Proximal Remote Sensing of Herbicide and Drought Stress in Field Grown Colocasia and Sweet Potato Plants by Sunlight-Induced Chlorophyll Fluorescence Imaging

Chlorophyll fluorescence is an indicator of plant photosynthetic activity and has been used to monitor the health status of vegetation. Several studies have exploited the application of red/far-red chlorophyll fluorescence ratio in detecting the impact of various types of stresses in plants. Recently, sunlight-induced chlorophyll fluorescence imaging has been used to detect and discriminate different stages of mosaic virus infection in potted cassava plants with a multi-spectral imaging system (MSIS). In this study, the MSIS is used to investigate the impact of drought and herbicide stress in field grown crop plants. Towards this control and treatment groups of colocasia and sweet potato plants were grown in laterite soil beds and the reflectance images of these crop plants were recorded up to 14-days of treatment at the Fraunhofer lines of O₂ B at 687 nm and O₂ A at 759.5 nm and the off-lines at 684 and 757.5 nm. The recorded images were analyzed using the Fraunhofer Line Discrimination technique to extract the sunlight-induced chlorophyll fluorescence component from the reflectance images of the plant leaves. As compared to the control group, the chlorophyll fluorescence image ratio (F ₆₈₇/F ₇₆₀) in the treatment groups of both the plant varieties shows an increasing trend with increase in the extent of stress. Further, the F ₆₈₇/F ₇₆₀ ratio was found to correlate with the net photosynthetic rate (Pn) and stomatal conductance (g_s) of leaves. The correlation coefficient (R ²) for the relationship of F ₆₈₇/F ₇₆₀ ratio with Pn were found to be 0.78, 0.79 and 0.78, respectively for the control, herbicide treated and drought treated colocasia plants, while these were 0.77, 0.86 and 0.88, respectively for sweet potato plants. The results presented show the potential of proximal remote sensing and the application F ₆₈₇/F ₇₆₀ fluorescence image ratio for effective monitoring of stress-induced changes in field grown plants.     

A hyperspectral experiment over tropical forests based on the EO-1 orbit change and PROSAIL simulation

ABSTRACT

We designed a unique hyperspectral experiment from the Earth Observing One (EO-1) orbit change to evaluate solar illumination effects over tropical forests in Brazil. Ten nadir-viewing Hyperion images collected over a fixed site and period of the year (July to August) were selected for analysis. We evaluated variations in reflectance and in 16 narrowband vegetation indices (VIs) with increasing solar zenith angle (SZA) from the pre-drift (2004–2008) to the EO-1 drift period (2011–2016). To detect changes in reflectance and shadows, we applied spectral mixture analysis (SMA) and principal component analysis (PCA) and calculated the similarity spectral angle (θ) between the vegetation spectra measured with variable SZA. The magnitude of the illumination effects was also evaluated from change-point analysis and nonparametric Mann-Whitney U tests applied over the time series. Finally, we complemented our experiment using the PROSAIL model to simulate the VIs variation with increasing SZA resultant from satellite drift. The results showed significant changes in Hyperion reflectance and VIs, especially when the EO-1 crossed the study area at earlier times and larger SZA in 2015 (9:05 a.m.; SZA = 59°) and 2016 (8:30 a.m.; SZA = 67°). Compared to the pre-drift period (10:30 a.m.; SZA = 45°), the SZA differences of 14° (2015) and 22° (2016) increased the shade fractions and decreased the vegetation brightness. PCA separated the pre-drift and drift reflectance datasets, showing shifts in scores due to changes in brightness. θ increased with SZA, indicating changes in the shape of the vegetation spectra with drift. For most VIs, the change-point analysis indicated 2015 (SZA = 59°) as the predominant year of detected changes. Compared to the EO-1 original orbit, the Plant Senescence Reflectance Index (PSRI), Anthocyanin Reflectance Index (ARI) and Structure Insensitive Pigment Index (SIPI) presented the largest positive changes during drift, while the Photochemical Reflectance Index (PRI), Visible Atmospherically Resistant Index (VARI) and Enhanced Vegetation Index (EVI) had the largest negative changes. The effect size of the illumination geometry on these VIs was large, as indicated by increasing values of the Cohen’s r metric toward 2016. The anisotropy of the Hyperion VIs was generally consistent with that from PROSAIL in the simulated pre-drift and drift periods. Focusing on structural indices, it affected the relationships between VIs and simulated leaf area index (LAI) at large SZA.     

Study of fractional vegetation cover using high spectral resolution data

A field experiment was conducted to study the effect of vegetation cover on soil spectra and relationship of spectral indices with vegetation cover. Multi-date spectral measurements were carried out on twelve wheat fields. Five sets of measurements were taken during the growth period of wheat crop. Field reflectance data were collected in the range 350 to 1800 nm using ASD spectroradiometer. Analysis of data was done to select narrow spectral bands for estimation of ground cover. The ratio of reflectance from vegetation covered soil and reflectance from bare soil indicated that spectral reflectance at 670 and 710 nm are the most sensitive bands. Two bands in visible (670 and 560 nm), three bands in near infrared (710, 870 and 1100 nm) and three bands in middle infrared (1480, 1700 and 1800 nm) were found highly correlated with fractional cover. Vegetation indices developed using narrow band spectral data have been found to be better than those developed using broad- band data for estimation of ground cover.     

Differentiation of oil seed crops by remote sensing based on their spectral reflectance pattern

The feasibility of differentiating four oil-seed crops viz., mustard, toria, yellow sarson and sunflower, based on their spectral reflectance in the visible and near infra red region was studied in a field experiment, The spectral vegetative index profiles, generated during the growth period of different oil seed crops indicated two vegetative growth peaks and a depression between the two peaks, due to the conspicuous yellow colour of flowers, which masked the green leaves. The magnitude of such depression in the spectral vegetation indices viz., ‘Greenness’ and ‘Perpendicular Vegetation Index’ (PVI), were of higher magnitude in yellow sarson. The flowering period parameters viz., flowering time, duration and intensity, deduced from the spectral vegetation indices were found to be beneficial in differentiating different oil-seed crops by remote sensing. A plot of ‘Brightness’ vs. ‘Greenness’ values determined during the growth of the crops formed typical clusters. The cluster representing toria crop was significantly different from the other crops, thereby making toria identifiable from others by remote sensing.     

Surface Reflectance Climate Data Records (CDRs) is a Reliable Landsat ETM+ Source to Study Chlorophyll Content in Pecan Orchards

We evaluated the relationships among three Landsat Enhanced Thematic Mapper (ETM+) datasets, top-of-atmosphere (TOA) reflectance, surface reflectance climate data records (surface reflectance-_CDR) and atmospherically corrected images using Fast Line-of-Sight atmospheric analysis of Spectral Hypercubes model (surface reflectance-_FLAASH) and their linkto pecan foliar chlorophyll content(chl-cont). Foliar chlorophyll content as determined with a SPAD meter, and remotely-sensed data were collected from two mature pecan orchards (one grown in a sandy loam and the other in clay loam soil) during the experimental period. Enhanced vegetation index derived from remotely sensed data was correlated to chl-cont. At both orchards, TOA reflectance was significantly lower than surface reflectance within the 550–2400 nm wavelength range. Reflectance from atmospherically corrected images (surface reflectance-_CDR and surface reflectance-_FLAASH) was similar in the shortwave infrared (SWIR: 1550–1750 and 2080–2350 nm) and statistically different in the visible (350–700 nm). Enhanced vegetation index derived from surface reflectance-_CDR and surface reflectance-_FLAASH had higher correlation with chl-cont than TOA. Accordingly, surface reflectance is an essential prerequisite for using Landsat ETM+  data and TOA reflectance could lead to miss-/or underestimate chl-cont in pecan orchards. Interestingly, the correlation comparisons (Williams t test) between surface reflectance-_CDR and chl-cont was statistically similar to the correlation between chl-cont and commercial atmospheric correction model. Overall, surface reflectance-_CDR, which is freely available from the earth explorer portal, is a reliable atmospherically corrected Landsat ETM+ image source to study foliar chlorophyll content in pecan orchards.     

Evaluating metrics derived from Landsat 8 OLI imagery to map crop cover

Developing techniques are required to generate agricultural land cover maps to monitor agricultural fields. Landsat 8 Operational Land Imager (OLI) offers reflectance data over the visible to shortwave-infrared range. OLI offers several advantages, such as adequate spatial and spectral resolution, and 16 day repeat coverage, furthermore, spectral indices derived from Landsat 8 OLI possess great potential for evaluating the status of vegetation. Additionally, classification algorithms are essential for generating accurate maps. Recently, multi-Grained Cascade Forest, which is also called deep forest, was proposed, and it was shown to give highly competitive performance for classification. However, the ability of this algorithm to generate crop maps with satellite data had not yet been evaluated. In this study, the reflectance at 7 bands and 57 spectral indices calculated from Landsat 8 OLI data were evaluated for its potential for crop type identification.     

Spectral reflectance of some typical Indian soils as affected by tillage and cover types

Measuring spectral reflectance of soils in situ which simulates measurements made from aircraft and by satellite scanner system has become an integral part of soil mapping using remote sensing techniques. A preliminary study has been conducted to measure the spectral reflectance of some typical red and black soils of India using a field radiometer (EXOTECH-100-A) and to study the changes in specrtral reflectance patterns due to tillage and crop and non-crop cover, The spectral reflectance were measured in four different pands of electromagnetic spectrum—two in visible (0.5-0.6μ and 0.6-0.7μ) and two in infrared (0.7-0.8μ and 0.8-1.0μ) region. Spectral reflectance curves were drawn from these values which helped in understanding the spectral separability and mixing of various red and black soil types. Black soils having grass cover showed maximum reflectance value followed by ploughed one and bare counterparts whereas, the order of decrease in spectral reflectance of red soils was bare soils> ploughed soil> soils with grass cover.     

Assessment of Aphid Infestation in Mustard by Hyperspectral Remote Sensing

Field experiment was conducted during 2009–10 and 2010–11 rabi season at research farm of IARI, New Delhi for assessing the aphid infestation in mustard. In aphid infested plant the LAI was 67 to 94% lower than healthy plant. Chlorophyll concentration decreased to 50% in infested plant as compared to healthy plant. Infestation was more severe in late sown crop and due to aphid infestation the percentage oil content and yield was reduced significantly. The spectral reflectance of aphid infested canopy and healthy canopy taken in the laboratory had significant difference in NIR region. In the visible region, the reflectance peak occurred in healthy canopy at around 550–560 nm while this peak was lower by 31% in the aphid infested canopy. The reflectance for healthy crop was found to be more in visible as well as NIR region as compared to aphid infested canopy. The most significant spectral bands for the aphid infestation in mustard are in visible (550–560 nm) and near infrared regions (700–1250 nm and 1950–2450 nm). The different level of aphid infestation can be identified in 1950–2450 nm spectral regions. Spectral indices viz NDVI, RVI, AI and SIPI had significant correlation with aphid infestation. Hence these indices could be used for identifying aphid infestation in mustard.     

Multiple statistical approaches for the discrimination of mangrove species of Rhizophoraceae using transformed field and laboratory hyperspectral data

This study aims at discriminating eight mangrove species of Rhizophoraceae family of Indian east coast using field and laboratory spectra in spectral range (350–2500 nm). Parametric and non-parametric statistical analyses were applied on spectral data in four spectral modes: (i) reflectance (ii) continuum removed, (iii) additive inverse and (iv) continuum removed additive inverse. We introduced continuum removal of inverse spectra to utilize the advantage of continuum removal in reflectance region. Non-parametric test gave better separability than parametric test. Principal component analysis and stepwise discriminant analysis were applied for feature reduction and to identify optimal wavelengths for species discrimination. To quantify the separability, Jeffries–Matusita distance measure was derived. Green (550 nm), red edge (680–720 nm) and water absorption region (1470 and 1850 nm) were found to be optimal wavelengths for species discrimination. The continuum removal of additive inverse spectra gave better separability than the continuum removed spectra.     

Multi-Index Soil Moisture Estimation from Satellite Earth Observations: Comparative Evaluation of the Topographic Wetness Index (TWI), the Temperature Vegetation Dryness Index (TVDI) and the Improved TVDI (iTVDI)

Soil moisture estimation from satellite earth observation has emerged effectively advantageous due to the high temporal resolution, spatial resolution, coverage, and processing convenience it affords. In this paper, we present a study carried out to estimate soil moisture level at every location within Enugu State Nigeria from satellite earth observation. Comparative analysis of multiple indices for soil moisture estimation was carried out with a view to evaluating the robustness, correlation, appropriateness and accuracy of the indices in estimating the spatial distribution of soil moisture level in Enugu State. Results were correlated and validated with In-Situ soil moisture observations from multi-sample points. To achieve this, the Topographic Wetness Index (TWI), based on digital elevation data, the Temperature Vegetation Dryness Index (TVDI) and an improved TVDI (iTVDI) incorporating air temperature and a Digital Elevation Model (DEM) were calculated from ASTER global DEM and Landsat images. Possible dependencies of the indices on land cover type, topography, and precipitation were explored. In-Situ soil moisture data were used to validate the derived indices. The results showed that there was a positive significant relationship between iTVDI versus TVDI (R = 0.53, P value < 0.05), while in iTVDI versus TWI (R = 0.00, P value > 0.05) and TVDI versus TWI (R = ?0.01, P value > 0.05) no significant relationship existed. There was a strong relationship between iTVDI and topography, land cover type, and precipitation than other indices (TVDI, TWI). In situ measured soil moisture values showed negative significant relationship with TVDI (R = ?0.52, P value < 0.05) and iTVDI (R = ?0.63, P value < 0.05) but not with TWI (R = ?0.10, P value > 0.05). The iTVDI outperformed the other two index; having a stronger relationship with topography, precipitation, land cover classes and soil moisture. It concludes that although iTVDI outperformed other indices (TVDI, TWI) in soil moisture estimation, the decision of which index to apply is dependent on available data, the intent of usage and spatial scale.     

Monitoring of <Emphasis Type="Italic">Aphis gossypii</Emphasis> Using Greenseeker and SPAD Meter

Cotton aphid (Aphis gossypii) is considered as one of the most important agriculture pest for the cotton production. However, it is generally labor-intensive and time-consuming to obtain some information of Cotton aphid with conventional methods through direct measurement by sampling in the field. This study explores the potential of using a new method to obtain information of the Cotton aphid rapidly. In our study, the cotton canopy spectral indices (NDVI, VI_2, REDrefc, NIRrefc) and chlorophyll concentration, obtained from hand-held high spectrometer GreenSeeker and chlorophyll meter SPAD-502 and Cotton aphid amount derived from the artificial field-based survey were used to uncover the relationship between Cotton aphid amount and canopy spectral index and SPAD value of the cotton in city of Shihezi, China. The results showed that NDVI and NIRrefc were negatively related to Cotton aphid amount. VI_2 content had a significant and positive relationship with its amount. The non-linear three cubic models with alate Aphid amount as independent variables have been established between VI_2 value and alatae Aphid amount, which could explain 92.37 % of VI_2 value variance. SPAD values were also significantly and negatively correlated to the Aphid amount. The non-linear logarithm model with wingless Aphid amount as independent variables was the best for uncovering the relationship between SPAD value and wingless Aphid amount, which could explain 85.48 % of SPAD value variance. The results demonstrate the establishment of the function model provides a theoretical basis and techniques for indirect and rapid monitoring and management of Cotton aphid.     

Generation of Vegetation Fraction and Surface Albedo Products Over India from Ocean Colour Monitor (OCM) Data Onboard Oceansat-2

The use of Local Area Coverage (LAC) data from Ocean Color Monitor (OCM) sensor of Oceansat-2 with its high radiometric resolution (12 bits/pixel) and 2-day repeat cycle for rapid monitoring of vegetation growth and estimating surface albedo for the Indian region is demonstrated in this study. For the vegetation monitoring, normalized difference vegetation index (NDVI) and vegetation fraction (VF) products were estimated by maximum value composite approach fortnightly and were resampled to 1 km. The surface albedo products were realized by converting narrow-band eight-band spectral reflectance OCM data to a) visible (300–700 nm) and b) broad band (300–3,000 nm) data. For validation, the derived products were compared with respective MODIS global products and found to be in good agreement.     

重金属铜污染植被光谱响应特征研究

重金属铜污染植被的反射光谱特性会发生明显改变。在本研究中,采用不同程度的铜污染土壤作为培养基质,选择春小麦、上海青两种农作物进行铜胁迫实验,获取了4个不同生育期、10个不同铜污染强度下的植被叶片的反射光谱,并采用铜污染叶片7个特征波段和光谱角的方法研究了铜污染叶片的光谱特征。结果表明,铜污染叶片光谱差异与作物时期和作物类型有关,可以采用叶片光谱角描述铜污染叶片与健康叶片的光谱差异。该方法只需与阈值做简单的比较,方法简便易行,而且对轻度及重度铜污染十分敏感。叶片光谱辐射传输模型反演结果表明铜污染叶片内部结构参数N明显变大,这也证明了铜污染使叶片内部结构更加散乱无序。在此基础上进一步建立了N与红肩处反射率值的线性关系,相关系数为0.978。本文为铜污染叶片光谱反射模型的建立提供了初步的数据基础与理论支持。     

Comparison of the Different Classifiers in Vegetation Species Discrimination Using Hyperspectral Reflectance Data

Feature selection methods play an important role in Hyperspectral Remote Sensing applications, especially in classification. This paper proposed a new Feature selection strategy for Hyperspectral dataset. This strategy was designed to help refine vegetation classification of 4 categories with 13 species vegetation which are the most common species in central China. An ASD field spectrometer (Analytical Spectral Device) was used to collect spectrum information of plant leaves from each species through 400 nm to 900 nm with 1 nm spectral resolution. Firstly, correlation between the physical/chemical characteristics of the leaves and the separability of each vegetation species was tested. Then, two feature selection methods, spectral angle and spectral distance, and the feature parameters extracted from spectral curves (FPESC) were used to build the feature space which would be the input space for the classifiers. At last, two linear classifiers, mahalanobis distance (MDC), and fisher linear discriminate analysis (FLDA), and a quadratic classifier, maximum likelihood (MLC), were used for vegetation species refine classification. The results showed that (1) there were no significant differences among 13 species on the leaf dry weight (physical parameter) and leaf chlorophyll content (chemical parameter); (2) FPESC of 13 species have distinctive differences and could be ideal features to discriminate these species; (3) The linear classifiers, MDC and FLDA, have better classification results in the experiments compared to the quadratic classifier MLC, where MDC has the highest classification accuracy which is above 96.2 %.     

Relationship of Hyperspectral Reflectance Indices with Leaf N and P Concentration,Dry Matter Accumulation and Grain Yield of Wheat

A field experiment was conducted on wheat during rabi season of year 2010–2011 and 2011–2012 at IARI, New Delhi to study the reflectance response of wheat to the nutrient omissions and identify the appropriate indices for assessing the nutrient deficiencies. Treatments comprised omission of N, P, K, S and Zn, 50% omission of N, P, and K, absolute control and optimum dose of nutrition (150–26.4–50–15–3 kg/ha N–P–K–S–Zn). The R² were significant and higher for the hyperspectral indices than the broad band vegetation indices. GMI-I, RI-2 dB and RI-3d, GNDVI, VOGa, VOGb, VOGc, ND₇₀₅, PRI, PSNDc and REIP had higher R² (>0.61) for the leaf N concentration. The hyperspectral indices having highly significant correlation with leaf P concentration were PSSRc, GMI-1, ZM, RI-half, VOGa, VOGb, VOGc, mSR and REIP. Among the indices analysed PSSRc, GMI-I, VOGa, RI-2 dB, RI-3 dB, GNDVI, VOGb, VOGc and ND₇₀₅ had almost a similar degree of relationship with DM accumulation with R² values ranging from 0.70 to 0.73. However, REIP displayed a higher degree of relationship with leaf N concentration, drymatter accumulation and grain yield as indicated by R² of 0.85, 0.81 and 0.95 (P = ≤0.01), respectively. It can be concluded from the study that among the hyperspectral indices REIP had a highly significant relationship with leaf N concentration, DM accumulation and grain yield. However, for leaf P concentration several hyperspectral indices viz PSSRc, GMI-1, ZM, RI-half, VOGa, VOGb, VOGc, mSR had though significant but almost similar R² values.     

Assessment of Late Blight Induced Diseased Potato Crops: A Case Study for West Bengal District Using Temporal AWiFS and MODIS Data

Remote sensing technology becomes an effective and inexpensive technique for detecting disease in vegetation. In this study, an attempt has been done to discriminate healthy and late blight affected crop using remote sensing based indices such as NDVI and LSWI. NDVI and LSWI spectral profiles between healthy and late blight affected crop shows large difference. Mean difference in reflectance between two acquired dates Jan. 10 and 29, 2009 crop clusters varied from 31.28 % in red band, 7.7 % in NIR band and 6.23 % in SWIR bands in healthy crops while in late blight affected crops it is ?15.5 % in red, 44.4 % in NIR and ?14.61 % in SWIR bands. Negative percentage differences in reflectance indicate reflectance increases from Jan. 10, 2009 to Jan. 29, 2009, while positive difference indicate decrease in reflectance between the two dates. Since potato is an irrigated crop, these differences in reflectance are attributed to prevalent disease at that time. It is found that severely affected areas are Bardhman, Arambag, Bishnupur, Ghatal and Hugli taluka with crop damage areas are 4036.66, 1138.68, 2025.23, 469.15, and 380.08 ha, respectively.     

Estimating soil salinity in Pingluo County of China using QuickBird data and soil reflectance spectra

Soil salinization is a worldwide environmental problem with severe economic and social consequences. In this paper, estimating the soil salinity of Pingluo County, China by a partial least squares regression (PLSR) predictive model was carried out using QuickBird data and soil reflectance spectra. At first, a relationship between the sensitive bands of soil salinity acquired from measured reflectance spectra and the spectral coverage of seven commonly used optical sensors was analyzed. Secondly, the potentiality of QuickBird data in estimating soil salinity by analyzing the correlations between the measured reflectance spectra and reflectance spectra derived from QuickBird data and analyzing the contributions of each band of QuickBird data to soil salinity estimation Finally, a PLSR predictive model of soil salinity was developed using reflectance spectra from QuickBird data and eight spectral indices derived from QuickBird data. The results indicated that the sensitive bands covered several bands of each optical sensor and these sensors can be used for soil salinity estimation. The result of estimation model showed that an accurate prediction of soil salinity can be made based on the PLSR method (R² = 0.992, RMSE = 0.195). The PLSR model's performance was better than that of the stepwise multiple regression (SMR) method. The results also indicated that using spectral indices such as intensity within spectral bands (Int1, Int2), soil salinity indices (SI1, SI2, SI3), the brightness index (BI), the normalized difference vegetation index (NDVI) and the ratio vegetation index (RVI) as independent model variables can help to increase the accuracy of soil salinity mapping. The NDVI and RVI can help to reduce the influences of vegetation cover and soil moisture on prediction accuracy. The method developed in this paper can be applied in other arid and semi-arid areas, such as western China.     

高光谱反演水稻叶面积指数的主成分分析法

为了通过水稻冠层反射光谱来提取水稻叶面积指数信息，尝试利用辐射传输模型PROSPECT+SAIL来模拟水稻冠层反射光谱， 比较了各植被指数中叶面积指数(LAI)和叶绿素浓度的相关性。在观察光谱曲线后发现，红边位置光谱可以较好地区分LAI和叶绿素 浓度二者引起光谱变化的差异。由此提出对700 nm~750 nm区间内的反射光谱做主成分变换，并利用第2主成分与LAI建立反演模型( 即主成分分析法)，取得了较好效果，表明在植被指数趋近于饱和以至于无法区分二者相关性时，主成分分析法可以作为一种简单 而有效提取水稻叶面积指数信息的补充手段。     

Calibration of ASTER and ETM + imagery using empirical line method—A case study of north-east of Hajjah, Yemen

This study is aimed at using the Empirical Line Method (ELM) to eliminate atmospheric effects with respect to visible and near infrared bands of advanced spaceborne thermal emission and reflection radiometer (ASTER) and enhanced thematic mapper plus (ETM+) data. Two targets (Amran limestone as light target and quartz-biotite-sericite-graphite schists as dark target), which were widely exposed and easy to identify in the imagery were selected. The accuracy of the atmospheric correction method was evaluated from three targets (vegetation cover, Amran limestone and Akbra shale) of the surface reflectance. Analytical spectral devices (ASD) FieldSpec3 was used to measure the spectra of target samples. ETM+ data were less influenced by the atmospheric effect when compared to ASTER data. Normalized differences vegetation indices (NDVI) displayed good results with reflectance data when compared with digital number (DN) data because it is highly sensitive to ground truth reflectance (GTR). Most of the differences observed before and after calibration of satellite images (ASTER and ETM+) were absorbed in the SWIR region.