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

Abstract

Accurately 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.

Adaptability Evaluation of the Spatiotemporal Fusion Model in the Summer Maize Planting Area of the Southeast Loess Plateau

“…Moreover, this area is also a typical hilly area of the Loess Plateau. The terrain fragmentation and the lack of water resources are the main factors limiting the development of agriculture in this area [11].…”

“…NPP is the amount of organic matter fixed with green plants per unit area and per unit time, and is the fraction of organic carbon fixed with photosynthesis minus the fraction consumed with plant respiration, which has a direct impact on crop yield [8]. Existing models for estimating NPP are generally divided into four categories: the climate productivity model [9], physiological and ecological process model [10], light use efficiency model [11], and ecological remote sensing coupling model [12]. Among them, the climate productivity model and physiological and ecological process model have complex mechanisms, require many parameters, and have large errors in regional scale simulation results [13].…”

“…Moreover, human factors have a great influence on parameter determination of the ecological remote sensing coupling model [14]. The light use efficiency model characterized by the Carnegie-Ames-Stanford Approach (CASA), with its few input parameters and strong operability, is widely used in the process of regional scale NPP estimation [11,15]. CASA, which can forecast crop yield at various scales, has the largest concordance with MODIS-NPP and observed values when compared to the statistical model and process model [16].…”

“…The Loess Plateau is a typical arid and semi-arid continental monsoon climate zone with a long history of watershed agriculture and valley reclamation, which plays an essential role in guaranteeing national food security and grain production [37]. The Yuncheng Basin is an important grain producing area in the southeast of the Loess Plateau, but it has a large degree of topographic fragmentation [11]. Due to the limited spatial resolution of the MODIS-NDVI data (500 m), it is challenging to distinguish the finer features of the spatial textures of different surface objects.…”

Sustainable application of GF-6 WFV satellite data in desert steppe: A village-scale grazing study in China

“…Satellite remote sensing technology has unique advantages in monitoring and estimating ecological parameters of surface vegetation (Wan et al, 2012;Zribi et al, 2016;Ruan et al, 2021). Compared with traditional field surveys and statistical methods, remote sensing monitoring can obtain a wider range of data with relatively little basic work reducing the time and personnel need while the frequency of obtaining ground-based information can be increased.…”