Vegetation Temperature Condition Index Research Articles

Investigating Drought and Flood Evolution Based on Remote Sensing Data Products over the Punjab Region in Pakistan

Over the last five decades, Pakistan experienced its worst drought from 1998 to 2002 and its worst flood in 2010. This study determined the record-breaking impacts of the droughts (1998–2002) and the flood (2010) and analyzed the given 12-year period, especially the follow-on period when the winter wheat crop was grown. We identified the drought, flood, and warm and cold edges over the plain of Punjab Pakistan based on a 12-year time series (2003–2014), using the vegetation temperature condition index (VTCI) approach based on Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data products. During the year 2010, the Global Flood Monitoring System (GFMS) model applied to the real-time Tropical Rainfall Measuring Mission (TRMM) rainfall incorporated data products into the TRMM Multi-Satellite Precipitation Analysis (TMPA) for the flood detection/intensity, stream flow, and daily accumulative precipitation, and presented the plain provisions to wetlands. This study exhibits drought severity, warm and cold edges, and flood levels using the VTCI drought-monitoring approach, which utilizes a combination of the normalized difference vegetation index (NDVI) with land surface temperature (LST) data products. It was found that during the years 2003–2014, the VTCI had a positive correlation coefficient (r) with the cumulative precipitation (r = 0.60) on the day of the year (D-073) in the winter. In the year 2010, at D-201, there was no proportionality (nonlinear), and at D-217, a negative correlation was established. This revealed the time, duration, and intensity of the flood at D-201 and D-217, and described the heavy rainfall, stream flow, and flood events. At D-233 and D-281 during 2010, a significant positive correlation was noticed in normal conditions (r = 0.95 in D-233 and r = 0.97 in D-281 during the fall of 2010), which showed the flood events and normality. Notably, our results suggest that VTCI can be used for drought and wet conditions in both rain-fed and irrigated regions. The results are consistent with anomalies in the GFMS model using the spatial and temporal observations of the MODIS, TRMM, and TMPA satellites, which describe the dry and wet conditions, as well as flood runoff stream flow and flood detection/intensity, in the region of Punjab during 2010. It should be noted that the flood (2010) affected the area, and the production of the winter wheat crop has consistently declined from 19.041 to 17.7389 million tons.

Assessing Impacts of Flood and Drought over the Punjab Region of Pakistan Using Multi-Satellite Data Products

The Punjab region of Pakistan faced significant losses from flash flooding in 2010 and experienced a multiyear drought during 1998–2002. The current study illustrates the drought and flood conditions using the multi-satellite data products derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Tropical Rainfall Measuring Mission (TRMM) as well as the TRMM Multi-satellite Precipitation Analysis (TMPA) satellites with high-quality resolution in the region of Punjab during 2010–2014. To determine the drought and flood events, we used the Vegetation Temperature Condition Index (VTCI) drought monitoring approach combined with the Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST) to identify the warm and cold edges (WACE) in the provision of soil moisture as well as the VTCI imagery using the MODIS-Aqua data products. We assessed the 2010 flood effect on the four years (2011–2014) of drought conditions during winter wheat crop seasons. The obtained VTCI imagery and precipitation data were utilized to validate the drought and flood conditions in the year 2010 and the drought conditions in the years 2011–2014 during the winter-wheat-crop season. It is worth mentioning that over the four years (2011–2014) of the Julian day~D-041 year, the VTCI shows a stronger link with the accumulative precipitation anomaly (r = 0.77). It was found that for D-201 during the 2010 flood was the relationship was nonlinear, and in D-217, there was a negative relationship which revealed the flood timing, duration, and intensity. For D-281, a correlation (r = 0.97) was noted during fall 2010, which showed the drought and flood extreme conditions for the winter-wheat-crop season in the year 2010–2014. In regard to 2010, the Global Flood Monitoring System (GFMS) model employs the TRMM and TMPA data products to display the study region during the 2010 flood events and validate the VTCI results. This study’s spatial and temporal observations based on the observed results of the MODIS, TRMM, and TMPA satellites are in good agreement with dry and wet conditions as well as the flood runoff stream flow and flood intensity. It demonstrates the flood events with high intensity compared with the normality of flood with the complete establishment of flood events and weather extremes during the year of 2011–2014, thereby highlighting the natural hazards impacts. Our findings show that the winter wheat harvest was affected by the 2010 monsoon’s summer high rain and floods in the plain of Punjab (Pakistan).

A deep learning framework combining CNN and GRU for improving wheat yield estimates using time series remotely sensed multi-variables

Accurate and timely crop yield estimation is crucial for crop market planning and food security. Combining remotely sensed big data with deep learning for yield estimation has attracted extensive attention. However, it is still challenging to understand and quantify the time cumulative effects of crop growth over time for crop yield estimation. In this study, we combined the powerful feature extraction capability of the convolutional neural network (CNN) and the advantage of time series memory of the gated recurrent unit (GRU) network to develop a novel deep learning model called CNN-GRU for estimating county-level winter wheat yields in the Guanzhong Plain using three remotely sensed variables, vegetation temperature condition index (VTCI), leaf area index (LAI) and fraction of photosynthetically active radiation (FPAR). The CNN-GRU model was able to extract features related to yield from the input variables and the accuracy of the proposed model (R2 = 0.64, RMSE = 462.56 kg/ha, MRE = 8.90 %) was higher than that of the GRU model (R2 = 0.62, RMSE = 479.79 kg/ha, MRE = 9.34 %), and the CNN-GRU model’s reliability and robustness were confirmed by applying the leave-one-year-out cross-validation. Furthermore, we applied the proposed CNN-GRU model to simulate the wheat yields in the Plain pixel by pixel and examined the spatiotemporal patterns of the estimated yields. The distribution of yields presented the spatial characteristics of low yields in the east and high yields in the west, and the inter-annual variation characteristics of overall stability and steady increase. Additionally, we explored the possibility of timely prediction of winter wheat yield and the contribution of the multi-variables at different growth stages to yield estimation based on the ability of deep learning to reveal cumulative effects and non-linear relationships between influencing factors and yield. It was found that the information reflected by the multi-variables from late March to late April was important for yield estimation and the best prediction could be achieved approximately 20 days before the harvest of winter wheat. Our study demonstrated that combining CNN and GRU was an efficient and promising approach to improve yield estimation, offering great promise for global crop yield estimation.

Estimating Above-Ground Biomass from Land Surface Temperature and Evapotranspiration Data at the Temperate Forests of Durango, Mexico

The study of above-ground biomass (AGB) is important for monitoring the dynamics of the carbon cycle in forest ecosystems. The emergence of remote sensing has made it possible to analyze vegetation using land surface temperature (LST), Vegetation Temperature Condition Index (VTCI) and evapotranspiration (ET) information. However, relatively few studies have evaluated the ability of these variables to estimate AGB in temperate forests. The aim of the present study was to evaluate the relationship of LST, VTCI and ET with AGB in temperate forests of Durango, Mexico, regarding each season of the year and to develop a AGB estimation model using as predictors LST, VCTI and ET, together with topographic, reflectance and Gray-Level Co-Occurrence Matrix (GLCM) texture variables. A semi-parametric model was generated to analyze the linear and non-linear responses of the predictive variables of AGB using a generalized linear model (GAM). The results show that the best predictors of AGB were longitude, latitude, spring LST, ET, elevation VTCI, NDVI (Normalized Difference Vegetation Index), slope and GLCM mean (R2 = 0.61; RMSE = 28.33 Mgha−1). The developed GAM model was evaluated with an independent dataset (R2 = 0.58; RMSE = 31.21 Mgha−1), suggesting the potential of this modeling approach to predict AGB for the analyzed temperate forest ecosystems.

Information fusion approach for downscaling coarse resolution scatterometer data

ABSTRACT The applications of scatterometer data (σ°) are limited due to their coarser resolution (25–50 km). Some image reconstruction techniques are available to generate high-resolution products, but they require various sensor parameters and multiset observation, making them complex to use. Therefore, this paper proposes an information fusion approach to disaggregate the coarse resolution σ° product. The coarse resolution backscattering signal includes the contribution from more than one land cover class, such as short vegetation, soil, urban and tall vegetation, the information of which can be obtained from normalised difference vegetation index (NDVI), vegetation temperature condition index (VTCI), and fraction cover of urban and forests, respectively. Disaggregating this coarse resolution pixel, an optimum weight information is required that provides the distribution of each class. Since the distribution of land cover classes is not homogeneous for every pixel, a variance-based fusion approach has been used to obtain the optimum weight factors to fuse NDVI, VTCI, and fraction cover. These weight factors are used to disaggregate every coarse-resolution pixel into high-resolution pixels. The developed model is applied to Sentinel-1 and Scatsat-1 level-3 products, and the obtained results are quite satisfactory.

Remote sensing-based assessment of ecosystem health by optimizing vigor-organization-resilience model: A case study in Fuzhou City, China

Although remote sensing technology has been used to evaluate regional ecosystem health for a long time, it is still necessary to find a suitable index system to better evaluate ecosystem health. This study aims to improve the ecosystem health measurement ability of remote sensing technology. This research was carried out in Fuzhou under the traditional Vigor-Organization-Resilience (VOR) framework by optimizing and improving the construction method of sub-indexes. Sub-indexes were constructed using spectral index analysis, landscape theoretical ecology model and spatial measurement. Three remote sensing datasets were used (1996, 2008 and 2021) to carry out remote sensing diagnosis of regional ecosystem health in the Fuzhou administrative region. The main research findings and conclusions were as follows. A new comprehensive vigor index (CVI) was developed by the principal component analysis (PCA) based on the four indicators: fractional vegetation cover (FVC), global vegetation moisture index (GVMI), vegetation temperature condition index (VTCI), normalized differential build-up and bare soil index (NDBSI). A new organizational index was constructed based on the landscape index. Four types of indexes, namely landscape heterogeneity (LH), landscape connectivity (LC), the shape characteristics of forest patches (CS) and the connectivity of forest patches (CC) were used as the main factors for calculating the organizational index. A resilience index calculation framework was proposed based on the habitat quality model. The temporal and spatial characteristics of ecosystem health were evaluated and analyzed. The regional ecosystem health value of the whole region reduced gradually, with average values of 0.3521 (1996), 0.3445 (2008) and 0.3345 (2021) respectively. The average reduction rate was 0.0007 per year (1996–2021). The proposed remote sensing diagnosis method provides a complete framework for solving the problems of measuring the dynamic evolution process and characteristics of regional ecosystem health.

Integrating an attention-based deep learning framework and the SAFY-V model for winter wheat yield estimation using time series SAR and optical data

Information on the spatial distribution of yields can be obtained over a large area by using remote sensing (RS) data. Combining Synthetic Aperture Radar (SAR), being sensitive to above ground biomass and soil moisture in all weather conditions, and optical data can improve the usability of RS data and provide a basis for pixel-based crop yield estimation (YE). In this study, an Upscaled Convolutional Gated Recurrent Unit model incorporated an attention mechanism (UpSc-AConvGRU model) was proposed to improve the estimation accuracy of the winter wheat growth parameter, Leaf Area Index (LAI). Gap filling the time series of optical data was done with backscatter coefficients, local incidence angles and polarimetric decomposition information from Sentinel-1 SAR imagery. The time series LAI estimated by the UpSc-AConvGRU model and Vegetation Temperature Condition Index (VTCI) retrieved from Sentinel-3 optical imagery were then used as state variables of the SAFY-V model to estimate winter wheat yield. The results showed that the proposed UpSc-AConvGRU model incorporated the Convolutional Block Attention Module (CBAM) can effectively improve the accuracy of LAI estimation, with RMSEs ranging from 0.413 to 0.699 m2 m2 for LAI estimated within main growth stages (MGSs) of winter wheat. The correlation between estimated LAI and Sentinel-3 retrieved LAI was generally higher at irrigated farmland compared to rain-fed farmland. The estimated LAI was closest to Sentinel-3 retrieved LAI at the green-up and late heading-filling stage of winter wheat, followed by the jointing and early heading-filling stage, and finally the milk maturity stage. There was good agreement between the SAFY-V model estimated and field measured winter wheat yields (R2 = 0.546, RMSE = 0.757 t ha−1), and the estimated yields at the pixel scale in the Guanzhong Plain, PR China were satisfactory. This study combined deep learning and crop growth modeling, proposed a new pixel scale winter wheat YE method.

Improving wheat yield estimates using data augmentation models and remotely sensed biophysical indices within deep neural networks in the Guanzhong Plain, PR China

Crop yield estimation and prediction constitutes a key issue in agricultural management, particularly under the context of demographic pressure and climate change. Currently, the main challenge in estimating crop yields based on remotely sensed data and data-driven methods is how to cope with small datasets and the limited amount of annotated samples. In order to cope with small datasets and the limited amount of annotated samples and improve the accuracy of winter wheat yield estimation in the Guanzhong Plain, PR China, this study proposed a method of combining generative adversarial networks (GANs) and convolutional neural network (CNN) for comprehensive growth monitoring of winter wheat, in which the remotely sensed leaf area index (LAI), vegetation temperature condition index (VTCI) and meteorological data at four growth stages of winter wheat during 2012–2017 were generated as the inputs of multi-layer convolutional neural networks (CNNs), and GAN was employed to artificially increase the number of training samples. Then, a linear regression model between the simulated comprehensive growth monitoring (I) and the measured yields was established to estimate yields of winter wheat in the Guanzhong Plain pixel by pixel. The final results showed when GAN was used to double the size of the training samples, and the simulation values obtained by CNN based on augmented samples using GAN provided a better training (R2 = 0.95, RMSE = 0.05), validation (R2 = 0.54, RMSE = 0.16) and testing (R2 = 0.50, RMSE = 0.14) performance than that just using the original samples. The achieved best pixel-scale yield estimation accuracy of winter wheat (R2 = 0.50, RMSE = 591.46 kg/ha) in the Guanzhong Plain. These results showed that small samples can be enlarged by GAN, thus, more important features for reflecting the growth conditions and yields of winter wheat from the remotely sensed indices and meteorological indices can be extracted, and indicated that CNN accompanied with GAN could contribute a lot to the comprehensive growth monitoring and yield estimation of winter wheat and data augmentation methods are extremely useful for the application of small samples in deep learning.

A copula model integrating atmospheric moisture demand and supply for vegetation vulnerability mapping

Drought caused by various meteorological factors negatively affects vegetation. Constructing a joint probability distribution between vegetation and drought information may be appropriate to understand the vulnerability of vegetation to drought. In this study, a copula-based trivariate joint probability model is proposed to investigate the effects of various aspects of meteorological drought on vegetation (vegetation drought). Because drought can be caused by insufficient precipitation or excessive evapotranspiration, the meteorological drought risk for vegetation was divided into two aspects (atmospheric moisture supply and moisture demand). The vulnerability of vegetation drought was mapped when two aspects of meteorological drought occurred separately or simultaneously at high spatial resolution using remote sensing data. The results revealed that the response of vegetation was significantly different depending on the climatic stressors. Although the sensitivity of vegetation to each drought condition varied from region to region, it was found that vegetation was more vulnerable to drought caused by atmospheric moisture demand in most regions of Far East Asia. It has also been shown that drought conditions, which overlapped with insufficient precipitation and excessive evapotranspiration, can drive vegetation to a far more lethal level. Meanwhile, through comparison with the existing VTCI, the proposed Normalized Vegetation Temperature Condition Index (nVTCI) was found to be able to more rationally monitor vegetation drought in the Far East Asian region.

An LSTM neural network for improving wheat yield estimates by integrating remote sensing data and meteorological data in the Guanzhong Plain, PR China

Crop growth condition and production play an important role in food management and economic development. Therefore, estimating yield accurately and timely is of vital importance for regional food security. The long short-term memory (LSTM) model represents a deep network structure to incorporating crop growth processes, which has been proven to accommodate different types and representations of data, recognize sequential patterns over long time spans, and capture complex nonlinear relationships. The LSTM model was developed to estimate wheat yield in the Guanzhong Plain by integrating meteorological data and two remotely sensed indices, vegetation temperature condition index (VTCI) and leaf area index (LAI) at the main growth stages. Considering the LSTM model has characteristics of memorizing time series information, we adopted different time steps to estimate wheat yield. The results showed that the accuracy of yield estimation was highest (RMSE = 357.77 kg/ha and R2 = 0.83) under two time steps and the input combination (meteorological data and two remotely sensed indices). We evaluated the yield estimation accuracy of the optimal LSTM model performance compared with the back propagation neural network (BPNN) and support vector machine (SVM). As a result, the LSTM model outperformed BPNN (R2 = 0.42 and RMSE = 812.83 kg/ha) and SVM (R2 = 0.41 and RMSE = 867.70 kg/ha), since its recurrent neural network structure that can incorporate nonlinear relationships between multi-features inputs and yield. To further validate the robustness of the optimal LSTM method, the correlations between estimated yield and measured yield at the irrigation sites and the rain-fed sites from 2008 to 2016 were analyzed, and the results demonstrated that the proposed model can serve as an effective approach for different type sampling sites and has better adaptability to interannual fluctuations of climate. Our findings demonstrated a reliable and promising approach for improving yield estimation.

Drought Index Mapping in Java Island Using Sentinel-3 SLSTR

As the most populous island globally, Java Island has various vulnerabilities to disasters ranging from geological to hydro-meteorological. One of the most common hydro-meteorological disasters is the drought that occurs every year in the dry season. This disaster causes crop failure, land and forest fires, and clean water shortages. In this study, the Sea and Land Surface Temperature Radiometer (SLSTR) instrument onboard the Sentinel-3 platform was used to map drought using the Vegetation Temperature Condition Index (VTCI) algorithm based on the scattering space technique of Land Surface Temperature (LST) and Normalized Different Vegetation Index (NDVI). During 2018, the highest and lowest LST occurred on September 28 (309 oK), and February 1 (278 oK); the highest and lowest NDVI occurred on July 1 (0.466) and November 13 (0.221). In comparison, the driest conditions indicated the lowest VTCI (0.162) on July 17 and the wettest on August 28 (0.508). The driest and wettest situation co-occurred: at the end of the dry session and the rainy session, it shows that the rain greatly contributes to high VTCI. A regular drought mapping needs to be performed as an effort to disaster risk reduction. Drought maps are then used as a spatial recommendation in reforestation intervention to reduce drought in the future.

A deep learning framework under attention mechanism for wheat yield estimation using remotely sensed indices in the Guanzhong Plain, PR China

The rapid and effective acquisition of crop yield information is critical to the stability of food markets and development and implementation of related policies. It is an important baseline observation that is used for ensuring regional and global food security. In this study, a novel deep learning framework was developed for winter wheat yield estimation using meteorological data and two remotely sensed indices, Vegetation Temperature Condition Index (VTCI) and Leaf Area Index (LAI) at the main growth stages of winter wheat in the Guanzhong Plain. The proposed deep learning model was based on Long Short-Term Memory (LSTM) neural network with an attention mechanism (ALSTM), which the main idea is to assign attention to the key parts of the input sequence that affect the target vectors so that the specific features can be accurately extracted. The ALSTM model provided an improved estimation accuracy (R2 = 0.63, MAPE = 8.20%, RMSE = 502.71 kg/ha, NRMSE = 11.15%) as compared with the LSTM (R2 = 0.55, MAPE = 13.46%, RMSE = 699.92 kg/ha, NRMSE = 15.52%). A validation based on leave-one-year-out-validation further substantiated the robustness of ALSTM with smaller values of NRMSE and MAPE (13.63% and 11.54%). We demonstrated that the ALSTM model provided good generalization ability for sampling sites under different farming systems, including irrigation and rain-fed sampling sites. In addition, we evaluated the relative importance of each input variable in determining yields based on stepwise sensitivity analysis. It was found that LAI at the heading-filling stage and the milk stage as well as VTCI at the jointing stage contributed more than other input feature variables towards the corresponding yield. In conclusion, our findings highlighted that the attention mechanism helped to improve the interpretability of neural networks and the ALSTM model along with remotely sensed biophysical indices can provide a reliable and robust estimation of crop yield. An accurate estimation of wheat yield is not only helping towards informed crop management decisions but it will improve efficiency and sustainability of farming operations.

Improving Wheat Yield Estimates by Integrating a Remotely Sensed Drought Monitoring Index Into the Simple Algorithm for Yield Estimate Model

Water stress is an important factor to be considered when using crop growth models for crop yields estimation. In this article, we propose a simple algorithm for yield estimate (SAFY) V model to estimate yields of winter wheat by integrating the time-series remotely sensed drought monitoring index, vegetation temperature condition index (VTCI), into SAFY model, and the fixed effective light-use efficiency parameter ( elue0 ) in the SAFY model is modified to a new varied parameter ( E ) in the SAFY-V model. The parameter E can accurately describe the changes of water stress at the four growth stages of winter wheat and has a high correlation with the field measured yields ( R 2 values are 0.28, 0.34, 0.31 and 0.31, respectively). The SAFY-V model integrating the time series leaf area index (LAIs) and VTCIs which has the highest accuracy on dry aerial mass estimation compared with the SAFY model and SAFY-WB model (a combination of the SAFY model and water balance model), can better alleviate the yield underestimation and overestimation and greatly improve the estimation accuracy of winter wheat yield especially in rain-fed farmlands ( R 2 = 0.48, MAE = 1.05 t/ha and NMAE = 15.6%), and the accuracy of winter wheat yields estimates at the county scale was also satisfactory ( R 2 = 0.49, MAE = 0.73 t/ha and NMAE = 16.1% for five years). The proposed SAFY-V model of this article has few model parameters and low computational cost, which provides a significant reference for crop yield estimation at a regional scale.

Effectiveness of upscaling the vegetation temperature condition index retrieved from Landsat data using an algorithm combining the trend surface analysis and the spatial variation weight method

ABSTRACT Taking the Guanzhong Plain of China as the research area, a method combining the trend surface analysis (TSA) method and the spatial variation weight method (SVWM) was used to upscale the vegetation temperature condition index (VTCI) retrieved from Landsat 8 from a finer spatial resolution to a coarser. The upscaled results were compared with VTCIs retrieved from Aqua MODIS to provide technical support for the comprehensive application of drought monitoring results on two spatial scales. Meanwhile, the upscaled methods were systematically evaluated in a case study according to various indicators, including the spatial distribution characteristics for drought, the statistical characteristics, the semivariogram function (SVF), the structural similarity (SSIM), the correlation coefficient (r), and the root mean square error (RMSE). The results show that the dominant class variability weighted (DCVW) method performed better than did the arithmetic average variability weighted (AAVW) and median pixel variability weighted (MPVW) methods in terms of the statistical characteristics, SSIM, correlation and RMSE, and the upscaling model based on the union of TSA and DCVW had the highest accuracy. The DCVW is a promising tool for upscaling Landsat-VTCI images of the Guanzhong Plain from finer to coarser spatial resolutions because of its efficiency and flexibility.

Evaluation of Drought Monitoring Effect of Winter Wheat in Henan Province of China Based on Multi-Source Data

Monitoring agricultural drought is important to food security and the sustainable development of human society. In order to improve the accuracy of soil moisture and winter wheat yield estimation, drought monitoring effects of optical drought index data, meteorological drought data, and passive microwave soil moisture data were explored during individual and whole growth periods of winter wheat in 2003–2011, taking Henan Province of China as the research area. The model of drought indices and relative meteorological yield of winter wheat in individual and whole growth periods was constructed based on multiple linear regression. Results showed a higher correlation between Moderate-Resolution Imaging Spectroradiometer (MODIS) drought indices and 10 cm relative soil moisture (RSM10) than 20 cm (RSM20) and 50 cm (RSM50). In the whole growth period, the correlation coefficient (R) between vegetation supply water index (VSWI) and RSM10 had the highest correlation (R = −0.206), while in individual growth periods, the vegetation temperature condition index (VTCI) was superior to the vegetation health index (VHI) and VSWI. Among the meteorological drought indices, the 10-day, 20-day, and 30-day standard precipitation evapotranspiration indices (SPEI1, SPEI2, and SPEI3) were all most relevant to RSM10 during individual and whole growth periods. RSM50 and SPEI3 had a higher correlation, indicating that deep soil moisture was more related to drought on a long time scale. The relationship between Advanced Microwave Scanning Radiometer for EOS soil moisture (AMSR-E SM) and VTCI was stable and significantly positive in individual and whole growth periods, which was better compared to VHI and VSWI. Compared with the drought indices and the relative meteorological yield in the city, VHI had the best monitoring effect during individual and whole growth periods. Results also showed that drought occurring at the jointing–heading stage can reduce winter wheat yield, while a certain degree of drought occurring at the heading–milk ripening stage can increase the yield. In the whole growth period, the combination of SPEI1, SPEI2, and VHI had the best performance, with a coefficient of determination (R2) of 0.282 with the combination of drought indices as the independent variables and relative meteorological yield as the dependent variable. In the individual growth period, the model in the later growth period of winter wheat performed well, especially in the returning green–jointing stage (R2 = 0.212). Results show that the combination of multiple linear drought indices in the whole growth period and the model in the returning green–jointing period could improve the accuracy of winter wheat yield estimation. This study is helpful for effective agricultural drought monitoring of winter wheat in Henan Province.

Monitoring maize growth on the North China Plain using a hybrid genetic algorithm-based back-propagation neural network model

Crop growth and early yield information is crucial for the establishment and adjustment of agricultural management plans. Timely, precise and regional assessments of crop growth conditions and production greatly benefit the national economy and agriculture. In this study, remotely sensed leaf area index (LAI) and vegetation temperature condition index (VTCI) data retrieved from the Global LAnd Surface Satellite (GLASS) and Moderate-resolution Imaging Spectroradiometer (MODIS) data were selected as the key indicators of maize growth and a hybrid genetic algorithm (GA)-based back-propagation neural network (BPNN) (GA-BPNN) model was applied to provide complementary information on maize growth at the main growth stage. GA-BPNN models with different architectures were established, and an architecture with 10, 9 and 1 nodes in the input, hidden and output layers, respectively, achieved the best training and testing performance. The root mean square error (RMSE) values of the training and testing samples were 588.2 kg/ha and 663.4 kg/ha, respectively. Thus, the hybrid model with the best architecture (10-9-1) was selected to calculate the values of the integrated growth monitoring index (G) at the regional scale with a 1 km spatial resolution in the study area from 2010 to 2018. The results showed that the monitored maize growth well reflected the actual situation and the correlations between G values and sites’ measured variables, such as the maize yield and soil relative humidity, were higher than that of a pure BPNN model. The linear relationship between the GA-BPNN-based G values and maize yields was analyzed to estimate the maize yield in the North China Plain. Most of the RMSE and mean absolute percentage error (MAPE) values between the estimated and actual maize yields were less than 700.0 kg/ha and 10.0%, respectively. Considering that the estimation errors of most statistical samples were small and there were no obvious differences between the estimated maize yields in the adjacent regions of the northern, central and southern plain, the GA-BPNN-based yield estimation model provided reliable and spatially continuous estimates of maize yield.

An IPSO-BP neural network for estimating wheat yield using two remotely sensed variables in the Guanzhong Plain, PR China

Early and accurate information of crop growth condition is vital for agricultural industry and food security, which gives rise to a strong demand for timely monitoring crop growth condition and estimating crop yields. This study selected the remotely sensed leaf area index (LAI) and vegetation temperature condition index (VTCI) which closely relate to crop growth and crop water stress as two key variables for indicating crop growth condition and estimating crop yields in the Guanzhong Plain, PR China. The single VTCI, the single LAI and the combination of VTCI and LAI at four growth stages of winter wheat (the turning green, jointing, heading-filling, and dough stages) were used as three input variable schemes of the back propagation (BP) neural network and the improved particle swarm optimization algorithm (IPSO)-BP neural network using a nonlinear decreasing inertia weight, respectively. The relative importance of the input variables to the output variable, yield of winter wheat, was used to determine the weight values of input variables at each growth stage. Based on the weights, the integrated index (I) was established, and then three linear regression models (weighted VTCIs, weighted LAIs, and I values) were established with yield data to estimate winter wheat yields. By calculating several statistical functions, i.e., coefficient of determination (R2) and probability value (P), the model between the I values and wheat yield performed better than those between the weighted VTCIs or weighted LAIs and wheat yields. The yield estimation model of I values by using the IPSO-BP neural network (R2 = 0.342) was found to be better than that using the BP neural network (R2 = 0.310). Therefore, we applied the model with better performance (R2 = 0.342) to map the regional winter wheat yields pixel by pixel in the Guanzhong Plain during 2011–2018, and analyzed the spatial and temporal characteristics of the estimated yields. Regarding the spatial distribution, the yields in the west part of the plain are the highest, followed by the central part, and the yields in the east part are lowest, consistent with previous studies. The estimated yields showed inter-annual fluctuations along with an increasing trend on the whole. Winter wheat yields were most depleted in 2013 and most abundant in 2015. These results were consistent with the actual situation of winter wheat production in the plain, which indicated that I can be used to provide a better quantification for monitoring regional winter wheat growth conditions and estimating crop yield. Thus, the approach of this study can provide significant benefit for regional crop production monitoring.

Developing a fused vegetation temperature condition index for drought monitoring at field scales using Sentinel-2 and MODIS imagery

Time-series high spatial resolution drought monitoring is essential for effective agricultural management. For more than a decade, the multiyear vegetation temperature condition index (VTCI) based on the Advanced Very High Resolution Radiometer (AVHRR) and Moderate Resolution Imaging Spectroradiometer (MODIS) has been applied to regional drought monitoring. However, the spatial resolutions of AVHRR and MODIS (1 km) often represent mixtures of built-up areas and different vegetation or crop types. In this paper, a framework is proposed to obtain a ten-day interval multiyear VTCI at field scales, which is fused from Sentinel-2 data with a fine spatial resolution (20 m) and ten-day interval Terra MODIS data with a coarse spatial resolution using the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM). This framework includes the disaggregation of the Terra MODIS land surface temperature (LST) using Sentinel-2 data and digital elevation model (DEM) data, the spatiotemporal fusion of normalized difference vegetation index (NDVI) and LST, and drought monitoring by the fused VTCI. The accuracy of this framework for regional drought monitoring was tested in the Guanzhong Plain of China. The results indicate that 1) the Sentinel-2 biophysical products combined with DEM data based on support vector regression can accurately disaggregate the Terra MODIS LST; 2) the ESTARFM has good capability to fuse the NDVI and LST derived from Sentinel-2 and Terra MODIS; 3) the multiyear spatiotemporally fused VTCI, a quantitative drought monitoring index at field scales, can be calculated by using the linear regression between the single-year fused VTCI based on the data in 2018 and the multiyear Terra MODIS VTCI based on an 18-year data record; and 4) the spatiotemporal fusion of the multiyear VTCI can significantly improve the drought monitoring accuracy in the winter wheat and woodland area of the Guanzhong Plain. From mid-March to early May, the multiyear spatiotemporally fused VTCIs have better correlation with the cumulative precipitation over the past 20 days (R2 is 0.83 in the entire study area) than the multiyear Terra MODIS VTCIs (R2 is 0.80 in the entire study area). The results of this study demonstrate the potential of using the spatiotemporal fusion algorithm to obtain the multiyear VTCI at field scales and provide an effective method for improving the accuracy of drought monitoring.

Satellite-derived vegetation temperature condition index to infer root zone soil moisture in semi-arid province of Rajasthan, India

Soil moisture is one of the key state variable to regulate the water and energy budgets at the soil-plant-atmosphere continuum. This study demonstrates the utility of Vegetation Temperature Condition Index (VTCI), developed from interpretation of NDVI–Ts space using MODIS, to estimate soil moisture status at a spatial resolution of 1-km in Rajasthan province. The key findings reveal that VTCI at 1-km resolution and computed for the whole image (Method-1) was found to be more representative of the average profile soil moisture condition than the other methods. Statistical regression between VTCI and in-situ volumetric soil moisture for the surface and at depths of 15, 30 and 45 cm for the prominent crops in Eastern Rajasthan showed a strong relation (R 2 = 0.63, p-value <0.01). Thus, it is suggested that VTCI is useful to infer soil moisture beneath the surface over semi-arid areas for agricultural drought assessment.

Drought Monitoring Using the Sentinel-3-Based Multiyear Vegetation Temperature Condition Index in the Guanzhong Plain, China

The vegetation temperature condition index (VTCI) has been shown to perform well for drought monitoring using multiyear advanced very high-resolution radiometer (AVHRR) and moderate resolution imaging spectroradiometer (MODIS) data. Compared to single-year VTCI, the VTCI calculated by multiyear data can quantitatively reflect the degree of drought and precipitation in a region. Sentinel-3 is a recently launched remote sensing satellite with high temporal resolution and is similar to the satellites carrying AVHRR and MODIS sensors. One year of Sentinel-3 data is available for calculating the VTCI, and given the need for developing quantitatively drought monitoring capabilities, the aim of this study is to investigate the methods used to calculate the potential multiyear Sentinel-3 VTCI for quantitative drought monitoring. This is based on a comparison with multiyear Terra MODIS VTCI and a correlation analysis with cumulative precipitation data. The analysis results indicate that the potential multiyear Sentinel-3 VTCI can be accurately calculated from the single-year Sentinel-3 VTCI based on the linear correlation between the single-year VTCI and multiyear VTCI derived from Terra MODIS, which do not exhibit obvious systematic deviations from the multiyear Terra MODIS VTCI (the absolute value of the average bias R 2 = 0.731, P < 0.001). Therefore, it is proposed that Sentinel-3 can successfully inherit the VTCI-based drought monitoring tasks from MODIS.