The Optimal Image Date Selection for Evaluating Cultivated Land Quality Based on Gaofen-1 Images

This study proposes a method for determining the optimal image date to improve the evaluation of cultivated land quality (CLQ). Five vegetation indices: leaf area index (LAI), difference vegetation index (DVI), enhanced vegetation index (EVI), normalized difference vegetation index (NDVI), and ratio vegetation index (RVI) are first retrieved using the PROSAIL model and Gaofen-1 (GF-1) images. The indices are then introduced into four regression models at different growth stages for assessing CLQ. The optimal image date of CLQ evaluation is finally determined according to the root mean square error (RMSE). This method is tested and validated in a rice growth area of Southern China based on 115 sample plots and five GF-1 images acquired at the tillering, jointing, booting, heading to flowering, and milk ripe and maturity stage of rice in 2015, respectively. The results show that the RMSEs between the measured and estimated CLQ from four vegetation index-based regression models at the heading to flowering stage are smaller than those at the other growth stages, indicating that the image date corresponding with the heading to flowering stage is optimal for CLQ evaluation. Compared with other vegetation index-based models, the LAI-based logarithm model provides the most accurate estimates of CLQ. The optimal model is also driven using the GF-1 image at the heading to flowering stage to map CLQ of the study area, leading to a relative RMSE of 14.09% at the regional scale. This further implies that the heading to flowering stage is the optimal image time for evaluating CLQ. This study is the first effort to provide an applicable method of selecting the optimal image date to improve the estimation of CLQ and thus advanced the literature in this field.

Download Full-text

The value of different vegetative indices (NDVI, GAI) for the assessment of yield potential of pea (Pisum sativum L.) at different growth stages and under varying management practices

Acta Agrobotanica ◽

10.5586/aa.1733 ◽

2018 ◽

Vol 71 (1) ◽

Cited By ~ 2

Author(s):

Agnieszka Klimek-Kopyra ◽

Tadeusz Zając ◽

Andrzej Oleksy ◽

Bogdan Kulig ◽

Anna Ślizowska

Keyword(s):

Vegetation Index ◽

Management Practices ◽

Normalized Difference Vegetation Index ◽

Negative Impact ◽

Yield Potential ◽

Growth Stages ◽

Maturity Stage ◽

Area Index ◽

Foliar Fertilizer ◽

Vegetative Indices

This research evaluated the NDVI (normalized difference vegetation index) and GAI (green area index) in order to indicate the productivity and developmental effects of Rhizobium inoculants and microelement foliar fertilizer on pea crops. Two inoculants, Nitragina (a commercial inoculant) and IUNG (a noncommercial inoculant gel) and a foliar fertilizer (Photrel) were studied over a 4-year period, 2009–2012. The cultivars chosen for the studies were characterized by different foliage types, namely a semileafless pea ‘Tarchalska’ and one with regular foliage, ‘Klif’. Foliar fertilizer significantly increased the length of the generative shoots and the number of fruiting nodes in comparison to the control, which in turn had a negative impact on the harvest index. Pea seed yield was highly dependent on the interaction between the years of growth and the microbial inoculant, and was greater for ‘Tarchalska’ (4.33 t ha−1). Presowing inoculation of seeds and foliar fertilization resulted in a significantly higher value of GAI at the flowering (3.91 and 3.81, respectively) and maturity stages (4.82 and 4.77, respectively), whereas the value of NDVI was higher for these treatments only at the maturity stage (0.67 and 0.79, respectively). A significantly greater yield (5.0–5.4 t ha−1) was obtained after inoculation with IUNG during the dry years.

Download Full-text

The GA-BPNN-Based Evaluation of Cultivated Land Quality in the PSR Framework Using Gaofen-1 Satellite Data

Sensors ◽

10.3390/s19235127 ◽

2019 ◽

Vol 19 (23) ◽

pp. 5127 ◽

Cited By ~ 3

Author(s):

Liu ◽

Peng ◽

Xia ◽

Hu ◽

Wang ◽

...

Keyword(s):

Remote Sensing ◽

Linear Model ◽

Vegetation Index ◽

Regional Scale ◽

Cultivated Land ◽

Land Quality ◽

Remote Sensing Technology ◽

Evaluation Approach ◽

Sensing Technology ◽

State Index

Rapid and efficient assessment of cultivated land quality (CLQ) using remote sensing technology is of great significance for protecting cultivated land. However, it is difficult to obtain accurate CLQ estimates using the current satellite-driven approaches in the pressure-state-response (PSR) framework, owing to the limitations of linear models and CLQ spectral indices. In order to improve the estimation accuracy of CLQ, this study used four evaluation models (the traditional linear model; partial least squares regression, PLSR; back propagation neural network, BPNN; and BPNN with genetic algorithm optimization, GA-BPNN) to evaluate CLQ for determining the accurate evaluation model. In addition, the optimal satellite-derived indicator in the land state index was selected among five vegetation indices (the normalized vegetation index, NDVI; enhanced vegetation index, EVI; modified soil-adjusted vegetation index, MSAVI; perpendicular vegetation index, PVI; and soil-adjusted vegetation index, SAVI) to improve the prediction accuracy of CLQ. This study was conducted in Conghua District of Guangzhou, Guangdong Province, China, based on Gaofen-1 (GF-1) data. The prediction accuracies from the traditional linear model, PLSR, BPNN, and GA-BPNN were compared using observations. The results demonstrated that (1) compared with other models (the traditional linear model: R2 = 0.14 and RMSE = 91.53; PLSR: R2 = 0.33 and RMSE = 74.58; BPNN: R2 = 0.50 and RMSE = 61.75), the GA-BPNN model based on EVI in the land state index provided the most accurate estimates of CLQ, with the R2 of 0.59 and root mean square error (RMSE) of 56.87, indicating a nonlinear relationship between CLQ and the prediction indicator; and (2) the GA-BPNN-based evaluation approach of CLQ in the PSR framework was driven to map CLQ of the study area using the GF-1 data, leading to an RMSE of 61.44 at the regional scale, implying that the GA-BPNN-based evaluation approach has the potential to map CLQ over large areas. This study provides an important reference for the high-accuracy prediction of CLQ based on remote sensing technology.

Download Full-text

Water and heat unit requirement in different growth stages of Soybean (Glycine max L. Merrill) at Bhopal

MAUSAM ◽

10.54302/mausam.v58i4.1437 ◽

2021 ◽

Vol 58 (4) ◽

pp. 537-542

Author(s):

I. J. VERMA ◽

H. P. DAS ◽

V. N. JADHAV

Keyword(s):

Crop Coefficient ◽

Growth Stages ◽

Maturity Stage ◽

Degree Days ◽

Flowering Stage ◽

Heat Unit ◽

Soybean Crop ◽

Pod Development ◽

Use Efficiency ◽

Different Growth Stages

In this study, ET data available on Soybean crop for Bhopal during 1991-95 have been utilized. With regard to water need of the crop, the life span of soybean has been divided into five important growth stages viz., seedling up to 2 weeks after sowing (WAS), vegetative (3-8 WAS), flowering (9-10 WAS), pod development (11-13 WAS), and maturity (14-15 WAS). In this paper, consumptive use of water (ET), Water Use Efficiency (WUE), Heat Units (HU), Heat Use Efficiency (HUE) and crop coefficient (Kc) for different growth stages of the crop have been computed and discussed. The study revealed that on an average, Soybean crop consumed about 450 mm of water. The average WUE was found to be 3.23 kg /ha/mm. It was also observed that WUE does not depend only on the total amount of water consumed by the crop but also indicates the importance of its distribution during various growth stages. On an average, the crop consumed nearly 7%, 36%, 24%, 25% and 8% of water during seedling, vegetative, flowering, pod development and maturity stage respectively. The crop consumed maximum amount of water during vegetative stage. However, the average weekly ET rate was found to be highest during flowering stage (nearly 52 mm). Average heat unit requirement of soybean was found to be 1694 degree-days. Maximum heat units were required during vegetative stage (638 degree days) followed by pod development stage (358 degree days). The average HUE was found to be 0.86 kg/ha/degree days. Crop coefficient (Kc) values varied in the range 0.30 – 0.45, 0.55 – 0.90, 1.00 – 1.15, 0.85 – 0.70 and 0.55 – 0.40 during seedling, vegetative, flowering, pod development and maturity stage respectively. The crop coefficient values attained the peak during the flowering stage.

Download Full-text

RGB Image-Derived Indicators for Spatial Assessment of the Impact of Broadleaf Weeds on Wheat Biomass

Remote Sensing ◽

10.3390/rs12182982 ◽

2020 ◽

Vol 12 (18) ◽

pp. 2982 ◽

Cited By ~ 1

Author(s):

Christelle Gée ◽

Emmanuel Denimal

Keyword(s):

Biomass Production ◽

Precision Agriculture ◽

Weed Management ◽

Vegetation Index ◽

Management Strategies ◽

Weed Competition ◽

Growth Stages ◽

Area Index ◽

Crop Weed Competition ◽

The Impact

In precision agriculture, the development of proximal imaging systems embedded in autonomous vehicles allows to explore new weed management strategies for site-specific plant application. Accurate monitoring of weeds while controlling wheat growth requires indirect measurements of leaf area index (LAI) and above-ground dry matter biomass (BM) at early growth stages. This article explores the potential of RGB images to assess crop-weed competition in a wheat (Triticum aestivum L.) crop by generating two new indicators, the weed pressure (WP) and the local wheat biomass production (δBMc). The fractional vegetation cover (FVC) of the crop and the weeds was automatically determined from the images with a SVM-RBF classifier, using bag of visual word vectors as inputs. It is based on a new vegetation index called MetaIndex, defined as a vote of six indices widely used in the literature. Beyond a simple map of weed infestation, the map of WP describes the crop-weed competition. The map of δBMc, meanwhile, evaluates the local wheat above-ground biomass production and informs us about a potential stress. It is generated from the wheat FVC because it is highly correlated with LAI (r2 = 0.99) and BM (r2 = 0.93) obtained by destructive methods. By combining these two indicators, we aim at determining whether the origin of the wheat stress is due to weeds or not. This approach opens up new perspectives for the monitoring of weeds and the monitoring of their competition during crop growth with non-destructive and proximal sensing technologies in the early stages of development.

Download Full-text

The validation of a model estimating the Leaf Area Index of grasslands in southern China

The Rangeland Journal ◽

10.1071/rj12025 ◽

2013 ◽

Vol 35 (3) ◽

pp. 245 ◽

Cited By ~ 1

Author(s):

Chengming Sun ◽

Zhengguo Sun ◽

Tao Liu ◽

Doudou Guo ◽

Shaojie Mu ◽

...

Keyword(s):

Leaf Area Index ◽

Leaf Area ◽

Vegetation Index ◽

Mean Squared Error ◽

Southern China ◽

Carbon Sink ◽

Measurement Data ◽

Light Transmittance ◽

Area Index ◽

Normalised Difference Vegetation Index

In order to estimate the leaf area index (LAI) over large areas in southern China, this paper analysed the relationships between normalised difference vegetation index (NDVI) and the vegetation light transmittance and the extinction coefficient based on the use of moderate resolution imaging spectroradiometer data. By using the improved Beer–Lambert Law, a model was constructed to estimate the LAI in the grassy mountains and slopes of southern China with NDVI as the independent variable. The model was validated with field measurement data from different locations and different years in the grassland mountains and slopes of southern China. The results showed that there was a good correlation between the simulated and observed LAI values, and the values of R2 achieved were high. The relative root mean squared error was between 0.109 and 0.12. This indicated that the model was reliable. The above results provided the theoretical basis for the effective management of the grassland resources in southern China and the effective estimation of grassland carbon sink.

Download Full-text

Effect of Rhizobium inoculation of seeds and foliar fertilization on productivity of Pisum sativum L.

Acta Agrobotanica ◽

10.5586/aa.2013.024 ◽

2013 ◽

Vol 66 (2) ◽

pp. 71-78 ◽

Cited By ~ 2

Author(s):

Tadeusz Zając ◽

Agnieszka Klimek-Kopyra ◽

Andrzej Oleksy

Keyword(s):

Pisum Sativum ◽

Photosynthetically Active Radiation ◽

Vegetation Index ◽

Foliar Application ◽

Yield Potential ◽

Grain Legume ◽

Growth Stages ◽

Area Index ◽

Active Radiation ◽

Experimental Field

Pea (Pisum sativum L.) is the second most important grain legume crop in the world which has a wide array of uses for human food and fodder. One of the major factors that determines the use of field pea is the yield potential of cultivars. Presently, pre-sowing inoculation of pea seeds and foliar application of microelement fertilizers are prospective solutions and may be reasonable agrotechnical options. This research was undertaken because of the potentially high productivity of the 'afila' morphotype in good wheat complex soils. The aim of the study was to determine the effect of vaccination with Rhizobium and foliar micronutrient fertilization on yield of the afila pea variety. The research was based on a two-year (2009–2010) controlled field experiment, conducted in four replicates and carried out on the experimental field of the Bayer company located in Modzurów, Silesian region. experimental field soil was Umbrisol – slightly degraded chernozem, formed from loess. Nitragina inoculant, as a source of symbiotic bacteria, was applied before sowing seeds. Green area index (GAI) of the canopy, photosynthetically active radiation (PAR), and normalized difference vegetation index (NDVI) were determined at characteristic growth stages. The presented results of this study on symbiotic nitrogen fixation by leguminous plants show that the combined application of Nitragina and Photrel was the best combination for productivity. Remote measurements of the pea canopy indexes indicated the formation of the optimum leaf area which effectively used photosynthetically active radiation. The use of Nitragina as a donor of effective Rhizobium for pea plants resulted in slightly higher GAI values and the optimization of PAR and NDVI. It is not recommended to use foliar fertilizers or Nitragina separately due to the slowing of pea productivity.

Download Full-text

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

10.21203/rs.3.rs-170131/v1 ◽

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.

Download Full-text

Combining Spectral and Texture Features of UAV Images for the Remote Estimation of Rice LAI throughout the Entire Growing Season

Remote Sensing ◽

10.3390/rs13153001 ◽

2021 ◽

Vol 13 (15) ◽

pp. 3001

Author(s):

Kaili Yang ◽

Yan Gong ◽

Shenghui Fang ◽

Bo Duan ◽

Ningge Yuan ◽

...

Keyword(s):

Vegetation Index ◽

Morphological Changes ◽

Texture Features ◽

Growing Season ◽

Estimation Accuracy ◽

Coefficient Of Determination ◽

Spectral Information ◽

Growth Stages ◽

Area Index ◽

Texture Information

Leaf area index (LAI) estimation is very important, and not only for canopy structure analysis and yield prediction. The unmanned aerial vehicle (UAV) serves as a promising solution for LAI estimation due to its great applicability and flexibility. At present, vegetation index (VI) is still the most widely used method in LAI estimation because of its fast speed and simple calculation. However, VI only reflects the spectral information and ignores the texture information of images, so it is difficult to adapt to the unique and complex morphological changes of rice in different growth stages. In this study we put forward a novel method by combining the texture information derived from the local binary pattern and variance features (LBP and VAR) with the spectral information based on VI to improve the estimation accuracy of rice LAI throughout the entire growing season. The multitemporal images of two study areas located in Hainan and Hubei were acquired by a 12-band camera, and the main typical bands for constituting VIs such as green, red, red edge, and near-infrared were selected to analyze their changes in spectrum and texture during the entire growing season. After the mathematical combination of plot-level spectrum and texture values, new indices were constructed to estimate rice LAI. Comparing the corresponding VI, the new indices were all less sensitive to the appearance of panicles and slightly weakened the saturation issue. The coefficient of determination (R2) can be improved for all tested VIs throughout the entire growing season. The results showed that the combination of spectral and texture features exhibited a better predictive ability than VI for estimating rice LAI. This method only utilized the texture and spectral information of the UAV image itself, which is fast, easy to operate, does not need manual intervention, and can be a low-cost method for monitoring crop growth.

Download Full-text

Photosynthetic characteristics of cotton are enhanced by altering the timing of mulch film removal

Journal of Cotton Research ◽

10.1186/s42397-021-00091-7 ◽

2021 ◽

Vol 4 (1) ◽

Author(s):

Zhanqin ZHANG ◽

Li ZHANG ◽

Haiyan TIAN ◽

Yuan NIU ◽

Xiangkun YANG

Keyword(s):

Adverse Effects ◽

Seedling Emergence ◽

Photosynthetic Characteristics ◽

Growth Stages ◽

Flowering Stage ◽

Area Index ◽

Significant Difference ◽

Mulch Film ◽

Cotton Plants ◽

Mulching Film

Abstract Background The photosynthetic parameters of cotton plants may be modified by the timing of film removal during their growing period. This study was undertaken during 2015–2017 in Xinjiang, China, to determine to what extent the film mulching removal time, 1 and 10 days before the first irrigation and 1 day before the second irrigation after seedling emergence, influenced cotton’s photosynthetic characteristics. The control group (CK) was film-mulched throughout the growth stages. Results The results suggested the following: (1) Removing mulching-film within 50 days since seedling emergence had adverse effects on soil temperature and moisture. (2) Film-removal before the first or second irrigation after emergence improved the net photosynthetic rate in cotton’s later flowering stage and its transpiration rate in mid and later flowering stages while enhancing the actual electron transport rate (ETR) and maximum electron transfer rate (ETRmax) between cotton photosystems I and II. (3) Film-removal treatment also increased cotton plants’ tolerance to high irradiation after emergence, the trend was more pronounced in the early flowering stage in wetter years. (4) Leaf area index (LAI) of cotton was reduced in the film-removal treatment for which the least accumulation of dry matter occurred in a drought year (i.e., 2015). (5) Film removal caused a yield decrease in the dry year (2015), and the earlier the film was removed, the more seriously the yield decreased. Removing mulching film before the second irrigation could increase the yield of XLZ42 in the rainy year (2016) and the normal rainfall year (2017). Early film removal can increase the yield of XLZ45 in the rainy year (2016). Conclusions Collectively, our study’s experimental results indicate that applying mulch film removal at an appropriate, targeted time after seedling emergence had no adverse effects on soil moisture and temperature, and improved the photosynthetic performance of cotton, thus increased cotton yield and fiber quality, but no significant difference was reached.

Download Full-text

Estimation of Paddy Rice Nitrogen Content and Accumulation Both at Leaf and Plant Levels from UAV Hyperspectral Imagery

Remote Sensing ◽

10.3390/rs13152956 ◽

2021 ◽

Vol 13 (15) ◽

pp. 2956

Author(s):

Li Wang ◽

Shuisen Chen ◽

Dan Li ◽

Chongyang Wang ◽

Hao Jiang ◽

...

Keyword(s):

Nitrogen Content ◽

Regression Models ◽

Growth Stage ◽

Vegetation Index ◽

Leaf Nitrogen ◽

Paddy Rice ◽

Growth Stages ◽

Nitrogen Accumulation ◽

Plant Nitrogen ◽

Stage Models

Remote sensing-based mapping of crop nitrogen (N) status is beneficial for precision N management over large geographic regions. Both leaf/canopy level nitrogen content and accumulation are valuable for crop nutrient diagnosis. However, previous studies mainly focused on leaf nitrogen content (LNC) estimation. The effects of growth stages on the modeling accuracy have not been widely discussed. This study aimed to estimate different paddy rice N traits—LNC, plant nitrogen content (PNC), leaf nitrogen accumulation (LNA) and plant nitrogen accumulation (PNA)—from unmanned aerial vehicle (UAV)-based hyperspectral images. Additionally, the effects of the growth stage were evaluated. Univariate regression models on vegetation indices (VIs), the traditional multivariate calibration method, partial least squares regression (PLSR) and modern machine learning (ML) methods, including artificial neural network (ANN), random forest (RF), and support vector machine (SVM), were evaluated both over the whole growing season and in each single growth stage (including the tillering, jointing, booting and heading growth stages). The results indicate that the correlation between the four nitrogen traits and the other three biochemical traits—leaf chlorophyll content, canopy chlorophyll content and aboveground biomass—are affected by the growth stage. Within a single growth stage, the performance of selected VIs is relatively constant. For the full-growth-stage models, the performance of the VI-based models is more diverse. For the full-growth-stage models, the transformed chlorophyll absorption in the reflectance index/optimized soil-adjusted vegetation index (TCARI/OSAVI) performs best for LNC, PNC and PNA estimation, while the three band vegetation index (TBVITian) performs best for LNA estimation. There are no obvious patterns regarding which method performs the best of the PLSR, ANN, RF and SVM in either the growth-stage-specific or full-growth-stage models. For the growth-stage-specific models, a lower mean relative error (MRE) and higher R2 can be acquired at the tillering and jointing growth stages. The PLSR and ML methods yield obviously better estimation accuracy for the full-growth-stage models than the VI-based models. For the growth-stage-specific models, the performance of VI-based models seems optimal and cannot be obviously surpassed. These results suggest that building linear regression models on VIs for paddy rice nitrogen traits estimation is still a reasonable choice when only a single growth stage is involved. However, when multiple growth stages are involved or missing the phenology information, using PLSR or ML methods is a better option.

Download Full-text