A Method for Estimating Alfalfa (Medicago sativa L.) Forage Yield Based on Remote Sensing Data

Abstract

Alfalfa (Medicago sativa L.) is a widely planted perennial legume forage plant with excellent quality and high yield. In production, it is very important to determine alfalfa growth dynamics and forage yield in a timely and accurate manner. This study focused on inverse algorithms for predicting alfalfa forage yield in large-scale alfalfa production. We carried out forage yield and aboveground biomass (AGB) field surveys at different times in 2022. The correlations among the reflectance of different satellite remote sensing bands, vegetation indices, and alfalfa forage yield/AGB were analyzed, additionally the suitable bands and vegetation indices for alfalfa forage yield inversion algorithms were screened, and the performance of the statistical models and machine learning (ML) algorithms for alfalfa forage yield inversion were comparatively analyzed. The results showed that (1) regarding different harvest times, the alfalfa forage yield inversion model for first-harvest alfalfa had relatively large differences in growth, and the simulation accuracy of the alfalfa forage yield inversion model was higher than that for the other harvest times, with the growth of the second- and third-harvest alfalfa being more homogeneous and the simulation accuracy of the forage yield inversion model being relatively low. (2) In the alfalfa forage yield inversion model based on a single parameter, the moisture-related vegetation indices, such as the global vegetation moisture index (GVMI), normalized difference water index (NDWI) and normalized difference infrared index (NDII), had higher coefficients of correlation with alfalfa forage yield/AGB, and the coefficients of correlation R2 values for the first-harvest alfalfa were greater than 0.50, with the NDWI correlation being the best with an R2 value of 0.60. (3) For the alfalfa forage yield inversion model constructed with vegetation indices and band reflectance as multiparameter variables, the random forest (RF) and support vector machine (SVM) simulation accuracy was higher than that of the alfalfa forage yield inversion model based on a single parameter; the first-harvest alfalfa R2 values based on the multiparameter RF and SVM models were both 0.65, the root mean square errors (RMSEs) were 329.74 g/m2 and 332.32 g/m2, and the biases were −0.47 g/m2 and −2.24 g/m2, respectively. The vegetation indices related to plant water content can be considered using a single parameter inversion model for alfalfa forage yield, the vegetation indices and band reflectance can be considered using a multiparameter inversion model for alfalfa forage yield, and ML algorithms are also an optimal choice. The findings in this study can provide technical support for the effective and strategic production management of large-scale alfalfa.

Digestate Application Methods and Rates with Regard to Greenhouse Gas Emissions and Crop Conditions

“…Agronomy 2024, 14, x FOR PEER REVIEW 6 of 18 (NDWI), which is sensitive to the water content of the plants [35], as is shown in Table 3. The data were processed using Google Earth Engine (Google LLC, San Francisco, CA, USA) qGIS (Open Source Geospatial Foundation), and STATISTICA (TIBCO, Palo Alto, CA, USA).…”

“…Therefore, five cloud-less images were selected with spatial resolutions of 10 m•px −1 . Crop status was derived from (a) the Normalized Difference Vegetation Index (NDVI), as one of the most common vegetation indexes that are used for local-scale management purposes as an direct indicator of plant health and growth [33,34] and (b) the Normalized Difference Water Index (NDWI), which is sensitive to the water content of the plants [35], as is shown in Table 3. The data were processed using Google Earth Engine (Google LLC, San Francisco, CA, USA) qGIS (Open Source Geospatial Foundation), and STATISTICA (TIBCO, Palo Alto, CA, USA).…”