Multi-Year Mapping of Major Crop Yields in an Irrigation District from High Spatial and Temporal Resolution Vegetation Index

Abstract

Crop yield estimation is important for formulating informed regional and national food trade policies. The introduction of remote sensing in agricultural monitoring makes accurate estimation of regional crop yields possible. However, remote sensing images and crop distribution maps with coarse spatial resolution usually cause inaccuracy in yield estimation due to the existence of mixed pixels. This study aimed to estimate the annual yields of maize and sunflower in Hetao Irrigation District in North China using 30 m spatial resolution HJ-1A/1B CCD images and high accuracy multi-year crop distribution maps. The Normalized Difference Vegetation Index (NDVI) time series obtained from HJ-1A/1B CCD images was fitted with an asymmetric logistic curve to calculate daily NDVI and phenological characteristics. Eight random forest (RF) models using different predictors were developed for maize and sunflower yield estimation, respectively, where predictors of each model were a combination of NDVI series and/or phenological characteristics. We calibrated all RF models with measured crop yields at sampling points in two years (2014 and 2015), and validated the RF models with statistical yields of four counties in six years. Results showed that the optimal model for maize yield estimation was the model using NDVI series from the 120th to the 210th day in a year with 10 days’ interval as predictors, while that for sunflower was the model using the combination of three NDVI characteristics, three phenological characteristics, and two curve parameters as predictors. The selected RF models could estimate multi-year regional crop yields accurately, with the average values of root-mean-square error and the relative error of 0.75 t/ha and 6.1% for maize, and 0.40 t/ha and 10.1% for sunflower, respectively. Moreover, the yields of maize and sunflower can be estimated fairly well with NDVI series 50 days before crop harvest, which implicated the possibility of crop yield forecast before harvest.

Estimating Growing Season Evapotranspiration and Transpiration of Major Crops over a Large Irrigation District from HJ-1A/1B Data Using a Remote Sensing-Based Dual Source Evapotranspiration Model

“…In future studies, crop yield [43] and crop water consumption estimated in this study can be integrated to assess water productivity and planting suitability of major crops in the study region, so as to find optimal crop planting pattern with less water consumption and more crop yield/benefit. Author Contributions: S.S. outlined the research topic, assisted with manuscript writing, and coordinated the revision activities.…”

“…Consequently, the reduction of irrigation water may have a negative impact on crop growth in the study area, and it is necessary to study whether the impact is beneficial or not. Summer maize and sunflower are dominant crops in the study area, and the proportion of maize and sunflower planting has increased significantly in recent years to over 80% of the total agricultural land after 2015 [36,43]. Accordingly, this study focused on maize and sunflower.…”

“…Accordingly, this study focused on maize and sunflower. From 2009 to 2015, the annual average yields of maize and sunflower in the study area ranged from 10.61 t/ha to 11.41 t/ha and from 3.54 t/ha to 3.87 t/ha, respectively [43].…”

“…The preprocessing procedures for HJ-1A/1B images mainly include radiometric calibration and atmospheric and geometric corrections, and more details can refer to Li (2014) [45] and Yu and Shang (2017) [36]. Then, the parameters of HTEM model can be retrieved from the pre-processed satellite Summer maize and sunflower are dominant crops in the study area, and the proportion of maize and sunflower planting has increased significantly in recent years to over 80% of the total agricultural land after 2015 [36,43]. Accordingly, this study focused on maize and sunflower.…”

Risk of Low Productivity is Dependent on Farm Characteristics: How to Turn Poor Performance into an Advantage

“…Hence, comprehensive data on production capacity are needed with good spatial and temporal coverage. Open satellite data haves opened many new solutions for agriculture and many of them support the large-scale transformation of agriculture: e.g., in the estimation of yields [19][20][21] as well as the identification of crops [16], crop conditions [22,23], pre-crop values for crop sequence [24] and providing sets of sustainable intensification indicators [25].…”