The Potential of Landsat NDVI Sequences to Explain Wheat Yield Variation in Fields in Western Australia

Long-term maps of within-field crop yield can help farmers understand how yield varies in time and space and optimise crop management. This study investigates the use of Landsat NDVI sequences for estimating wheat yields in fields in Western Australia (WA). By fitting statistical crop growth curves, identifying the timing and intensity of phenological events, the best single integrated NDVI metric in any year was used to estimate yield. The hypotheses were that: (1) yield estimation could be improved by incorporating additional information about sowing date or break of season in statistical curve fitting for phenology detection; (2) the integrated NDVI metrics derived from phenology detection can estimate yield with greater accuracy than the observed NDVI values at one or two time points only. We tested the hypotheses using one field (~235 ha) in the WA grain belt for training and another field (~143 ha) for testing. Integrated NDVI metrics were obtained using: (1) traditional curve fitting (SPD); (2) curve fitting that incorporates sowing date information (+SD); and (3) curve fitting that incorporates rainfall-based break of season information (+BOS). Yield estimation accuracy using integrated NDVI metrics was further compared to the results using a scalable crop yield mapper (SCYM) model. We found that: (1) relationships between integrated NDVI metrics using the three curve fitting models and yield varied from year to year; (2) overall, +SD marginally improved yield estimation (r = 0.81, RMSE = 0.56 tonnes/ha compared to r = 0.80, RMSE = 0.61 tonnes/ha using SPD), but +BOS did not show obvious improvement (r = 0.80, RMSE = 0.60 tonnes/ha); (3) use of integrated NDVI metrics was more accurate than SCYM (r = 0.70, RMSE = 0.62 tonnes/ha) on average and had higher spatial and yearly consistency with actual yield than using SCYM model. We conclude that sequences of Landsat NDVI have the potential for estimation of wheat yield variation in fields in WA but they need to be combined with additional sources of data to distinguish different relationships between integrated NDVI metrics and yield in different years and locations.

Download Full-text

Joint Assimilation of Leaf Area Index and Soil Moisture from Sentinel-1 and Sentinel-2 Data into the WOFOST Model for Winter Wheat Yield Estimation

Sensors ◽

10.3390/s19143161 ◽

2019 ◽

Vol 19 (14) ◽

pp. 3161 ◽

Cited By ~ 7

Author(s):

Haizhu Pan ◽

Zhongxin Chen ◽

Allard de Wit ◽

Jianqiang Ren

Keyword(s):

Soil Moisture ◽

Data Assimilation ◽

Winter Wheat ◽

Crop Yield ◽

Wheat Yield ◽

Crop Growth ◽

Open Loop ◽

Yield Estimation ◽

Spatiotemporal Resolution ◽

Sentinel 2

It is well known that timely crop growth monitoring and accurate crop yield estimation at a fine scale is of vital importance for agricultural monitoring and crop management. Crop growth models have been widely used for crop growth process description and yield prediction. In particular, the accurate simulation of important state variables, such as leaf area index (LAI) and root zone soil moisture (SM), is of great importance for yield estimation. Data assimilation is a useful tool that combines a crop model and external observations (often derived from remote sensing data) to improve the simulated crop state variables and consequently model outputs like crop total biomass, water use and grain yield. In spite of its effectiveness, applying data assimilation for monitoring crop growth at the regional scale in China remains challenging, due to the lack of high spatiotemporal resolution satellite data that can match the small field sizes which are typical for agriculture in China. With the accessibility of freely available images acquired by Sentinel satellites, it becomes possible to acquire data at high spatiotemporal resolution (10–30 m, 5–6 days), which offers attractive opportunities to characterize crop growth. In this study, we assimilated remotely sensed LAI and SM into the Word Food Studies (WOFOST) model to estimate winter wheat yield using an ensemble Kalman filter (EnKF) algorithm. The LAI was calculated from Sentinel-2 using a lookup table method, and the SM was calculated from Sentinel-1 and Sentinel-2 based on a change detection approach. Through validation with field data, the inverse error was 10% and 35% for LAI and SM, respectively. The open-loop wheat yield estimation, independent assimilations of LAI and SM, and a joint assimilation of LAI + SM were tested and validated using field measurement observation in the city of Hengshui, China, during the 2016–2017 winter wheat growing season. The results indicated that the accuracy of wheat yield simulated by WOFOST was significantly improved after joint assimilation at the field scale. Compared to the open-loop estimation, the yield root mean square error (RMSE) with field observations was decreased by 69 kg/ha for the LAI assimilation, 39 kg/ha for the SM assimilation and 167 kg/ha for the joint LAI + SM assimilation. Yield coefficients of determination (R2) of 0.41, 0.65, 0.50, and 0.76 and mean relative errors (MRE) of 4.87%, 4.32%, 4.45% and 3.17% were obtained for open-loop, LAI assimilation alone, SM assimilation alone and joint LAI + SM assimilation, respectively. The results suggest that LAI was the first-choice variable for crop data assimilation over SM, and when both LAI and SM satellite data are available, the joint data assimilation has a better performance because LAI and SM have interacting effects. Hence, joint assimilation of LAI and SM from Sentinel-1 and Sentinel-2 at a 20 m resolution into the WOFOST provides a robust method to improve crop yield estimations. However, there is still bias between the key soil moisture in the root zone and the Sentinel-1 C band retrieved SM, especially when the vegetation cover is high. By active and passive microwave data fusion, it may be possible to offer a higher accuracy SM for crop yield prediction.

Download Full-text

Identification of the most appropriate adaptation for rice and wheat in the face of climate change in eastern India

Journal of Water and Climate Change ◽

10.2166/wcc.2021.446 ◽

2021 ◽

Author(s):

Madhuri Dubey ◽

Ashok Mishra ◽

Rajendra Singh

Keyword(s):

Climate Change ◽

Food Security ◽

Crop Yield ◽

Negative Impact ◽

Wheat Yield ◽

Sowing Date ◽

Irrigation Scheduling ◽

Fertilizer Management ◽

Eastern India ◽

Adaptation Options

Abstract The changing climate affects natural resources that impart a negative impact on crop yield and food security. It is thus imperative to identify agro-climate wise, area-specific adaptation options to ensure food security. This study, therefore, evaluated some feasible adaptation options for two staple food grain crops, rice and wheat, in different agro-climatic regions (ACRs) of Eastern India. Alteration in transplanting date, seedling age, and fertilizer management (rate and split of fertilizer) for rice; and sowing date, fertilizer management, and deficit irrigation scheduling for wheat, are assessed as adaptation options. Crop environment and resource synthesis (DSSAT) model is used to simulate the crop yield using different plausible adaptation options to projected climate scenarios. Findings show that shifting transplanting/sowing date, and nitrogen fertilizer application at 120% of recommended nitrogen dose with four splits could be an effective adaptation for rice and wheat crops. Results also emphasize that transplanting of 18 days older seedlings may be beneficial in rice cultivation. In contrast, irrigation at a 30–40% deficit of maximum available water would sustain the wheat yield under climate change conditions. This study suggests the best combination of adaptation options under climate change conditions in diverse ACRs, which may assist agriculturists in coping with climate change.

Download Full-text

MULTI STAGE WHEAT YIELD ESTIMATION USING DIFFERENT MODEL UNDER SEMI ARID REGION OF INDIA

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-3-w6-263-2019 ◽

2019 ◽

Vol XLII-3/W6 ◽

pp. 263-267

Author(s):

A. Vashisth ◽

P. Krishanan ◽

D. K. Joshi

Keyword(s):

Crop Yield ◽

Cropping Systems ◽

Wheat Yield ◽

Grain Filling ◽

Wheat Crop ◽

Yield Estimation ◽

Filling Stage ◽

Multi Stage ◽

Dssat Model ◽

Grain Filling Stage

Abstract. Crop yield estimation before harvest is required for marketing, pricing, storage, import, export etc. Productivity of cropping systems under various weather, management and policy scenarios can be predicted successfully by simulation models. Due to increase in input cost of agricultural operation, agriculture produces become costly. Therefore, crop yield estimation in the agriculture becomes essential. Weather variability causes the losses in the yield. Therefore, model based on weather parameters, soil parameter and crop parameters can provide reliable crop yield estimation in advance. For estimating the multi stage wheat crop yield, experiments were conducted at research farm of IARI, New Delhi during Rabi 2016&ndash;17 and Rabi 2017&ndash;18. Crop yield were estimated by weather based and crop simulation model. Percentage deviation of estimated yield by observed yield at flowering and grain filling stage was &minus;5.1 and 2.0 by weather based model, 4.3 and 2.1 by InfoCrop model, 10.2 and 9.0 by DSSAT model during Rabi 2016&ndash;17 and 5.3 and 5.9 by weather based model, 2.3 and 2.2 by InfoCrop model, &minus;10.8 and &minus;9.6 by DSSAT model during Rabi 2017&ndash;18 respectively. Among the three models opted for estimating the yield at flowering and grain filling stage, InfoCrop model gave better results followed by weather based and DSSAT model. Therefore, this model can be used for multi stage wheat crop yield estimation at district as well as regional level.

Download Full-text

Determination of Appropriate Remote Sensing Indices for Spring Wheat Yield Estimation in Mongolia

Remote Sensing ◽

10.3390/rs11212568 ◽

2019 ◽

Vol 11 (21) ◽

pp. 2568 ◽

Cited By ~ 4

Author(s):

Battsetseg Tuvdendorj ◽

Bingfang Wu ◽

Hongwei Zeng ◽

Gantsetseg Batdelger ◽

Lkhagvadorj Nanzad

Keyword(s):

Remote Sensing ◽

Crop Yield ◽

Spring Wheat ◽

Drought Index ◽

Wheat Yield ◽

Condition Index ◽

Yield Prediction ◽

Yield Estimation ◽

Water Index ◽

Remote Sensing Indices

In Mongolia, the monitoring and estimation of spring wheat yield at the regional and national levels are key issues for the agricultural policy and food management as well as for the economy and society as a whole. The remote sensing data and technique have been widely used for the estimation of crop yield and production in the world. For the current research, nine remote sensing indices were tested that include normalized difference drought index (NDDI), normalized difference water index (NDWI), vegetation condition index (VCI), temperature condition index (TCI), vegetation health index (VHI), normalized multi-band drought index (NMDI), visible and shortwave infrared drought index (VSDI), and vegetation supply water index (VSWI). These nine indices derived from MODIS/Terra satellite have so far not been used for crop yield prediction in Mongolia. The primary objective of this study was to determine the best remote sensing indices in order to develop an estimation model for spring wheat yield using correlation and regression method. The spring wheat yield data from the ground measurements of eight meteorological stations in Darkhan and Selenge provinces from 2000 to 2017 have been used. The data were collected during the period of the growing season (June–August). Based on the analysis, we constructed six models for spring wheat yield estimation. The results showed that the range of the root-mean-square error (RMSE) values of estimated spring wheat yield was between 4.1 (100 kg ha−1) to 4.8 (100 kg ha−1), respectively. The range of the mean absolute error (MAE) values was between 3.3 to 3.8 and the index of agreement (d) values was between 0.74 to 0.84, respectively. The conclusion was that the best model would be (R2 = 0.55) based on NDWI, VSDI, and NDVI out of the nine indices and could serve as the most effective predictor and reliable remote sensing indices for monitoring the spring wheat yield in the northern part of Mongolia. Our results showed that the best timing of yield prediction for spring wheat was around the end of June and the beginning of July, which is the flowering stage of spring wheat in this study area. This means an accurate yield prediction for spring wheat can be achieved two months before the harvest time using the regression model.

Download Full-text