scholarly journals Deriving crop calendar using NDVI time-series

2014 ◽  
Vol XL-8 ◽  
pp. 869-873 ◽  
Author(s):  
J. H. Patel ◽  
M. P. Oza
Keyword(s):  
Vegetation Index ◽  
Spectral Response ◽  
Temporal Analysis ◽  
Growth Cycle ◽  
Crop Growth ◽  
Data Series ◽  
Time Interval ◽  
Growth Stages ◽  
Seasonal Behavior ◽  
Crop Calendar

Agricultural intensification is defined in terms as cropping intensity, which is the numbers of crops (single, double and triple) per year in a unit cropland area. Information about crop calendar (i.e. number of crops in a parcel of land and their planting & harvesting dates and date of peak vegetative stage) is essential for proper management of agriculture. Remote sensing sensors provide a regular, consistent and reliable measurement of vegetation response at various growth stages of crop. Therefore it is ideally suited for monitoring purpose. The spectral response of vegetation, as measured by the Normalized Difference Vegetation Index (NDVI) and its profiles, can provide a new dimension for describing vegetation growth cycle. The analysis based on values of NDVI at regular time interval provides useful information about various crop growth stages and performance of crop in a season. However, the NDVI data series has considerable amount of local fluctuation in time domain and needs to be smoothed so that dominant seasonal behavior is enhanced. Based on temporal analysis of smoothed NDVI series, it is possible to extract number of crop cycles per year and their crop calendar. <br><br> In the present study, a methodology is developed to extract key elements of crop growth cycle (i.e. number of crops per year and their planting – peak - harvesting dates). This is illustrated by analysing MODIS-NDVI data series of one agricultural year (from June 2012 to May 2013) over Gujarat. Such an analysis is very useful for analysing dynamics of kharif and rabi crops.

Comparison of UAV RGB Imagery and Hyperspectral Remote-sensing Data for Monitoring Winter-wheat Growth

2021 ◽  
Author(s):  
Haikuan Feng ◽  
Huilin Tao ◽  
Chunjiang Zhao ◽  
Zhenhai Li ◽  
Guijun Yang
Keyword(s):  
Remote Sensing ◽  
Spectral Index ◽  
Vegetation Index ◽  
Crop Growth ◽  
Hyperspectral Data ◽  
Growth Stages ◽  
Growth Monitoring ◽  
Estimation Model ◽  
Area Index ◽  
Simple Ratio

Abstract Background: Although crop-growth monitoring is important for agricultural managers, it has always been a difficult research topic. However, unnamed aerial vehicles (UAVs) equipped with RGB and hyperspectral cameras can now acquire high-resolution remote-sensing images, which facilitates and accelerates such monitoring. Results: To explore the effect of monitoring a single crop-growth indicator and multiple indicators, this study combines six growth indicators (plant nitrogen content, above-ground biomass, plant water content, chlorophyll, leaf area index, and plant height) into a new comprehensive growth index (CGI). We investigate the performance of RGB imagery and hyperspectral data for monitoring crop growth based on multi-time estimation of the CGI. The CGI is estimated from the vegetation indices based on UAV hyperspectral data treated by linear, nonlinear, and multiple linear regression (MLR), partial least squares (PLSR), and random forest (RF). The results show that (1) the RGB-imagery indices red reflectance (r), the excess-red index(EXR), the vegetation atmospherically resistant index(VARI), and the modified green-red vegetation index(MGRVI) , as well as the spectral indices consisting of the linear combination index (LCI), the modified simple ratio index(MSR), the simple ratio vegetation index(SR), and the normalized difference vegetation index (NDVI)are more strongly correlated with the CGI than a single growth-monitoring indicator (2) The CGI estimation model is constructed by comparing a single RGB-imagery index and a spectral index, and the optimal RGB-imagery index corresponding to each of the four growth stage in order is r, r, r, EXR; the optimal spectral index is LCI for all four growth stages. (3) The MLR, PLSR, and RF methods are used to estimate the CGI. The MLR method produces the best estimates. (4) Finally, the CGI is more accurately estimated using the UAV hyperspectral indices than using the RGB-image indices.Conclusions: UAVs carrying RGB cameras and hyperspectral cameras have high inversion CGI accuracy and can judge the overall growth of wheat can provide a reference for monitoring the growth of wheat.

LIMITES CRÍTICOS DE NDVI PARA ESTIMATIVA DO POTENCIAL PRODUTIVO DO MILHO

2018 ◽  
Vol 17 (1) ◽  
pp. 91 ◽  
Author(s):  
ANDRÉ LUIS VIAN ◽  
CHRISTIAN BREDEMEIER ◽  
PAULO REGIS FERREIRA DA SILVA ◽  
ANTÔNIO LUIS SANTI ◽  
CECÍLIA PAZ GIORDANO DA SILVA ◽  
...  
Keyword(s):  
Zea Mays ◽  
Grain Yield ◽  
Avena Sativa ◽  
Vegetation Index ◽  
Agricultural Practices ◽  
Growth Cycle ◽  
Yield Potential ◽  
Growth Stages ◽  
Different Growth Stages ◽  
Critical Limits

 RESUMO - A estimativa do potencial produtivo da cultura do milho ao longo do ciclo de desenvolvimento é uma das novas práticas agrícolas que vêm sendo utilizadas para qualificar o manejo da cultura. Neste sentido, destaca-se a inserção de sensores de vegetação, com a finalidade de realizar o monitoramento do desenvolvimento e da condição nutricional da cultura ao longo do seu ciclo. O objetivo do presente trabalho foi determinar os limites críticos do Índice de vegetação por diferença normalizada (NDVI) para a determinação de classes de potencial produtivo do milho em diferentes estádios fenológicos, utilizando sensor óptico ativo de vegetação (Greenseeker). O experimento foi conduzido na EEA/UFRGS, durante a safra agrícola 2013/2014. Os tratamentos consistiram de diferentes épocas de dessecação da aveia branca (Avena sativa L.) antes da semeadura da cultura do milho. As avaliações com o sensor óptico ativo foram realizadas nos estádios fenológicos V3, V5, V6, V7 e V8. Os resultados mostraram que o NDVI medido pelo sensor Greenseeker foi eficiente na predição da produtividade de milho em diferentes estádios fenológicos. Os limites críticos de NDVI, os quais correspondem a diferentes classes de potencial produtivo, podem ser identificados de maneira rápida e precisa entre os estádios fenológicos V3 a V8 e esta informação pode ser empregada para a adubação nitrogenada em taxa variável de acordo com o potencial produtivo estimado. Palavras-chave: Greenseeker, índice de vegetação, Zea mays, NDVI. CRITICAL LIMITS OF NDVI FOR YIELD POTENTIAL ESTIMATION IN MAIZE ABSTRACT - The estimation of grain yield potential of maize along the growth cycle is one of new agricultural practices that have been used to qualify crop management. In this sense, the use of vegetation sensors can be highlighted, in order to carry out the monitoring of plant development and nutritional condition throughout its development. The objective of this study was to indicate NDVI critical limits for determining grain yield potential classes of maize in different growth stages using an active optical vegetation sensor (Greenseeker). The experiment was carried out in the 2013/14 growing season, in Eldorado do Sul, State of Rio Grande do Sul, southern Brazil. Treatments consisted of different dissecation timing of oat (Avena sativa L.) before corn sowing. Evaluations with the active optical sensor were done at growth stages V3, V5, V6, V7, and V8. Results showed that NDVI measured by the sensor Greenseeker was efficient in identifying maize grain productivity at different growth stages. NDVI critical limits that correspond to different yield potential levels in maize can be quickly and precisely identified between V3 and V8 growth stages and this information can be used for site-specific nitrogen fertilization according to the estimated yield potential. Keywords: Greenseeker, vegetation index, Zea mays, NDVI.

scholarly journals Crop Growth Stage GPP-Driven Spectral Model for Evaluation of Cultivated Land Quality Using GA-BPNN

Agriculture ◽  
2020 ◽  
Vol 10 (8) ◽  
pp. 318
Author(s):  
Mingbang Zhu ◽  
Shanshan Liu ◽  
Ziqing Xia ◽  
Guangxing Wang ◽  
Yueming Hu ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
Empirical Bayes ◽  
Growth Stage ◽  
Spectral Response ◽  
Crop Growth ◽  
Spectral Model ◽  
Support Vector ◽  
Growth Stages ◽  
Cultivated Land ◽  
Land Quality

Rapid and accurate evaluation of cultivated land quality (CLQ) using remotely sensed images plays an important role for national food security and social stability. Current approaches for evaluating CLQ do not consider spectral response relationships between CLQ and spectral indicators based on crop growth stages. This study aimed to propose an accurate spectral model to evaluate CLQ based on late rice phenology. In order to increase the accuracy of evaluation, the Empirical Bayes Kriging (EBK) interpolation was first performed to scale down gross primary production (GPP) products from a 500 m spatial resolution to 30 m. As an indicator, the ability of MODIS-GPPs from critical growth stages (tillering, jointing, heading, and maturity stages) was then investigated by combining Pearson correlation analysis and variance inflation factor (VIF) to select the phases of CLQ evaluation. Finally, a linear Partial Least Squares Regression (PLSR) and two nonlinear models, including Support Vector Regression (SVR) and Genetic Algorithm-Based Back Propagation Neural Network (GA-BPNN), were driven to develop an accurate spectral model of evaluating CLQ based on MODIS-GPPs. The models were tested and compared in the Conghua and Zengcheng districts of Guangzhou City, Guangdong, China. The results showed that based on field measured GPP data, the validation accuracy of 30 m spatial resolution MODIS GPP products with a root mean square error (RMSE) of 7.43 and normalized RMSE (NRMSE) of 1.59% was higher than that of the 500 m MODIS GPP products, indicating that the downscaled 30 m MODIS GPP products by EBK were more appropriate than the 500 m products. Compared with PLSR (R2 = 0.38 and RMSE = 87.97) and SVR (R2 = 0.64 and RMSE = 64.38), the GA-BPNN model (R2 = 0.69 and RMSE = 60.12) was more accurate to evaluate CLQ, implying a non-linear relationship of CLQ with the GPP spectral indicator. This is the first study to improve the accuracy of estimating CLQ using the rice growth stage GPP-driven spectral model by GA-BPNN and can thus advance the literature in this field.

scholarly journals Mapping Crop Phenology in Near Real-Time Using Satellite Remote Sensing: Challenges and Opportunities

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Feng Gao ◽  
Xiaoyang Zhang
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Real Time ◽  
Land Surface ◽  
Vegetation Index ◽  
Growing Season ◽  
Crop Growth ◽  
Growth Stages ◽  
Crop Phenology ◽  
Crop Growth Stages

Crop phenology is critical for agricultural management, crop yield estimation, and agroecosystem assessment. Traditionally, crop growth stages are observed from the ground, which is time-consuming and lacks spatial variability. Remote sensing Vegetation Index (VI) time series has been used to map land surface phenology (LSP) and relate to crop growth stages mostly after the growing season. In recent years, high temporal and spatial resolution remote sensing data have allowed near-real-time mapping of crop phenology within the growing season. This paper summarizes two classes of near-real-time mapping methods, i.e., curve-based and trend-based approaches. The curve-based approaches combine the time series VIs and crop growth stages from historical years with the current observations to estimate crop growth stages. The curve-based approaches are capable of a short-term prediction. The trend-based approaches detect upward or downward trends from time series and confirm the trends using the increasing or decreasing momentum and VI thresholds. The trend-based approaches only use current observations. Both curve-based and trend-based approaches are promising in mapping crop growth stages timely. Nevertheless, mapping crop phenology near real-time is challenging since remote sensing observations are not always sensitive to crop growth stages. The accuracy of crop phenology detection depends on the frequency and availability of cloud-free observations within the growing season. Recent satellite datasets such as the harmonized Landsat and Sentinel-2 (HLS) are promising for mapping crop phenology within the season over large areas. Operational applications in the near future are feasible.

Novel Clustering Schemes for Full and Compact Polarimetric SAR Data: A Case Study for Rice Phenology Characterization

2020 ◽  
Author(s):  
Subhadip Dey ◽  
Avik Bhattacharya ◽  
Debanshu Ratha ◽  
Dipankar Mandal ◽  
Heather McNairn ◽  
...  
Keyword(s):  
Morphological Characteristics ◽  
Temporal Analysis ◽  
Crop Growth ◽  
Unsupervised Clustering ◽  
Degree Of Polarization ◽  
Growth Stages ◽  
Phenological Stages ◽  
Sar Images ◽  
Sar Data ◽  
Type Parameter

Information on rice phenological stages from Synthetic Aperture Radar (SAR)images is of prime interest for in-season monitoring. Often, prior in-situ measurements of phenology are not available. In such situations, unsupervised clustering of SAR images might help in discriminating phenological stages of a crop throughout its growing period. Among the existing unsupervised clustering techniques using full-polarimetric (FP) SAR images, the eigenvalue-eigenvector based roll-invariant scattering-type parameter, and the scattering entropy parameter are widely used in the literature. In this study, we utilize a unique target scattering-type parameter, which jointly uses the Barakat degree of polarization and the elements of the polarimetric coherency matrix. In particular, the degree of polarization attributes to scattering randomness from a target. The scattering randomness in crops increases with advancements in its growth stages due to the development of branches and foliage. Hence, the degree of polarization varies with changes in the crop growth stages. Besides, the elements of the coherency matrices are directly related to the crop geometry as well as soil and crop water content. There-fore, this complementarity information captures the scattering randomness at each crop growth stage while taking into account diverse crop morphological characteristics. Likewise, we also utilize an equivalent parameter proposed for compact-polarimetric (CP) SAR data. These scattering-type parameters are analogous to the Cloude-Pottier’s parameter for FP SAR data and the ellipticity parameter for CP SAR data. Besides this, we also introduce new clustering schemes for both FP and CP SAR data for segmenting diverse scattering mechanisms across the phenological stages of rice. In this study, we use the RADARSAT-2 FP and simulated CP SAR data acquired over the Indian test site of Vijayawada under the Joint Experiment for Crop Assessment and Monitoring (JECAM) initiative. The temporal analysis of the scattering-type parameters and the new clustering schemes help us to investigate detailed scattering characteristics from rice across its phenological stages.<div>(Submitted to ISPRS journal)</div>

scholarly journals Growth Stages Classification of Potato Crop Based on Analysis of Spectral Response and Variables Optimization

Sensors ◽  
2020 ◽  
Vol 20 (14) ◽  
pp. 3995 ◽  
Author(s):  
Ning Liu ◽  
Ruomei Zhao ◽  
Lang Qiao ◽  
Yao Zhang ◽  
Minzan Li ◽  
...  
Keyword(s):  
Growth Stage ◽  
Spectral Response ◽  
Classification Model ◽  
Growth Stages ◽  
Potato Field ◽  
Spectral Bands ◽  
Svm Model ◽  
Selection Algorithms ◽  
Potato Crops

Potato is the world’s fourth-largest food crop, following rice, wheat, and maize. Unlike other crops, it is a typical root crop with a special growth cycle pattern and underground tubers, which makes it harder to track the progress of potatoes and to provide automated crop management. The classification of growth stages has great significance for right time management in the potato field. This paper aims to study how to classify the growth stage of potato crops accurately on the basis of spectroscopy technology. To develop a classification model that monitors the growth stage of potato crops, the field experiments were conducted at the tillering stage (S1), tuber formation stage (S2), tuber bulking stage (S3), and tuber maturation stage (S4), respectively. After spectral data pre-processing, the dynamic changes in chlorophyll content and spectral response during growth were analyzed. A classification model was then established using the support vector machine (SVM) algorithm based on spectral bands and the wavelet coefficients obtained from the continuous wavelet transform (CWT) of reflectance spectra. The spectral variables, which include sensitive spectral bands and feature wavelet coefficients, were optimized using three selection algorithms to improve the classification performance of the model. The selection algorithms include correlation analysis (CA), the successive projection algorithm (SPA), and the random frog (RF) algorithm. The model results were used to compare the performance of various methods. The CWT-SPA-SVM model exhibited excellent performance. The classification accuracies on the training set (Atrain) and the test set (Atest) were respectively 100% and 97.37%, demonstrating the good classification capability of the model. The difference between the Atrain and accuracy of cross-validation (Acv) was 1%, which showed that the model has good stability. Therefore, the CWT-SPA-SVM model can be used to classify the growth stages of potato crops accurately. This study provides an important support method for the classification of growth stages in the potato field.

scholarly journals Coastal Mangrove Response to Marine Erosion: Evaluating the Impacts of Spatial Distribution and Vegetation Growth in Bangkok Bay from 1987 to 2017

2020 ◽  
Vol 12 (2) ◽  
pp. 220 ◽  
Author(s):  
Han Xiao ◽  
Fenzhen Su ◽  
Dongjie Fu ◽  
Qi Wang ◽  
Chong Huang
Keyword(s):  
Spatial Distribution ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Mangrove Ecosystem ◽  
Early Growth ◽  
Growth Stages ◽  
Human Disturbances ◽  
Vegetation Growth ◽  
Two Parameters ◽  
The Impact

Long time-series monitoring of mangroves to marine erosion in the Bay of Bangkok, using Landsat data from 1987 to 2017, shows responses including landward retreat and seaward extension. Quantitative assessment of these responses with respect to spatial distribution and vegetation growth shows differing relationships depending on mangrove growth stage. Using transects perpendicular to the shoreline, we calculated the cross-shore mangrove extent (width) to represent spatial distribution, and the normalized difference vegetation index (NDVI) was used to represent vegetation growth. Correlations were then compared between mangrove seaside changes and the two parameters—mangrove width and NDVI—at yearly and 10-year scales. Both spatial distribution and vegetation growth display positive impacts on mangrove ecosystem stability: At early growth stages, mangrove stability is positively related to spatial distribution, whereas at mature growth the impact of vegetation growth is greater. Thus, we conclude that at early growth stages, planting width and area are more critical for stability, whereas for mature mangroves, management activities should focus on sustaining vegetation health and density. This study provides new rapid insights into monitoring and managing mangroves, based on analyses of parameters from historical satellite-derived information, which succinctly capture the net effect of complex environmental and human disturbances.

scholarly journals The STAFF-DWP wave instrument on the DSP equatorial spacecraft: description and first results

2005 ◽  
Vol 23 (8) ◽  
pp. 2785-2801 ◽  
Author(s):  
N. Cornilleau-Wehrlin ◽  
H. St. Alleyne ◽  
K. H. Yearby ◽  
B. de la Porte de Vaux ◽  
A. Meyer ◽  
...  
Keyword(s):  
Electromagnetic Waves ◽  
Broad Band ◽  
Temporal Analysis ◽  
Time Interval ◽  
Magnetic Fluctuations ◽  
Quiet Time ◽  
Waveform Data ◽  
Power Intensity ◽  
First Case ◽  
First Results

Abstract. The STAFF-DWP wave instrument on board the equatorial spacecraft (TC1) of the Double Star Project consists of a combination of 2 instruments which are a heritage of the Cluster mission: the Spatio-Temporal Analysis of Field Fluctuations (STAFF) experiment and the Digital Wave-Processing experiment (DWP). On DSP-TC1 STAFF consists of a three-axis search coil magnetometer, used to measure magnetic fluctuations at frequencies up to 4 kHz and a waveform unit, up to 10 Hz, plus snapshots up to 180 Hz. DWP provides several onboard analysis tools: a complex FFT to fully characterise electromagnetic waves in the frequency range 10 Hz-4 kHz, a particle correlator linked to the PEACE electron experiment, and compression of the STAFF waveform data. The complementary Cluster and TC1 orbits, together with the similarity of the instruments, permits new multi-point studies. The first results show the capabilities of the experiment, with examples in the different regions of the magnetosphere-solar wind system that have been encountered by DSP-TC1 at the beginning of its operational phase. An overview of the different kinds of electromagnetic waves observed on the dayside from perigee to apogee is given, including the different whistler mode waves (hiss, chorus, lion roars) and broad-band ULF emissions. The polarisation and propagation characteristics of intense waves in the vicinity of a bow shock crossing are analysed using the dedicated PRASSADCO tool, giving results compatible with previous studies: the broad-band ULF waves consist of a superimposition of different wave modes, whereas the magnetosheath lion roars are right-handed and propagate close to the magnetic field. An example of a combined Cluster DSP-TC1 magnetopause crossing is given. This first case study shows that the ULF wave power intensity is higher at low latitude (DSP) than at high latitude (Cluster). On the nightside in the tail, a first wave event comparison - in a rather quiet time interval - is shown. It opens the doors to future studies, such as event timing during substorms, to possibly determine their onset location.

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