Normalized Difference Vegetation Index Curve Research Articles

Mapping Main Grain Crops and Change Analysis in the West Liaohe River Basin with Limited Samples Based on Google Earth Engine

It is an important issue to explore achieving high accuracy long-term crop classification with limited historical samples. The West Liaohe River Basin (WLRB) serves as a vital agro-pastoral ecotone of Northern China, which experiences significant changes in crop planting structure due to a range of policy. Taking WLRB as a case study, this study constructed multidimensional features for crop classification suitable for Google Earth Engine cloud platform and proposed a method to extract main grain crops using sample augmentation and model migration in case of limited samples. With limited samples in 2017, the method was employed to train and classify crops (maize, soybean, and rice) in other years, and the spatiotemporal changes in the crop planting structure in WLRB from 2014 to 2020 were analyzed. The following conclusions were drawn: (1) Integrating multidimensional features could discriminate subtle differences, and feature optimization could ensure the accuracy and efficiency of classification. (2) By augmenting the original sample size by calculating the similarity of the time series NDVI (normalized difference vegetation index) curves, migrating the random forest model, and reselecting the samples for other years based on the model accuracy scores, it was possible to achieve a high crop classification accuracy with limited samples. (3) The main grain crops in the WLRB were primarily distributed in the northeastern and southern plains with lower elevations. Maize was the most predominant crop type with a wide distribution. The planting area of main grain crops in the WLRB exhibited an increasing trend, and national policies primarily influenced the variations of planting structure in maize and soybean. This study provides a scheme for extracting crop types from limited samples with high accuracy and can be applied for long-term crop monitoring and change analysis to support crop structure adjustment and food security.

Automatically identifying the vegetation destruction and restoration of various open-pit mines utilizing remotely sensed images: Auto-VDR

The spatio-temporal pattern of vegetation disturbance by open-pit mining activities such as the disturbance area and magnitude are important for coordinated development of surface mining and environmental protection. This paper proposes an automatic method (Auto-VDR) for identifying vegetation destruction and restoration of various open-pit mines. The proposed method consists of three steps: construction of the sample dataset, identification of disturbance types, and extraction of disturbance time and magnitude. First, a sample dataset with three types was constructed according to the interannual trends of Normalized Difference Vegetation Index (NDVI) in mining areas: “Restoration after destruction (RAD)”, “Destruction without restoration (DWR)” and “No destruction (ND)”. Second, in order to accommodate to various open-pit mines with different background conditions (original vegetation cover prior to mining, loss rate and magnitude of vegetation due to mining, etc.), the “Shape-based distance” was used to match the NDVI curves of each pixel to the three disturbance types. Third, the disturbance time and magnitude in the destroyed regions were extracted based on the vegetated/bare ground threshold (α). The Shendong coal base in China was selected as the study area consisting of 133 open-pit coal mines. Annual NDVI was generated using Landsat time-series data. The maps of vegetation destruction and restoration caused by surface mining during 1990–2021 were drawn with accuracies of 0.82 and 0.87, respectively. The identification accuracy of the Auto-VDR is higher than Landsat-based detection of Trends in Disturbance and Recovery (LandTrendr) and Continuous Change Detection and Classification (CCDC). The results show that from 1990 to 2021, a total of 400.08 km2 of vegetation has been destroyed, accounting for 35.93% of the study area. Furthermore, restoration activities mainly began in 2010, lagging mining activities by approximately three years. The accumulated area of restoration was 177.91 km2 and accounts for 15.98% of the study area. The vegetation destruction at the Shendong coal base has gone through four stages, with 2008, 2015 and 2018 as splits, corresponding to the developing stage of the China coal industry. In particular, the area of vegetation destruction per year reached the maximum of 61.16 km2 in 2008, which was 1.38 times the total area mined over the past 17 years. There were 14% of the 133 open-pit mines in Shendong coal base with a restoration rate of 0.8 and 59% of the mines with a restoration rate of 0.5. This method has been demonstrated to be able to extract vegetation destruction and restoration processes accurately and rapidly for various open-pit mines at a scale of coal base, which provides an important reference for supervising the impact of mining on the environment and assessing the effectiveness of ecological restoration.

Yield estimation of summer maize based on multi‐source remote‐sensing data

AbstractAccurately estimating regional‐scale crop yields is substantial in determining current agricultural production performance and effective agricultural land management. The Yuncheng Basin is an important grain‐producing area in the Shanxi Province. This paper used Sentinel 2A with a spatial resolution of 10 m and MODIS with a temporal resolution of 1 d in 2020. The spatial and temporal nonlocal filter‐based fusion model (STNLFFM) was used to obtain fused data with a spatial resolution of 10 m and a temporal resolution of 1 d, combined with the Carnegie–Ames–Stanford Approach (CASA) and light‐use efficiency model to achieve summer maize (Zea mays L.) yield estimation. The results showed that the fused normalized difference vegetation index (NDVI) could inherit the spatial Sentinel‐2A NDVI details and express the spatial differences between smaller features more effectively. The STNLFFM NDVI curve was consistent with the actual summer maize growth condition, which accurately reflects the NDVI trend and local abrupt change information during the summer maize growth period. Moreover, the fused NDVI was influenced by topographic differences and artificial irrigation factors, whereas the summer maize yield in mountainous and plateau areas of the Yuncheng Basin was <5,000 kg ha−1 and those in the alluvial plain of the Sushui River reached 8,000 kg ha−1. The accuracy of the yield estimation model constructed based on STNLFFM NDVI (mean absolute percentage error [MAPE] = 5.47%, −13.74% ≤ relative error [RE] ≤0.12%) was significantly higher than that of the model based on MODIS NDVI (MAPE = 15.65%, −19.67% ≤ RE ≤ 20.88%), indicating that the use of spatio‐temporal fusion technology can effectively improve the summer maize yield estimation accuracy.

Comparison of Stationary and Mobile Canopy Sensing Systems for Maize and Soybean in Nebraska, USA

HighlightsMultispectral sensors mounted on the center pivot lateral were able to capture differences between rainfed and irrigated crop.Canopy temperature was strongly associated among stationary and pivot-mounted sensors with coefficient of determination ranging between 0.88 and 0.99.A cooling effect of about 2°C was observed in canopy temperature data collected from pivot mounted sensors for irrigated soybean crop.Abstract. Accurate knowledge of plant and field characteristics is crucial for irrigation management. Irrigation can potentially be better managed by utilizing data collected from various sensors installed on different platforms. The accuracy and repeatability of each data source are important considerations when selecting a sensing system suitable for irrigation management. The objective of this study was to compare data from multispectral (red and near-infrared bands) and thermal (long wave thermal infrared band) sensors mounted on different platforms to investigate their comparative usability and accuracy. The different sensor platforms included stationary posts fixed on the ground, the lateral of a center pivot irrigation system, unmanned aircraft systems (UAS), and Planet (PlanetScope multispectral imager, Planet Labs, Inc., San Francisco, Calif.) satellites. The surface reflectance data from multispectral (MS) sensors were used to compute the Normalized Difference Vegetation Index (NDVI) and Soil Adjusted Vegetation Index (SAVI). The experimental plots were managed with rainfed and irrigated treatments. Irrigation was applied according to a spatial evapotranspiration model informed with Planet satellite imagery. The NDVI and SAVI curves computed from the different sensing systems exhibited similar patterns and were able to capture differences between the rainfed and irrigated treatments when the crops were approaching senescence. Strong correlations were observed for canopy temperature measurements between the stationary and pivot-mounted infrared thermometer (IRT) sensors (p-value of less than 0.01 for the correlations) when canopy were scanned with no irrigation application (dry scans). The best correlation was obtained for the irrigated maize, which yielded r2 of 0.99, RMSE of 0.4°C, and MAE of 0.3°C. The correlation for the canopy temperature data collected during dry scan between UAS and pivot-mounted thermal sensors was weak with r2 = 0.26 to 0.28, larger RMSE values of 3.7°C and MAE values of 3.4°C. Secondary analysis between thermal data from stationary and pivot-mounted IRTs collected during wet scans (during an irrigation event) demonstrated reduced canopy temperature from pivot-mounted IRTs by approximately 2°C for irrigated soybean due to wetting of the canopy by the irrigation. Understanding the performance of these sensor systems is valuable in configuring practical design and operational considerations when using sensor feedback for irrigation management. Keywords: Center pivots, Irrigation, Multispectral, Remote sensing, Thermal, Unmanned aircraft systems.

Genetic dissection of seasonal vegetation index dynamics in maize through aerial based high-throughput phenotyping.

Plant phenotyping under field conditions plays an important role in agricultural research. Efficient and accurate high-throughput phenotyping strategies enable a better connection between genotype and phenotype. Unmanned aerial vehicle-based high-throughput phenotyping platforms (UAV-HTPPs) provide novel opportunities for large-scale proximal measurement of plant traits with high efficiency, high resolution, and low cost. The objective of this study was to use time series normalized difference vegetation index (NDVI) extracted from UAV-based multispectral imagery to characterize its pattern across development and conduct genetic dissection of NDVI in a large maize population. The time series NDVI data from the multispectral sensor were obtained at five time points across the growing season for 1,752 diverse maize accessions with a UAV-HTPP. Cluster analysis of the acquired measurements classified 1,752 maize accessions into two groups with distinct NDVI developmental trends. To capture the dynamics underlying these static observations, penalized-splines (P-splines) model was used to obtain genotype-specific curve parameters. Genome-wide association study (GWAS) using static NDVI values and curve parameters as phenotypic traits detected signals significantly associated with the traits. Additionally, GWAS using the projected NDVI values from the P-splines models revealed the dynamic change of genetic effects, indicating the role of gene-environment interplay in controlling NDVI across the growing season. Our results demonstrated the utility of ultra-high spatial resolution multispectral imagery, as that acquired using a UAV-based remote sensing, for genetic dissection of NDVI.

Prediction of plant growth based on statistical methods and remote sensing data

Based on the data provided from the new satellite constellation, the crop phonology can be mapped accurately with a high spatial and temporal resolution. The potential of Sentinel-2 (high spatial and temporal resolution, 10 m and 5 days) is investigated in order to monitor and forecast the crop growth over different olive groves located in Sfax, Tunisia, from the beginning of 2016 to the end of 2020. The normalized difference vegetation index (NDVI) is used as an indicator of vegetation health due to its high correlation with crop growth health and productivity, and it is particularly forecasted to predict the trends. The prediction is done using two different statistical methods, autoregressive (AR) and Markov chain (MC), based on historical data derived from Sentinel-2 data. The prediction is applied for three different areas having various vegetation types and density. Moreover, for an accurate and precise prediction, our study areas are divided into different homogeneous clusters using the well-known Gaussian mixture model. Furthermore, the performances of our approach are assessed by means of the root mean squared error (RMSE) between the predicted results and the actual data. Globally, the obtained results for all the clusters of each area show that the forecast, using both methods, is accurate where the error is less than 5%. Nevertheless, the MC model displays the highest performance where the predicted NDVI curves of the different study areas are the closest to the actual observation. MC (resp. AR) precision is of 97% with RMSE = 0.025 (resp. 95% and RMSE = 0.041) for all the clusters of Jbeniana and Limaya, and 99% with RMSE = 0.0073 (resp. 98% and RMSE = 0.0127) for the four clusters of Chaâl.

Start of the Green Season and Normalized Difference Vegetation Index in Alaska’s Arctic National Parks

Daily Normalized Difference Vegetation Index (NDVI) values from the MODIS Aqua and Terra satellites were compared with on-the-ground camera observations at five locations in northern Alaska. Over half of the spring rise in NDVI was due to the transition from the snow-covered landscape to the snow-free surface prior to the deciduous leaf-out. In the fall after the green season, NDVI fluctuated between an intermediate level representing senesced vegetation and lower values representing clouds and intermittent snow, and then dropped to constant low levels after establishment of the permanent winter snow cover. The NDVI value of snow-free surfaces after fall leaf senescence was estimated from multi-year data using a 90th percentile smoothing spline curve fit to a plot of daily NDVI values vs. ordinal date. This curve typically showed a flat region of intermediate NDVI values in the fall that represent cloud- and snow-free days with senesced vegetation. This “fall plateau” was readily identified in a large systematic sample of MODIS NDVI values across the study area, in typical tundra, shrub, and boreal forest environments. The NDVI level of the fall plateau can be extrapolated to the spring rising leg of the annual NDVI curve to approximate the true start of green season.

Phenology as accuracy metrics for vegetation index forecasting over Tunisian forest and cereal cover types

ABSTRACT Different studies on predicting future green cover changes exist with various success levels. Each one focuses on a different story about which model is more appropriate. Therefore, finding a suitable model remains a difficult task due to the remotely sensed data issues and the complexity of the vegetation cover change process. Despite the unicity of vegetation indices time series, the forecasting assessment relies basically on the commonly used forecast error measurements (e.g. Mean Square Error (MSE), Root MSE (RMSE), and the symmetric Mean Absolute Percentage Error (sMAPE), etc.) which may not reflect the real potential of the fitted forecasting model. Herein, the experimentation of forecasting vegetation indices employing two univariate time series models and new accuracy metrics is discussed. Box Jenkins models (Seasonal AutoRegressive Integrated Moving Average (ARIMA) model) and neural network model (nonlinear autoregressive (NAR) model) are applied individually and coupled (NAR-NAR and NAR-ARIMA) based on a multi-resolution analysis-wavelet transform. These models’ forecasting ability is evaluated using 16-days Moderate Resolution Imaging Spectroradiometer Normalized Difference Vegetation Index (NDVI) time series data of two different vegetation cover types of northwestern area in Tunisia. The major finding highlights firstly the importance of integration of the decomposition step to show off the hidden components of vegetation indices. The commonly used accuracy measures show that the coupled neural networks model outperforms other models. Then, interesting conclusions were drawn when phenological metrics are used as performance measures. Herein, Box Jenkins forecasting model generates a better NDVI curve shape than hybrid models despite their low RMSE measures. This is mainly due to good estimation of some phenological events, namely, the amplitude, the peak and the season’s length. Generally, Box Jenkin model excels at handling quick variations. By contrast, combined models show better phenological metrics’ estimation when the observations are complex or describe long time periods. Based on these findings, we suggest that the choice of the evaluation metric must be related to the future forecaster interest.

A Gaussian Kernel-Based Spatiotemporal Fusion Model for Agricultural Remote Sensing Monitoring

Time series normalized difference vegetation index (NDVI) is the primary data for agricultural remote sensing monitoring. Due to the tradeoff between a single sensor's spatial and temporal resolutions and the impacts of cloud coverage, the time series NDVI data cannot serve well for precision agriculture. In this study, a Gaussian kernel-based spatiotemporal fusion model (GKSFM) was developed to fuse high-resolution NDVI (Landsat) and low-resolution NDVI (MODIS) to produce a daily NDVI product at a 30-m spatial resolution. Considering that the NDVI curve of crop in each growing season can be characterized by Gaussian function, GKSFM used the Gaussian kernel to fit the nonlinear relationship between the high-resolution NDVI and the low-resolution NDVI, to obtain a more reasonable temporal increment. The experimental results show that GKSFM outperformed the comparative models in different proportions of cropland/noncropland and different crop phenology. In addition, GKSFM was also applied for crop mapping of Mishan County by fusing the NDVI images during the crop growing season. This study demonstrates that the accuracy of the proposed method can be improved in the midseason of crops.

Winter wheat growth assessment using Temporal Normalized Phenology Index (TNPI) in Bhuj Taluka, Gujarat State, India

In the present study on winter crop growth monitoring in Bhuj Taluka of Kachchh district was carried out using multi-temporal Sentinel-2 multi-spectral data (spatial resolution 10-m). Sentinel-2 multi-spectral data for the period from October-2018 to March-2019 was analysed in this study. The major objective of this study was to generate crop growth profiles of different crops and identify the relationships between crop phenology and crop growth stages by generating Normalized Difference Vegetation Index (NDVI) profiles of wheat and mustard crops.The results of spectral behavior of wheat (normal & late sown) and mustard during active growth stages indicated that these winter crops have distinct spectral signatures in Sentinel-2 spectral bands. The NDVI growth profiles of wheat and mustard indicate the temporal variations during different growth stages of these crops. The maximum temporal variations were observed during early growth stages to flowering & grain filling stages. The difference in sowing dates of wheat and mustard crops result in different phenological stages of these winter crops. The NDVI curve of Rabi Season-2018-19 was divided into different phenological growth phases namely, i) Phase-I: G0 (Spectral emergence) to G1(Flag leaf emergence), Phase-II: G1 (Flag leaf emergence) to Gmax (Flowering and Grain filling), Phase-III: Gmax (Flowering and Grain filling) to G2 (Dough Stage) and Phase-IV: G2 (Dough Stage) to G3 (Physiological Maturity). The temporal changes in the NDVI was analysed by computing the Temporal Normalized Phenology Index (TNPI) between different phenological phases from G0 to G3.Mean TNPI values of wheat crop for different phenological phases were computed which indicated that rising part of NDVI profile of growth phases from G0 to Gmax higher TNPI values whereas the declining part of NDVI profile of growth phases from Gmax to G3 showed lower TNPI values. The difference NDVI images were generated using the NDVI images of two different phenological phases of wheat crop which represent the temporal changes in wheat crop greenness, where “0” represents no temporal change and “1” represents the maximum temporal change in vegetation greenness across wheat crop areas in the study area.

Early high‐moisture wheat harvest improves double‐crop system: II. Soybean growth and yield

AbstractDouble cropping soybean [Glycine max (L.) Merr.] after winter wheat (Triticum aestivum L.) increases total food production without additional land. However, double‐crop soybean usually yields less than full‐season soybean, mainly due to late planting. We evaluated double‐crop soybean growth and yield as affected by early planting immediately after high‐moisture wheat harvest across 20 site‐years in five Mid‐Atlantic states during 2015–2017. At each site, six soybean cultivars from relative maturity group (rMG) 3.1–5.9 were planted at three to five dates in a 4‐ to 14‐d interval. Soybean growth, measured by normalized difference vegetation index (NDVI) across the growing season, was affected only by planting date. Although NDVI peaked near the R5 stage, it took 9–27 more days to reach the peak NDVI (0.84–0.98) for early‐planted soybean than for late‐planted soybean. Relative yield declined with planting dates, which explained 41–81% of the relative yield variability. The yield loss from delayed planting was greater in the north (33–80%; Pennsylvania, Maryland, and Delaware) than in the south (20–27%; Virginia, North Carolina) due to longer delay in planting and shorter growing season in the north. Soybean NDVI from the R1–R6 stages was associated with yield, with the strongest association (R2 = .55–.57) at the R2 and R3 stages. The area under the NDVI curve (AUNDVIC) was also strongly associated (R2 = .77) with relative yield, indicating an excellent tool for explaining double‐crop soybean yield loss due to poor growth. High‐moisture wheat harvest facilitated soybean planting 4–21 d earlier, which increased growth and yield.

Detecting and Assessing Nondominant Farmland Area with Long-Term MODIS Time Series Images

While most land use and land cover (LULC) studies have focused on modeling, change detection and driving forces at the class or categorical level, few have focused on the subclass level, especially regarding the quality change within a class such as farmland. The concept of nondominant farmland area (NAF) is proposed in this study to assess within class variability and quantify farmland areas where poor environmental conditions, unsuitable natural factors, natural disasters or unsustainable management practices lead to poor crop growth and thus low yield. A 17-year (2000–2016) time series of the Normalized Difference Vegetation Index (NDVI) was used to develop a NAF extraction model with abnormal features in the NDVI curves and subsequently applied to Heilongjiang province in China. The NAF model was analyzed and assessed from three aspects: agricultural disasters, soil types and medium- and low-yield fields, to determine dominant factors of the NAF patterns. The results suggested that: (1) the NAF model was able to extract a variety of NAF types with an overall accuracy of ~80%. The NAF area accumulated more than 8 years in 17 years is 6.20 thousand km2 in Heilongjiang Province, accounting for 3.75% of the total cultivated land area; (2) the NAF had significant spatial clustering characteristics and temporal variability. 53.24% of the NAF accumulated more than 8 years in 17 years is mainly concentrated in the west of Heilongjiang Province. The inter-annual NAF variability was related with meteorological variations, topography and soil properties; and (3) the spatial and temporal NAF patterns seem to reflect a cumulative impact of meteorological disasters, poor farmland quality, and soil degradation on crop growth. The determinant factors of the observed NAF patterns differed across regions, and must be interpreted in the local context of topography, soil properties and meteorological environment. Spatial and temporal NAF variability could provide useful, diagnostic information for precision farmland management.

Remote Sensing Monitoring of Vegetation Dynamic Changes after Fire in the Greater Hinggan Mountain Area: The Algorithm and Application for Eliminating Phenological Impacts

Fires are frequent in boreal forests affecting forest areas. The detection of forest disturbances and the monitoring of forest restoration are critical for forest management. Vegetation phenology information in remote sensing images may interfere with the monitoring of vegetation restoration, but little research has been done on this issue. Remote sensing and the geographic information system (GIS) have emerged as important tools in providing valuable information about vegetation phenology. Based on the MODIS and Landsat time-series images acquired from 2000 to 2018, this study uses the spatio-temporal data fusion method to construct reflectance images of vegetation with a relatively consistent growth period to study the vegetation restoration after the Greater Hinggan Mountain forest fire in the year 1987. The influence of phenology on vegetation monitoring was analyzed through three aspects: band characteristics, normalized difference vegetation index (NDVI) and disturbance index (DI) values. The comparison of the band characteristics shows that in the blue band and the red band, the average reflectance values of the study area after eliminating phenological influence is lower than that without eliminating the phenological influence in each year. In the infrared band, the average reflectance value after eliminating the influence of phenology is greater than the value with phenological influence in almost every year. In the second shortwave infrared band, the average reflectance value without phenological influence is lower than that with phenological influence in almost every year. The analysis results of NDVI and DI values in the study area of each year show that the NDVI and DI curves vary considerably without eliminating the phenological influence, and there is no obvious trend. After eliminating the phenological influence, the changing trend of the NDVI and DI values in each year is more stable and shows that the forest in the region was impacted by other factors in some years and also the recovery trend. The results show that the spatio-temporal data fusion approach used in this study can eliminate vegetation phenology effectively and the elimination of the phenology impact provides more reliable information about changes in vegetation regions affected by the forest fires. The results will be useful as a reference for future monitoring and management of forest resources.

TRENDS AND VULNERABILITY ASSESSMENT OF METEOROLOGICAL AND AGRICULTURAL DROUGHT CONDITIONS OVER INDIAN REGION USING TIME-SERIES (1982–2015) SATELLITE DATA

Abstract. The vulnerability and trends of meteorological as well as agricultural drought conditions over Indian region was studied using long-term (1982–2015) gridded precipitation and time-series normalized difference vegetation index (NDVI) data. The Climate Hazards Group Infra-Red Precipitation with Station (CHIRPS) precipitation data (~5 km) was used to compute Standardized precipitation index (SPI) at 3-month time scale for Indian summer monsoon season (June-September). Subsequently, the long-term Global Inventory Modelling and Mapping Studies (GIMMS) time-series NDVI data (~8 km) was interpolated at daily scale and smoothened using Savitzky and Golay filtering method. Further, the time-series NDVI data was transformed into several phenological parameters using threshold and derivative approach. As integrated NDVI, i.e. the area under seasonal NDVI curve, is able to represent the anomalies in seasonal agricultural production, it was transformed into standardized vegetation index (SVI) using empirical distribution. Several drought parameters, e.g. magnitude and extent, were computed at district level based on the SPI and SVI values, where values with SPI or SVI less than minus one was considered as meteorological and agricultural drought year, respectively. The trends of drought magnitude and extent for both the meteorological and agricultural drought were estimated using Sen’s slope. The direction of trends and magnitude were found to be varying spatially across different parts of Indian region. Further, the mean SPI/SVI values along with drought frequency were utilized to categorize entire Indian agricultural area into different vulnerable zones during three decades separately. The overall drought vulnerability was found to be decreasing over time.

Mapping Winter Crops in China with Multi-Source Satellite Imagery and Phenology-Based Algorithm

Timely and accurate mapping of winter crop planting areas in China is important for food security assessment at a national level. Time-series of vegetation indices, such as the normalized difference vegetation index (NDVI), are widely used for crop mapping, as they can characterize the growth cycle of crops. However, with the moderate spatial resolution optical imagery acquired by Landsat and Sentinel-2, it is difficult to obtain complete time-series curves for vegetation indices due to the influence of the revisit cycle of the satellite and weather conditions. Therefore, in this study, we propose a method for compositing the multi-temporal NDVI, in order to map winter crop planting areas with the Landsat-7 and -8 and Sentinel-2 optical images. The algorithm composites the multi-temporal NDVI into three key values, according to two time-windows—a period of low NDVI values and a period of high NDVI values—for the winter crops. First, we identify the two time-windows, according to the time-series of the NDVI obtained from daily Moderate Resolution Imaging Spectroradiometer observations. Second, the 30 m spatial resolution multi-temporal NDVI curve, derived from the Landsat-7 and -8 and Sentinel-2 optical images, is composited by selecting the maximal value in the high NDVI value period, and the minimal and median values in the low NDVI value period, using an algorithm of the Google Earth Engine. Third, a decision tree classification method is utilized to perform the winter crop classification at a pixel level. The results indicate that this method is effective for the large-scale mapping of winter crops. In the study area, the area of winter crops in 2018 was determined to be 207,641 km2, with an overall accuracy of 96.22% and a kappa coefficient of 0.93. The method proposed in this paper is expected to contribute to the rapid and accurate mapping of winter crops in large-scale applications and analyses.

Stay-green associates with low water soluble carbohydrates at flowering in oat

High throughput phenotyping of canopy senescence based on Normalized Difference Vegetation Index (NDVI) could be useful in plant breeding. The aim of this paper was to assess the association between grain yield and canopy senescence quantified with NDVI in oat lines selected for grain yield and milling quality.Sixteen lines were grown in four winter-rainfall environments where logistic curves between NDVI and thermal time from anthesis (GS60) were fitted to return five traits: maximum NDVI, NDVImax; minimum NDVI, NDVImin; thermal time to 50% senescence, x50; rate of senescence, rate; and the area under the NDVI curve, area. Across sources of variation, residuals of yield after removing the effect of phenology correlated with area (r = 0.69), NDVImax (r = 0.67), x50 (r = 0.57), NDVImin (r = 0.51) and rate (r = −0.30), all significant at P < 0.05. All five senescence traits correlated negatively with concentration of water soluble carbohydrates at anthesis, particularly area (r = −0.75, P < 0.0001). There was no correlation for yield between environments in 5 out of 6 comparisons (broad sense heritability = 0.39) and water soluble carbohydrates correlated between environments in 6 out of 6 comparisons (broad sense heritability = 0.89). Correlations between environments were irregular for senescence traits. Selection for low concentration of water soluble carbohydrates could increase oat grain yield by improving both grain number per m2 and leaf area duration.

Reflectance as a decision-making tool for the application of fungicide to the Asian Soybean Rust

The objective of this study was to evaluate the application of GreenSeeker® (radiometer) the ideal time of application of fungicide on soybeans and the relationship between NDVI (Normalized Difference Vegetation Index) and productivity. Two experiments were conducted at the Universidade Estadual de Londrina (2012/2013 and 2013/2014 crops), both with experimental design of random blocks (6x4). The treatments aimed at obtaining a disease gradient (Asian soybean rust) by means of different numbers and times of application of fungicide. The quantification of the severity of the disease was made through the diagram scale and the readings of NDVI. Were analyzed the total mass, the 1.000 grains mass, the humidity, and productivity in kg ha-1. A decrease in productivity was observed in connection with the delay in the application of the fungicide and the same happened in the area below the NDVI curve. At the same time when a statistical difference between the treatments of the severity was observed there was also a significant statistical difference (P&lt;0.05) between the values of NDVI. The NDVI contributes to decision making about the best moment for the application of fungicide in the culture of soy for the control of Asian rust and presents regular results for the estimation of productivity.

Phenological metrics-based crop classification using HJ-1 CCD images and Landsat 8 imagery

ABSTRACTCrop type data are an important piece of information for many applications in agriculture. Extracting crop type using remote sensing is not easy because multiple crops are usually planted into small parcels with limited availability of satellite images due to weather conditions. In this research, we aim at producing crop maps for areas with abundant rainfall and small-sized parcels by making full use of Landsat 8 and HJ-1 charge-coupled device (CCD) data. We masked out non-vegetation areas by using Landsat 8 images and then extracted a crop map from a long-term time-series of HJ-1 CCD satellite images acquired at 30-m spatial resolution and two-day temporal resolution. To increase accuracy, four key phenological metrics of crops were extracted from time-series Normalized Difference Vegetation Index curves plotted from the HJ-1 CCD images. These phenological metrics were used to further identify each of the crop types with less, but easier to access, ancillary field survey data. We used crop area data from the Jingzhou statistical yearbook and 5.8-m spatial resolution ZY-3 satellite images to perform an accuracy assessment. The results show that our classification accuracy was 92% when compared with the highly accurate but limited ZY-3 images and matched up to 80% to the statistical crop areas.

Multi-Year Mapping of Maize and Sunflower in Hetao Irrigation District of China with High Spatial and Temporal Resolution Vegetation Index Series

Crop identification in large irrigation districts is important for crop yield estimation, hydrological simulation, and agricultural water management. Remote sensing provides an opportunity to visualize crops in the regional scale. However, the use of coarse resolution remote sensing images for crop identification usually causes great errors due to the presence of mixed pixels in regions with complex planting structure of crops. Therefore, it is preferable to use remote sensing data with high spatial and temporal resolutions in crop identification. This study aimed to map multi-year distributions of major crops (maize and sunflower) in Hetao Irrigation District, the third largest irrigation district in China, using HJ-1A/1B CCD images with high spatial and temporal resolutions. The Normalized Difference Vegetation Index (NDVI) series obtained from HJ-1A/1B CCD images was fitted with an asymmetric logistic curve to find the NDVI characteristics and phenological metrics for both maize and sunflower. Nine combinations of NDVI characteristics and phenological metrics were compared to obtain the optimal classifier to map maize and sunflower from 2009 to 2015. Results showed that the classification ellipse with the NDVI characteristic of the left inflection point in the NDVI curve and the phenological metric from the left inflection point to the peak point normalized, with mean values of corresponding grassland indexes achieving the minimum mean relative error of 10.82% for maize and 4.38% for sunflower. The corresponding Kappa coefficient was 0.62. These results indicated that the vegetation and phenology-based classifier using HJ-1A/1B data could effectively identify multi-year distribution of maize and sunflower in the study region. It was found that maize was mainly distributed in the middle part of the irrigation district (Hangjinhouqi and Linhe), while sunflower mainly in the east part (Wuyuan). The planting sites of sunflower had been gradually expanded from Wuyuan to the north part of Hangjinhouqi and Linhe. These results were in agreement with the local economic policy. Results also revealed the increasing trends of both maize and sunflower planting areas during the study period.

Toward mapping crop progress at field scales through fusion of Landsat and MODIS imagery

The ability to regionally monitor crop progress and condition through the growing season benefits both crop management and yield estimation. In the United States, these metrics are reported weekly at state or district (multiple counties) levels by the U.S. Department of Agriculture (USDA) National Agricultural Statistics Service (NASS) using field observations provided by trained local reporters. However, the ground data collection process supporting this effort is time consuming and subjective. Furthermore, operational crop management and yield estimation efforts require information with more granularity than at the state or district level. This paper evaluates remote sensing approaches for mapping crop phenology using vegetation index time-series generated by fusing Landsat and MODIS (Moderate Resolution Imaging Spectroradiometer) surface reflectance imagery to improve temporal sampling over that provided by Landsat alone. The case study focuses on an agricultural region in central Iowa from 2001 to 2014. Our objectives are 1) to assess Landsat-MODIS data fusion results over cropland; 2) to map crop phenology at 30m resolution using fused surface reflectance data; and 3) to identify the relationships between remotely sensed crop phenology metrics and the crop progress stages reported by NASS. The results show that detailed spatial and temporal variability in vegetation development across this landscape can be identified using the fused Landsat-MODIS data. The mean difference (bias) in Normalized Difference Vegetation Index (NDVI) between actual Landsat observations and the fused Landsat-MODIS data, generated for Landsat overpass dates, is in the range of −0.011 to 0.028 for every year. The derived phenological metrics show distinct features for different crops and natural vegetation at field scales. Strong correlations are observed between remotely sensed phenological stages, based on NDVI curve inflection points, and the observed crop physiological growth stages from the NASS Crop Progress (CP) reports. The green-up dates detected from remote sensing data typically occurred during crop vegetative stages when 2–4 leaves were developed for both corn and soybeans, or about 1–3weeks after the reported emergence dates when the plant were first visible to ground-based observers. Despite being a lagging indicator, remotely sensed green-up can be used effectively to backcast emergence, e.g. as input to spatially distributed crop models. The differences in green-up date between corn and soybean were 8–10days, consistent with the offset in emergence dates reported by NASS at district level. The reported harvest dates were typically about 2–3weeks after the dormancy stage was detected via remote sensing for corn and about 1–2weeks for soybeans. This suggests that probable harvest times for individual fields may be predicted 1–3weeks ahead using remote sensing data. The results suggest that crop phenology and certain growth stages at field scales (30m spatial resolution) can be linked and mapped by integrating imagery from multiple remote sensing platforms.