of classified map indicates that land features of Surat Thani on map show an accuracy of 91.13% with different land features on ground. Both optical (LANDSAT-8) and synthetic aperture radar (ALOS-2) remote sensing data are used for above ground biomass (AGB) assessment. Biomass that stores in branch and stem of tree is called as above ground biomass. Twenty ground sample plots of 30 m × 30 m utilized for biomass calculation from allometric equations. Optical remote sensing calculates the biomass based on the spectral indices of Soil Adjusted Vegetation Index (SAVI) and Ratio Vegetation Index (RVI) by regression analysis (R2 = 0.813). Synthetic aperture radar (SAR) is an emerging technique that uses high frequency wavelengths for biomass estimation. HV backscattering of ALOS-2 shows good relation (R2 = 0.74) with field calculated biomass compared to HH (R2 = 0.43) utilizes for biomass model generation by linear regression analysis. Combination of both optical spectral indices (SAVI, RVI) and HV (ALOS-2) SAR backscattering increases the plantation biomass accuracy to (R2 = 0.859) compared to optical (R2 = 0.788) and SAR (R2 = 0.742).

Spatiotemporal patterns of burned areas,fire drivers,and fire probability across the equatorial Andes

Field-based fire studies in the equatorial Andes indicate that fires are strongly associated with biophysical and anthropogenic variables.However,fire controls and fire regimes at the regional scale remain undocumented.Therefore,this paper describes spatial and temporal burned-area patterns,identifies biophysical and anthropogenic fire drivers,and quantifies fire probability across 6°of latitude and 3°of longitude in the equatorial Andes.The spatial and temporal burned-area analysis was carried out based on 18 years(2001-2018)of the MCD64 A1 MODIS burned-area product.Climate,topography,vegetation,and anthropogenic variables were integrated in a logistic regression model to identify the significance of explanatory variables and determine fire occurrence probability.A total of 5779 fire events were registered during the 18 years of this study,located primarily along the western cordillera of the Andes and spreading from North to South.Eighty-eight percent of these fires took place within two fire hotspots located in the northwestern and southwestern corners of the study area.Ninety-nine percent occurred during the second part of the year,between June and December.The largest density of fires was primarily located on herbaceous vegetation and shrublands.Results show that mean monthly temperature,precipitation and NDVI during the prefire season,the location of land cover classes such as forest and agriculture,distance to roads and urban areas,slope,and aspect were the most important determinants of spatial and temporal fire distribution.The logistic regression model achieved a good accuracy in predicting fire probability(80%).Probability was higher in the southwestern and northern corners of the study area,and lower towards the north in the western and eastern piedmonts of the Andes.This analysis contributes to the understanding of fires in mountains within the tropics.The results here presented have the potential to contribute to fire management and control in the region.

An examination of thematic research,development,and trends in remote sensing applied to conservation agriculture

Conservation agriculture seeks to reduce environmental degradation through sustainable management of agricultural land.Since the 1990s,agricultural research has been conducted using remote sensing technologies;however,few previous reviews have been conducted focused on different conservation management practices.Most of the previous literature has focused on the application of remote sensing in agriculture without focusing exclusively on conservation practices,with some only providing a narrative review,others using biophysical remote sensing for quantitative estimates of the bio-geo-chemical-physical properties of soils and crops,and few others focused on single agricultural management practices.This paper used the preferred reporting items for systematic review(PRISMA)methodology to examine the last 30 years of thematic research,development,and trends associated with remote sensing technologies and methods applied to conservation agriculture research at various spatial and temporal scales.A set of predefined key concepts and keywords were applied in three databases:Scopus,Web of Science,and Google Scholar.A total of 188 articles were compiled for initial examination,where 68 articles were selected for final analysis and grouped into cover crops,crop residue,crop rotation,mulching,and tillage practices.Publications on conservation agriculture research using remote sensing have been increasing since 1991 and peaked at 10 publications in 2020.Among the 68 articles,94%used a pixel-based,while only 6%used an object-based classification method.Prior to 2005,tillage practices were abundantly studied,then crop residue was a focused theme between 2004 and 2012.From 2012 to 2020,the focus shifted again to cover crops.Ten spectral indices were used in 76%of the 68 studies.This examination offered a summary of the new potential and identifies crucial future research needs and directions that could improve the contribution of remote sensing to the provision of long-term operational services for various conservation agriculture applications.

A spatial frequency/spectral indicator-driven model for estimating cultivated land quality using the gradient boosting decision tree and genetic algorithm-back propagation neural network

Cultivated land quality(CLQ)is related to national food security.Rapid and high-precision monitoring of CLQ is crucial for the sustainable development of agriculture.However,current satellite image-based evaluation methods that only consider the crop's spatial spectrum characteristics in the key growth stages cannot accurately estimate CLQ.This study proposes a new method based on time-series spectral data of crop growth to improve the accuracy of CLQ estimation.This study was conducted in the Conghua District of Guangzhou,Guangdong Province,China.The results showed that seven spectral indicators were determined as the optimal indicators based on the gradient boosting decision tree(GBDT)and variance inflation factor(VIF).And the genetic algorithm-back propagation neural network(GA-BPNN)model provided more accurate CLQ estimates than the partial least squares regression(PLSR)model,indicating a nonlinear relationship between CLQ and the indicators.In addition,the GA-BPNN model with a normalized root mean square error(NRMSE)of 9.91%demonstrates the excellent potential for mapping CLQ over large areas.The model based on the seven optimal indicators of crop phenology provided higher accuracy than the GA-BPNN model based on the normalized difference vegetation index(NDVI)indicators in the spatial domain,significantly decreasing the NRMSE of the CLQ estimates by 3.17%.This further implied that the spectral indicators in the spatial frequency domain can improve the accuracy of estimating CLQ.

Anomalous behaviour detection using one-class support vector machine and remote sensing images: a case study of algal bloom occurrence in inland waters

Algal blooms are a frequent subject in scientific discussions and are the focus of many recent studies,mainly due to their adverse effect on society.Given the lack of ground truth data and the need to develop tools for their detection and monitoring,this research proposes a novel method to automate detection.Concepts derived from multi-temporal image series processing,spectral indices and classification with Oneclass Support Vector Machine(OC-SVM)are used in this proposal.Imagery from multi-spectral sensors on Landsat-8 and MODIS were acquired through the Google Earth Engine API(GEE API).In order to evaluate our method,two bloom detection case studies(Lake Erie(USA)and Lake Taihu(China))were performed.Comparisons were made with methods based on spectral index thresholds.Also,to demonstrate the performance of the OC-SVM classifier compared to other machine learning methods,the proposal was adapted to be used with a Random Forest(RF)classifier,having its results added to the analysis.In situ measurements show that the proposed method delivers highly accurate results compared to spectral index thresholding approaches.However,a drawback of the proposal refers to its higher computational cost.The application of the new method to a real-world bloom case is demonstrated.

Evaluation of high yielding soybean germplasm under water limitation

Limited information is available for soybean root traits and their plasticity under drought stress. To date, no studies have focused on examining diverse soybean germ- plasm for regulation of shoot and root response under water limited conditions across varying soil types. In this study, 17 genetically diverse soybean germplasm lines were selected to study root response to water limited conditions in clay （trial 1） and sandy soil （trial 2） in two target environments. Physiological data on shoot traits was measured at multiple crop stages ranging from early vegetative to pod filling. The phenotypic root traits, and biomass accumulation data are collected at pod filling stage. In trial 1, the number of lateral roots and forks were positively correlated with plot yield under water limitation and in trial 2, lateral root thickness was positively correlated with the hill plot yield. Plant Introduction （PI） 578477A and 088444 were found to have higher later root number and forks in clay soil with higher yield under water limitation, in sandy soil, P1458o2o was found to have a thicker lateral root system and higher yield under water limitation. The genotypes identified in this study could be used to enhance drought tolerance of elite soybean cultivars through improved root traits specific to target environments.

Spatial pattern analysis of post-fire damages in the Menderes District of Turkey

Forest fires,whether caused naturally or by human activity can have disastrous effects on the environment.Turkey,located in the Mediterranean climate zone,experiences hundreds of forest fires every year.Over the past two decades,these fires have destroyed approximately 308000 ha of forest area,threatening the sustainability of its ecosystem.This study analyzes the forest fire that occurred in the Menderes region of Izmir on July 1,2017,by using pre-and post-fire Sentinel 2(10 m and 20 m)and Landsat 8(30 m)satellite images,MODIS and VIIRS fire radiative power(FRP)data(1000 m and 375 m,respectively),and reference data obtained from a field study.Hence,image processing techniques integrated with the Geographic Information System(GIS)database were applied to a satellite image data set to monitor,analyze,and map the effects of the forest fire.The results show that the land surface temperature(LST)of the burned forest area increased from 1 to 11°C.A high correlation(R=0.81)between LST and burn severity was also determined.The burned areas were calculated using two different classification methods,and their accuracy was compared with the reference data.According to the accuracy assessment,the Sentinel(10 m)image classification gave the best result(96.43%for Maximum Likelihood,and 99.56%for Support Vector Machine).The relationship between topographical/forest parameters,burn severity and disturbance index was evaluated for spatial pattern distribution.According to the results,the areas having canopy closure between 71%–100%and slope above 35%had the highest burn incidence.As a final step,a spatial correlation analysis was performed to evaluate the effectiveness of MODIS and VIIRS FRP data in the postfire analysis.A high correlation was found between FRPslope,and FRP-burn severity(0.96 and 0.88,respectively).

Chlorophyll, anthocyanin, and gas exchange changes assessed by spectroradiometry in Fragaria chiloensis under salt stress

Chlorophyll and anthocyanin contents provide a valuable indicator of the status of a plant’s physiology, but to be more widely utilized it needs to be assessed easily and non‐destructively. This is particularly evident in terms of assessing and exploiting germplasm for plant‐breeding programs. We report, for the first time, experiments with Fragaria chiloensis（L.）Duch. and the estimation of the effects of response to salinity stress（0, 30, and 60 mmol NaCl/L） in terms of these pigments content and gas exchange. It is shown that both pigments（which interestingly, themselves show a high correlation） give a good indication of stress response. Both pigments can be accurately predicted using spectral reflectance indices（SRI）;however, the accuracy of the predictions was slightly improved using multilinear regression analysis models and genetic algorithm analysis. Specifically for chlorophyll content, unlike other species, the use of published SRI gave better indications ofstress response than Normalized Difference Vegetation Index.The effect of salt on gas exchange is only evident at the highest concentration and some SRI gave better prediction performance than the known Photochemical Reflectance Index. This information will therefore be useful for identifying tolerant genotypes to salt stress for incorporation in breeding programs.

Integrated sensor system for monitoring rice growth conditions based on unmanned ground vehicle system

Ground-based platform systems provide a good tool for monitoring and managing crop conditions in precision agriculture applications and have been widely used for monitoring crop conditions.To develop an unmanned ground vehicle system(UGVS)based multi-sensors and test the feasibility of this system for monitoring rice conditions,an UGVS was developed to collect real-time rice condition information including NDVI values,reflectance measurements and crop canopy temperature in this study.Major components of the integrated system are GreenSeeker R100 system,hyper-spectroradiometer and infrared temperature sensor.The leaf area index(LAI)was measured by the CGMD302 Spectrometer.The Independent Samples T-Test method and the one way ANOVA method were used to determine the best spectral indices and analyze the relationship between the vegetation indices and rice LAI.It was found that the two best spectral indices for estimating LAI were NDVI(860 nm and 750 nm)with the correlation coefficient(R^(2))at 0.745 and RVI(853 nm and 751 nm)with the R^(2)at 0.724.The results show the UGVS can support multi-source information acquisition and is useful for crop management and precision agriculture applications.