scholarly journals Rice Yield Estimation Based on Vegetation Index and Florescence Spectral Information from UAV Hyperspectral Remote Sensing

2021 ◽  
Vol 13 (17) ◽  
pp. 3390
Author(s):  
Fumin Wang ◽  
Xiaoping Yao ◽  
Lili Xie ◽  
Jueyi Zheng ◽  
Tianyue Xu
Keyword(s):  
Remote Sensing ◽  
Field Experiments ◽  
Rice Yield ◽  
Vegetation Indices ◽  
Estimation Method ◽  
Estimation Accuracy ◽  
Spectral Information ◽  
Growth Stages ◽  
Yield Estimation ◽  
First Derivative

Rice floret number per unit area as one of the key yield structure parameters is directly related to the final yield of rice. Previous studies paid little attention to the effect of the variations in vegetation indices (VIs) caused by rice flowering on rice yield estimation. Unmanned aerial vehicles (UAV) equipped with hyperspectral cameras can provide high spatial and temporal resolution remote sensing data about the rice canopy, providing possibilities for flowering monitoring. In this study, two consecutive years of rice field experiments were conducted to explore the performance of florescence spectral information in improving the accuracy of VIs-based models for yield estimates. First, the florescence ratio reflectance and florescence difference reflectance, as well as their first derivative reflectance, were defined and then their correlations with rice yield were evaluated. It was found that the florescence spectral information at the seventh day of rice flowering showed the highest correlation with the yield. The sensitive bands to yield were centered at 590 nm, 690 nm and 736 nm–748 nm, 760 nm–768 nm for the first derivative florescence difference reflectance, and 704 nm–760 nm for the first derivative florescence ratio reflectance. The florescence ratio index (FRI) and florescence difference index (FDI) were developed and their abilities to improve the estimation accuracy of models basing on vegetation indices at single-, two- and three-growth stages were tested. With the introduction of florescence spectral information, the single-growth VI-based model produced the most obvious improvement in estimation accuracy, with the coefficient of determination (R2) increasing from 0.748 to 0.799, and the mean absolute percentage error (MAPE) and the root mean squared error (RMSE) decreasing by 11.8% and 10.7%, respectively. Optimized by flowering information, the two-growth stage VIs-based model gave the best performance (R2 = 0.869, MAPE = 3.98%, RMSE = 396.02 kg/ha). These results showed that introducing florescence spectral information at the flowering stage into conventional VIs-based yield estimation models is helpful in improving rice yield estimation accuracy. The usefulness of florescence spectral information for yield estimation provides a new idea for the further development and improvement of the crop yield estimation method.

scholarly journals Agricultural Land Suitability Assessment Using Satellite Remote Sensing-Derived Soil-Vegetation Indices

Land ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 223
Author(s):  
Rubaiya Binte Mostafiz ◽  
Ryozo Noguchi ◽  
Tofael Ahamed
Keyword(s):  
Remote Sensing ◽  
Satellite Remote Sensing ◽  
Agricultural Land ◽  
Expert Knowledge ◽  
Vegetation Indices ◽  
Land Suitability ◽  
Assessment System ◽  
Yield Estimation ◽  
Suitability Assessment ◽  
Land Suitability Assessment

Satellite remote sensing technologies have a high potential in applications for evaluating land conditions and can facilitate optimized planning for agricultural sectors. However, misinformed land selection decisions limit crop yields and increase production-related costs to farmers. Therefore, the purpose of this research was to develop a land suitability assessment system using satellite remote sensing-derived soil-vegetation indicators. A multicriteria decision analysis was conducted by integrating weighted linear combinations and fuzzy multicriteria analyses in a GIS platform for suitability assessment using the following eight criteria: elevation, slope, and LST vegetation indices (SAVI, ARVI, SARVI, MSAVI, and OSAVI). The relative priorities of the indicators were identified using a fuzzy expert system. Furthermore, the results of the land suitability assessment were evaluated by ground truthed yield data. In addition, a yield estimation method was developed using indices representing influential factors. The analysis utilizing equal weights showed that 43% of the land (1832 km2) was highly suitable, 41% of the land (1747 km2) was moderately suitable, and 10% of the land (426 km2) was marginally suitable for improved yield productions. Alternatively, expert knowledge was also considered, along with references, when using the fuzzy membership function; as a result, 48% of the land (2045 km2) was identified as being highly suitable; 39% of the land (2045 km2) was identified as being moderately suitable, and 7% of the land (298 km2) was identified as being marginally suitable. Additionally, 6% (256 km2) of the land was described as not suitable by both methods. Moreover, the yield estimation using SAVI (R2 = 77.3%), ARVI (R2 = 68.9%), SARVI (R2 = 71.1%), MSAVI (R2 = 74.5%) and OSAVI (R2 = 81.2%) showed a good predictive ability. Furthermore, the combined model using these five indices reported the highest accuracy (R2 = 0.839); this model was then applied to develop yield prediction maps for the corresponding years (2017–2020). This research suggests that satellite remote sensing methods in GIS platforms are an effective and convenient way for agricultural land-use planners and land policy makers to select suitable cultivable land areas with potential for increased agricultural production.

scholarly journals Grain Yield Estimation in Rice Breeding Using Phenological Data and Vegetation Indices Derived from UAV Images

Agronomy ◽  
2021 ◽  
Vol 11 (12) ◽  
pp. 2439
Author(s):  
Haixiao Ge ◽  
Fei Ma ◽  
Zhenwang Li ◽  
Changwen Du
Keyword(s):  
Grain Yield ◽  
Low Cost ◽  
Vegetation Indices ◽  
Model Performance ◽  
Estimation Accuracy ◽  
Accurate Estimation ◽  
Rice Breeding ◽  
Yield Estimation ◽  
Phenological Data ◽  
Significant Difference

The accurate estimation of grain yield in rice breeding is crucial for breeders to screen and select qualified cultivars. In this study, a low-cost unmanned aerial vehicle (UAV) platform mounted with an RGB camera was carried out to capture high-spatial resolution images of rice canopy in rice breeding. The random forest (RF) regression techniques were used to establish yield models by using (1) only color vegetation indices (VIs), (2) only phenological data, and (3) fusion of VIs and phenological data as inputs, respectively. Then, the performances of RF models were compared with the manual observation and CERES-Rice model. The results indicated that the RF model using VIs only performed poorly for estimating yield; the optimized RF model that combined the use of phenological data and color VIs performed much better, which demonstrated that the phenological data significantly improved the model performance. Furthermore, the yield estimation accuracy of 21 rice cultivars that were continuously planted over three years in the optimal RF model had no significant difference (p > 0.05) with that of the CERES-Rice model. These findings demonstrate that the RF model, by combining phenological data and color Vis, is a potential and cost-effective way to estimate yield in rice breeding.

scholarly journals CLASSIFICATION OF LISS IV IMAGERY USING DECISION TREE METHODS

2016 ◽  
Vol XLI-B8 ◽  
pp. 1061-1066 ◽  
Author(s):  
Amit Kumar Verma ◽  
P. K. Garg ◽  
K. S. Hari Prasad ◽  
V. K. Dadhwal
Keyword(s):  
Remote Sensing ◽  
Decision Tree ◽  
Vegetation Indices ◽  
Spectral Information ◽  
Main Concern ◽  
Crop Land ◽  
Sensing Applications ◽  
Crop Classification ◽  
Decision Tree Method ◽  
Tree Method

Image classification is a compulsory step in any remote sensing research. Classification uses the spectral information represented by the digital numbers in one or more spectral bands and attempts to classify each individual pixel based on this spectral information. Crop classification is the main concern of remote sensing applications for developing sustainable agriculture system. Vegetation indices computed from satellite images gives a good indication of the presence of vegetation. It is an indicator that describes the greenness, density and health of vegetation. Texture is also an important characteristics which is used to identifying objects or region of interest is an image. This paper illustrate the use of decision tree method to classify the land in to crop land and non-crop land and to classify different crops. In this paper we evaluate the possibility of crop classification using an integrated approach methods based on texture property with different vegetation indices for single date LISS IV sensor 5.8 meter high spatial resolution data. Eleven vegetation indices (NDVI, DVI, GEMI, GNDVI, MSAVI2, NDWI, NG, NR, NNIR, OSAVI and VI green) has been generated using green, red and NIR band and then image is classified using decision tree method. The other approach is used integration of texture feature (mean, variance, kurtosis and skewness) with these vegetation indices. A comparison has been done between these two methods. The results indicate that inclusion of textural feature with vegetation indices can be effectively implemented to produce classifiedmaps with 8.33% higher accuracy for Indian satellite IRS-P6, LISS IV sensor images.

scholarly journals Estimating Vertical Distribution of Leaf Water Content within Wheat Canopies after Head Emergence

2021 ◽  
Vol 13 (20) ◽  
pp. 4125
Author(s):  
Weiping Kong ◽  
Wenjiang Huang ◽  
Lingling Ma ◽  
Lingli Tang ◽  
Chuanrong Li ◽  
...  
Keyword(s):  
Vertical Distribution ◽  
Water Content ◽  
Near Infrared ◽  
Field Experiments ◽  
Estimation Method ◽  
Middle Layer ◽  
Leaf Water ◽  
Leaf Water Content ◽  
Growth Stages ◽  
Spectral Indices

Monitoring vertical profile of leaf water content (LWC) within wheat canopies after head emergence is vital significant for increasing crop yield. However, the estimation of vertical distribution of LWC from remote sensing data is still challenging due to the effects of wheat spikes and the efficacy of sensor measurement from the nadir direction. Using two-year field experiments with different growth stages after head emergence, N rates, wheat cultivars, we investigated the vertical distribution of LWC within canopies, the changes of canopy reflectance after spikes removal, the relationship between spectral indices and LWC in the upper-, middle- and bottom-layer. The interrelationship among vertical LWC were constructed, and four ratio of reflectance difference (RRD) type of indices were proposed based on the published WI and NDWSI indices to determine vertical distribution of LWC. The results indicated a bell shape distribution of LWC in wheat plants with the highest value appeared at the middle layer, and significant linear correlations between middle-LWC vs. upper-LWC and middle-LWC vs. bottom-LWC (r ≥ 0.92) were identified. The effects of wheat spikes on spectral reflectance mainly occurred in near infrared to shortwave infrared regions, which then decreased the accuracy of LWC estimation. Spectral indices at the middle layer outperformed the other two layers in LWC assessment and were less susceptible to wheat spikes effects, in particular, the newly proposed narrow-band WI-4 and NDWSI-4 indices exhibited great potential in tracking the changes of middle-LWC (R2 = 0.82 and 0.84, respectively). By taking into account the effects of wheat spikes and the interrelationship of vertical LWC within canopies, an indirect induction strategy was developed for modeling the upper-LWC and bottom-LWC. It was found that the indirect induction models based on the WI-4 and NDWSI-4 indices were more effective than the models obtained from conventional direct estimation method, with R2 of 0.78 and 0.81 for the upper-LWC estimation, and 0.75 and 0.74 for the bottom-LWC estimation, respectively.

scholarly journals Classification of Landsat-8 Imagery Based On Pca And Ndvi Methods

2019 ◽  
Vol 8 (10) ◽  
pp. 4321-4325
Keyword(s):  
Remote Sensing ◽  
Satellite Image ◽  
Vegetation Indices ◽  
Region Of Interest ◽  
The Other ◽  
Spectral Information ◽  
Landsat 8 ◽  
Statistical Parameters ◽  
Sensing Applications

Remote sensing is an important issue in satellite image classification. In developing a significant sustainable system in agriculture farming, the major concern for remote sensing applications is the crop classification mechanism. The other important application in remote sensing is urban classification which gives the information about houses, roads, buildings, vegetation etc. A superior indicator for the presence of vegetation can be computed from the vegetation indices of a satellite image. This indicator supports in describing the health of vegetation through the image attributes like greenness and density. The other parameter in detecting objects or region of interest is an image is the texture. A satellite image contains spectral information and can be represented by more spectral bands and classification is very tough task. Generally, Classification of individual pixels in satellite images is based on the spectral information. In this research paper Principle component analysis and combination of PCA and NDVI classification methods are applied on Landsat-8 images. These images are acquired from USGS. The performance of these methods is compared in statistical parameters such as Kappa coefficient, overall accuracy, user’s accuracy, precision accuracy and F1 accuracy. In this work existing method is PCA and proposed method is PCA+NDVI. Experimental results shows that the proposed method has better statistical values compared to existing method.

scholarly journals Time-series Multi-spectral Imaging in Soybean for Improving Biomass and Genomic Prediction Accuracy

2021 ◽  
Author(s):  
Kengo Sakurai ◽  
Yusuke Toda ◽  
Hiromi Kajiya-Kanegae ◽  
Yoshihiro Ohmori ◽  
Yuji Yamasaki ◽  
...  
Keyword(s):  
Genomic Prediction ◽  
Prediction Accuracy ◽  
Field Experiments ◽  
Developmental Stages ◽  
Early Stage ◽  
Vegetation Indices ◽  
Optimal Timing ◽  
Growth Stages ◽  
Ms Imaging ◽  
Irrigation Levels

Multi-spectral (MS) imaging enables the measurement of characteristics important for increasing the prediction accuracy of genotypic and phenotypic values for yield-related traits. In this study, we evaluated the potential application of temporal MS imaging for the prediction of above-ground biomass (AGB) and determined which developmental stages should be used for accurate prediction in soybean. Field experiments with 198 accessions of soybean were conducted with four different irrigation levels. Five vegetation indices (VIs) were calculated using MS images from soybean canopies from early to late growth stages. To predict the genotypic values of AGB, VIs at the different growth stages were used as secondary traits in a multi-trait genomic prediction. The accuracy of the prediction model increased starting at an early stage of growth (31 days after sowing). To predict phenotypic values of AGB, we employed multi-kernel genomic prediction. Consequently, the prediction accuracy of phenotypic values reached a maximum at a relatively early growth stage (38 days after sowing). Hence, the optimal timing for MS imaging may depend on the irrigation levels.

scholarly journals INTEGRATION OF HUMAN PARTICIPATORY SENSING AND ARCHIVES OF REMOTE SENSING OBSERVATIONS FOR FIELD LEVEL CROP PHENOLOGY ESTIMATION

2018 ◽  
Vol XLII-4 ◽  
pp. 547-550
Author(s):  
S. A. Sawant ◽  
J. D. Mohite ◽  
S. Pappula
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Vegetation Indices ◽  
Crop Growth ◽  
Growth Stages ◽  
Growth Monitoring ◽  
Specific Information ◽  
Crop Phenology ◽  
Field Level ◽  
Sentinel 2

<p><strong>Abstract.</strong> The rise in global population has increased food and water demand thereby causing excessive pressure on existing resources. In developing countries with fragmented land holdings there exists constant pressure on available water and land resources. Obtaining field scale crop specific information is challenging task. Advent of open freely available multi-temporal remote sensing observations with improved radiometric resolution the possibilities for near real / real time applications has increased. In this study and an attempt has been made to establish operational model for field level crop growth monitoring using integrated approach of crowd sourcing and time series of remote sensing observations. The time series of Sentinel 2 (A and B) satellite has been used to estimate crop growth related components such as vegetation indices and crop growth stage and crop phenology. In initial stage high valued cereal crop Wheat has been selected. The field level information (i.e. 108 Wheat fields) collected using mobile based agro-advisory platform mKRISHI&amp;reg; has been used to extract time series of Sentinel 2 observations (44 scenes for year 2016 and 2018). The moving average has been used for filling gaps in the time series of vegetation indices. The BFAST and GreenBrown package in R were used for detecting breaks in vegetation index time series and estimating crop growth stages. Analysis shows that the estimated crop phenology parameters were in better agreement with the field observations. In future more crops from different agro-climatic conditions will be considered for providing field level crop management advisory.</p>

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