1.

INTRODUCTION

The forage crop industry is an important part of modern agriculture and an important focus for adjusting and optimizing China’s agricultural structure[1]. In 2020, forage crops grown in China mainly included silage corn, forage oats, annual ryegrass, and alfalfa, with a cultivated area of nearly 5.4 million hectares and a dry weight of about 71.6 million tons, an increase of about 24 million tons over 2015[2]. Forage crops are a renewable resource, and rapid monitoring of their growth and yield is indispensable for scientific management and sustainable use. It is of great scientific significance to carry out the research and application of rapid and accurate monitoring methods for the growth of forage crops, which is of great scientific significance for the management and protection of forage resources and the improvement of the ecological environment.

Growth is an important basis for predicting forage crop yield. Traditionally, ground manual surveys have been used to measure vegetation growth information such as vegetation coverage, height, development period, and grass yield of forage crops[3]. This method is time-consuming, laborious, limited in coverage, and greatly affected by human factors, which cannot quickly and comprehensively reflect the temporal and spatial variability of large-scale planting areas, which affects the timeliness of formulating forage resource management measures[4]. Remote sensing monitoring of forage crop growth is a macroscopic dynamic monitoring of growth status, process, and yield in a large area by using remote sensing methods combined with ground observation and climate reality[5], which has the characteristics of high timeliness, wide coverage, short detection period, few human interference factors, and low cost, which provides a new technical method for large-scale, rapid and accurate monitoring of forage crop growth[6].

2.

BRIEF DESCRIPTION OF REMOTE SENSING MONITORING IN FORAGE CROP GROWTH

2.2

Principles of Remote Sensing Monitoring

As the basis for the analysis and interpretation of remote sensing data, the spectral characteristics of ground objects are studied, and the basis for the selection of sensor bands is also designed. The spectral curves of healthy green vegetation show obvious “peak-valleys”: there is a high reflection zone in the near-infrared band, chlorophyll has a strong absorption of visible light, and there are troughs in the spectral curve near 450 nm and 680 nm, with peaks around 550 nm. The degree of development, water content, and chlorophyll content of each leaf are different, which makes the absorption and reflection of incident light different, so the spectral information is closely related to vegetation biomass[15]. Remote sensing yield estimation is to analyze the optical properties of plant leaves and measure their reflectance in different wavebands according to the spectral curve, and analyze the relationship with yield through modeling, so as to estimate yield[16]. The growth and development stage, pests and diseases, nutrient content and water and fertilizer deficit of forage crops can be well reflected by the spectral characteristics of remote sensing images. Remote sensing imaging can be used to obtain dense and information-rich datasets without contact, and various physical and biochemical parameters of forage crops can be inverted by combining spectral data analysis and processing methods[17], as shown in Figure 1.

Figure.1

The process of monitoring forage crop growth by by remote sensing

3.

APPLICATION OF REMOTE SENSING TECHNOLOGY IN FORAGE CROP GROWTH MONITORING

The application of remote sensing observation to crop monitoring and yield estimation began in the 70s of the last century, but the coupling of crop models and remote sensing observations began in the late 80s of the last century. Since 1996, Canada has monitored crop growth by comparing the NDVI curve with weekly growth changes[21]. Since 1998, the European Union has developed a growth monitoring method based on the NDVI change process based on the CORINE1:100,000 land use database[22]. In China, in the early nineties of the twentieth century, Huang et al.[23-25] began to study and model the relationship between natural grassland yield and satellite data. In 2008, Mao et al.[26] studied the relationship between forage yield and vegetation index NDVI, and established a linear model and an exponential model with good fitting results, and estimated forage yield in Qinghai Province. Qian et al.[27] analyzed the trends of grassland area and yield in Fukang City, Xinjiang over a period of 20 years (1990, 1999 and 2008) based on remote sensing classification and yield estimation models. In 2020, Yu et al.[28] combined ground monitoring data and high-resolution images to establish an inversion model to analyze the changes of nutrient content in natural grassland. Based on the previous data of satellite remote sensing monitoring, the relationship between vegetation index and forage crop yield is studied and established at point and surface, and remote sensing technology is an effective means to estimate the grass yield of forage crops on a large spatial scale.

4.

MAIN TECHNICAL BOTTLENECK AT PRESENT

Although the latest remote sensing technologies such as hyperspectral have the advantages of rapid, non-destructive and accurate detection, there are still some technical bottlenecks in the monitoring of forage crop growth, which limit their wide application.

First of all, remote sensing satellites have a long revisit period, low resolution, and are susceptible to weather and terrain, especially in areas where optical remote sensing is difficult to apply to cloudy and rainy areas, and there are homogeneous and homomorphic foreign bodies in a small number of areas[29], which is unavoidable for optical remote sensing interpretation.

Second, the resolution of the remote sensing data used is generally tens of meters, 100 meters and below, and the precision and accuracy have not fully met the needs, and there are still certain difficulties in the ground verification of remote sensing monitoring of forage crop growth. To improve the accuracy and reliability of monitoring, it is necessary to conduct in-depth research on the temporal and spatial resolution of remote sensing images, the quantitative processing of remote sensing, and the growth mechanism of forage crops[9].

Thirdly, most of the researchers engaged in remote sensing monitoring of forage crop growth are engaged in remote sensing monitoring or forage crop science, the former lacks in-depth research on the growth mechanism of forage crops, and the latter lacks the breadth and depth of remote sensing spectroscopy professional and technical personnel in remote sensing applications, how to promote the deep integration of remote sensing technology and forage crop science to improve the accuracy of monitoring and the training of interdisciplinary professionals still has a long way to go in the future.

5.

PROSPECT

Although there are not many studies on the application of remote sensing technology to monitor the physiological indicators and environmental stress of forage vegetation, and there is a lack of spectral database of forage crops, we can learn from the experience gained by remote sensing technology in the production of other crops, combine the growth characteristics of forage crops, make up for the shortcomings of traditional monitoring technology, and initially realize fast and non-destructive forage growth monitoring. Promoting the deep integration of remote sensing technology and grassland science to improve the accuracy of monitoring and the training of interdisciplinary professionals will lay the foundation for the realization of digital forage monitoring. In the next ten years, with the launch of more and more high-resolution domestic satellites and the in-depth implementation of high-resolution special projects, the construction of national space infrastructure will be further promoted. With the development of big data, artificial intelligence, Internet of Things, Internet+ and other technologies, remote sensing technology will develop rapidly in the field of forage crop growth monitoring, multi-source remote sensing data fusion, interdisciplinary improvement of monitoring accuracy, artificial intelligence and big data applications.